Karapan Network Journal : Journal Computer Technology and Mobile Ad Hoc Network
Karapan Network Journal: Journal of Computer Technology and Mobile Ad Hoc Network is an open-access journal published by Omah Tabing Publisher, issued quarterly (October, January, May, and July). The journal publishes scholarly articles on distributed sensor networks and mobile ad hoc networks, covering algorithms, architectures, data processing, and their applications in computer science, engineering, and applied sciences.
Pemanfaatan Aplikasi Business Intelligence Berbasis Web untuk Optimalisasi Manajemen Stok dan Strategi Promosipada Toko Parfum
erkembangan teknologi cloud computing mendorong penggunaan private cloud storage sebagai solusi penyimpanan data yang aman dan fleksibel dalam lingkungan jaringan lokal (LAN). Namun, implementasi sistem tersebut tidak terlepas dari berbagai ancaman keamanan seperti sniffing, unauthorized access, dan serangan jaringan lainnya. Oleh karena itu, diperlukan penerapan protokol keamanan yang tepat untuk menjaga kerahasiaan, integritas, dan ketersediaan data. Penelitian ini bertujuan untuk menganalisis penerapan protokol keamanan jaringan pada implementasi private cloud storage berbasis server Ubuntu. Metode penelitian yang digunakan adalah pendekatan deskriptif–eksploratif dengan kombinasi analisis konfigurasi sistem dan pengujian keamanan jaringan. Protokol yang dianalisis meliputi SSH, HTTPS, SSL/TLS, serta firewall (UFW). Hasil penelitian menunjukkan bahwa penerapan protokol keamanan tersebut mampu meningkatkan perlindungan sistem terhadap akses tidak sah dan serangan jaringan. Penggunaan HTTPS berhasil mengenkripsi data transmisi, sementara konfigurasi firewall membatasi akses ke port tertentu. Dengan demikian, implementasi private cloud berbasis Ubuntu yang dilengkapi dengan protokol keamanan yang tepat dapat meningkatkan keamanan sistem secara signifikan
Copyright (c) 2026 Moh imam Hidayatullah (Penulis); Akmal (Penerjemah) https://creativecommons.org/licenses/by/4.0
Introduction to Informatics Research: Concepts, Methodology, and Role in Information Technology Development
Research is a crucial aspect of the advancement of knowledge, including within the field of informatics. Informatics research plays a vital role in generating innovative solutions to various information technology challenges—such as software development, artificial intelligence, cybersecurity, data analysis, and information systems. This article aims to explain the fundamental concepts of informatics research, the stages of research methodology, and the field's role in supporting the evolution of information technology. The study employs a literature review approach, examining various books, scholarly articles, and relevant reference materials. The findings indicate that an understanding of research methodology is essential for conducting research that is systematic, objective, valid, and scientifically sound. Furthermore, informatics research serves as the foundation for creating technological innovations that address the needs of society, industry, and the education sector. Consequently, mastering research concepts and methodologies is a necessary competency for students and researchers in the field of informatics.
Informatika, Artificial Intelligence, Internet of Things, Cloud Computing, Big Data, Tren Riset.
Copyright (c) 2026 fadil baitul rahman, Dr.fauzan prastyo eka putra, M.kom. (Penulis) https://creativecommons.org/licenses/by/4.0
Cloud Computing: Tinjauan Sistematis Arsitektur, Keamanan, dan Optimasi Performa pada Era Transformasi Digital
Perkembangan teknologi informasi dan komunikasi yang pesat telah mendorong adopsi cloud computing secara masif di berbagai sektor industri. Meskipun menawarkan berbagai keuntungan berupa skalabilitas, fleksibilitas, dan efisiensi biaya, cloud computing menghadirkan tantangan kritis pada aspek keamanan data, privasi, keandalan layanan, dan optimasi performa jaringan. Penelitian ini bertujuan menganalisis perkembangan terkini arsitektur cloud computing, mengidentifikasi tantangan keamanan utama, mengevaluasi strategi optimasi performa, serta mengusulkan kerangka kerja terintegrasi melalui metode Systematic Literature Review (SLR) berdasarkan panduan PRISMA terhadap 50 publikasi ilmiah dari jurnal terindeks dan prosiding konferensi internasional periode 2008–2025. Hasil penelitian menunjukkan bahwa tren arsitektur cloud computing bergerak ke arah hybrid cloud yang dikelola melalui Software-Defined Networking (SDN) dan Network Function Virtualization (NFV), sementara integrasi teknologi 5G dan edge computing membuka peluang layanan dengan latensi ultra-rendah untuk mendukung ekosistem IoT. Pendekatan zero-trust security yang dikombinasikan dengan verifikasi integritas data berbasis blockchain serta deteksi ancaman real-time berbasis kecerdasan buatan merupakan solusi keamanan paling menjanjikan yang teridentifikasi dari literatur. Penelitian ini menyimpulkan bahwa kerangka kerja terintegrasi SDN, NFV, dan zero-trust security merupakan pendekatan efektif untuk menjawab tantangan arsitektur, keamanan, dan performa cloud computing secara simultan, khususnya untuk mendukung transformasi digital dan pengembangan smart city berbasis IoT di Indonesia.
Copyright (c) 2026 kevin ananda (Penulis) https://creativecommons.org/licenses/by/4.0
Analysis of the Role of Smart Agriculture in Supporting Digital Transformation in the Agricultural Sector
Digital transformation in the agricultural sector has become a strategic necessity in addressing global challenges such as climate change, resource constraints, and the demand for increased productivity and efficiency. Smart Agriculture emerges as a technology-based approach that integrates the Internet of Things (IoT), smart sensors, artificial intelligence, and data analysis to support modern agricultural management. This study aims to analyze the role of Smart Agriculture in supporting digital transformation in the agricultural sector and to identify the benefits and challenges of its implementation. The study employs a literature review method with a qualitative approach through the process of identifying, selecting, analyzing, and synthesizing various relevant scientific literature. The results of the analysis show that the application of Smart Agriculture can improve operational efficiency and lead to savings in resource use (water and fertilizer), enhance the accuracy of decision-making through real-time data, and support increased productivity and the quality of agricultural yields. In addition, the integration of technologies such as IoT and AI contributes to monitoring land conditions, predicting crop yields, and the early detection of pests and plant diseases. However, challenges have been identified, including limited digital infrastructure, low levels of technological literacy among farmers, and high implementation costs. Smart Agriculture has proven to play a significant role in supporting the digital transformation of the agricultural sector, although its success is heavily influenced by the readiness of the technology ecosystem and human resources. Further research is recommended to examine implementation through field case studies and to develop technology adoption models that are better adapted to local conditions.
Keywords: Smart Agriculture, Digital Transformation, Internet of Things, Artificial Intelligence, Agricultural Sector
Copyright (c) 2026 M. Husnol Hidayat (Penulis) https://creativecommons.org/licenses/by/4.0
Implementasi dan Analisis Kinerja Jaringan 5G dalam Meminimalkan Latensi pada Game Multiplayer Real-Time
Perkembangan industri game multiplayer real-time menuntut infrastruktur jaringan yang mampu menyediakan latensi rendah, throughput tinggi, dan stabilitas koneksi yang konsisten. Penelitian ini bertujuan untuk mengimplementasikan dan menganalisis kinerja jaringan generasi kelima (5G) dalam meminimalkan latensi pada lingkungan game multiplayer real-time. Pendekatan yang digunakan meliputi pengujian jaringan 5G dengan memanfaatkan teknologi Ultra-Reliable Low-Latency Communication (URLLC), Mobile Edge Computing (MEC), dan network slicing untuk mengalokasikan sumber daya jaringan secara optimal bagi trafik game. Pengujian dilakukan pada skenario game first-person shooter (FPS) dan massively multiplayer online (MMO) dengan mengukur parameter Quality of Service (QoS) meliputi latensi, jitter, packet loss, dan throughput. Hasil implementasi menunjukkan bahwa jaringan 5G dengan konfigurasi MEC mampu mereduksi latensi rata-rata hingga 8,3 ms dibandingkan jaringan 4G LTE yang mencapai 45,7 ms. Penerapan network slicing berhasil mengisolasi trafik game dari trafik umum sehingga packet loss turun menjadi 0,12% dan jitter terjaga di bawah 2 ms. Analisis kinerja membuktikan bahwa implementasi 5G dengan arsitektur MEC dan URLLC secara signifikan meningkatkan pengalaman bermain (Quality of Experience/QoE) dan menjadi solusi infrastruktur yang viable untuk mendukung ekosistem game multiplayer real-time generasi berikutnya
Copyright (c) 2026 Rafael Ardiansyah, Nabila Ambarwati (Penulis) https://creativecommons.org/licenses/by/4.0
Literature Study: Migration Strategies and Zero Trust Authentication Methods to Secure Microservices Architecture in a Multi-Cloud Environment
The massive adoption of microservices architecture has pushed many organizations to move from monolithic systems to distributed ecosystems running across multiple cloud providers (multi-cloud), in order to avoid reliance on a single vendor, boost resilience, and meet regional compliance requirements. However, this decentralization also greatly expands the attack surface, especially during the migration phase when legacy systems and new microservices run side by side in security conditions that aren’t fully mature, making them vulnerable to lateral movement and unauthorized access escalation.Traditional perimeter-based security models have proven to be no longer adequate for such dynamic and fragmented environments. This study uses a Systematic Literature Review (SLR) approach to identify, evaluate, and synthesize various migration strategies and security mechanisms relevant to securing microservices architectures in multi-cloud environments. By reviewing literature from reputable academic databases over the past five years, this study examines three main security mechanism domains: service mesh with mutual TLS (mTLS) encryption, token-based authorization through API gateways (OAuth2/JWT), and a Zero Trust architecture that applies continuous identity verification for every service request. The synthesis results indicate that there is no single mechanism that is optimal for all scenarios;Each approach comes with trade-offs between protection strength, communication latency between services, and cross-cloud operational complexity. This study also maps research gaps related to the lack of a migration framework that integrates security from the design stage (security-by-design), and proposes a development direction towards an adaptive hybrid architecture that combines the advantages of service mesh and Zero Trust to support secure, efficient, and scalable microservices migration in next-generation multi-cloud ecosystems.
Copyright (c) 2026 Giovani sapta purnama A, Radhitya Dwi Akmal Purnomo (Penulis) https://creativecommons.org/licenses/by/4.0
Implementasi radius server berbasis ip publik pada vps mikrotik untuk autentikasi terpusat jaringan rt rw net
Abstrak Jaringan RT RW Net berkembang sebagai solusi alternatif pemerataan internet berbasis komunitas. Namun, autentikasi pengguna yang masih dilakukan secara manual dan lokal pada masing-masing perangkat MikroTik menyulitkan pengelolaan serta membatasi fleksibilitas pengguna. Penelitian ini bertujuan mengimplementasikan RADIUS Server berbasis IP publik yang diinstal pada VPS MikroTik sebagai sistem autentikasi terpusat untuk jaringan RT RW Net di Kecamatan Proppo dan Camplong. Sistem ini memungkinkan pengguna login dari berbagai lokasi dengan satu akun, serta memungkinkan admin melakukan manajemen terpusat menggunakan User Manager. Pengujian dilakukan menggunakan Wireshark untuk mengukur delay, throughput, dan efektivitas sistem. Hasilnya menunjukkan peningkatan efisiensi administrasi, keamanan jaringan, dan kemudahan akses pengguna. RT RW Net has developed as an alternative solution to provide equitable internet access within communities. However, user authentication is still performed manually and locally on each MikroTik device, which complicates management and limits user flexibility. This study aims to implement a centralized authentication system using a public IP-based RADIUS Server installed on a MikroTik VPS for the RT RW Net in Proppo and Camplong Districts. The system allows users to log in from various locations using a single account and enables administrators to manage the network centrally through the User Manager. Testing was conducted using Wireshark to measure delay, throughput, and system effectiveness. The results show improvements in administrative efficiency, network security, and user accessibility. Keywords: RADIUS Server, MikroTik, Autentikasi Terpusat, RT RW Net, User Manager, VPS. Article history: Received 5 April 20XX, first decision 22 April 20XX, accepted 22 August 20XX, available online 28 October 20XX
Copyright (c) 2026 m fadli, Zainol Fahmi, m.angga baitur rahman (Penulis) https://creativecommons.org/licenses/by/4.0
Future Directions of Informatics Research: An Introduction to Artificial Intelligence (AI)-Based Research Opportunities
Today, Artificial Intelligence (AI) is no longer merely a supporting computing technology but has become a central catalyst for digital evolution in the field of informatics. While specific AI algorithms are highly capable of executing focused instructions, this scientific field is undergoing a crucial shift toward Generative AI, multimodal capabilities, and the automation of software creation. This massive change ultimately creates significant gaps in conventional research paradigms. This research focuses on systematically mapping and identifying emerging study focuses, essential technological prospects, and the probability of socio-technical obstacles in the field of AI-integrated informatics for the next ten years. This investigation applies an in-depth literature study approach combined with a bibliometric evaluation of various peer-reviewed scientific publications from 2022 to 2026. This process summarizes various thematic groups spanning the disciplines of software engineering, data science, and cybersecurity. The evaluation successfully identified three crucial research directions: (1) AI-accelerated software development, which can increase code writing speed by up to 45%, but also increases the risk of security vulnerabilities by 18%; (2) autonomous database structures capable of autonomously managing search indexing; and (3) the application of Explainable AI (XAI), which functions to reduce the level of opacity in neural networks by presenting decision-making processes that are transparently understandable to human users. This exploration confirms the massive shift from AI-powered informatics systems to fully autonomous and self-healing infrastructure. However, the lack of standard guidelines regarding data management and the ethics of AI utilization remains the biggest obstacle today. Therefore, further studies must focus on designing security measurement standards for computational code produced by Large Language Models (LLMs), along with expanding the implementation of XAI in real time for critical facilities.
Copyright (c) 2026 Suro Jalil (Penulis); Waliyur Rohman (Penerjemah) https://creativecommons.org/licenses/by/4.0
Informatics Research as the Foundation for Developing Information Systems and Technology
Informatics research is a scientific activity that plays an important role in the development of systems and information technology. Through a systematic research process, informatics research produces various concepts, methods, algorithms, as well as technological innovations that can be applied to solve problems in various fields. The aim of this research is to examine the role of informatics research as a foundation in developing effective, efficient, and user-oriented information systems and technology. The method used is a literature study by reviewing various scientific sources related to the development of informatics research and its implementation in information technology development. The findings show that informatics research contributes to improving software quality, developing artificial intelligence, managing databases, information security, cloud computing, and Internet of Things (IoT)-based technologies. In addition, informatics research supports the process .Besides that, informatics research supports the innovation process by providing a scientific approach in analyzing needs, system design, implementation, and technology evaluation. Therefore, informatics research becomes the main foundation in creating information systems and technologies that are adaptive, innovative, and capable of meeting the challenges of digital development in the modern era.
Informatics Research, Information Systems, Information Technology, Technology Innovation, System Development.
Copyright (c) 2026 nur beilqis anastasya, alfiyatus zahroh (Penulis) https://creativecommons.org/licenses/by/4.0
Implementing Big Data Analytics to Support Business Intelligence in the Era of Digital Transformation
Digital transformation drives organizations to handle large and complex amounts of data as a foundation for decision-making. This makes Big Data Analytics a highly influential technology in strengthening Business Intelligence capabilities to create more precise, faster, and strategic information. The purpose of this study is to examine the use of Big Data Analytics to support Business Intelligence in the era of digital transformation and to identify the benefits and obstacles in its implementation. The methodology used is a qualitative study with a literature study approach, involving the collection, selection, and analysis of various scientific journals, books, and articles related to the research theme. The findings of this study indicate that combining Big Data Analytics with Business Intelligence can improve the quality of data analysis, accelerate the presentation of information directly, and support more effective and data-driven decision-making. In addition, the application of this technology also helps organizations predict business trends, understand customer behavior, and improve operational efficiency. However, the implementation process still faces challenges related to data quality, information security, technological infrastructure, and the need for skilled human resources. Therefore, the application of Big Data Analytics can be an important strategy in optimizing Business Intelligence so that organizations can increase competitiveness and adapt to developments in digital transformation.
Big Data Analytics, Business Intelligence, Digital Transformation, Data Analysis, and Decision Making.
Copyright (c) 2026 Hoirul Umam, Septiana Wulandari (Penulis) https://creativecommons.org/licenses/by/4.0
Integration of Artificial Intelligence and Business Intelligence in the Era of Digital Transformation: Opportunities and Challenges
The rapid development of digital technology is driving organizations to utilize data as a strategic asset to support decision-making. In this context, the integration of Artificial Intelligence (AI) and Business Intelligence (BI) is one approach widely implemented to increase the effectiveness of data analysis and produce more accurate, faster, and valuable information for organizations. AI enables systems to perform predictions, recognize patterns, and automate analysis processes, while BI plays a role in collecting, managing, and presenting data into information that is easily understood by decision-makers. This study aims to examine the opportunities and challenges of AI and BI integration in the era of digital transformation through a literature review method. Data were obtained from various scientific journals, proceedings, and academic publications relevant to the research topic. The study results indicate that AI and BI integration can improve the quality of data-driven decision-making, accelerate the analysis process, increase operational efficiency, and support the formulation of more appropriate business strategies. However, its implementation still faces various challenges, such as suboptimal data quality, limited human resources competent in AI and BI, high technology investment costs, and issues of data security and privacy. Therefore, an appropriate implementation strategy, adequate technological infrastructure support, and increased human resource competency are required so that the integration of AI and BI can provide maximum benefits for organizations in facing the dynamics of digital transformation.
Artificial Intelligence, Business Intelligence, Digital Transformation, Data-Driven Decision Making, Data Analytics
Copyright (c) 2026 Resty Amylia Margareta, Wulidati Trixiana (Penulis) https://creativecommons.org/licenses/by/4.0
Digital Transformation in Student Learning: A Systematic Literature Review on Access, Usage, and the Impact of Information Technology
Digital transformation has fundamentally changed the landscape of higher education, especially in how students access, use, and leverage information technology (IT) for learning activities. This study aims to synthesize the latest scientific evidence on students' access to digital infrastructure, patterns of using various platforms and applications, factors influencing IT adoption, and its impact on academic achievement and psychological well-being. The method used is a systematic literature review (SLR) based on the PRISMA 2020 protocol, with searches conducted in the Scopus, Web of Science, SINTA, and Google Scholar databases for the period 2019–2026. Out of 287 articles identified, 40 articles met the inclusion criteria and were analyzed thematically. The results show that access to devices and the internet is still uneven, with 68% of urban students having high-speed internet compared to only 34% in rural areas. IT usage is mostly dominated by Learning Management Systems (94%) and communication apps (89%). The strongest predictors of IT adoption are digital self-efficacy (β=0.74) and subjective norms (β=0.61), while technology anxiety (β=-0.52) is the main barrier. The positive impacts of IT include increased learning independence (74%), time flexibility (89%), and access to global references (82%), but it also triggers digital fatigue (67%) and social isolation (58%). This study concludes that digital transformation offers great opportunities as well as serious challenges that require evidence-based policies and a human-centered approach in its implementation.
Copyright (c) 2026 ALVIN HIDAYAT (Penulis); M. FEBRI DWIDANA SAPUTRA (Penerjemah) https://creativecommons.org/licenses/by/4.0
Rancang Bangun Orang-Orangan Sawah Pintar Berbasis Internet of Things untuk Pengendalian Hama Burung pada Tanaman Padi
Hama burung merupakan salah satu faktor yang menyebabkan penurunan produktivitas tanaman padi, terutama pada fase pengisian bulir hingga menjelang panen. Petani umumnya masih menggunakan orang-orangan sawah konvensional yang memiliki keterbatasan karena bersifat pasif dan memerlukan pengawasan secara manual. Perkembangan teknologi Internet of Things (IoT) memberikan peluang untuk mengembangkan sistem pengusiran hama yang lebih efektif dan otomatis. Penelitian ini bertujuan merancang dan mengimplementasikan orang-orangan sawah pintar berbasis Internet of Things yang mampu mendeteksi keberadaan burung, melakukan pengusiran secara otomatis, serta menyediakan fasilitas monitoring secara real-time. Sistem dikembangkan menggunakan mikrokontroler ESP32 yang terintegrasi dengan sensor Passive Infrared (PIR), sensor ultrasonik HC-SR04, motor servo, modul DFPlayer Mini, dan platform monitoring berbasis Blynk. Pengujian dilakukan pada prototipe menggunakan metode simulasi untuk mengevaluasi akurasi sensor, waktu respons sistem, keandalan komunikasi IoT, serta efektivitas pengusiran burung. Hasil pengujian menunjukkan bahwa sensor PIR memiliki tingkat akurasi deteksi rata-rata sebesar 92,0%, sedangkan sensor ultrasonik HC-SR04 memiliki akurasi pengukuran sebesar 98,07%. Sistem mampu merespons objek yang terdeteksi dengan rata-rata waktu respons sebesar 1,32 detik. Pengujian komunikasi IoT menunjukkan tingkat keberhasilan pengiriman data sebesar 98,0%. Selain itu, sistem berhasil mengurangi aktivitas burung pada area pengamatan dengan rata-rata efektivitas pengusiran sebesar 83,35%. Orang-orangan sawah pintar berbasis Internet of Things yang dikembangkan mampu bekerja secara otomatis dalam mendeteksi dan mengusir hama burung pada tanaman padi. Integrasi sensor, aktuator, dan teknologi IoT terbukti mampu meningkatkan efektivitas pengendalian hama serta mendukung penerapan konsep Smart Farming pada sektor pertanian modern. Penelitian selanjutnya perlu dilakukan pada kondisi persawahan yang sebenarnya untuk memperoleh hasil yang lebih representatif.
Internet of Things, Smart Farming, Orang-Orangan Sawah Pintar, Hama Burung, ESP32, Tanaman Padi.
Copyright (c) 2026 Maktsuful Ghummah, Moh Amrullah (Penulis) https://creativecommons.org/licenses/by/4.0
Analysis of Consumer Behavior Using Business Intelligence and Machine Learning in Digital Marketplaces
The rapid growth of digital marketplaces has led to a significant increase in the volume and diversity of transaction data. This condition requires methods capable of transforming large-scale data into valuable information that can support organizational decision-making. In this context, Business Intelligence (BI) is utilized to process and present historical data through various visualization techniques, while Machine Learning is employed to identify customer behavior patterns and predict potential future transaction trends. This study aims to analyze consumer behavior in digital marketplaces through the integration of Business Intelligence and Machine Learning to generate predictive insights that support more accurate, effective, and data-driven business decisions. The research approach encompasses Business Intelligence processes, including Extract, Transform, Load (ETL), data warehouse development, and the design of interactive dashboards to visualize sales performance and customer activities. Subsequently, Machine Learning techniques are applied to develop predictive models based on historical transaction data, purchase frequency, product categories, and customer attributes. Model performance is evaluated using several metrics, including accuracy, precision, recall, and F1-score. The findings indicate that Business Intelligence effectively provides relevant insights into sales trends, top-performing products, and customer purchasing patterns through intuitive visual representations. Furthermore, the Machine Learning implementation successfully develops predictive models capable of estimating repurchase likelihood and identifying potential customers with satisfactory performance levels. These results demonstrate that the integration of Business Intelligence and Machine Learning offers more comprehensive analytical capabilities compared to descriptive approaches that rely solely on historical data. The combination of Business Intelligence and Machine Learning has proven effective in supporting predictive consumer behavior analysis within digital marketplace environments. In addition to providing insights into past transaction patterns, this approach delivers predictive intelligence that can be utilized to formulate marketing strategies, improve customer retention, and enhance long-term business performance.
Copyright (c) 2026 kevin oktavian pratama, maskana ramadhanj (Penulis) https://creativecommons.org/licenses/by/4.0
Analysis of the Needs of a Website-Based Asset Management Information System for Government Agencies Using the PIECES Framework
Effective asset management in government institutions requires an information system that can support accurate, efficient, and accountable asset recording, monitoring, control, and reporting. Manual or semi-computerized asset management can lead to data duplication, reporting delays, difficulties in tracking assets, and weak data change control. The purpose of this study is to analyze the requirements of a web-based asset management information system for government institutions using the PIECES framework. This study uses a descriptive qualitative approach through a literature review of 50 articles relevant to asset management systems, government information systems, web-based systems, the PIECES framework, information technology governance, and system security. The analysis was conducted by classifying the literature findings into six PIECES dimensions: Performance, Information, Economy, Control, Efficiency, and Service. The results show that the required system must include features for asset data management, asset categories, asset locations, users, transactions, maintenance, supporting documents, quick search, dashboards, automated reports, audit trails, data export, and data backup. Non-functional requirements include access, system performance, ease of use, compatibility, scalability, and ease of maintenance. This study concludes that the PIECES framework can be used systematically to formulate requirements for a web-based asset management information system that is aligned with the characteristics of government institutions.
Copyright (c) 2026 Nadiva Qolbi (Penulis); Noera (Penerjemah) https://creativecommons.org/licenses/by/4.0
Internet of Things (IoT) System Architecture for Smart Drip Irrigation of Melon Plants
The implementation of the Internet of Things (IoT) in precision agriculture for melon cultivation currently faces challenges in the form of low network efficiency and high latency in the actuation process, largely due to the system's reliance on cloud-centric computing. This study aims to design a four-layer IoT system architecture for distributed smart drip irrigation, with calibration tailored to the specific agronomic characteristics of melon plants. The approach used is a Systematic Literature Review (SLR) with the PRISMA protocol as a standard reference. The data synthesis process is carried out by comparing 23 recent literature sources (2023–2025) that discuss IoT-based irrigation architectures. The research findings indicate that cloud-based irrigation architectures are highly vulnerable to system failures, especially when applied to agricultural land with marginal conditions. To address this, this study proposes a new architecture that combines Edge Computing at the Middleware layer using the lightweight MQTT communication protocol. By moving the decision-making process to the local level, this system is capable of producing actuation responses with near-zero latency. In addition, the irrigation algorithm is specifically designed to align with the vegetative growth phase and the ripening phase of melon fruit, in order to regulate water stress levels in a controlled manner to increase the fruit's sweetness (Brix). The main contribution of this research is the development of a blueprint for a Local-Intelligence-based IoT architecture that is resilient to connectivity disruptions, efficient in its use, and relevant for application in horticultural practices.
Copyright (c) 2026 Habiburrahman (Penulis); Halim (Penerjemah) https://creativecommons.org/licenses/by/4.0
Penerapan Text Mining untuk Klasifikasi Berita Online
Pesatnya perkembangan portal berita daring di Indonesia — termasuk Kompas.com, Detik.com, dan CNN Indonesia — menghasilkan ratusan artikel yang dipublikasikan setiap hari dalam berbagai kategori topik, sehingga menimbulkan tantangan berupa kelebihan informasi (information overload). Pengelompokan berita secara manual sudah tidak lagi efisien untuk skala besar dan rentan terhadap inkonsistensi manusia. Text mining, sebagai cabang data mining yang berfokus pada data teks tidak terstruktur, telah menunjukkan potensi besar dalam mengotomatisasi klasifikasi dokumen. Studi-studi sebelumnya yang menggunakan Naïve Bayes, Support Vector Machine (SVM), serta pembobotan Term Frequency–Inverse Document Frequency (TF-IDF) pada korpus berita berbahasa Indonesia melaporkan tingkat akurasi antara 82%–95%. Namun, evaluasi komparatif yang sistematis terhadap pipeline praproses pada dataset berita Indonesia yang seimbang dan multi-kategori masih belum banyak dieksplorasi dalam literatur.Penelitian ini bertujuan untuk mengembangkan dan mengevaluasi sistem klasifikasi otomatis berbasis text mining pada artikel berita daring berbahasa Indonesia dalam lima kategori topik: politik, ekonomi, olahraga, teknologi, dan hiburan.Penelitian ini menggunakan pendekatan kuantitatif. Dataset sebanyak 1.500 artikel berita (300 per kategori) dikumpulkan melalui web scraping otomatis menggunakan Python dan BeautifulSoup dari tiga portal berita daring utama di Indonesia. Tahapan praproses teks meliputi empat langkah berurutan: case folding, tokenisasi, penghapusan stopword menggunakan daftar stopword Bahasa Indonesia yang telah dikurasi, serta stemming menggunakan library Sastrawi. Ekstraksi fitur dilakukan dengan metode pembobotan TF-IDF. Tiga algoritma klasifikasi terawasi digunakan dan dibandingkan, yaitu Naïve Bayes (NB), Support Vector Machine (SVM dengan kernel linear), dan K-Nearest Neighbor (K-NN, k=5). Pembagian data dilakukan menggunakan stratified split 80:20 antara data latih dan data uji. Kinerja model dievaluasi menggunakan metrik akurasi, precision, recall, dan F1-score berdasarkan confusion matrix.SVM menghasilkan akurasi klasifikasi tertinggi sebesar 91,67%, dengan precision 91,3%, recall 91,7%, dan F1-score 91,4%. Naïve Bayes berada di posisi berikutnya dengan akurasi 85,33% (F1-score: 84,9%), sedangkan K-NN menghasilkan 78,67% (F1-score: 78,1%). Analisis per kategori menunjukkan bahwa kategori politik dan olahraga menghasilkan F1-score tertinggi, masing-masing sebesar 94,2% dan 93,8%, sedangkan kategori teknologi memiliki F1-score terendah yaitu 86,1% akibat adanya tumpang tindih semantik dengan kategori ekonomi. Penggunaan stemming Sastrawi meningkatkan akurasi keseluruhan sebesar 4,2 poin persentase dibandingkan baseline tanpa stemming pada ketiga algoritma.Text mining yang dikombinasikan dengan ekstraksi fitur TF-IDF dan klasifikasi SVM terbukti efektif dalam mengotomatisasi pengelompokan berita online berbahasa Indonesia, dengan akurasi melebihi 91% pada lima kategori topik. Stemming berbasis Sastrawi terbukti sebagai langkah praproses yang krusial dalam meningkatkan performa klasifikasi pada teks Bahasa Indonesia. Namun, ambiguitas semantik antara kategori yang saling berdekatan (teknologi dan ekonomi) masih menjadi keterbatasan pada pendekatan bag-of-words. Penelitian selanjutnya disarankan untuk mengeksplorasi model bahasa berbasis transformer seperti IndoBERT dan representasi kata kontekstual untuk mengatasi tumpang tindih semantik, serta integrasi pemrosesan aliran berita secara real-time dan klasifikasi multi-label guna merepresentasikan kompleksitas konten berita daring modern.
Text Mining, Klasifikasi Teks, Berita Online, Machine Learning, TF-IDF
Copyright (c) 2026 andreas fiki, Panji cahya prasetyo (Penulis) https://creativecommons.org/licenses/by/4.0
Utilizing Business Intelligence to Help Companies Manage Data
The development of information technology has led to an increase in the amount of data generated by companies in various business activities. Poorly managed data can make it difficult to obtain accurate information and hinder the decision-making process. Therefore, technology is needed that can process data into valuable information for companies. One solution that can be implemented is Business Intelligence (BI). This study aims to analyze the use of Business Intelligence in helping companies manage data and support information-based decision-making processes. The research method used is a descriptive qualitative method through a literature study by analyzing the concepts of Business Intelligence, data management, business analysis, and the implementation of decision support systems. The results show that Business Intelligence can help companies integrate data from various sources, improve the quality of data processing, and present information through easy-to-understand reports and dashboards. The implementation of BI also provides benefits in increasing operational efficiency, accelerating the analysis process, and helping management determine business strategies based on accurate data. Thus, Business Intelligence can be a strategic solution for companies in optimizing data management and increasing competitiveness in the digital era.
Copyright (c) 2026 mohammad efendi, mohammad ifandi (Penulis) https://creativecommons.org/licenses/by/4.0
A Comprehensive Analysis of Future Smart Agriculture Ecosystems: Integration of Low-Cost IoT Architecture, Energy-Efficient Sensor Networks (WSNs), Artificial Intelligence (AI), and Supply Chain Traceability Systems
Global population growth and unpredictable climate conditions have massively threatened food security, thus demanding a transition in agricultural management from conventional methods to Smart Agriculture based on the Internet of Things (IoT). Although IoT offers revolutionary potential, its widespread adoption is often hampered by the high cost of implementing commercial devices, inefficient power consumption in Wireless Sensor Networks (WSN), data transmission security vulnerabilities, and the lack of integration of data from the field to the consumer. This study presents a comprehensive analytical overview of the future agricultural IoT ecosystem to address these challenges. At the hardware architecture level, the analysis shows that the implementation of an open-source, cross-domain Monitoring and Control Framework (MCF) has been proven to reduce system implementation costs by up to 20 times compared to existing commercial solutions on the market. Furthermore, at the network layer, WSN performance and efficiency are optimized through an intelligent cluster head selection function that considers residual energy, distance, and Signal to Noise Ratio (SNR). This efficiency approach, when combined with linear congruential generator-based data encryption, has been proven to reduce routing overhead by up to 26% while securing data from cyber threats. At the analytical processing level, there is a shift from centralized cloud processing to real-time edge AI and deep learning directly at the edge of the network device. This integration of Artificial Intelligence (AI) facilitates full automation such as crop yield prediction, plant disease detection, and the autonomous operation of Unmanned Aerial Vehicles (UAVs). Finally, in the downstream sector, the use of smart objects with RFID technology and Electronic Product Code (EPC) facilitates the Traceability system in Agricultural Supply Chain Management (APSCM). This comprehensive integration of low-cost innovation, energy-efficient networks, precise AI analytics, and product traceability will be the main foundation in creating a transparent and sustainable future food security ecosystem.
Copyright (c) 2026 Yuris Ikrar RABBANI, Wildan Ramadhani (Penulis) https://creativecommons.org/licenses/by/4.0
Analysis of Artificial Intelligence Technology Acceptance in Students Using the Technology Acceptance Model (TAM) Approach
The development of digital technology in higher education in recent years has shown rapid growth, particularly in supporting student learning processes and research activities. However, there is still a gap between students' ability to utilize technology and their research competencies, particularly in processing information critically and structured. This study aims to examine the influence of digital literacy and information technology utilization on student research competencies. This study used a quantitative approach with a survey method, where data were obtained through questionnaires from 100 student respondents and analyzed using multiple linear regression. The results showed that digital literacy and information technology utilization simultaneously had a positive and significant influence on student research competencies, contributing 62%. Individually, digital literacy had a stronger influence than information technology utilization. This finding indicates that digital literacy plays a role not only as a technical skill but also as a cognitive foundation in supporting the research process, while information technology functions as a supporting tool in accessing and processing information. In conclusion, both variables play an important role in improving student research competencies, although there are still other factors that have not been analyzed in this study. Future research is recommended to add variables such as learning motivation and academic environment to obtain more comprehensive results.
Copyright (c) 2026 AGUS SALIM (Penulis); Ubaidillah Hamdi Putra Pratama (Penerjemah) https://creativecommons.org/licenses/by/4.0
Smart Farming Technology Integration to Mitigate Crop Failure Risks on Small-Scale Farms
The development of digital technology has encouraged the implementation of Smart Farming as a solution to increase productivity and efficiency in the agricultural sector. This technology integrates the Internet of Things (IoT), environmental sensors, and data-driven monitoring systems to help farmers manage their land more effectively. However, small-scale farmers still face the risk of crop failure due to weather changes, drought, unstable soil moisture, and limited access to information on land conditions. This study aims to determine how the integration of Smart Farming technology can be used to mitigate the risk of crop failure in small-scale agricultural land. The study used a quantitative method with an applied research approach. Data were obtained through observation, literature review, and sensor data collection covering air temperature, air humidity, soil moisture, light intensity, and rainfall. Data were analyzed descriptively to identify land conditions and potential risks that could affect crop yields. The study resulted in the design of a Smart Farming system that integrates IoT sensors, a database, and a monitoring dashboard. The system is capable of monitoring land conditions in real time, automatically displaying environmental parameters, and providing early warning notifications when land conditions are outside the optimal limits for plant growth. System integration enables farmers to obtain more accurate information to determine land management actions. The integration of Smart Farming technology has the potential to reduce the risk of crop failure through more effective land condition monitoring and data-driven decision-making. This research demonstrates that the use of digital technology can support increased productivity in small-scale agriculture. Future research can focus on implementing the system directly in the field and developing artificial intelligence-based predictive models to improve the accuracy of risk mitigation.
Smart Farming, Internet of Things, Digital Agriculture, Risk Mitigation, Crop Failure, Small-Scale Farming.
Copyright (c) 2026 Achmed Abdillah, Ach. Nur Maulidi (Penulis) https://creativecommons.org/licenses/by/4.0
Implementasi Business Intelligence dalam Visualisasi Data Penjualan Produk pada UMKM
Perkembangan teknologi informasi mendorong UMKM untuk memanfaatkan data sebagai dasar pengambilan keputusan bisnis. Namun, sebagian besar UMKM masih mengalami kesulitan dalam mengolah data penjualan yang terus meningkat sehingga informasi yang dihasilkan belum dimanfaatkan secara optimal. Tujuan: Penelitian ini bertujuan untuk mengimplementasikan Business Intelligence dalam visualisasi data penjualan produk pada UMKM guna menghasilkan informasi yang lebih mudah dipahami dan mendukung pengambilan keputusan berbasis data. Metode: Penelitian menggunakan metode deskriptif dengan pendekatan implementatif. Tahapan penelitian meliputi pengumpulan data transaksi penjualan, data cleaning, transformasi data, analisis Business Intelligence, visualisasi data, dan evaluasi dashboard. Data yang digunakan berupa data penjualan produk UMKM yang kemudian diolah menjadi berbagai indikator kinerja dan disajikan dalam bentuk dashboard interaktif. Hasil: Hasil penelitian menunjukkan bahwa implementasi Business Intelligence mampu menyajikan informasi penjualan secara lebih efektif melalui visualisasi data. Dashboard yang dibangun menampilkan total penjualan sebesar Rp45.000.000 dengan total produk terjual sebanyak 1.250unit dan 450 transaksi. Analisis menunjukkan adanya tren peningkatan penjualan dari Januari hingga Juni serta kategori makanan sebagai penyumbang terbesar dengan kontribusi sebesar 45% terhadap total penjualan. Selain itu, produk Keripik Singkong menjadi produk dengan tingkat penjualan tertinggi. Kesimpulan: Implementasi Business Intelligence berhasil mengubah data transaksi menjadi informasi yang informatif dan mudah dipahami sehingga dapat membantu pelaku UMKM dalam melakukan analisis bisnis dan pengambilan keputusan secara lebih cepat, tepat, dan berbasis data. Penelitian selanjutnya dapat mengembangkan sistem dengan fitur prediksi penjualan dan analisis perilaku pelanggan untuk meningkatkan akurasi perencanaan bisnis.
Copyright (c) 2026 Ahmad Fauzan (Penulis); khoirul mufid (Penerjemah) https://creativecommons.org/licenses/by/4.0
Proactive Retail Intelligence: Integrating Apriori Algorithm and Telegram API for Product Inventory Optimization
The development of digitalization in the micro, small, and medium enterprise (MSME) sector has resulted in an increase in the volume of transaction data stored through Point of Sales (POS) systems. However, most of this data is still used as historical archives and has not been optimally utilized to support business decision-making. This condition often causes business actors to experience delays in recognizing consumer purchasing patterns that have the potential to be used to optimize sales strategies and inventory management. This study aims to design and implement a Proactive Business Intelligence system that can transform transaction data analysis results into operational recommendations automatically and in real-time. The method used is Market Basket Analysis with the Apriori algorithm to extract association patterns between products based on support, confidence, and lift ratio values. The research dataset consists of retail transaction data transformed using the One-Hot Encoding technique so that it can be processed in the form of a binary matrix. Association rules that meet the threshold of confidence ≥ 0.80 and lift ratio > 1.20 are then processed by the filtering engine and sent to users through the Telegram Bot API integration. The results showed that the system successfully identified a strong complementary relationship between Ground Coffee and Palm Sugar products with a confidence value of 0.88 and a lift ratio of 1.33. Furthermore, black-box testing showed that the entire process, from data reading to notification delivery, only took an average of 1.48 seconds. These findings demonstrate that the integration of the Apriori algorithm and the Telegram Bot API can support faster, more proactive, and more efficient decision-making for micro and medium-sized retail businesses.
Copyright (c) 2026 ANISA TRIYANA, AURA ILLA SARI (Penulis) https://creativecommons.org/licenses/by/4.0
Application of Machine Learning in Smart Agriculture to Predict Crop Yields Based on Environmental Factors
The development of digital technology in the agricultural sector has led to the emergence of the concept of Smart Agriculture as a solution to increase agricultural productivity and efficiency through the use of data and artificial intelligence. The main problem in modern agriculture is the uncertainty of environmental conditions such as temperature, humidity, rainfall, light intensity, and soil conditions that can affect crop production yields. This study aims to apply Machine Learning methods to predict crop yields based on environmental factors to assist the decision-making process in the agricultural sector. The research method used is a quantitative approach with an experimental method, namely through the stages of collecting environmental data and crop yield data, data preprocessing, building a Machine Learning model, and evaluating model performance using prediction error measurement parameters. The results show that the Machine Learning model is able to process environmental factor data to find patterns of relationships with crop productivity and produce a prediction system that can be used to support agricultural decisions. The preprocessing process and feature selection are important factors in improving model quality because environmental data has complex and dynamic characteristics. The conclusion of this study shows that the application of Machine Learning in Smart Agriculture can be an effective approach to improving the accuracy of crop yield predictions and supporting data-driven agricultural management. Further research can be developed by integrating real-time IoT sensor data and testing various Machine Learning algorithms to obtain models with more optimal prediction performance.
Smart Agriculture, Machine Learning, Crop Prediction, Environmental Factors, Artificial Intelligence.
Copyright (c) 2026 Mohammad Faisol (Penulis); Syamsul Arifin (Penerjemah) https://creativecommons.org/licenses/by/4.0
Classification of Batik Types (Hand-Written, Stamped, and Printed) Using Gray Level Co-Occurrence Matrix Feature Extraction and Support Vector Machine
Batik is one of Indonesia's cultural heritages that has various production techniques, including hand-drawn batik, stamped batik, and printed batik. Despite having different production processes, these three types of batik often display similar motifs, making it difficult for the general public to distinguish them through visual observation alone. The identification process, which still relies on manual observation, also requires specialized knowledge and experience, potentially leading to errors in determining the type of batik. Therefore, a technology-based approach is needed that can perform identification more objectively and consistently. This study aims to build a classification model for batik types by utilizing digital image processing and machine learning techniques. The research stages include image preprocessing, texture feature extraction using the Gray Level Co-occurrence Matrix (GLCM), and the classification process using the Support Vector Machine (SVM) algorithm. The dataset used consists of 300 batik images grouped into three classes: hand-drawn batik, stamped batik, and printed batik. The extracted texture features include contrast, correlation, energy, and homogeneity, which are then used as input attributes in the classification model. The test results show that the developed model is able to achieve an accuracy value of 90.00%, a precision of 90.25%, a recall of 90.00%, and an F1-score of 90.05%. These findings indicate that the combination of GLCM and SVM methods is effective in representing the texture characteristics of batik images and is able to distinguish batik types automatically with a good level of accuracy. This research is expected to support the use of computer vision technology in the batik identification process and contribute to efforts to preserve Indonesian culture through the application of digital technology.
Copyright (c) 2026 AHMAD Farizi, ABD ROSYID (Penulis) https://creativecommons.org/licenses/by/4.0
AI Readiness Framework for Higher Education: Sistesis Multidimensi Kesiapan Adopsi Generative AI di Kampus
Perkembangan Generative Artificial Intelligence (GenAI) telah mendorong transformasi signifikan dalam ekosistem pendidikan tinggi melalui pemanfaatan chatbot cerdas, sistem pembelajaran adaptif, dan asisten penulisan akademik. Kendati demikian, akselerasi adopsi teknologi berbasis kecerdasan buatan generatif ini sering kali memicu kerentanan etis dan kepanikan pedagogis akibat ketidaksiapan fondasi institusional. Penelitian ini bertujuan untuk mengidentifikasi, menganalisis, dan mensintesis faktor-faktor penentu serta tantangan struktural yang memengaruhi kesiapan perguruan tinggi dalam mengadopsi GenAI secara efektif dan bertanggung jawab. Dengan menerapkan pendekatan kualitatif deskriptif melalui metode Systematic Literature Review (SLR) yang mengacu pada protokol Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), pelacakan artikel ilmiah dilakukan secara ketat pada basis data Scopus, ScienceDirect, dan Google Scholar dalam rentang publikasi 2020–2026. Data dianalisis secara induktif menggunakan analisis tematik untuk mengkristalisasi kode-kode literatur menjadi klaster tema makro. Hasil sintesis menunjukkan bahwa kesiapan perguruan tinggi secara komprehensif dipengaruhi oleh tiga dimensi utama, yaitu kesiapan infrastruktur (infrastructure readiness), kesiapan kompetensi (competency readiness), dan kesiapan tata kelola (governance readiness). Kesiapan infrastruktur mencakup stabilitas teknologi komputasi awan dan pelokalan peladen; kesiapan kompetensi meliputi penguasaan rekayasa perintah (prompting) dan literasi epistemis civitas akademika; sedangkan kesiapan tata kelola bertumpu pada kebijakan etika, regulasi hak cipta, dan mitigasi risiko mutu pembelajaran. Sebagai kontribusi teoretis dan panduan praktis, penelitian ini mengusulkan kerangka konseptual AI Readiness Framework for Higher Education (AIRF-HE). Kerangka integratif ini diharapkan dapat memandu para pengambil kebijakan di perguruan tinggi dalam merancang strategi implementasi AI yang adaptif, etis, aman, dan berkelanjutan.
Copyright (c) 2026 Barrotut Taqiyah, Sri Maulani (Penulis) https://creativecommons.org/licenses/by/4.0
Analysis of an Internet of Things (IoT)-Based Soil Moisture Monitoring System to Support the Irrigation of Chili Plants
Soil moisture is one of the key factors affecting the growth and productivity of chili pepper plants. Soil moisture monitoring, which is still done manually, often results in improper watering because it relies on visual observations and farmers’ experience. Advances in the Internet of Things (IoT) offer a solution through monitoring systems capable of providing real-time information on soil conditions. This study aims to analyze an Internet of Things (IoT)-based soil moisture monitoring system to support the irrigation process for chili pepper plants. The study employs a quantitative method using a systems analysis approach. Data were obtained through soil moisture measurements using sensors integrated with a microcontroller, an internet network, an IoT platform, and a monitoring dashboard. The sensor readings were then analyzed using quantitative descriptive methods to evaluate the system’s ability to support irrigation decision-making. The results showed that the system was capable of monitoring soil moisture in real time and displaying the data via the monitoring dashboard. The soil moisture values obtained during consecutive observations were 35%, 42%, 55%, 68%, 74%, and 63%. At soil moisture levels of 35% and 42%, the system identified the soil as being in the “dry” category, indicating a need for watering. After watering, the moisture level rose to 74%, indicating that the soil was in the “wet” category. The system also successfully transmitted data automatically via the internet, allowing for remote monitoring. The IoT-based soil moisture monitoring system has proven capable of supporting more effective and efficient watering of chili plants by providing accurate, real-time information on soil conditions. Future research could develop the system with an automatic watering feature to improve the efficiency of crop management.
Keywords: Internet of Things (IoT), Soil Moisture, Real-Time Monitoring, Chili Plant Irrigation, Smart Farming.
Verditya Puja Dharmawan Verditya Puja Dharmawan
Copyright (c) 2026 Rafael Ainur Hayat, Verditya Puja Dharmawan (Penulis) https://creativecommons.org/licenses/by/4.0
Implementing Machine Learning to Predict Plant Irrigation Needs in an IoT-Based Smart Agriculture System
The development of Internet of Things (IoT) and Machine Learning technology has opened up opportunities in the development of Smart Agriculture to improve the efficiency of agricultural resource management, particularly in crop irrigation systems. Conventional irrigation management often leads to water waste and reduces crop productivity. The purpose of this study is to implement Machine Learning in predicting crop irrigation needs in an Internet of Things-based Smart Agriculture system so that water use can be carried out more effectively and efficiently. The research method uses a quantitative method with a system development approach that integrates IoT sensors and Machine Learning algorithms. Environmental data is obtained from soil moisture, air temperature, and humidity sensors collected in real-time, then through preprocessing, model training, and testing to produce predictions of crop irrigation needs. The results show that the system successfully collects and processes environmental data automatically through IoT devices. The Machine Learning model is able to analyze the relationship between soil moisture, air temperature, and air humidity to produce predictions of crop water needs in real-time. The integration of IoT and Machine Learning enables the continuous land monitoring process and helps determine the timing and amount of irrigation more precisely than conventional methods. The implementation of Machine Learning in an IoT-based Smart Agriculture system can support decision-making in irrigation management more accurately and efficiently. The developed system has the potential to increase agricultural productivity while optimizing water resource use. Future research could focus on developing more complex predictive models utilizing more diverse environmental data to improve the system's accuracy.
Copyright (c) 2026 AHMAD HAMDANI, MOH. IZZUL HAQ RAMDLANI (Penulis) https://creativecommons.org/licenses/by/4.0
IoT Implementation in Machine Learning-Based Automatic Irrigation System for Smart Agriculture
Water volumetric losses and decreased crop productivity in the agricultural sector are often caused by traditional irrigation systems that are inflexible in dealing with fluctuating environmental variables. To address this problem, this research integrates IoT architecture and Machine Learning intelligent computing to create an automatic irrigation system that is water-efficient and capable of maintaining ideal soil quality. This quantitative experiment utilizes an ESP32 microcontroller, a humidity sensor, and a DHT22 temperature sensor as instruments for collecting direct environmental data. This data is evaluated by an ML model (based on local weather data) to dynamically determine the volume and schedule of watering. The device's performance was analyzed over several weeks of operation and compared to a conventional automatic irrigation system. The system recorded 94.5% accuracy in projecting soil hydration needs. Empirical testing proved that this smart device is capable of saving water by 28.4% compared to conventional systems, while ensuring stable soil moisture within agronomic parameter limits (60%–80%). The synergy of IoT and machine learning has proven to provide a precise solution for water management in today's smart agriculture. Future research should be directed at the application of deep learning algorithms for long-term microweather predictions and implementation on a more complex variety of crop types.
Copyright (c) 2026 Imam (Penulis); Moh. Gilank Alamsyah (Penerjemah) https://creativecommons.org/licenses/by/4.0
Analisis Peran Sistem Informasi dalam Meningkatkan Efisiensi Administrasi Sekolah
Abstract Perkembangan teknologi informasi telah mendorong transformasi digital di berbagai sektor, termasuk pendidikan, sehingga sekolah mulai memanfaatkan sistem informasi untuk mendukung proses administrasi, seperti pengelolaan data siswa, data guru, keuangan, jadwal pelajaran, dan arsip dokumen. Penerapan sistem informasi diharapkan mampu mengatasi berbagai kendala administrasi konvensional, seperti proses yang lambat, kesalahan pencatatan, serta kesulitan dalam penyimpanan dan pencarian data. Penelitian ini bertujuan untuk menganalisis peran sistem informasi dalam meningkatkan efisiensi administrasi sekolah serta mengidentifikasi manfaat dan tantangan dalam penerapannya. Penelitian menggunakan metode studi literatur dengan mengkaji berbagai jurnal ilmiah, buku, dan artikel yang relevan mengenai sistem informasi dalam administrasi sekolah. Hasil kajian menunjukkan bahwa penerapan sistem informasi mampu meningkatkan efisiensi administrasi melalui percepatan pengolahan data, peningkatan akurasi informasi, pengurangan kesalahan pencatatan, kemudahan penyimpanan dan pencarian data, serta mendukung pengambilan keputusan yang lebih cepat dan tepat. Selain itu, sistem informasi meningkatkan kualitas layanan administrasi kepada seluruh warga sekolah. Namun, implementasinya masih menghadapi kendala, seperti keterbatasan infrastruktur teknologi, kompetensi sumber daya manusia, dan proses adaptasi terhadap perubahan teknologi. Dengan demikian, sistem informasi memiliki peran yang signifikan dalam meningkatkan efisiensi administrasi sekolah, sehingga diperlukan dukungan infrastruktur, pelatihan pengguna, dan komitmen manajemen agar implementasinya dapat berjalan secara optimal.
Sistem Informasi, Administrasi Sekolah, Efisiensi Administrasi, Teknologi Informasi, Transformasi Digital.
Copyright (c) 2026 Seindi pujiastutik Dwiyanti, Tryamina Salsabila (Penulis) https://creativecommons.org/licenses/by/4.0