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.
Dynamics of Informatics Research in the Industry 4.0 Era
Research Technological developments in the Industry 4.0 era have brought significant changes in various fields, including informatics. The current dynamics of informatics research are characterized by the increasing integration of technologies such as artificial intelligence, big data, the Internet of Things (IoT), and cloud computing in various sectors of life. This study aims to examine the developments, trends, and challenges in informatics research in the Industry 4.0 era. The method used is a literature study of various scientific journals and related publications. The results of the study indicate that informatics research plays a vital role in supporting digital transformation, increasing system efficiency, and encouraging innovation in various sectors, such as education, health, and industry. However, there are challenges that must be faced, such as data security, privacy, and human resource readiness. Therefore, an appropriate strategy is needed in the development of informatics research to be able to adapt to the rapid and complex technological developments in the Industry 4.0 era.
Informatics Research Dynamics, Industry 4.0, Artificial Intelligence, Big Data, Internet of Things, Digital Transformation
Copyright (c) 2026 Abduhu Rafik, Romeo Djefzy Romadhoni (Penulis) https://creativecommons.org/licenses/by/4.0
Smart Agriculture: Integrasi Sensor Dan Data Analytics Dalam Sistem Pertanian Modern
Perkembangan teknologi digital mendorong transformasi sektor pertanian menuju sistem yang lebih modern melalui penerapan smart agriculture berbasis sensor dan analisis data. Namun, implementasi teknologi ini masih menghadapi tantangan dalam integrasi sistem dan pemanfaatan data secara optimal. Tujuan: Penelitian ini bertujuan untuk merancang dan mengkaji sistem smart agriculture yang mengintegrasikan sensor dan data analytics guna meningkatkan efisiensi dan efektivitas pengelolaan pertanian. Metode: Penelitian ini menggunakan pendekatan rekayasa sistem dengan metode eksperimental, melibatkan perancangan perangkat keras dan lunak, implementasi sensor berbasis IoT, serta analisis data menggunakan teknik statistik dan machine learning. Hasil: Sistem yang dikembangkan mampu melakukan monitoring real-time terhadap kelembapan tanah, suhu, dan pH dengan interval 10 menit, serta menghasilkan rekomendasi irigasi yang adaptif. Hasil pengujian menunjukkan tingkat akurasi sensor yang baik dengan deviasi rendah serta peningkatan efisiensi penggunaan air berdasarkan kondisi aktual lahan. Kesimpulan: Integrasi sensor dan data analytics terbukti efektif dalam mendukung pengambilan keputusan berbasis data pada sistem pertanian modern. Penelitian ini berhasil mencapai tujuan yang ditetapkan, meskipun masih diperlukan pengembangan lebih lanjut terkait infrastruktur, biaya, dan literasi pengguna untuk implementasi yang lebih luas.
Copyright (c) 2026 fauzan, khoirul (Penulis) https://creativecommons.org/licenses/by/4.0
Cloud Computing-Based Smart Agriculture Decision Support System (DSS) for Medium-Scale Farmers
In the era of Agriculture 4.0, the main challenge for medium-scale farmers is the inability to process massive data into tactical decisions and the high cost of IT infrastructure. Purpose: This study aims to design and test the effectiveness of a cloud computing-based Decision Support System (DSS) in optimizing the use of resources and crop yields. Method: This experimental study uses a system development methodology (SDLC) that integrates IoT with a hybrid cloud architecture, where soil and weather sensor data are processed through a Rule-Based Reasoning algorithm. Result: The test results showed a data transmission reliability rate of 98.5% with a decision recommendation accuracy of 92%.Significantly, this system is able to reduce water consumption by up to 32%, decrease fertilizer usage by 20%, and lower operational costs by 27%, which contributes to an estimated increase in crop yield of 10.3%. Conclusion: The implementation of a cloud-based DSS has proven to be an efficient and scalable solution for medium-scale farmers to migrate towards precision agriculture. Future research should integrate deep learning algorithms for visual pest detection to enhance the comprehensiveness of this intelligent system.
Copyright (c) 2026 Giovani Sapta Purnama A, fadil baiutul rahman (Penulis) https://creativecommons.org/licenses/by/4.0
Implementation of IoT-Based Smart Agriculture for Agricultural Land Monitoring
The agricultural sector currently faces a number of challenges, ranging from climate anomalies, water availability crises, inefficient land monitoring, and the continued prevalence of manual management practices. Advances in Internet of Things (IoT) technology have paved the way for the use of Smart Agriculture to optimize real-time monitoring of land conditions. This study aims to design and implement an IoT-based Smart Agriculture system for monitoring agricultural land, enabling faster, more precise, and more efficient monitoring. The approach adopted is Research and Development (R&D), encompassing the stages of needs identification, system design, prototype assembly, device testing, and performance evaluation. This system was developed using a microcontroller integrated with soil moisture sensors, temperature and humidity sensors, and a soil pH meter. The data obtained is then transmitted via an internet connection to a server to be presented on a web dashboard interface. Test results show that the device successfully reads land conditions independently and transfers data to the server at specific time intervals. The soil moisture reader component is capable of tracking water volume fluctuations, both in dry and post-irrigation soil conditions. Temperature and humidity sensors display consistent data following daily weather dynamics, while the soil pH sensor produces accuracy within a reasonable tolerance range. The dashboard is proven capable of presenting up-to-date information, graphic visualizations, and warning notifications when soil moisture levels drop below the minimum limit. The implementation of Smart Agriculture with IoT technology has proven effective as a solution for monitoring today's agricultural land. This innovation can boost the efficiency of monitoring planted areas and facilitate the rapid determination of cultivation decisions. For future improvements, the system can be directed towards the integration of automatic irrigation features and predictive analytics supported by artificial intelligence (AI).
Copyright (c) 2026 Mohammad Faisol, Syamsul Arifin (Penulis) https://creativecommons.org/licenses/by/4.0
Implementation of Smart Agriculture Using Cloud Computing for Agricultural Data Management
Advances in digital technology are driving the transformation of the agricultural sector toward data-driven systems; however, agricultural data management still faces challenges in terms of integration, accessibility, and efficiency. Objective: This study aims to implement a cloud-based smart agriculture system to improve the effectiveness of real-time agricultural data management. Methods: This study employs an experimental approach by designing and implementing an Internet of Things (IoT)-based system integrated with a cloud platform, followed by performance testing of the system, including sensor accuracy, data transmission reliability, and information accessibility. Results: The results indicate that the system can transmit data with a success rate exceeding 95% and low latency, while generating accurate data with minimal deviation from field conditions. The system also improves resource efficiency, particularly in irrigation management based on soil moisture data. Conclusion: The implementation of a cloud-based smart agriculture system has proven effective in enhancing the quality of agricultural data management, although challenges remain in network and security aspects that require further development.
Keywords: smart agriculture, cloud computing, Internet of Things, agricultural data management, precision agriculture
Copyright (c) 2026 Moh. Ali Sobit, Riko Adi Setiawan (Penulis) https://creativecommons.org/licenses/by/4.0
Autonomous Integration of Internet of Things and Causal Machine Learning-Based Business Intelligence for Precision Agriculture Financial Risk Mitigation
Climate uncertainty and environmental data dynamics pose major challenges to the stability of agricultural sector profitability in the Economy 5.0 era. This research aims to develop an autonomous Business Intelligence (BI) system that synchronizes Internet of Things (IoT) rain sensor data with real-time financial profit projections. Using a systems development approach, this framework leverages conceptual data models to integrate heterogeneous data streams into a structured Big Data architecture. The system's analytical core combines a Bi-Directional Long Short-Term Memory (Bi-LSTM) algorithm for precise rainfall prediction with Causal Machine Learning to analyze cause-and-effect relationships across economic metrics. The results show that this integration enables transparent prescriptive decision-making in pre-rain risk mitigation strategies, such as optimizing fertilization and harvest schedules. The system's strength lies in its autonomous ability to detect data drift and independently retrain models to maintain accuracy amidst changing extreme weather patterns. This implementation significantly contributes to operational efficiency and increased profit margins by transforming IoT data into robust and adaptive business intelligence.
Autonomous Bussiness Intelligence, IoT, Bi-LSTM, Causal Machine Learning, Agricultural Profitability
Copyright (c) 2026 Anisa Triyana, Aura Illa Sari (Penulis) https://creativecommons.org/licenses/by/4.0
Introduction to Research Methodology in Informatics
Research in informatics has made a significant contribution to the development of technology and innovation based on computing systems. As the use of digital technology increases, the need for structured and scientifically sound research is becoming increasingly important. Therefore, an understanding of research methodology is fundamental for both students and researchers. Research methodology serves as a guideline for conducting the research process systematically, logically, and in accordance with scientific principles. This study aims to provide a comprehensive overview of the basic concepts of research methodology in informatics, including types of methods, research stages, and data collection and analysis techniques. The approach used is a literature review, reviewing various scientific sources such as books and journals. The results of the study indicate that informatics research combines theoretical and practical aspects, from analysis to system development. The selection of the right method significantly influences the quality and accuracy of research results.
Research Methodology, Informatics, Data Collection Techniques, Data Analysis, Scientific Research
Copyright (c) 2026 Ryan Putra Pratama Agustin, Dimas Prasetya Januar Dyansah (Penulis) https://creativecommons.org/licenses/by/4.0
Analisis Peran Teknologi Informasi dalam Kehidupan Sehari-hari
Perkembangan teknologi informasi yang pesat telah membawa perubahan signifikan dalam berbagai aspek kehidupan manusia. Teknologi informasi kini tidak hanya digunakan sebagai alat bantu, tetapi juga menjadi bagian integral dalam aktivitas sehari-hari, seperti komunikasi, pendidikan, pekerjaan, hingga transaksi ekonomi. Penelitian ini bertujuan untuk menganalisis peran teknologi informasi dalam kehidupan sehari-hari serta dampaknya terhadap efisiensi, efektivitas, dan kualitas hidup masyarakat. Penelitian ini bertujuan untuk mendapatkan pemahaman yang komprehensif tentang perkembangan dan pemanfaatan teknologi informasi di berbagai bidang melalui penelitian literatur, termasuk buku, jurnal ilmiah, dan artikel. Hasil penelitian menunjukkan bahwa teknologi informasi memiliki banyak manfaat, seperti membuat komunikasi lebih mudah melalui media digital, meningkatkan akses ke pendidikan dan informasi, dan mendukung aktivitas ekonomi melalui platform digital. Namun, penggunaan teknologi informasi juga dapat memiliki efek negatif, seperti menjadi ketergantungan pada sumber daya yang tidak dapat diakses. Dengan demikian, dapat disimpulkan bahwa teknologi informasi memainkan peran yang sangat penting dalam kehidupan sehari-hari karena memberikan banyak kemudahan dan manfaat. Oleh karena itu, untuk memaksimalkan manfaatnya dan mengurangi dampak negatifnya, teknologi informasi harus digunakan dengan bijak dan bertanggung jawab.
Teknologi Informasi, Kehidupan Sehari-hari, Dampak Teknologi, Digitalisasi, Produktivitas.
Copyright (c) 2026 Nurul Maudy Apriyani, Nur Hidayah Titiani (Penulis) https://creativecommons.org/licenses/by/4.0
The Role of Computer Science Research in the Development of Business Intelligence to Support Data-Driven Decision-Making
Advances in information technology in the digital age have prompted organizations to use data as a vital source of information to strengthen decision-making processes. However, disorganized data management often hinders quick and accurate access to information. Therefore, it is necessary to utilize technologies that can process data into useful information, one of which is the implementation of Business Intelligence. In this context, computer science research plays a crucial role in developing methods, systems, and technologies that support efficient data management and analysis. This study aims to explore the contribution of informatics research to the development of Business Intelligence in supporting data-driven decision-making. The method applied in this study is an analytical study with a qualitative approach, through literature review. Data were collected from various reference sources such as scientific journals, books, and scientific articles related to this topic. The data analysis process was conducted using a descriptive method that included a review and conclusions based on information obtained from various sources. The results of this study indicate that computer science research contributes to enhancing the capabilities of Business Intelligence systems to manage data, generate relevant information and facilitate more effective, efficient, and data-driven decision-making.
Keywords: Computer Science Research, Business Intelligence, Decision Making, Data, Information Technology
Resty Amylia Margareta Resty Amylia Margareta
Copyright (c) 2026 Resty Amylia Margareta, Wulidati Trixiana (Penulis) https://creativecommons.org/licenses/by/4.0
Implementasi Internet of Things (IoT) pada Sistem Penetasan Telur Otomatis untuk Meningkatkan Tingkat Keberhasilan Penetasan di Pamekasan
Di wilayah Pamekasan, proses penetasan telur pada peternakan unggas masih banyak dilakukan dengan metode konvensional yang memanfaatkan lampu sebagai sumber panas utama di dalam inkubator. Metode ini digunakan untuk menjaga suhu agar tetap hangat selama proses perkembangan embrio di dalam telur. Namun, sistem konvensional tersebut memiliki beberapa kelemahan, terutama dalam menjaga kestabilan suhu dan kelembaban di dalam inkubator. Perubahan suhu lingkungan serta tidak adanya sistem pengendalian otomatis sering menyebabkan kondisi di dalam inkubator menjadi tidak stabil. Hal ini dapat mempengaruhi perkembangan embrio dan berdampak pada rendahnya tingkat keberhasilan penetasan telur. Penelitian ini bertujuan untuk merancang dan mengembangkan sistem penetas telur otomatis berbasis Internet of Things (IoT) yang mampu melakukan pengendalian serta pemantauan kondisi inkubator secara real-time. Metode penelitian yang digunakan adalah metode rancang bangun sistem, yang meliputi tahap perancangan perangkat, proses implementasi, serta pengujian sistem yang telah dibuat. Sistem ini memanfaatkan sensor suhu dan kelembaban untuk mendeteksi kondisi di dalam inkubator, mikrokontroler sebagai pusat pengendali, serta aktuator yang berfungsi untuk menyesuaikan kondisi suhu dan kelembaban agar tetap berada pada tingkat yang optimal. Berdasarkan hasil pengujian yang telah dilakukan, sistem yang dirancang mampu menjaga suhu inkubator pada kisaran optimal yaitu sekitar 37–38°C. Selain itu, sistem juga mampu menjaga kestabilan kondisi inkubator secara lebih baik dibandingkan dengan metode penetasan konvensional. Dengan demikian, sistem penetas telur otomatis berbasis IoT ini diharapkan dapat meningkatkan efektivitas serta keberhasilan dalam proses penetasan telur.
Internet of Things; Penetas Telur; Otomatisasi; Monitoring; Inkubator
Copyright (c) 2026 Maktsuful Ghummah, Moh Amrullah (Penulis) https://creativecommons.org/licenses/by/4.0
Implementasi Deep Learning untuk Klasifikasi dan Prediksi Data pada Sistem Rekomendasi Berbasis Machine Learning
Perkembangan pesat teknologi komputasi telah mendorong adopsi luas sistem rekomendasi berbasis machine learning dalam berbagai domain industri, namun masih terdapat tantangan pada sistem rekomendasi konten yang umumnya bersifat konvensional dan berbasis collaborative filtering sehingga membatasi akurasi dan relevansi rekomendasi. Penelitian ini bertujuan untuk mengimplementasikan Deep Learning (DL), khususnya arsitektur Convolutional Neural Network dan Recurrent Neural Network (CNN-RNN), dalam menganalisis pola data pengguna guna menghasilkan rekomendasi yang lebih akurat dan personal. Metode yang digunakan meliputi integrasi model deep learning untuk feature extraction (ekstraksi fitur) dan collaborative prediction (prediksi kolaboratif) agar sistem mampu memahami preferensi serta pola perilaku pengguna secara mendalam. Pengembangan sistem dilakukan menggunakan pendekatan Cross-Industry Standard Process for Data Mining (CRISP-DM) dan diimplementasikan dalam bentuk prototipe sistem rekomendasi, dilengkapi dengan evaluasi performa model menggunakan metrik (Mean Absolute Error) dan (Root Mean Square Error) pada arsitektur cloud computing. Hasil pengujian menunjukkan bahwa model deep learning mampu memprediksi preferensi pengguna dengan tingkat akurasi yang tinggi, serta proses pelatihan dan inferensi model dapat berjalan secara efisien dan stabil, sehingga sistem mampu memberikan rekomendasi yang relevan, personal, dan sesuai konteks kebutuhan pengguna. Dengan demikian, penerapan deep learning dalam analisis data terbukti efektif dalam mengatasi keterbatasan sistem rekomendasi konvensional, meningkatkan kepuasan pengguna, serta menciptakan pengalaman yang lebih adaptif dalam ekosistem digital.
Deep Learning, Machine Learning, Convolutional Neural Network, Sistem Rekomendasi, rekomendasi konten, Collaborative Filtering, Prediksi Data.
Copyright (c) 2026 Radhit Purnomo (Penulis) https://creativecommons.org/licenses/by/4.0
Analysis and Design of an IoT-Based Broiler Chicken Coop Temperature Monitoring System Using a Smart Agriculture Approach
This study was motivated by the need for a more effective broiler chicken coop temperature monitoring system, given that conventional methods still have limitations in terms of accuracy, data continuity, and response speed to temperature changes. The objective of this study is to analyze the requirements and design an Internet of Things (IoT)-based temperature monitoring system using a Smart Agriculture approach to support real-time coop environment management. The method used is a technical research approach, which includes the stages of needs analysis, system design, and design evaluation. The system was designed using a DHT22 temperature sensor, an ESP32 microcontroller, and a web-based monitoring platform to display temperature data in real-time. The results show that the system is capable of real-time temperature monitoring, with the current temperature at 28.4°C, which falls within the safe range of 24.0–32.0°C. The temperature change graph shows fluctuations with a highest temperature of 31.2°C and a lowest temperature of 24.8°C, as well as a duration of out-of-range conditions lasting 15 minutes and 30 seconds. Additionally, the system successfully provided automatic notifications when temperatures exceeded normal limits. The conclusion of this study is that an IoT-based temperature monitoring system using a Smart Farming approach can improve efficiency, accuracy, and responsiveness in broiler chicken coop management. Further research is recommended to develop a system equipped with integrated automatic controls and improved network reliability.
Keywords: Internet of Things, Smart Farming, Temperature Monitoring, Broiler Chickens, Real-Time System
Kevin Oktavia Pratama Kevin Oktavia Pratama
Copyright (c) 2026 Maskana Ramadhani, Kevin Oktavia Pratama (Penulis) https://creativecommons.org/licenses/by/4.0
Analysis of the Application of Smart Agriculture in Monitoring Soil Moisture for Plant Growth
This study was motivated by the development of Smart Agriculture, which enables more accurate monitoring of agricultural environmental conditions through a data-driven approach. Soil moisture is one of the primary factors influencing plant growth; however, in conventional practices, such monitoring is still often conducted manually and lacks accuracy. This study aims to analyze the application of Smart Agriculture in soil moisture monitoring and to examine its relationship with plant growth. The method used is a literature review with a descriptive-analytical approach, in which data were obtained from various literature sources and analyzed to identify patterns of relationships between soil moisture and plant growth indicators. The results indicate a non-linear relationship between soil moisture and plant growth, with optimal growth occurring within a soil moisture range of 20%–50% volumetric. Conditions below 20% result in stunted growth, while levels above 50% cause reduced growth due to waterlogging. Growth indicators such as plant height, number of leaves, and visual condition show the best performance under optimal soil moisture conditions. The findings of this study confirm that the application of Smart Farming in soil moisture monitoring can support more accurate data-driven decision-making in agricultural management. Further research is recommended to develop practical applications of IoT-based systems to improve the accuracy and efficiency of monitoring.
Keywords: Smart Agriculture, Soil Moisture, Plant Growth, Data-Driven Monitoring, Agricultural Analysis
Verditya Puja Dharmawan Verditya Puja Dharmawan
Copyright (c) 2026 M. Husnol Hidayat, Verditya Puja Dharmawan, Rafael Ainur Hayat (Penulis) https://creativecommons.org/licenses/by/4.0
The Application of Business Intelligence to Support Data-Driven Decision-Making in Computer Science Research
This study aims to analyze the application of Business Intelligence (BI) as a tool to support data-driven decision-making in computer science research, so that the process of data analysis and interpretation can be conducted in a more structured, accurate, and efficient manner. The method used is a quantitative approach with a descriptive-explanatory design involving several stages, namely data collection from various sources, data preprocessing, data warehouse construction, analysis using data mining and OLAP techniques, and presentation of results through an interactive dashboard. The variables examined include data quality, analysis speed, information accuracy, and the effectiveness of visualization in supporting decision-making. The research results indicate that BI implementation enhances data quality through cleaning and integration processes, resulting in more consistent and analysis-ready data. Additionally, the analysis process successfully identifies patterns, trends, and relationships among variables that support objective decision-making. The use of interactive dashboards has also proven to facilitate data comprehension and accelerate real-time decision-making. Overall, the implementation of BI provides significant improvements in terms of efficiency, accuracy, and decision quality compared to conventional approaches, leading to the conclusion that BI is an effective strategy for generating
Copyright (c) 2026 mohammad efendi, mohammad ifandi (Penulis) https://creativecommons.org/licenses/by/4.0
Analisis Kepuasan Pengguna terhadap Sistem Informasi pada UMKM Menggunakan Kuesioner
Transformasi digital di sektor Usaha Mikro, Kecil, dan Menengah (UMKM) mengakselerasi adopsi sistem informasi sebagai alat penting dalam pengelolaan data dan proses pengambilan keputusan. Akan tetapi, kesuksesan penerapan sistem informasi tidak hanya bergantung pada faktor teknis saja, tetapi juga pada pandangan dan tingkat kepuasan para pengguna. Tujuan dari penelitian ini adalah untuk mengevaluasi tingkat kepuasan pengguna terhadap sistem informasi di UMKM dengan menggunakan pendekatan kuantitatif yang berlandaskan kuesioner. Alat ukur penelitian ini dirancang dengan skala Likert, yang mencerminkan aspek kemudahan penggunaan, kualitas informasi, serta keandalan sistem. Data berhasil dikumpulkan dari para pelaku UMKM yang telah menerapkan sistem informasi dalam kegiatan operasional sehari-hari mereka. Penelitian ini menggunakan pendekatan evaluatif yang mengutamakan pengguna sebagai tolok ukur utama bagi keberhasilan sistem, sejalan dengan penelitian sebelumnya yang menunjukkan bahwa tingkat kepuasan pengguna dipengaruhi oleh kualitas sistem dan layanan informasi. Penerapan kuesioner sebagai alat pengumpulan data telah terbukti efektif untuk mengukur pandangan pengguna mengenai sistem informasi dalam berbagai konteks, termasuk sistem akademik dan perpustakaan. Hasil penelitian ini diharapkan dapat memberikan wawasan empiris mengenai tingkat kepuasan pengguna serta mengidentifikasi elemen-elemen sistem yang perlu ditingkatkan, sekaligus menjadi acuan rekomendasi bagi pengembang dan pelaku UMKM dalam memaksimalkan penggunaan sistem informasi — tidak sekadar sebagai alat operasional, melainkan juga sebagai pendukung pengambilan keputusan yang strategis dan berkelanjutan.
kepuasan pengguna, sistem informasi, UMKM, kuesioner, analisis data
Copyright (c) 2026 tryamina salsabila (Penulis) https://creativecommons.org/licenses/by/4.0
Sales Data Analysis Using a Business Intelligence Dashboard to Support Strategic Decision-Making
Advances in information technology are driving organizations to leverage data as a strategic asset; however, many are still unable to effectively process sales data into decision-supporting insights. This study aims to analyze sales data using a Business Intelligence-based dashboard to support strategic decision-making. This study employs a quantitative approach using a descriptive-analytical method through the stages of data collection, preprocessing, the ETL (Extract, Transform, Load) process, and data visualization in an interactive dashboard. The results show that total sales reached Rp 2,456,789,000 with a growth of 12.45%, total transactions of 15,672 increased by 8.23%, and total products sold of 32,456 increased by 10.15%. Trend analysis indicates a consistent increase, with peak sales in October reaching Rp 360 million. The electronics category contributed the most at 40.20%, followed by fashion at 26.64%, while the retail customer segment dominated with a 40.20% contribution. The BI dashboard proved effective in presenting sales data in an informative manner and generating relevant insights to support data-driven strategic decision-making. This study successfully achieved its objectives; however, further development is recommended to integrate predictive analytics to improve the accuracy of business planning.
Keywords: Business Intelligence, Sales Dashboard, Data Analysis, Decision Making, Data Visualization
Copyright (c) 2026 Ilyana, Deni Mahmudi (Penulis) https://creativecommons.org/licenses/by/4.0
The Application of Business Intelligence and Data Mining Analysis for Predicting Product Sales Trends
Rapid advancements in information technology have driven the use of data as the foundation for business decision-making, with Business Intelligence and Data Mining emerging as the primary approaches for effectively processing and analyzing sales data. This study aims to implement the integration of Business Intelligence and Data Mining to accurately predict product sales trends. This study employs a quantitative approach involving the collection of historical sales data, preprocessing, the development of a BI dashboard, and predictive modeling using time series analysis as part of Data Mining techniques. The results show that the BI dashboard is capable of displaying sales trends visually and informatively, while the predictive model produces values close to actual data with an average difference of around 5 million rupiah per period and a low error rate. Seasonal patterns and sales growth trends during specific periods were consistently identified. The integration of Business Intelligence and Data Mining has proven effective in improving the accuracy of sales trend predictions and supporting data-driven decision-making. This research successfully achieved its intended objectives, although further development is still needed by incorporating external variables and more complex prediction methods to improve model accuracy in the future.
Keywords: Business Intelligence, Data Mining, Sales Forecasting, Time Series, BI Dashboard
Copyright (c) 2026 Barrotut Taqiyah, Sri maulani (Penulis) https://creativecommons.org/licenses/by/4.0
Digital Transformation in Agriculture Through Smart Agriculture: A Computer Science Research Perspective
Digital transformation in the agricultural sector is advancing rapidly through the implementation of smart agriculture, which integrates information technologies such as the Internet of Things (IoT), big data, and artificial intelligence to improve efficiency and productivity. However, the adoption of these technologies still faces various challenges, particularly in terms of infrastructure and digital literacy. This study aims to identify the role of information technology in supporting agricultural digital transformation and to design an integrated smart agriculture system framework. The study employs a literature review method using a qualitative-descriptive approach, involving the collection and analysis of relevant scientific sources, followed by conceptual analysis to develop a system model. The results indicate that smart agriculture comprises five main layers: data collection, communication, data processing, applications, and automation. The integration of these technologies generates real-time information—such as harvest predictions, irrigation recommendations, and pest detection—which enhances decision-making accuracy and resource efficiency. Additionally, the system features a feedback loop mechanism that enables continuous performance improvement. This study confirms that smart agriculture holds great potential for supporting the digital transformation of agriculture, although its implementation requires infrastructure support and capacity building for users. Further research is recommended to develop systems that are simpler and more adaptable to local conditions.
Keywords: Smart Agriculture, Internet of Things, Digital Transformation, Artificial Intelligence, Agricultural Information Systems
Moh. Izzul Haq Ramadlani Moh. Izzul Haq Ramadlani
Copyright (c) 2026 Ahmad Hamdani, Moh. Izzul Haq Ramadlani (Penulis) https://creativecommons.org/licenses/by/4.0
Pengembangan IoT & Artificial Intelligence dalam Smart Agriculture untuk Deteksi Penyakit Tanaman
Penelitian ini mengkaji penerapan teknologi IoT & Artificial Intelligence dalam sistem pertanian cerdas guna meningkatkan kemampuan deteksi penyakit tanaman secara lebih cepat dan tepat. Sistem yang dikembangkan dirancang untuk memantau kondisi tanaman secara berkelanjutan melalui sensor serta melakukan identifikasi penyakit berdasarkan analisis data lingkungan dan citra tanaman.Pendekatan penelitian menggunakan metode eksperimen berbasis machine learning yang meliputi pengumpulan data, tahap praproses, pelatihan model, hingga evaluasi performa sistem. Hasil pengujian menunjukkan bahwa sistem mampu memberikan tingkat akurasi yang tinggi dalam mendeteksi penyakit tanaman serta menghasilkan informasi secara real-time.Selain itu, pemanfaatan IoT memungkinkan proses pemantauan dilakukan secara terus-menerus sehingga mendukung peningkatan efisiensi dalam pengelolaan pertanian. Namun demikian, terdapat beberapa kendala seperti variasi kondisi lingkungan dan keterbatasan dataset. Oleh karena itu, pengembangan lanjutan diperlukan, terutama dengan memanfaatkan metode deep learning yang lebih kompleks serta data yang lebih beragam
Copyright (c) 2026 Ach. Ramadani (Penulis); muhammad faruq (Penerjemah) https://creativecommons.org/licenses/by/4.0
Smart Agriculture as an IT Innovation to Support Food Security
Food security has become a strategic issue amid rising production demands and limited agricultural resources, making technology-based innovations such as smart agriculture necessary to improve efficiency and productivity. This study aims to design an information technology-based smart agriculture system capable of directly monitoring environmental conditions and assisting in decision-making processes for agricultural management. This study employs a quantitative method using a system development approach, encompassing the design of an IoT architecture, the implementation of environmental sensors, and system performance testing across various distances and operational conditions. The designed system is capable of monitoring environmental parameters such as soil moisture (40%–75%), air temperature (26°C–34°C), and light intensity (300–900 lux) with stability. Communication performance shows a packet delivery ratio of 100% at close range and remains above 90% at distances exceeding 150 meters, with latency ranging from 150–450 ms. Energy consumption was successfully reduced through duty cycling and sleep mode mechanisms, enabling the system to operate for extended periods. Conclusion: This study demonstrates that the implementation of IT-based smart agriculture can enhance monitoring efficiency and support data-driven decision-making. The developed system has proven to be reliable, efficient, and has the potential for widespread application to support food security. Further research is recommended to integrate more complex artificial intelligence analytics to enhance the system’s predictive capabilities and automation.
Keywords: Smart Agriculture, Internet of Things, Environmental Monitoring, Food Security, Energy Efficiency
Copyright (c) 2026 Ach. Nur Maulidi, Achmed Abdillah (Penulis) https://creativecommons.org/licenses/by/4.0
The Role of Computer Science Research in the Development of Business Intelligence Information Systems to Support Decision-Making
Rapid advancements in information technology in the digital age have prompted many organizations to treat data as a strategic asset to aid in the decision-making process. One technology frequently implemented is Business Intelligence (BI), which enables the transformation and analysis of data into meaningful information. This study aims to explore the contribution of computer science research to the development of BI systems to support the decision-making process. The methodology employed consists of a qualitative approach through a literature review, involving the collection of information from academic journals, books, and other relevant academic sources. The research findings indicate that computer science research plays a crucial role in creating technologies such as Data Warehousing, Data Mining, Online Analytical Processing (OLAP), machine learning, and big data analysis, which enhance the capabilities of BI systems in processing and analyzing data. Furthermore, BI systems supported by computer science research can present information interactively and in an easily understandable manner, thereby improving the effectiveness and accuracy of decision-making. However, there are several challenges that still need to be addressed, such as data quality issues, data integration, human resource limitations, and data security concerns. Therefore, ongoing research and development in computer science is necessary to optimize BI systems in supporting decision-making within organizations.
Keywords: Computer Science Research, Business Intelligence, Decision Making, Data Analysis, and Information Systems.
Copyright (c) 2026 Septiana Wulandari, Hoirul Umam (Penulis) https://creativecommons.org/licenses/by/4.0
Introduction to Informatics Research Basic Concepts and Research Methodology
Informatics research is an important foundation for students and researchers in understanding the scientific process in the field of information technology. This article discusses the basic concepts of informatics research, including the definition of research, objectives, and characteristics of scientific research. Furthermore, it explains various types of research methodologies commonly used, such as qualitative, quantitative, and mixed methods research. Research in informatics focuses not only on software development but also includes data, artificial intelligence, information systems, and human-computer interaction. The research process is explained systematically, starting from problem discussion, literature review, method determination, data collection, and analysis and reporting of research results. By understanding the basic concepts and appropriate research methodology, researchers are expected to be able to produce valid, systematic scientific work that can make a real contribution to the development of informatics.
Copyright (c) 2026 nur beilqis anastasya, alfiyatus zahroh (Penulis) https://creativecommons.org/licenses/by/4.0
Pendekatan Hybrid Business Intelligence untuk Meningkatkan Akurasi Pengambilan Keputusan
Penelitian ini mengkaji penerapan pendekatan Hybrid Business Intelligence (BI) yang mengintegrasikan metode analisis data tradisional dengan teknologi modern dalam rangka meningkatkan akurasi pengambilan keputusan. Pendekatan ini memadukan keunggulan sistem BI konvensional, seperti data warehouse dan Online Analytical Processing (OLAP), dengan teknologi mutakhir seperti machine learning dan analitik prediktif. Integrasi tersebut diharapkan mampu menghasilkan sistem analitik yang lebih komprehensif, adaptif, dan responsif terhadap dinamika lingkungan bisnis yang semakin kompleks. Metode penelitian yang digunakan adalah studi literatur dengan pendekatan analisis komparatif terhadap berbagai penelitian terdahulu yang relevan. Data yang dianalisis berasal dari jurnal ilmiah nasional dan internasional yang membahas perkembangan Business Intelligence, penerapan teknologi analitik modern, serta pengambilan keputusan berbasis data. Proses analisis dilakukan dengan mengidentifikasi, membandingkan, dan mensintesis berbagai konsep serta temuan penelitian untuk memperoleh pemahaman yang lebih mendalam. Hasil penelitian menunjukkan bahwa pendekatan Hybrid Business Intelligence mampu mengoptimalkan kelebihan dari masing-masing metode, baik dari sisi pengolahan data historis maupun kemampuan prediktif. Sistem ini tidak hanya menghasilkan informasi deskriptif, tetapi juga mampu memberikan insight yang lebih akurat dan relevan untuk mendukung pengambilan keputusan strategis. Selain itu, fleksibilitas dalam integrasi berbagai sumber data serta kemampuan adaptasi terhadap perubahan menjadi keunggulan utama dari pendekatan ini. Secara keseluruhan, penelitian ini memberikan kontribusi dalam pengembangan konsep Business Intelligence yang lebih inovatif dan efektif. Pendekatan Hybrid BI diharapkan dapat menjadi solusi dalam meningkatkan kualitas pengambilan keputusan serta mendorong organisasi untuk lebih optimal dalam memanfaatkan data sebagai aset strategis di era digital.
Copyright (c) 2026 mohammat dafid, fathur rosi (Penulis) https://creativecommons.org/licenses/by/4.0
Penerapan Artificial Intelligence dalam Analisis Teks untuk Meningkatkan Interaksi pada Game Berbasis Natural Language Processing
Perkembangan industri video game mendorong terciptanya pengalaman bermain yang semakin imersif dan realistis, namun masih terdapat kendala pada sistem dialog Non-Player Character (NPC) yang umumnya bersifat statis dan berbasis decision tree sehingga membatasi fleksibilitas interaksi pemain. Penelitian ini bertujuan untuk mengimplementasikan Artificial Intelligence (AI), khususnya Natural Language Processing (NLP), dalam menganalisis masukan teks pemain guna menghasilkan interaksi yang lebih dinamis dan kontekstual. Metode yang digunakan meliputi integrasi model NLP untuk intent classification (klasifikasi niat) dan sentiment analysis (analisis sentimen) agar sistem mampu memahami maksud serta emosi dalam bahasa alami pemain. Pengembangan sistem dilakukan menggunakan pendekatan System Development Life Cycle (SDLC) dan diimplementasikan dalam bentuk purwarupa game interaktif, lengkap dengan evaluasi performa latensi jaringan (Quality of Service) dan sistem keamanan (firewall) pada arsitektur cloud server. Hasil pengujian menunjukkan bahwa model NLP mampu menginterpretasikan struktur kalimat dengan tingkat akurasi yang tinggi, serta pertukaran data komunikasi dapat berjalan stabil dan aman, sehingga NPC dapat memberikan respons yang adaptif, variatif, dan sesuai konteks. Dengan demikian, penerapan AI dalam analisis teks terbukti efektif dalam mengatasi keterbatasan dialog statis, meningkatkan keterlibatan pemain, serta menciptakan pengalaman interaksi yang lebih responsif dalam lingkungan virtual.
Copyright (c) 2026 Moh. Rafael Kamil Ardiansyah, Nabila Ambarwati (Penulis) https://creativecommons.org/licenses/by/4.0
Tinjauan Sistematis Metodologi Penelitian Informatika: Tren, Tantangan, dan Arah Masa Depan
Selama satu dekade terakhir, bidang informatika telah mengalami percepatan luar biasa yang mengubah cara penelitian dilakukan baik dalam pendekatan metodologis, teknik analisis, maupun keterpaduan lintas disiplin. Penelitian ini berupaya menelususri dinamika tersebut dengan mengidentifikasi tren metodologi penelitian terkini, menyoroti tantangan fundamental yang muncul, dan menawarkan arah pengembangan metodologi yang lebih adaptif serta berkelanjutan. Studi ini menggunakan mixed methods yang mengombinasikan tinjauan sistematis terhadap beberapa artikel ilmiah internasional. Data dikumpulkan berdasarkan protokol PRISMA melalui systematic literature review dan dianalisis menggunakan perpaduan statistik deskriptif dan analisis tematik. Temuan menunjukkan kecenderungan kuat terhadap penggunaan metodologi kuantitatif berbasis data terutama machine learning dan data mining yang kemudian diimbangi oleh meningkatnya pemanfaatan mixed methods dalam riset interdisipliner. Beberapa tantangan utama yang teridentifikasi mencakup rendahnya tingkat replikasi hasil penelitian, kompleksitas pengelolaan data berskala besar, serta integrasi aspek sosial dan etika ke dalam kerangka konseptual terpadu yang memetakan hubungan antara tren metodologis, hambatan struktural, dan arah masa depan disiplin. Kontribusi ini diharapkan dapat memperkuat fondasi pengembangan metodologi penelitian informatika yang lebih tangguh, transparan, dan relavan dengan kebutuhan konteksual, khususnya di negara berkembang seperti Indonesia.
metodologi penelitian, informatika, systematic review, reproducibility.
Copyright (c) 2026 Noera Khairiyati Zulkaranen, nadiva qolbi (Penulis) https://creativecommons.org/licenses/by/4.0
Literature Review: IoT Device Authentication Mechanism to Prevent Injection Attacks on Smart Agriculture Database
The implementation of Internet of Things (IoT) technology in smart agriculture ecosystems has brought significant transformations in resource management efficiency, but on the other hand, it has given rise to very complex cybersecurity challenges, particularly regarding the protection of data integrity in central databases. IoT devices, which generally have limited computing and energy resources, often become weak points exploited by attackers as vectors to launch injection attacks, such as SQL Injection or NoSQL Injection, which can result in sensor data manipulation, theft of sensitive information, and even the failure of automatic irrigation or fertilization systems. This Systematic Literature Analysis (SLR) was conducted to identify, analyze, and synthesize various IoT device authentication mechanisms that are most effective in mitigating the risk of injection attacks on smart agriculture infrastructure. Through an in-depth review of scientific literature from the past five years, this study explores various authentication schemes ranging from energy-efficient lightweight cryptography protocols, token-based authentication systems such as JWT and OAuth2, to the use of blockchain technology to ensure decentralized and unmanipulated device identity. The analysis shows that while strong authentication methods are crucial, there is a significant trade-off between the level of security complexity and battery power consumption of sensor nodes in the field. The findings in this SLR provide a comprehensive overview of the current state of agricultural IoT security research, highlighting the advantages of each mechanism in validating data payloads before they are entered into the database, and identifying current research gaps that require the development of more adaptive, lightweight, and secure cross-layer hybrid authentication protocols to ensure operational sustainability and data reliability in future-generation smart farming systems.
Copyright (c) 2026 Yuris Ikrar RABBANI, Wildan Ramadhani (Penulis) https://creativecommons.org/licenses/by/4.0
Exploring the Ethics and Risks of Artificial Intelligence in Automated Decision Making
<pre id="tw-target-text" class="tw-data-text tw-text-large tw-ta" dir="ltr" tabindex="-1" role="text" data-placeholder="Terjemahan" data-ved="2ahUKEwijguCU-5SUAxVDaXADHZJsKa0Q3ewLegQIDBAV" aria-label="Teks terjemahan: The transformation of crucial sectors such as law enforcement, healthcare, and finance is currently heavily influenced by the adoption of Artificial Intelligence (AI) in Automated Decision-Making (ADM) mechanisms. However, the increased independence of these systems brings new issues related to algorithm opacity, or the "black box" phenomenon. This poses a serious obstacle to ensuring accountability, transparency, and the protection of basic human rights amidst massive automation. This research is designed to examine the various ethical consequences and technical threats that accompany ADM systems. The primary focus of the analysis is directed at the influence of algorithmic bias and the lack of system interpretability on the principles of social justice and individual freedom. This research applies a qualitative approach through a literature review of 45 scientific articles and case studies published between 2020 and 2026. The methodological process was carried out by dissecting central themes related to global ethical standards and accountability structures in the use of algorithms. The data shows that the majority of the ADM systems studied (around 70%) contain "historical bias" derived from learning data, thus triggering discriminatory actions against marginalized communities. Furthermore, a substantial regulatory gap was identified, with contemporary legal systems unable to define who should be held accountable for errors resulting from automated decisions. The study concluded that the efficiency offered by ADM has not been matched by ethical maturity, so human oversight remains essential. The implementation of Explainable AI (XAI) and mandatory algorithm audits are crucial solutions to mitigate these risks. Going forward, international regulatory standardization governing legal liability in the artificial intelligence ecosystem is needed.">The transformation of crucial sectors such as law enforcement, healthcare, and finance is currently heavily influenced by the adoption of Artificial Intelligence (AI) in Automated Decision-Making (ADM) mechanisms. However, the increased independence of these systems brings new issues related to algorithm opacity, or the "black box" phenomenon. This poses a serious obstacle to ensuring accountability, transparency, and the protection of basic human rights amidst massive automation. This research is designed to examine the various ethical consequences and technical threats that accompany ADM systems. The primary focus of the analysis is directed at the influence of algorithmic bias and the lack of system interpretability on the principles of social justice and individual freedom. This research applies a qualitative approach through a literature review of 45 scientific articles and case studies published between 2020 and 2026. The methodological process was carried out by dissecting central themes related to global ethical standards and accountability structures in the use of algorithms. The data shows that the majority of the ADM systems studied (around 70%) contain "historical bias" derived from learning data, thus triggering discriminatory actions against marginalized communities. Furthermore, a substantial regulatory gap was identified, with contemporary legal systems unable to define who should be held accountable for errors resulting from automated decisions. The study concluded that the efficiency offered by ADM has not been matched by ethical maturity, so human oversight remains essential. The implementation of Explainable AI (XAI) and mandatory algorithm audits are crucial solutions to mitigate these risks. Going forward, international regulatory standardization governing legal liability in the artificial intelligence ecosystem is needed.
Copyright (c) 2026 Suro Jalil, Waliyur Rohman (Penulis) https://creativecommons.org/licenses/by/4.0
Implementasi Private Cloud Storage Berbasis Arsitektur Client-Server Menggunakan Kontainerisasi Docker
Perkembangan teknologi digital saat ini memicu lonjakan volume data yang sangat signifikan dalam aktivitas sehari-hari. Dalam pengelolaan aset digital tersebut, ketergantungan pada media penyimpanan fisik konvensional serta layanan public cloud kerap menimbulkan sejumlah hambatan. Risiko kerusakan perangkat, mahalnya biaya langganan untuk kapasitas yang besar, hingga timbulnya kekhawatiran terkait privasi informasi menjadi kendala yang cukup sering dihadapi. Situasi tersebut pada akhirnya memengaruhi efisiensi pengelolaan berkas dan membatasi fleksibilitas pengguna dalam mengakses data secara cepat. Penelitian ini difokuskan pada pengembangan sebuah sistem private cloud storage yang dirancang untuk menyediakan alternatif ruang penyimpanan lokal yang aman, efisien secara sumber daya, dan mudah diakses melalui jaringan. Pengembangan infrastruktur ini dimulai dari kegiatan studi literatur, analisis kebutuhan perangkat keras, hingga perancangan topologi jaringan dan pemodelan menggunakan Unified Modeling Language (UML). Pada tahap implementasi, sistem operasi Ubuntu Server digunakan sebagai fondasi utama, sementara teknologi kontainerisasi Docker dimanfaatkan untuk menjalankan layanan aplikasi cloud secara terisolasi agar kinerja komputer tetap ringan. Hasil pengembangan menunjukkan bahwa sistem ini berhasil menyediakan berbagai fitur operasional penting, seperti autentikasi keamanan (login), manajemen direktori (unggah, unduh, dan hapus berkas), akses perangkat secara serentak, serta pemantauan sisa kapasitas penyimpanan secara real-time. Kehadiran sistem private cloud ini membantu mempercepat proses transfer data antar-perangkat di jaringan lokal, meminimalisir risiko kebocoran privasi, dan memberikan kendali penuh kepada pengguna. Dengan demikian, infrastruktur cloud mandiri berbasis Docker ini menjadi solusi teknologi yang sangat layak untuk menggantikan ketergantungan pada layanan komputasi awan komersial, sehingga manajemen aset digital dapat berjalan dengan lebih aman, cepat, dan ekonomis.
Copyright (c) 2026 Moh imam Hidayatullah, Akmal (Penulis) https://creativecommons.org/licenses/by/4.0
Penerapan Metode Systematic Literature Review (SLR) dalam Memetakan Topik Riset Unggulan Mahasiswa Informatika
Latar Belakang: Perkembangan ilmu komputasi yang pesat menciptakan ekosistem penelitian yang kompleks, sehingga pemetaan literatur yang komprehensif sangat krusial untuk menyingkap tren riset mutakhir, mengidentifikasi celah penelitian, dan mencegah duplikasi karya ilmiah di kalangan mahasiswa informatika. Tujuan: Penelitian ini bertujuan untuk memetakan secara sistematis topik riset unggulan, metodologi dominan, serta tren interdisipliner pada publikasi riset informatika dari tahun 2020 hingga 2025. Metode: Penelitian ini murni merupakan tinjauan pustaka yang mengadopsi metodologi Systematic Literature Review (SLR) yang sepenuhnya didasarkan pada referensi yang ada tanpa pengambilan data di lapangan. Melalui perangkat lunak analitik, 50 artikel primer yang terbit antara tahun 2020 hingga 2025 diseleksi secara ketat dari basis data akademik untuk kemudian dilakukan ekstraksi data tematik. Hasil: Analisis tematik mengategorikan 50 literatur tersebut ke dalam empat klaster utama, yaitu: 1) Kecerdasan Buatan dan NLP (fokus pada model Transformers dan deteksi hate speech); 2) Rekayasa Perangkat Lunak (arsitektur microservice dan pola keamanan); 3) Teknologi Pendidikan (aplikasi Metaverse dan sistem tutoring cerdas); serta 4) Informatika Kesehatan (pemantauan kesehatan mental dan otomasi dataset klinis). Kesimpulan: Hasil kajian ini berhasil memetakan lintasan riset informatika, menunjukkan pergeseran fokus yang jelas menuju solusi sosio-teknis interdisipliner. Peta jalan riset berhasil dirumuskan untuk menjadi panduan akademis, dan penelitian selanjutnya disarankan untuk mengkaji implementasi AI yang etis serta keamanan siber berskala besar pada berbagai domain terapan tersebut.
Systematic Literature Review, Informatics, Research Trends, Computer Science, Mapping, Student Research
Copyright (c) 2026 andreas fiki, Panji cahya prasetyo (Penulis) https://creativecommons.org/licenses/by/4.0
Review of Informatics Research in Information Technology Development
Informatics research plays a strategic role in driving the development of information technology in today's digital era. This study aims to review various informatics research findings that contribute to the development of information technology, both in theory and implementation. The method used in this study is a literature review, reviewing various scientific journals, books, and relevant publications. The review results indicate that informatics research makes significant contributions in various fields, such as software development, information systems, data security, artificial intelligence (AI), and big data processing. Furthermore, informatics research plays a role in increasing the efficiency, accuracy, and speed of information processing, thus supporting more informed decision-making. Furthermore, the rapid development of information technology demands continuous innovation in informatics research to address challenges such as cybersecurity, data privacy, and complex system integration. Therefore, collaboration between academia, industry, and government is a crucial factor in developing applicable and impactful informatics research. Thus, this review confirms that informatics research is not only a theoretical basis, but also a key driver in creating innovative, adaptive, and sustainable information technology in the future.
Informatics Research, Information Technology, Innovation, Information Systems
Copyright (c) 2026 Rayyan (Penulis) https://creativecommons.org/licenses/by/4.0