Al'Adzkiya International of Computer Science and Information Technology Journal
Computer Science, Computer Engineering and Informatics: Data Science Artificial Intelligence, Machine Learning, Neural Network, Computer Architecture, Parallel and Distributed Computer, Pervasive Computing, Computer Network, Embedded System, Human—Computer Interaction, Virtual/Augmented Reality, Computer Security, Software Engineering (Software: Lifecycle, Management, Engineering Process, Engineering Tools and Methods), Programming (Programming Methodology and Paradigm), Data Engineering (Data and Knowledge level Modelling, Information Management (DB) practices, Knowledge Based Management System, Knowledge Discovery in Data), Network Traffic Modelling, Performance Modelling, Dependable Computing, High Performance Computing, Computer Security, , Networking Technology, Optical Communication Technology, Next Generation Media, Robotic Instrumentation, Information Search Engine, Multimedia Security, Computer Vision, Distributed Computing System, Mobile Processing, Next Network Generation, Computer Network Security, Natural Language Processing, Cognitive Systems. Management Informatics, Information System and developmental economics : Human-Machine Interface, Stochastic Systems, Information Theory, Intelligent Systems, IT Governance, Smart City, e-Learning, Business Intelligence, Information Retrieval, Business Process, Financial Technology (Fintech). Telecommunication and Information Technology: Modulation and Signal Processing for Telecommunication, Information Theory and Coding, Antenna and Wave Propagation, Wireless and Mobile Communications, Radio Communication, Communication Electronics and Microwave, Radar Imaging, Distributed Platform, Communication Network and Systems, Telematics Services and Security Network. Instrumentation and Mathematics: Optimal, Robust and Adaptive Controls, Non Linear and Stochastic Controls, Modelling and Identification, Robotics, Image Based Control, Hybrid and Switching Control, Process Optimization and Scheduling, Control and Intelligent Systems, Artificial Intelligent and Expert System, Fuzzy Logic and Neural Network, Complex Adaptive Systems.
Articles
63 Documents
<![CDATA[Design and Implementation of an Arduino-Based PIR Sensor System for Automated Bird Pest Control in Agricultural Areas ]]>
<![CDATA[Ade Sumaedi]]>;
<![CDATA[Angelina Hadriani]]>;
<![CDATA[Agus Suhendi]]>
<![CDATA[ Al'adzkiya International of Computer Science and Information Technology (AIoCSIT) Journal Vol 7, No 1 (2026) ]]>
Publisher : <![CDATA[Al'Adzkiya ]]>
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DOI: 10.55311/aiocsit.v7i1.357
<![CDATA[Indonesia relies heavily on agriculture as a fundamental pillar of its economy, with a large proportion of the population depending on crop cultivation, particularly rice farming. However, farmers frequently encounter recurring challenges each planting season, including attacks from various pests such as sparrows that feed on ripening rice grains. Traditionally, farmers use simple manual deterrent tools made from recycled cans hung around the rice field and connected with ropes that must be pulled to create noise. This conventional approach requires continuous human effort and is often ineffective, allowing birds to consume significant portions of the crop before harvest. As a result, farmers may experience losses reaching approximately 642 kilograms within a seven-month cultivation period. In response to this problem, this study proposes the development of an automated bird repellent system based on an Arduino microcontroller integrated with a Passive Infrared (PIR) sensor to detect bird movement through infrared radiation. When motion is detected, a buzzer is activated to generate sound that scares birds away from the field. The implementation of this automated system helps prevent birds from approaching the rice plants before harvest, thereby significantly reducing crop damage and eliminating farmer lossesKeyword : Hama Burung, Mikrokontroler Arduino, Sensor PIR, Buzzer, LCD I2C]]>
<![CDATA[Monitoring Expiration and Beyond-Use Dates of Non-Compounded Drugs Through Rule-Based and Machine Learning Approaches ]]>
<![CDATA[Marfuah Marfuah]]>;
<![CDATA[Masparudin Masparudin]]>;
<![CDATA[Raymond Erz Saragih]]>
<![CDATA[ Al'adzkiya International of Computer Science and Information Technology (AIoCSIT) Journal Vol 7, No 1 (2026) ]]>
Publisher : <![CDATA[Al'Adzkiya ]]>
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DOI: 10.55311/aiocsit.v7i1.392
<![CDATA[Public knowledge regarding the time limit for drug usage is still limited, despite existing educational efforts. Many people rely solely on the Expiration Date (ED) stated on the primary packaging, which indicates the post-production shelf life. However, once the packaging is opened, the usage limit no longer refers to the ED, but instead to the Beyond Use Date (BUD), which depends on the drug type, time, form, aroma, and color. The application of a Rule-Based Expert System, through a series of IF-THEN rules and machine learning. Three methods were used: decision tree (DT), Gaussian Naive Bayes (GBN), and K-Nearest Neighbor (KNN). The results showed that the decision tree method (95%), the GBN method (94%), and the KNN method (81%) can assist in monitoring drug use limits, minimizing losses, and enhancing user awareness and compliance to prevent medication errors.]]>
<![CDATA[Comparing Naïve Bayes and SVM for Public Sentiment on Online Gambling Policies ]]>
<![CDATA[Siti Qomariah]]>;
<![CDATA[Widya Noviana Noor]]>;
<![CDATA[Nazwa Risanti]]>
<![CDATA[ Al'adzkiya International of Computer Science and Information Technology (AIoCSIT) Journal Vol 7, No 1 (2026) ]]>
Publisher : <![CDATA[Al'Adzkiya ]]>
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DOI: 10.55311/aiocsit.v7i1.381
<![CDATA[Online gambling has become a major digital and social issue in Indonesia, prompting the government to implement various enforcement policies such as website blocking, account suspension, and advertisement filtering. However, illegal gambling content continues to circulate widely on social media platforms, generating diverse public reactions. This study aims to analyze public sentiment toward online gambling policies in Indonesia using Natural Language Processing (NLP) techniques and compare the performance of Naïve Bayes and Support Vector Machine (SVM) algorithms. Data were collected from Twitter (X), Facebook, and Instagram, resulting in 1,110 social media posts. The dataset was processed through text preprocessing, stemming, stopword removal, and TF-IDF feature extraction. To address class imbalance, the Synthetic Minority Oversampling Technique (SMOTE) was applied before model training. The experimental results show that SVM achieved better performance with an accuracy of 91.84%, while Naive Bayes achieved 86.39%. WordCloud and confusion matrix analyses indicate that negative sentiment is dominated by dissatisfaction with slow enforcement and persistent gambling advertisements, whereas positive sentiment reflects public support for stricter regulation. These findings demonstrate that SVM is more effective for Indonesian-language sentiment classification and can support data-driven evaluation of digital governance policies.]]>
