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All Journal Teknika Sistemasi: Jurnal Sistem Informasi RABIT: Jurnal Teknologi dan Sistem Informasi Univrab JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Jurnal SOLMA JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Informatika Kaputama (JIK) Jurnal Ilmiah ILKOMINFO - Ilmu Komputer & Informatika Journal of Computer Science, Information Technology and Telecommunication Engineering (JCoSITTE) JINAV: Journal of Information and Visualization Rambideun : Jurnal Pengabdian Kepada Masyarakat International Journal of Engineering, Science and Information Technology Jurnal Tika jeti Brilliance: Research of Artificial Intelligence Aptekmas : Jurnal Pengabdian Kepada Masyarakat Sisfo: Jurnal Ilmiah Sistem Informasi Jurnal Teknologi Terapan and Sains 4.0 Journal of Mathematics Instruction, Social Research and Opinion Jurnal Pendidikan Sains Indonesia (Indonesian Journal of Science Education) MEUSEURAYA : JURNAL PENGABDIAN MASYARAKAT Journal Of Artificial Intelligence And Software Engineering Jurnal Malikussaleh Mengabdi Journal of Advanced Computer Knowledge and Algorithms Gameology and Multimedia Expert JuTISI (Jurnal Teknik Informatika dan Sistem Informasi) Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN) Proceedings of Malikussaleh International Conference on Multidisciplinary Studies (MICoMS) Journal of Algorithmic Digital Engineering and Networks Jurnal Pengabdian Masyarakat Teknologi Informasi (PERANTI)
Rini Meiyanti
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Journal : journal of applied informatics and computing

 1 
Determining Eligibility for Smart Indonesia Program (PIP) Recipients Using the Backpropagation Method Rizkya, Ghinni; Nurdin, Nurdin; Meiyanti, Rini
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.9733

Abstract

The government provides financial assistance, educational opportunities, and expands access for students from poor or vulnerable families through the Smart Indonesia Program (PIP). At Madrasah Ibtidaiyah Negeri 20 Bireuen, the selection process for underprivileged students is still carried out manually by homeroom teachers by collecting data on students and their parents. This study aims to design, implement, and evaluate a classification method using the Backpropagation Neural Network to determine the eligibility of PIP scholarship recipients. The dataset consists of 309 entries, comprising 217 training data and 92 testing data, collected from MIN 20 Bireuen students between 2021 and 2023. The attributes used include father's occupation, mother's occupation, father's income, mother's income, number of dependents, number of vehicles, home ownership status, and card ownership status. Prior to training, the data were normalized using Min-Max scaling. The model was built with one hidden layer using a hard-limit activation function and a learning rate of 0.01. The classification results are categorized as "Eligible" and "Not Eligible". The model achieved an accuracy of 98%, precision of 100%, recall of 95%, and F1-score of 97%.
Method Design of an IoT-Based Automatic Pest Repellent System Prototype for Agriculture Kamaruzzaman, Hilda Zulfira; Ula, Munirul; Meiyanti, Rini
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10632

Abstract

Indonesia, as an agricultural country, still faces serious challenges in the farming sector, particularly pest attacks from birds and insects that significantly reduce rice productivity and may lead to crop failure. The use of traditional methods and chemical pesticides is considered ineffective and has negative impacts on health and the environment. This study aims to design a prototype of an automated pest repellent system for agriculture based on the Internet of Things (IoT) that is environmentally friendly, energy-efficient, and easy to operate by local farmers. The research method employed a prototyping approach, which includes problem identification, hardware and software design, testing, and system evaluation. The device consists of a NodeMCU ESP32 microcontroller, a PIR sensor to detect pest movement, relay, ultrasonic speaker, electric net, and solar panel as the main power source. Testing on a miniature rice field model showed that the system could detect pest movement at a distance of approximately 5 meters and automatically activate the ultrasonic speaker with a range of 50–100 meters to repel birds, and the electric net to catch insects at night. Energy consumption is primarily supplied by the solar panel, and a fully charged battery can power the system for about 3 hours without sunlight. The detection success rate reached more than 85% with consistent actuator response. This system has proven to reduce pesticide dependency, is environmentally friendly, and has the potential to increase rice farming efficiency.
Application of the Rule-Based Pattern Matching Method to Detection of Types of Mujarrad and Mazid Verbs in the Qur'an Based on Arabic Morphology Patterns Suci Khairani; Rini Meiyanti; Nunsina Nunsina
Journal of Applied Informatics and Computing Vol. 10 No. 4 (2026): August 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i4.13235

Abstract

Arabic has a complex morphological system, particularly in the formation of fi’il (verbs), which in sharaf are classified into fi’il mujarrad and fi’il mazid. Fi’il mujarrad refers to basic verbs without additional letters, while fi’il mazid involves added letters that form specific wazan (patterns). The main problem addressed in this study is the similarity of morphological patterns between fi’il and non-fi’il, which affects classification accuracy. This study aims to identify fi’il in Surah Al-Baqarah and classify them into mujarrad and mazid, along with their wazan patterns, automatically using a rule-based pattern matching approach. The method applies rules based on Arabic morphological patterns, such as fi’il mudhari’ prefixes, word length, and the presence of additional letters in accordance with sharaf principles.The data consist of Qur’anic text processed through preprocessing stages, including load data and tokenization. The system detected 916 candidate fi’il, of which 468 data points were used for evaluation by comparison with manual annotations using a confusion matrix.The results show that the system achieved an accuracy of 75% for fi’il type classification, with precision, recall, and F1-score of 0.77, 0.75, and 0.75, respectively. For wazan classification, the system achieved an accuracy of 69.23%, with weighted average precision of 0.66, recall of 0.69, and F1-score of 0.65. These findings indicate that the rule-based approach is sufficiently effective in detecting fi’il mujarrad and mazid, although performance for certain wazan patterns remains limited due to structural similarities. Therefore, further development of more specific rules and integration with machine learning methods are recommended to improve system accuracy.  
Co-Authors Agam Muarif Ahmad Junaidi Aidilof, Hafizh Al Kautsar Aji Anggara Andri Alfitra Angga Pratama Ar Razi Arief Rahman Armelia Dafrina Asrianda Asrianda Asrillah Asrillah Ayu Ramazani Azmi, Win Azwir, Andrea Micola Bustami Bustami Chaliza Nur, Wan Amalia Cut Agusniar Cut Lika Mestika Sandy Cut Lika Mestika Sandy Dahlan Abdullah Dahlan Abdullah Eva Darnila Eva Darnila Fahmi Izhari Izhari Faiz Syukri Arta Faiz Faris Bagaswara Fasdarsyah Fasdarsyah Fatayati, Nufus Fitri*, Zahratul Fuadi, Wahyu Fuzna Febriani Habib Muharry Yusdartono Hafidh Rafif, Teuku Muhammad Hamsi, Widia Harahap, Ilham Taruna Harahap, Lina Mardiana Hasan Dalimunthe, Amir Kamaruzzaman, Hilda Zulfira Kautsar, Al Khairul Anshar Lidya Rosnita Lina Mardiana Harahap M. Raiyan Firdaus Mamat, Rizalman Bin Maryana Maryana Maryana Maryana Mey Suci Br Pardosi Mirza Mirza Muchlis Abdul Muthalib Muhammad Muhammad Alif Al Fattah Muhammad Faisal Muhammad Fikry Muhammad Ikhwani Muhammad Muaz Munauwar Muhammad Muaz Munauwar Muhammad Muhammad Mulyawan, Rizka Munirul Ula Muthiah Riani Harahap Mutia Zahara Na'syakban, Irvan Nadya Putri Dwinta Nunsina Nunsina Nunsina Nunsina Nunsina, Nunsina Nurdin Nurdin Putri Yesi Ramadhani Rahma Fitria Rahma Fitria Rahma Fitria, Rahma Raisya Kamila Rangkuti, Haris Yunanda Rara Audia Utami Rizal Rizal Rizal, Reyhan Achmad Rizki Suwanda Rizkya, Ghinni Ruzanna, Arina Safriana Safriana Safwandi Safwandi Safwandi Said Fadlan Anshari Sandy, Cut Lika Mestika Serlina Serlina Suci Khairani Sujacka Retno Sukiman, T. Sukma Achriadi Syibral Malasyi, Syibral Wahyu Fuadi Yesy Afrillia Zahratul Fitri, Zahratul Zainuddin Ginting Zalfie Ardian Zara Yunizar