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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Informatika dan Teknik Elektro Terapan Jurnal Ilmiah KOMPUTASI Sinkron : Jurnal dan Penelitian Teknik Informatika RABIT: Jurnal Teknologi dan Sistem Informasi Univrab JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Inotera Jurnal Sisfokom (Sistem Informasi dan Komputer) ILKOM Jurnal Ilmiah Jurnal Infomedia MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer IJISTECH (International Journal Of Information System & Technology) JOURNAL OF SCIENCE AND SOCIAL RESEARCH JOURNAL OF INFORMATICS AND COMPUTER SCIENCE Jurnal Sistem Komputer dan Informatika (JSON) Journal of Computer Science, Information Technology and Telecommunication Engineering (JCoSITTE) IJISTECH Rambideun : Jurnal Pengabdian Kepada Masyarakat International Journal of Engineering, Science and Information Technology jeti JURNAL ILMIAH GLOBAL EDUCATION JUSTIN (Jurnal Sistem dan Teknologi Informasi) Jurnal Teknik Informatika dan Elektro (JURTIE) TECHSI - Jurnal Teknik Informatika Sisfo: Jurnal Ilmiah Sistem Informasi Jurnal Teknologi Terapan and Sains 4.0 Jurnal Kemitraan Responsif untuk Aksi Inovatif dan Pengabdian Masyarakat Journal Of Artificial Intelligence And Software Engineering Journal of Advanced Computer Knowledge and Algorithms Applied Engineering, Innovation, and Technology Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN)
Munirul Ula
Universitas Malikussaleh
Agriculture, Biological Sciences & Forestry Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation Other

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Journal : journal of applied informatics and computing

 1 
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.
Analysis of Customer Churn Classification for Sinarmas Syariah Lhokseumawe Insurance Services Using Deep Learning Tabnet and Explainable AI Syarifah Muliana; Taufiq Taufiq; Munirul Ula
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.12864

Abstract

Customer churn is a major challenge in the insurance industry because it directly affects customer retention, business sustainability, and company profitability. Early identification of customers at risk of churn is therefore essential for developing effective retention strategies. This study proposes an interpretable customer churn prediction framework for Sinarmas Syariah Lhokseumawe Insurance Services by integrating TabNet deep learning with Shapley Additive Explanations (SHAP). The dataset consists of 2,000 customer records containing demographic information, insurance transactions, premium payments, claims history, and customer interaction data. Due to the imbalanced class distribution, the Synthetic Minority Oversampling Technique (SMOTE) was applied exclusively to the training dataset to improve model learning while preventing data leakage. Model performance was evaluated using Accuracy, Precision, Recall, F1-Score, and ROC-AUC metrics. The experimental results demonstrate that the proposed approach achieved an accuracy of 98.77%, precision of 86.67%, recall of 92.86%, F1-score of 89.66%, and ROC-AUC of 0.995, indicating excellent classification performance. Furthermore, SHAP analysis revealed that communication, premi_2025, and reason_to_purchase were the most influential features affecting churn predictions. These findings highlight the importance of customer engagement, premium management, and purchasing motivations in customer retention. The proposed TabNet-SHAP framework provides both high predictive performance and model interpretability, making it a valuable decision-support tool for customer retention strategies in the insurance sector.
Implementation of a Hybrid Model Using Principal Component Analysis, K-Means, and Naïve Bayes for Tuition Fee Category Prediction Nurdin Nurdin; Jessika Jessika; Munirul Ula
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.v10i4.13184

Abstract

The determination of Tuition Fee Categories in higher education institutions is commonly conducted through manual verification of students’ socioeconomic documents, which may lead to subjectivity and inconsistencies in decision-making. This study proposes a hybrid machine learning approach that integrates Principal Component Analysis (PCA), K-Means Clustering, and Naïve Bayes Classifier within a semi-supervised learning framework for student socioeconomic classification based on pseudo-labels generated from clustering results. The dataset used in this study consists of 452 student records with 12 socioeconomic attributes obtained from the New Student Admission system of STAIN Teungku Dirundeng Meulaboh in 2025. Data preprocessing includes attribute selection, categorical transformation using One Hot Encoding, and feature standardization. PCA is applied to reduce dimensionality from 18 features to 12 principal components while retaining 95% of the total variance. The processed data are clustered using K-Means with the optimal number of clusters determined as 8 based on Elbow and Silhouette Score analysis. These clusters are used as pseudo-labels for training the Naïve Bayes classifier. Experimental results show that the proposed model achieves 98.89% training accuracy and 97.80% testing accuracy, with a weighted average F1-score of 0.98. The results indicate that the proposed hybrid approach is effective in capturing underlying socioeconomic patterns and provides a stable classification performance. However, the model is based on pseudo-labels rather than official tuition fee categories. Therefore, further validation using real labeled data is recommended to enhance generalizability and practical applicability.
Co-Authors Abdullah, ⁠Dahlan Affan Syafiq Azzikri Afif, Muhammad Athallah Agustriya, Manda Al-Ghiyats, Said Ananda Faridhatul Ulva Ar Razi Arnawan Hasibuan Azzikri, Affan Syafiq ⁠Dahlan Abdullah Bustami Bustami Bustami Bustami Cut Agusniar Dahlan Abdullah Dara Farhiyah Dhani, Saniah Dinda, Dinda Fadillah, Rizky Fahruddin Fahruddin Fajriana, F Fajriana, Fajriana Fasdarsyah Fasdarsyah Fidyatun Nisa Fikhri, Aditya Aziz Fitri, Anisa Amelia Fuddin, Mudhya Hamdhana, Defry Hasan Dalimunthe, Amir Husaini Jessika Jessika Kamaruzzaman, Hilda Zulfira KURNIAWATI - Kurniawati Kurniawati Lailatul Husna Lidya Rosnita M David Khalid Mey Suci Br Pardosi Muhammad Daud Muhammad Fauzan Muhammad Fikry Muhammad Ikhwanus Muhammad Muhammad Muhammad Yani, Muhammad Mutammimul Ula Muthalib, Muchlis Abd Nadia Saphira Nanda Imanda Nurdin Nurdin Nurdin Nurdin Nurdin Nurul Aula Nurul Husna Putri Agustina Dewi Putri, Nazirah Allisya Rahman, Ashri Nurhajizah Ridha, Ridha Rini Meiyanti Rita Afridah Rizal Rizal Rizal S.Si., M.IT, Rizal Rizal Tjut Adek Rizki Suwanda Rizky Putra Fhonna Rizky, Rahmat Rozzi Kesuma Dinata Rusadi, Athirah Said Fadlan Anshari Saiful Kiram Salimuddin, Salimuddin Sayed Fachrurrazi Sayed Fachrurrazi Sayuti, Muhammad Siagian, Tania Annisa Sinambela, Reza Syahputra Siska Amelia Melani Siti Aminah Sudarti, Atrida Sujacka Retno Susanti Susanti Syahrul Andika Lubis Syarifah Muliana Taufiq Taufiq Taufiq Taufiq Tiara Oktavia Ulfah, Julia Veri Ilhadi Wahidatunnisa Nasution Yasin, Fijri Ahmad Yessi Apprilia Yesy Aflillia Yopy Anfelia Yulisda, Desvina Yuni SariBr Sitepu Zailani Mohamed Sidek Zara Yunizar