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All Journal SAMUDERA Jurnal Informatika dan Teknik Elektro Terapan e-Jurnal Arus Elektro Indonesia Sistemasi: Jurnal Sistem Informasi RABIT: Jurnal Teknologi dan Sistem Informasi Univrab JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Information System for Educators and Professionals : Journal of Information System JOURNAL OF APPLIED INFORMATICS AND COMPUTING METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Journal on Education Jurnal Informatika Kaputama (JIK) JATI (Jurnal Mahasiswa Teknik Informatika) STEAM Engineering : Journal of Science, Technology, Education And Mechanical Engineering MOTIVECTION : Journal of Mechanical, Electrical and Industrial Engineering Medika Teknika : Jurnal Teknik Elektromedik Indonesia Jurnal Sistem Informasi dan Sains Teknologi Jurnal Pengabdian kepada Masyarakat Nusantara International Journal of Engineering, Science and Information Technology Jurnal Tika Malikussaleh Journal of Mechanical Science Technology Jurnal Abdi Masyarakat Indonesia Andalasian International Journal of Applied Science, Engineering, and Technology Jurnal Mandiri IT Jurnal Janitra Informatika dan Sistem Informasi Jurnal Minfo Polgan (JMP) Journal of Renewable Energy, Electrical, and Computer Engineering Journal Of Human And Education (JAHE) Jurnal Pendidikan dan Pembelajaran Jurnal Sains dan Teknologi Jurnal Energi Elektrik Jurnal Teknologi Terapan and Sains 4.0 Mafaza : Jurnal Pengabdian Masyarakat INFORMATION SYSTEM FOR EDUCATORS AND PROFESSIONALS : Journal of Information System International Review of Practical Innovation, Technology and Green Energy (IRPITAGE) Saree : Research in Gender Studies Innovative: Journal Of Social Science Research Jaliye: Jurnal Abdimas, Loyalitas, dan Edukasi Jurnal Malikussaleh Mengabdi Jurnal Polimesin PUSAKA: Jurnal Pengabdian Masyarakat Indonesian Journal of Technical and Vocational Education Training Jurnal Pendidikan Teknik Mesin Jurnal Informatika Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN) Jurnal Ilmu Komputer Aceh ITEJ (Information Technology Engineering Journals) Jurnal Pengabdian Masyarakat Teknologi Informasi (PERANTI)
Taufiq Taufiq
Universitas Malikussaleh
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Journal : journal of applied informatics and computing

 1 
Design of an Internet of Things (IoT)-Based Fish Feeder System Using an Android Application Ariyandi, Zulham; Taufiq, Taufiq; Nunsina, Nunsina
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.v9i4.10072

Abstract

Fish farming plays a crucial role in aquaculture, where feed management is a key factor affecting productivity and operational costs. This research presents the design and implementation of an Internet of Things (IoT)-based automatic fish feeder system, integrated with a custom Android application. The system uses an ESP32 microcontroller to control a load cell sensor for accurate feed weighing, an ultrasonic sensor to monitor feed availability, servo motors for feed release mechanisms, and a DC motor for feed dispersion. Firebase Realtime Database serves as the data communication medium between the hardware and mobile application, enabling real-time control and monitoring. A rule-based control logic is implemented to execute scheduled or manual feeding processes. Experimental results show a feed weight accuracy of ±5 grams, with feeding operations completed within 1.5 minutes and an average throw distance of 287.8 cm. The system supports automatic alerts, scheduling, feed history logging, and remote access via the application. Compared to conventional manual methods, the system reduces feed waste, increases portion accuracy, and decreases feeding time by over 75%. These features demonstrate the system’s capability to enhance feeding efficiency, reduce labor dependency, and support sustainable and scalable fish farming practices through automation and real-time monitoring.
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.
Comparative Analysis of Random Forest and Long Short-Term Memory for Predicting Optical Power Degradation in FTTH Networks Hermansyah Hermansyah; Taufiq Taufiq; Defry Hamdhana; Munirul Ula; Muhammad Ikhwanus
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.v10i4.13717

Abstract

Fiber-to-the-Home (FTTH) networks are widely used to provide high-speed broadband services, but optical power degradation can reduce network performance and service quality. This study compares Random Forest (RF) and Long Short-Term Memory (LSTM) for predicting FTTH network conditions classified as Normal, Warning, and Critical. The study used 63,145 historical records collected from 58 Optical Network Terminals (ONTs) between March and May 2026. To provide a fair comparison, RF was trained using engineered tabular features, including lag and rolling-window statistics, while LSTM used six-step sequential data representing approximately the previous six hours. Model performance was evaluated using accuracy, precision, recall, F1-score, Mean Absolute Error (MAE), Root Mean Square Error (RMSE), training time, and inference time. The results show that RF substantially outperformed LSTM, achieving 98.41% accuracy, precision, recall, and F1-score, with an MAE of 0.0173 and RMSE of 0.1420. RF also required only 2.667 seconds for training and 0.0102 ms for inference, compared with 189.98 seconds and 0.1856 ms for LSTM. Per-class evaluation confirmed that RF performed well across all network conditions, with precision and recall above 99% for Normal, above 94% for Warning, and above 90% for Critical. A strict chronological train-test split further confirmed the robustness of RF, which achieved 98.59% accuracy. Feature importance analysis showed that historical optical power, particularly lag-based features, was the most influential predictor of network degradation. These findings indicate that FTTH optical power degradation can be effectively modeled using engineered tabular features rather than a purely sequential approach. Finally, the RF model was integrated into a web-based monitoring dashboard with WhatsApp-based early warnings to support proactive FTTH network maintenance.
Performance Comparison of Point-to-Point and Point-to-Multipoint Fiber Optic Networks Using QoS Parameters and the Analytical Hierarchy Process (AHP) Husni Husni; Taufiq Taufiq; Defry Hamdhana; Muhammad Daud; Asrianda Asrianda
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.v10i4.13728

Abstract

The rapid growth of internet traffic in Indonesia, including in Aceh Province, requires an optical fiber network infrastructure that is both efficient and reliable. Two fundamental architectures, Point-to-Point (P2P) and Point-to-Multipoint (P2MP), offer different trade-offs between service quality and cost efficiency, yet a quantitative comparison that combines Quality of Service (QoS) measurement with a structured multi-criteria decision-making method remains limited. This study experimentally measures the throughput, delay, jitter, packet loss, and bandwidth of P2P and P2MP networks implemented at Universitas Almuslim, Bireuen, Aceh, using iPerf3 and Wireshark under varying client loads of 1, 2, 4, 8, and 16 users, and applies the Analytical Hierarchy Process (AHP) to weight the QoS criteria and rank the two architectures. The results show that P2P consistently outperforms P2MP on every QoS parameter, maintaining an average throughput of 95 Mbps, a delay of 2.68 ms, a jitter of 0.85 ms, a packet loss of 0.054%, and a fixed bandwidth of 100 Mbps, whereas P2MP degrades progressively as the number of users increases. AHP weighting identified throughput as the most influential criterion (0.50), followed by bandwidth (0.26), delay (0.13), jitter (0.07), and packet loss (0.03), with a Consistency Ratio of 0.054, confirming that the pairwise judgments were consistent. The resulting AHP scores were 9.00 for P2P and 2.66 for P2MP, indicating that P2P is the more optimal architecture for QoS-sensitive deployments, while P2MP remains advantageous where cost efficiency and wide coverage are prioritized.
Co-Authors Abdul Rasyid Abubakar Abubakar Dabet Adi Setiawan Agustival, Refi Alchalil Alchalil Alqhifari, Azka Amri Amri Andre Heri Bakriansyah Ar Razi Ariyandi, Zulham Arnawan Hasibuan Asran Asran Asri Asri Asrianda Asrianda Azwinur Azwinur Badriana, Badriana Bakhtiar Bakhtiar Chairil Pratama Cut Yuliani Sari cut Dahlan Abdullah Dedi Fariadi Defry Hamdhana Desy Sary Ayunda Diana Khairani Sofyan Diana, Diana Edo Roska Ezwarsyah Ezwarsyah Fadlisyah Fadlisyah Fajriana Fakhruddin Ahmad Nasution Fatimah Fatimah Ferri Safriwardy Gery Ulayya Hardi Hafizh Al Kautsar Aidilof Hamdhana, Defry Hamzani Hamzani Hardi, Gery U. Hardiansyah, Juni Hermansyah Hermansyah Husni Husni Ikramullah Ikramullah Iqlima Iqlima Iqlima, Iqlima Irsan Efendi Rangkuti Ishak Ishak Islami Fatwa Johan, T.M. Jumadi Khairul Fuadi Khalsiah Khalsiah M Azkal Azkia M Furqan M Sayuti Madanhar Manik Maizuar Maizuar Maryana Maryana Maryana Maulita, Maya Meutia Fadilla Misbahul Jannah Muchlis Abdul Muthalib Muhammad Daud Muhammad Ikhwanus Muhammad Raihan Rangkuti Muhammad Ridhani Muhammad Sayuti Muhammad Yusuf Muhammad Zakaria Muhammad, Muhammad Muhibuddin Muhibuddin Mujahid, Alvin Munar Munar Munar Munirul Ula Munirul Ula Munzir Absa Nasrah, Sayni Nasution, Fakhruddin A Novi Sylvia Nunsina, Nunsina Nurdin Nurdin Nurdin Nurdin Nursyafri Hafniza Pathia Pathia Pranata, Fajar Putra, Ardiansyah Rahmatina Rahmatina Raihan Putri Rajudin Rajudin Ratna Rita Sovianto Riza Andriani Riza Andriani Riza Mirza Rizki Suwanda Rosdiana Rosdiana Ruwaida Ruwaida Salahuddin Salahuddin Selamat Meliala Siraj Suhaili Sahibul Muna Sujacka Retno Sulaiman . Sulhatun Sulhatun Syahri, Alfis Syamsul Bahri Syamsul Bahri Syarifah Muliana Syukriah Syukriah Syukriah Taufik Taufik Teguh Brahmana Trisna Trisna Wawan Syahputra YANITA YANITA Yasir Amani Yogi Satya Zara Yunizar Zulfikar Ali Buto Zulmiardi zura, zuraimi