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All Journal ILKOMNIKA: Journal of Computer Science and Applied Informatics Journal of Welding Technology Jurnal Pengabdian Masyarakat Bangsa
Rokhim, Imam Nur
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Journal : ILKOMNIKA: Journal of Computer Science and Applied Informatics

 1 
Design and Development of a Fisherman's Trap Location Marking Application Using Android-Based Maps Picker and Tracking Kuswanto, Teguh Junian; Hamka, M. Saiful Rahman; Febrianti, Santi; Rokhim, Imam Nur; Nabila, Aisyah Nur
ILKOMNIKA Vol 8 No 1 (2026): Volume 8, Number 1, April 2026
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28926/ilkomnika.v8i1.816

Abstract

The fisheries sector in Indonesia is predominantly composed of small-scale traditional fishermen who still rely on manual methods to determine and track the locations of fishing gear, such as fish traps (bubu). This condition often leads to the loss of fishing gear due to environmental factors such as weather conditions, ocean currents, and limited access to modern navigation technologies. Therefore, there is a need for an information technology–based solution that can assist fishermen in digitally marking and tracking the locations of their fishing gear. This study aims to design and develop an Android-based application capable of marking and tracking the deployment locations of fish traps using a Maps Picker and Global Positioning System (GPS) in real time. The system development adopts the Waterfall model, consisting of requirement analysis, system design, implementation, testing, and evaluation phases. The application is developed using React Native as the primary framework, integrated with the Google Maps API and SQLite as a local database to support offline-first functionality. System testing is conducted using a black-box testing approach. The results indicate that all application features function according to the specified requirements and demonstrate a satisfactory level of tracking accuracy. Therefore, the proposed system is considered effective in assisting fishermen in marking and tracking fishing gear locations efficiently, while also supporting the digital transformation of the small-scale fisheries sector in Indonesia.
Naive Bayes Predictive Model for Failure Detection in CODLAG Propulsion Systems Rokhim, Imam Nur; Kuswanto, Teguh Junian; Dioktyanto, Mudzakkir; Abdullah, Hidayat; Dewi, Yulia Puspa
ILKOMNIKA Vol 8 No 1 (2026): Volume 8, Number 1, April 2026
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28926/ilkomnika.v8i1.831

Abstract

This study implements a Naive Bayes Classifier algorithm to detect failures in gas turbines operating within a CODLAG (Combined Diesel-Electric and Gas) propulsion system. The complexity of hybrid propulsion systems necessitates reliable data-driven monitoring methods to support early anomaly detection and predictive maintenance. An open-access dataset from Kaggle was utilized as the source of gas turbine operational data, with five key parameters (GTn, T48, ṁf, P1, and P2) selected due to their strong correlation with turbine thermodynamic performance. Following data preprocessing and an 80:20 train–test split, the model was trained to classify operating conditions into Normal and Faulty states. The evaluation results demonstrate an accuracy of 86.89%, accompanied by high precision and recall values, indicating the model’s capability to identify anomalies with minimal misclassification. Furthermore, the Receiver Operating Characteristic (ROC) curve yields an Area Under the Curve (AUC) of 0.96, reflecting strong discriminative performance. These findings confirm that the Naive Bayes approach is computationally efficient and suitable for real-time implementation within shipboard Condition-Based Monitoring (CBM) systems, thereby enhancing the reliability and operational efficiency of CODLAG propulsion systems.
Co-Authors Abdullah, Hidayat Agustina, Nur Aini Amalia Dinda Dewi, Yulia Puspa Dioktyanto, Mudzakkir Febrianti, Santi Hamka, M. Saiful Rahman Kuswanto, Teguh Junian Muhammad Iqbal FIrdaus, Muhammad Nabila, Aisyah Nur Putra, Ekananda Sulistyo Rochmad, Alif Nur