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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Malcom: Indonesian Journal of Machine Learning and Computer Science Jurnal Informatika dan Rekayasa Perangkat Lunak
Sahrial Ihsani Ishak
Universitas Dian Nusantara
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering

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Development of a Portable Smart Feeder Based on Scheduling and Precision Dosing for Fish Feeding in Aquaculture Arif Dwi Kuncoro; Tri Widodo; Yuri Rahmanto; Sahrial Ihsani Ishak
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 6 No. 2 (2026): MALCOM April 2026
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v6i2.2559

Abstract

Freshwater and marine fish farming is the backbone of the fisheries economy, especially for small to medium-scale farmers. Feed contributes approximately 60–70% of operational costs, so inaccurate feed scheduling and dosing can increase production costs, reduce feed efficiency, and increase waste. This research developed a Portable Smart Feeder based on precision scheduling and dosing, equipped with gradual feeding (sub-doses with intervals) and a spreader for even feed distribution. Development was carried out using a prototype method, then the system performance was tested through experiments in two ponds for 14 days (one sorting cycle): Pond A (conventional) and Pond B (using the system). Evaluation included feed consumption, mortality, and growth measured through a sorting process (grading) on ??the 14th day. The results showed that Pond B produced 70.90 kg of harvested biomass with 28.00 kg of feed and 13 fish mortality, while Pond A produced 68.17 kg of biomass with 33.80 kg of feed and 250 fish mortality. The FCR value of Pond B is 0.91:1, better than Pond A 1.20:1, so the system shows increased feed efficiency and maintenance stability.
Clustering of Provincial Health Vulnerability Levels in Indonesia Using the K-Means Method Okma Arnilia; Sahrial Ihsani Ishak; Tri Widodo; I Gusti Nyoman Agung Bisma Tatwa
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 7 No. 1 (2026): Volume 7 Number 1 March 2026
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jatika.v7i1.1469

Abstract

This study aims to classify the health vulnerability levels of 38 provinces in Indonesia based on health and socio-economic indicators in 2024, including the number of hospitals, access to adequate sanitation, access to safe drinking water, stunting prevalence, number of health facilities, population size, and the percentage of poor population. The analysis began with data normalization using the z-score method to standardize variable scales and prevent dominance by indicators with larger value ranges. Following normalization, the optimal number of clusters was determined using the Elbow method by examining the decrease in inertia across different k-values. Based on the inertia pattern and cluster stability, the optimal number of clusters was identified as K=4, which adequately represents the variation in health vulnerability. The clustering results were subsequently visualized in a spatial map using Indonesia’s provincial administrative boundaries. The visualization revealed clear geographical variation across regions, with Cluster 1 representing provinces with very good health conditions, Cluster 2 good conditions, Cluster 3 moderate conditions, and Cluster 4 provinces requiring special attention regarding health indicators. These findings provide a comprehensive overview of health vulnerability distribution in Indonesia and are expected to inform policymakers and stakeholders in prioritizing region-based health interventions, strengthening health development strategies, and promoting more equitable national health services.
Image-Based Food Classification for Nutritional Information Estimation Using Deep Learning Sahrial Ihsani Ishak; Sri Dianing Asri; Bias Yulisa Geni; Okma Arnilia; Tri Widodo; Diva Maulana Ilham
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 3 (2026): Article Research July 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i3.16170

Abstract

This study aims to develop an image-based food classification application integrated with nutritional information retrieval using a deep learning approach. The proposed system is designed to recognize food types from images and provide nutritional information based on an Indonesian food nutrition database. The method involves collecting a dataset of 8,248 images representing 38 categories of Indonesian traditional foods, performing image preprocessing and data augmentation, and developing a Convolutional Neural Network (CNN) model based on the MobileNetV2 architecture through transfer learning. Model performance was evaluated using a 3-fold stratified cross-validation strategy and measured using accuracy, precision, recall, and F1-score metrics. Experimental results showed that the proposed model achieved average accuracy, precision, recall, and F1-score values of 98.85%, 98.88%, 98.85%, and 98.85%, respectively, demonstrating robust and consistent classification performance across the validation folds. The trained model was subsequently deployed into a mobile application using TensorFlow Lite to support real-time food classification and nutritional information presentation. The main contribution of this study is the development of an end-to-end mobile system that integrates deep learning-based food classification with an Indonesian food nutrition database, enabling users to obtain calorie, protein, fat, and carbohydrate information quickly and conveniently for dietary monitoring and health awareness.  
Co-Authors Arif Dwi Kuncoro Bias Yulisa Geni Diva Maulana Ilham I Gusti Nyoman Agung Bisma Tatwa Okma Arnilia Sri Dianing Asri Tri Widodo Yuri Rahmanto