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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Malcom: Indonesian Journal of Machine Learning and Computer Science Electrician : Jurnal Rekayasa dan Teknologi Elektro Journal of Artificial Intelligence and Technology Information Jurnal Informatika dan Rekayasa Perangkat Lunak
Sahrial Ihsani Ishak
Universitas Dian Nusantara
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering 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.  
A Hybrid SITDE Weighting and HYBSO Method in Decision Support Systems for Warehouse Employee Selection Tri Widodo; Okma Arnilia; Iryanto Chandra; Sahrial Ihsani Ishak; Setiawansyah Setiawansyah
Journal of Artificial Intelligence and Technology Information (JAITI) Vol. 4 No. 3 (2026): Volume 4 Number 3 September 2026
Publisher : PT. Tech Cart Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58602/jaiti.v4i3.344

Abstract

Warehouse employee selection is a complex process because it involves various heterogeneous criteria, both quantitative and qualitative, such as physical tests, accuracy, work experience, discipline, and teamwork ability. A selection process that still relies on subjective judgment risks producing inconsistent and suboptimal decisions. This study proposes a hybrid method that integrates skewness impact through distributional evaluation (SITDE) for objective determination of criteria weights with a hybrid solution algorithm (HYBSO) for systematic ranking of alternatives. The results of applying this method indicate that Work Experience has the highest weight of 0.2813, followed by the Discipline Test (0.1969) and Physical Test (0.1926), while the Accuracy Test (0.1641) and Teamwork Test (0.1652) have lower weights. The final ranking results show that Candidate HW ranks first with a score of 0.3596, followed by Candidate FN (0.7066) and Candidate DK (0.9162). The sensitivity analysis evaluates the stability of the ranking results under changes in criterion weights across 20 scenarios, demonstrating the robustness of the proposed SITDE-HYBSO method and identifying candidates whose ranking positions are more sensitive to variations in criterion importance. These findings confirm that the SITDE-HYBSO hybrid method is capable of producing objective, consistent, and accountable employee selection decisions, while also providing an adaptive mechanism to respond to changes in organizational priorities.
Application of MobileNetV2 for Coffee Bean Ripeness Classification to Support Smart Agriculture Angga Bayu Santoso; Okma Arnilia; Sahrial Ihsani Ishak; I Gusti Nyoman Agung Bisma Tatwa; Riki Rikardo
Electrician : Jurnal Rekayasa dan Teknologi Elektro Vol. 20 No. 3 (2026)
Publisher : Department of Electrical Engineering, Faculty of Engineering, Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/elc.v20n3.3155

Abstract

This study addresses the problem of manual classification of coffee bean ripeness, which is often inconsistent and inefficient, potentially affecting product quality and market value. To overcome this issue, the study proposes an automated classification approach using a deep learning model based on MobileNetV2. The objective of this study is to develop an accurate and efficient model for classifying coffee beans into four ripeness levels: Dark, Green, Light, and Medium, while ensuring its applicability in real-world conditions with limited resources. The dataset consists of 1,200 images collected from coffee plantations in Indonesia, processed through resizing, normalization, and data augmentation to improve generalization. The model was trained using transfer learning and evaluated using accuracy, precision, recall, and F1-score metrics. The results show that the model achieves an accuracy of 98.33% on the testing dataset, with a precision of 0.985, recall of 0.983, and F1-score of 0.983. When implemented in a web-based application, it achieves 97.8% accuracy with an average response time of 1.8 seconds and is capable of handling up to 100 concurrent users with stable performance. These findings indicate that the proposed model is effective and reliable for supporting automated coffee bean classification and enhancing efficiency in post-harvest processing.
Co-Authors Angga Bayu Santoso Arif Dwi Kuncoro Arnilia, Okma Bias Yulisa Geni Diva Maulana Ilham I Gusti Nyoman Agung Bisma Tatwa Iryanto Chandra Okma Arnilia Riki Rikardo Setiawansyah Setiawansyah Sri Dianing Asri Tri Widodo Yuri Rahmanto