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Jurusan Akuakultur Gedung Teladan di Fakultas Pertanian, Perikanan, dan Biologi Universitas Bangka Belitung Jl. Balunijuk, Kecamatan Mewarang, Bangka 33172
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Journal of Aquatropica Asia
Published by Universitas Bangka Belitung
ISSN : 24073601     EISSN : 27217574     DOI : https://doi.org/10.33019/aquatropica
Core Subject : Science, Agriculture,
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Immunology & microbiology
Journal of Aquatropica Asia (JoAA) is an open access scientific periodical managed by the Department of Aquaculture, Bangka Belitung University. JoAA Journal involvement is carried out 2 (two) times a year, namely in July and December in the form of the main manuscript is an article that contains the results of research (research articles). Other manuscript contributions can be in the form of short articles (short communication), articles review, and also special issues. Articles received will be reviewed by reviewers managed by the Editor in Chief before the manuscript is accepted and approved for publication in JoAA. The Journal of Aquatropica Asia (JoAA) accepts articles written in Indonesian or English with regard to aquaculture studies and aquatic ecology in the broadest sense covering aspects of reproduction, nutrition and feed, genetics, physiology, morphology, health of aquatic organisms, water quality, plankton, conservation, and other aspects relevant to the field of aquaculture.
Arjuna Subject : Ilmu Pertanian dan Biologi (Semua) - Ilmu Perairan
Articles 171 Documents
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OPTIMIZATION OF A FLAT-DIE PELLETIZING MACHINE FOR FISH FEED PRODUCTION USING MACHINE LEARNING MODELS Olusegun, Adedipe; I.O, Yusuf; D.E, Ibiyeye
Journal of Aquatropica Asia Vol 10 No 2 (2025): Journal of Aquatropica Asia
Publisher : Program Studi Akuakultur, Universitas Bangka Belitung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33019/kbq3ty27

Abstract

High-quality fish feed production is a critical factor in the success of aquaculture systems. The quality of feed pellets, particularly their durability (pellet durability index), directly affects feed efficiency and aquatic environmental quality. This study applied machine learning approaches to predict and optimize pellet durability index in a flat die pelletizing machine used for fish feed production. The dataset consisted of 3,000 observations with 13 operational features collected through IoT sensors. Descriptive statistics were used to summarize overall data characteristics. The target variable was binarized to classify pellets as acceptable or non-acceptable. To address data imbalance, the Synthetic Minority Oversampling Technique (SMOTE) was applied, followed by data cleaning and feature scaling to ensure uniformity. The preprocessed dataset was split into training and testing subsets. Six machine learning models were developed and evaluated using four performance metrics. Logistic Regression and Support Vector Machine achieved the best performance, with accuracy, recall, precision, and F1-score values of 0.756, 1.000, 0.756, and 0.861, respectively. The results indicate that operational factors in the fish feed pelletization process can effectively train predictive models to assess feed quality. The integration of machine learning in fish feed manufacturing offers potential improvements in production efficiency and feed quality for commercial aquaculture applications.

Page 18 of 18 | Total Record : 171

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