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All Journal JURNAL TEKNOLOGI DAN OPEN SOURCE Jurnal Sistem Komputer dan Informatika (JSON)
Muhammad Ikhsan Wibowo
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Computer Science & IT Control & Systems Engineering

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Peningkatan Akurasi Klasifikasi Ikan kepe-kepe (Famili Chaetodontidae) dengan EfficientNetV2 dan Bayesian Hyperparameter Tuning Putu Mahendra, I Gusti Agung; Muhammad Ikhsan Wibowo; Zuliar Efendi
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 7 No. 2 (2025): Desember 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v7i2.9028

Abstract

Identifikasi cepat dan akurat spesies Chaetodontidae penting untuk monitoring keanekaragaman hayati laut, namun pendekatan manual tidak skala dan rentan kesalahan pada dataset besar. GAP riset yang kami tangani adalah: (i) ketiadaan kajian yang secara khusus mengombinasikan EfficientNetV2 dengan Bayesian hyperparameter tuning untuk klasifikasi Chaetodontidae, dan (ii) belum adanya evaluasi yang menekankan efisiensi penalaan adaptif beserta dampaknya terhadap performa. Kebaruan (novelty) studi ini ialah perancangan pipeline ringkas-efisien berbasis EfficientNetV2 dengan Bayesian Optimization   (10 percobaan) pada learning rate, dropout, dan unfreeze backbone, dipadukan augmentasi kuat (MixUp, CutMix) serta regularisasi (label smoothing, L2). Dataset mencakup 1.427 citra/13 spesies dengan praproses center-crop 80% dan resize 224×224. Konfigurasi terbaik (unfreeze=True, dropout=0,2, LR 3,73×10⁻⁴) mencapai val-accuracy 92,75% dan akurasi uji 97%, dengan precision–recall rata-rata >95%, menunjukkan generalisasi yang baik bahkan pada kelas bermorfologi mirip. Dibanding penalaan manual/grid, pendekatan ini lebih hemat eksperimen sekaligus meningkatkan akurasi. Temuan tersebut menegaskan bahwa integrasi EfficientNetV2 + Bayesian tuning efektif dan siap diadopsi untuk sistem identifikasi–monitoring ikan berbasis citra pada konteks konservasi laut Indonesia.
Comparative Forecasting of Kalimantan Palm Oil Production Using Classical, Machine Learning, and Deep Learning Models I Gusti Agung Putu Mahendra; Muhammad Ikhsan Wibowo; I Gusti Ayu Nandia Lestari
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 9 No. 1 (2026): Jurnal Teknologi dan Open Source, June 2026
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v9i1.5765

Abstract

Palm oil production forecasting in Kalimantan was important because production patterns differed across regions and changed over time. However, previous forecasting approaches often relied on limited classical models and did not sufficiently compare statistical, machine learning, and deep learning methods on regional panel time-series data. This study compared several forecasting models for palm oil production in Kalimantan using annual district and city-level data from 2010 to 2024. The dataset was transformed into panel time-series format, consisting of 56 regional series and 840 observations. Data from 2010 to 2022 were used for training, while data from 2023 to 2024 were used for testing. The evaluated models included Naive Forecast, Autoregressive Integrated Moving Average, Holt Linear Trend, Light Gradient Boosting Machine, Extreme Gradient Boosting, Long Short-Term Memory, Gated Recurrent Unit, and Neural Basis Expansion Analysis for Time Series. The results showed that Long Short-Term Memory achieved the best performance based on root mean squared error and coefficient of determination, while Naive Forecast performed best based on absolute and percentage error metrics. These findings indicated that deep learning was effective for reducing large prediction errors, but simple forecasting remained competitive for stable regional production patterns.
Co-Authors I Gusti Agung Putu Mahendra I Gusti Ayu Nandia Lestari Putu Mahendra, I Gusti Agung Zuliar Efendi