Article Per Year (5 Year)
p-Index From 2021 - 2026
2.826
P-Index
This Author published in this journals
All Journal TEKNIK INFORMATIKA TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) International Journal of Advances in Intelligent Informatics JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Mantik Indonesian Journal of Electrical Engineering and Computer Science Science in Information Technology Letters Journal of Applied Computer Science and Technology (JACOST) International Journal of Engineering, Science and Information Technology Yayasan Cita Cendikiawan Al Khwarizmi Prioritas : Jurnal Pengabdian Kepada Masyarakat Proceeding of International Conference on Information Science and Technology Innovation (ICoSTEC)
Hartono Hartono
Universitas Potensi Utama, Medan, Indonesia
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Transportation Veterinary Other

Published : 22 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 22 Documents
Search

 1   2   3 
Addressing Class Imbalance in Oil Palm Disease and Micronutrient Deficiency Detection Using Meta-Learned Transfer Metric Learning Hartono, Hartono; Ongko, Erianto
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 5 (2025): October 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i5.6857

Abstract

Class imbalance is a major challenge in oil palm disease and nutrient deficiency detection, where healthy samples dominate while diseased or deficient cases are underrepresented, often leading to biased models with high false-negative rates. To address this issue, this study proposes MetaTMLDA (Meta-Learned Transfer Metric Learning with Distribution Alignment), a hybrid framework that combines Transfer Metric Learning (TML) with MW-FixMatch. TML learns discriminative and domain-invariant features, while MW-FixMatch employs a meta-learned weighting mechanism to adaptively reweight samples, improving sensitivity to minority classes and enhancing robustness against pseudo-label noise. Experiments on four public datasets—Ganoderma Disease Detection, Palm Oil Leaf Disease, and Leaf Nutrient Detection for Boron and Magnesium—demonstrated that the proposed method consistently outperforms TML-DA, MW-FixMatch, SMOTE, Random Undersampling, and Biased SVM. On the smaller datasets (Ganoderma and Palm Oil Leaf Disease), MetaTMLDA achieved accuracy of 0.976, precision 0.951, recall 0.915, Cohen’s Kappa 0.912, and macro F1-score 0.933 for Ganoderma, and accuracy of 0.980, precision 0.972, recall 0.957, Kappa 0.911, and macro F1-score 0.964 for Palm Oil Leaf Disease. On the larger datasets (Boron and Magnesium), the model reached near-perfect accuracy of 0.995, with precision up to 0.967, recall up to 0.973, Kappa above 0.919, and macro F1-scores up to 0.969, highlighting its robustness and balanced predictive performance. These findings confirm that MetaTMLDA effectively addresses both class imbalance and domain shift, providing a scalable solution for precision agriculture through earlier and more reliable detection of oil palm health issues.
Sosialisasi Pemanfaatan IoT Berbasis Machine Learning pada Deteksi Penyakit Tanaman Sawit untuk Pertanian Berkelanjutan di Dusun I Bukit Gantung Desa Sumber Mulyo Hartono; Zuhanda, M. Khahfi; Rahman, Sayuti; Kuswardani, Retna Astuti; Suswati; Zen, Muhammad; Ongko, Erianto; Suryati, Lili
Dedikasi Sains dan Teknologi (DST) Vol. 5 No. 2 (2025): Artikel Pengabdian Nopember 2025
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/dst.v5i2.7148

Abstract

Kegiatan Pengabdian kepada Masyarakat (PkM) ini dilaksanakan untuk meningkatkan literasi digital petani sawit di Dusun I Bukit Gantung, Desa Sumber Mulyo, melalui sosialisasi pemanfaatan Internet of Things (IoT) berbasis machine learning untuk smart agriculture dalam deteksi dini penyakit tanaman sawit. Pelatihan dirancang dalam empat tahapan, yaitu persiapan perangkat, penyampaian materi konseptual, praktik instalasi IoT, penerapan machine learning, serta pendampingan lapangan. Evaluasi dilakukan menggunakan pre-test dan post-test untuk mengukur perubahan kompetensi peserta terhadap konsep IoT, machine learning, instalasi sensor, dan interpretasi hasil deteksi. Berdasarkan analisis, evaluasi hasil pelatihan menunjukkan peningkatan sebesar 47%, dengan kenaikan tertinggi pada keterampilan instalasi sensor dan membaca hasil aplikasi (+54%). Penerapan teknologi ini membantu petani melakukan deteksi penyakit lebih cepat dan akurat sehingga penggunaan pestisida dapat ditekan melalui penyemprotan selektif. Selain menghasilkan peningkatan kompetensi teknis, kegiatan ini meningkatkan keberterimaan teknologi di kalangan petani, terbukti dari 14 dari 15 kelompok yang secara konsisten menggunakan perangkat IoT pascapelatihan. Secara sosial, program ini mendorong perubahan perilaku kolektif menuju praktik budidaya yang lebih aman, efisien, dan berbasis data. Implementasi ini menunjukkan bahwa integrasi IoT–machine learning mampu memperkuat keberlanjutan pertanian sawit sekaligus meningkatkan kualitas pengelolaan kebun masyarakat, serta memberikan dasar penting bagi pengembangan sistem monitoring kesehatan tanaman yang lebih komprehensif dan adaptif pada skala komunitas.
Co-Authors Ammar Yasir Nasution Andi Rahmadsyah Asmah Indrawati B. Herawan Hayadi Dahlan Abdullah Desniarti Dian Maya Sari Erianto Ongko Erianto Ongko Erianto Ongko Erianto Ongko Erianto Ongko Erianto Ongko Erna Budhiarti Nababan Firman Syahputra Gio, Prana Ugiana Irwan Daniel Jaka Kusuma Lili Suryati Meli Handayani Muhammad Khahfi Zuhanda Muhammad Sadikin Muhammad Zen, Muhammad Nur Anzelina Opim Salim Sitompul Rahmad Syah, Rahmad Rahman, Sayuti Retna Astuti Kuswardani Rezzy Eko Caraka Rika Rosnelly Rika Rosnelly Rika Rosnelly Rika Rosnelly Rubianto Samsul A Rahman Sidik Hasibuan Sembiring, Arnes Silvia Lestari Sugeng Riyadi Suswati Tulus Tulus Wanayumini Wulan Dari Yeni Risyani Zakarias Situmorang