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All Journal JBIO: jurnal biosains (the journal of biosciences) Abdimas JURNAL PENGABDIAN KEPADA MASYARAKAT JURNAL HAMA DAN PENYAKIT TUMBUHAN TROPIKA Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Agrosains: Jurnal Penelitian Agronomi Agrotekma: Jurnal Agroteknologi dan Ilmu Pertanian JMM (Jurnal Masyarakat Mandiri) Community Development Journal: Jurnal Pengabdian Masyarakat AGRISAINS: Jurnal Ilmiah Magister Agribisnis Pelita Masyarakat Jurnal Ilmiah Pertanian ( JIPERTA) Budapest International Research in Exact Sciences (BirEx Journal) JPM: JURNAL PENGABDIAN MASYARAKAT Yayasan Cita Cendikiawan Al Khwarizmi Journal of Natural Sciences International Journal of Economic, Business, Accounting, Agriculture Management and Sharia Administration (IJEBAS) Warta Perkaretan Prioritas : Jurnal Pengabdian Kepada Masyarakat IRA Jurnal Pengabdian Kepada Masyarakat
Asmah Indrawati
Universitas Medan Area
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

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A Hybrid Framework Combining U-Net, Ant Colony Optimization, and CNN for Rice Leaf Disease Classification under Class Imbalance Erianto Ongko; Asmah Indrawati; Sukiman Sukiman
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 6 (2025): December 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i6.6910

Abstract

Accurate classification of rice plant diseases is essential for early intervention and precision agriculture. However, real-world datasets often suffer from complex backgrounds, high-dimensional features, and severe class imbalances, which compromise classification performance. This study proposes an integrated framework combining image segmentation using U-Net, feature selection via Ant Colony Optimization (ACO), hybrid sampling to handle class imbalance, and final classification using a Convolutional Neural Network (CNN). Segmentation isolates disease-affected areas, ACO optimizes feature subsets, and hybrid sampling balances class distribution using undersampling and SMOTE. The proposed method was tested on four rice leaf disease datasets—Brown Spot, Leaf Blast, Leaf Blight, and Leaf Scald—exhibiting significant class imbalance. Experimental results show that the proposed approach outperforms baseline models (SegNet, PspNet, and E-Net) across multiple metrics: Accuracy, IoU, Precision, and Recall. This indicates the framework’s robustness and potential for real-world deployment in precision agriculture. Future work will focus on model compression and real-time implementation in IoT systems.
Enhanced RegNetY-400MF for Fruit Fly Species Classification: Fine-Tuning Strategies and Data Balancing for Improved Accuracy Sayuti Rahman; Asmah Indrawati; Muhammad Zen; Billiam Zealtiel; Shabila Shaharani Tanjung
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 6 (2025): December 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i6.6973

Abstract

Fruit fly infestations pose a significant threat to agricultural productivity, especially in chili plantations, which can cause substantial yield losses. Accurate and rapid species classification is crucial for implementing targeted pest control strategies. This study developed a computationally efficient fruit fly species classification model using a deep learning approach that focused on improving accuracy with fine tuning and class balancing strategies. The dataset consists of 1049 images across 4 fruit fly species, captured in a natural plantation environment and available at www.inaturalist.org. The model evaluated several lightweight Convolutional Neural Network (CNN) architectures, including MobileNetV3-Small, RegNetY-400MF, and SqueezeNet among others, with RegNetY-400MF emerging as the best performing model, achieving a validation accuracy of 96.10% and a macro F1 score of 95.70%. The models tested in this study included several lightweight Convolutional Neural Network (CNN) architectures, including MobileNetV3-Small, RegNetY-400MF, and SqueezeNet, among others. RegNetY-400MF proved to be the best performing model, achieving a validation accuracy of 96.10% and a macro F1 score of 95.70%. Compared to other state-of-the-art models, RegNetY-400MF demonstrated higher accuracy while maintaining a lower number of parameters (8.3 million) and reduced computational complexity (0.41 GFLOPs). This makes the model highly suitable for real-time applications in resource-constrained agricultural environments. The model offers a practical solution for fruit fly species detection, enabling early and accurate identification of pest infestations in chili plantations, thereby reducing the risk of crop failure. By providing an efficient and scalable pest control tool, the model supports precision pest management, improves yield stability, and contributes to sustainable agriculture.
Application of Cow Manure and Dolomite on Mung Bean (Vigna radiata L.) Plants Roswita Oesman; Rahmaniah Rahmaniah; Asmah Indrawati; Abdul Rahman
Agrosains : Jurnal Penelitian Agronomi Vol 25, No 2 (2023): Agrosains: Jurnal Penelitian Agronomi
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/agsjpa.v25i2.71482

Abstract

The plant growth and yield could be influenced by the soil ameliorants. This study was to obtain the effect of cow manure, dolomite, and their interactions on the growth and yield of mung bean plants. This study was conducted at Marindal II Village, Patumbak Subdistrict, Deli Serdang District, North Sumatra from February to May 2022. A factorial randomized block design was selected in this study with the first factor being the dosage of cow manure (0; 2.5; 5 tons ha-1) and the second factor was the dolomite lime (0; 1; 2 kg plot-1) within three replications. Data were analyzed using ANOVA and further tested by Duncan at P<0.05. The results showed an increase in dry weight, seed weight plot-1, 100-seed weight, and yield ha-1 of mung bean were found in 5 tons ha-1 of cow manure dose by 19.06%; 103.62%; 6.48%; and 2.04 times. The plant height and dry weight of mung bean as well as soil pH also increased in 2 kg plot-1 of dolomite dose were 24.55; 15.68; and 7.68%. Their interaction of cow manure and dolomite had a slight impact on the growth and yield of mung bean plants
Pemanfaatan Teknologi Digital untuk Meningkatkan Produktivitas dan Kemandirian Peternak Ikan Desa Mariendal II Bayu Aditya Pratama; Nasaruddin Nur Hasibuan; Sayuti Rahman; Dadan Ramdan; Rahmad Syah; Hartono; M. Khahfi Zuhanda; Arnes Sembiring; Asmah Indrawati; Habib Satria; Iqbal Giffari Ritonga
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.v5i2.7875

Abstract

Kegiatan Pengabdian kepada Masyarakat (PkM) ini bertujuan untuk meningkatkan produktivitas dan kemandirian peternak ikan air tawar di Desa Mariendal II melalui penerapan sistem feeder dan monitoring pakan ikan berbasis Internet of Things (IoT). Permasalahan utama mitra meliputi pemberian pakan yang masih dilakukan secara manual, ketidakteraturan jadwal pakan, pemborosan pakan, serta keterbatasan literasi teknologi. Metode pelaksanaan kegiatan menggunakan pendekatan partisipatif yang meliputi tahap persiapan, pelatihan dan implementasi sistem IoT, pendampingan, serta evaluasi. Evaluasi dilakukan menggunakan pre-test dan post-test untuk mengukur peningkatan pemahaman dan keterampilan peserta. Hasil kegiatan menunjukkan peningkatan rata-rata kompetensi peserta sebesar 47%, dengan peningkatan tertinggi pada keterampilan instalasi dan pengoperasian perangkat serta kemampuan membaca hasil monitoring pakan. Nilai effect size yang sangat besar menunjukkan bahwa peningkatan kompetensi dipengaruhi secara signifikan oleh intervensi kegiatan. Selain itu, penerapan sistem feeder otomatis memberikan dampak operasional berupa peningkatan keteraturan pemberian pakan, pengurangan pemborosan pakan, serta efisiensi waktu dan tenaga kerja peternak. Tingginya tingkat keberterimaan teknologi tercermin dari konsistensi penggunaan sistem oleh sebagian besar peserta setelah kegiatan berakhir. Dengan demikian, kegiatan PkM ini membuktikan bahwa penerapan teknologi IoT yang tepat guna dan disertai pendampingan berkelanjutan mampu meningkatkan efisiensi budidaya ikan sekaligus mendorong kemandirian peternak secara berkelanjutan.
Optimalisasi Nutrisi AB Mix dan Giberelin terhadap Pertumbuhan Vegetatif Melon Hidroponik Dutch Bucket Muhammad Ilham; Asmah Indrawati; Saipul Sihotang
Agrotekma: Jurnal Agroteknologi dan Ilmu Pertanian Vol. 10 No. 2 (2026): Agrotekma: Jurnal Agroteknologi dan Ilmu Pertanian, 10 (2) Juni 2026
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/agr.v10i2.6586

Abstract

The vegetative growth of melon (Cucumis melo L.) grown in hydroponic systems is influenced by proper nutrient management and the application of plant growth regulators. This study aimed to evaluate the effects of AB Mix nutrient concentration, gibberellic acid (GA₃), and their interaction on the vegetative growth of melon cultivated in a Dutch Bucket hydroponic system. The experiment was conducted at the CV. BestFarm Greenhouse, Deli Serdang Regency, North Sumatra, Indonesia, from December 2025 to March 2026 using a factorial Completely Randomized Design (CRD). The treatments consisted of three AB Mix concentrations (1800, 2200, and 2600 ppm) and four GA₃ concentrations (0, 40, 60, and 80 ppm). The results showed that AB Mix had a highly significant effect on several vegetative growth parameters, including plant height, number of leaves, leaf area, stem diameter, and vine length, with 2200 ppm producing the optimum vegetative growth. GA₃ application and its interaction with AB Mix had no significant effect on all observed vegetative growth parameters. These findings indicate that optimum AB Mix nutrition plays a more important role than GA₃ application in improving the vegetative growth of melon cultivated under a Dutch Bucket hydroponic system.
Co-Authors Abdul Rahman Abdul Rahman Afrinaldi Lubis Agung Syahputra Andre Hasudungan Lubis Antahar, Diva Arnes Sembiring Azwana Azwana Bayu Aditya Pratama Beby Masitoh Beby Masitoh Billiam Zealtiel Dadan Ramdan Deddi Prima Putra Dian Noviandri Erianto Ongko Erianto Ongko Ernita Siahaan Fachru Yuzairi Gepin Sianipar Gusmeizal Gusmeizal Habib Satria Habib Satria Habibi, Khusnan Hartono Hartono Hartono Heru Kurniawan Ilham hidayat INDRA HERMAWAN Iqbal Giffari Ritonga Junaidi Junaidi Khafiz, Khafiz Khafiz, Khafiz M. Agung Saputra M M. Khahfi Zuhanda Mitra Musika Lubis Moranain Mungkin Muhammad Ilham Muhammad Khahfi Zuhanda Muhammad Usman Muhammad Usman, Muhammad Muhammad Yogie Ernanda Muhammad Zen Nasaruddin Nur Hasibuan Noer, Zoelherly Rahmad B.Y Syah Rahman, Sayuti Rahmaniah Rahmaniah Retna Astuti Rio Saputra Silaban Rizal Aziz Rizal Aziz Rizal Aziz, Rizal Roswita Oesman Roy Fernando Saipul Sihotang Sayuti Rahman Sembiring, Arnes Shabila Shaharani Tanjung Siti Mardiana Sukiman Sukiman Sumihar Hutapea Suswati . Suswati Suswati suswati suswati Suswati Suswati Syahbudin Hasibuan Syahputra, Agung Syahputra, Agung Tumpal HS Siregar Tumpal HS Siregar, Tumpal HS Zulheri Noer