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p-Index From 2021 - 2026
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All Journal Musamus Journal of Technology & Information Journal of Embedded Systems, Security and Intelligent Systems
Hasanudin Jayawardana
Universitas Musamus
Computer Science & IT Engineering

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SISTEM PENDUKUNG KEPUTUSAN PEMILIHAN BENIH PADI UNGGUL MENGGUNAKAN METODE SIMPLE ADDITVE WEIGHTING (SAW) BERBASIS WEB Selfina Pare; Hasanudin Jayawardana; Jarot Budiasto; Kumbiningsih Kumbiningsing
Musamus Journal of Technology & Information Vol 5 No 01 (2022): Musamus Journal of Technology & Information (MJTI)
Publisher : Musamus University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35724/mjti.v5i01.5203

Abstract

Padi merupakan tanaman yang paling banyak ditanam para petani, karena merupakan bahan makanan pokok. Ada beberapa faktor yang berpengaruh pada hasil panen salah satunya yaitu, pemilihan benih padi. Pemilihan benih adalah faktor penentu utama keberhasilan atau kegagalan, karena harus sesuai kondisi pasca tanam atau kondisi lahan tersebut. Faktor yang kedua yaitu penanganan atau perawatan, faktor ini berkaitan dengan cara penanaman maupun perawatan padi dari mulai tanam hingga panen. Petani di kampung Amunkay masih menggunakan cara lama yaitu menanam benih badi berdasrkan keinginan maupun kepemilikan, sehingga hasil panen yang didapatkan tidak sesuai harapan, karena setiap padi memiliki kelebihan dan kekurangan serta perawatan yang berbeda. Untuk itu dibutuhkan sistem yang dapat membantu petani memilih benih padi dalam upaya meningkatkan hasil panen. Penulis bertujuan untuk membuat Sistem Pendukung Keputusan Pemilihan Benih Padi Unggul. Pemilihan benih padi dapat dibantu dengan menggunakan metode Simple Additive Weighting (SAW) dengan kriteria: umur tanaman, potensi hasil, ketahanan terhadap hama, rasa nasi, kerebahan, dan harga benih. Tools yang digunakan yaitu xampp dengan metode pengujian menggunakan metode Blackbox dan kuesioner dengan perhitungan skala likert. Setelah dilakukan metode pengujian Blackbox dan perhitungan skala likert hasil yang diperoleh bahwa sistem pendukung keputusan pemilihan benih padi dapat membantu pemilihan benih padi dengan hasil presentase yaitu 80%.
Accuracy–Efficiency Trade-off Analysis of Five Lightweight CNN Architectures for Mobile-Deployable Corn Leaf Disease Classification Jarot Budiasto; Hasanudin Jayawardana; Tri Kustanti Rahayu; Tatik Melinda Tallulembang
Journal of Embedded Systems, Security and Intelligent Systems Vol 7 No 3 (2026): September 2026
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v7i3.12264

Abstract

Purpose – Corn leaf disease diagnosis in resource-constrained agricultural settings requires mobile-deployable models that maintain a practical balance between classification accuracy, model size, and on-device latency. This study aims to provide empirical guidance for selecting lightweight Convolutional Neural Network (CNN) architectures by systematically analyzing the accuracy–efficiency trade-offs of five models for corn leaf disease classification. Design/methods/approach – MobileNetV2, MobileNetV3-Small, MobileNetV3-Large, EfficientNetB0, and NASNetMobile were evaluated on the PlantVillage Corn dataset comprising 4,188 images across four classes under identical experimental settings. The models were trained using a two-phase strategy and converted into standard and dynamic-range quantized TensorFlow Lite formats. Evaluation covered classification accuracy, macro F1-score, model size, Android on-device inference latency, Pareto frontier and radar analyses, and pairwise McNemar's tests with Yates continuity correction. Findings – EfficientNetB0 achieved the highest accuracy (95.25%) and macro F1-score (93.77%). MobileNetV3-Small offered the strongest efficiency under the tested Android CPU setting, reaching 94.54% accuracy with a 1.18 MB dynamic-range quantized TensorFlow Lite model and 3.89 ± 0.04 ms standard inference. The top three models were statistically comparable (p = 0.6625-1.0000). Research implications/limitations – Standard TensorFlow Lite is preferable for low-latency Android CPU deployment, whereas dynamic-range quantized TensorFlow Lite supports storage-constrained offline distribution. However, the findings are limited to the PlantVillage benchmark dataset and testing on a single mid-range Android device. Originality/value – This study integrates lightweight CNN benchmarking, TensorFlow Lite deployment, real-device Android testing, accuracy–efficiency trade-off analysis, and statistical validation to support evidence-based mobile agricultural AI model selection.
Co-Authors JAROT BUDIASTO Kumbiningsih Kumbiningsing Selfina Pare, Selfina Tatik Melinda Tallulembang Tri Kustanti Rahayu