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All Journal JITK (Jurnal Ilmu Pengetahuan dan Komputer) Prosiding Snastikom
Poningsih
STIKOM Tunas Bangsa
Computer Science & IT Library & Information Science

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Penerapan Data Mining Dalam Menentukan Pola Penjualan Alat Tulis Kantor (Atk) Menggunakan Algoritma Apriori Pada Toko Novacom Aldo Daniel Purba; Poningsih; Widodo Saputra; Dedy Kristianto Lumbantobing; Sumarno
SNASTIKOM Vol. 2 No. 1 (2023): SEMINAR NASIONAL TEKNOLOGI INFORMASI & KOMUNIKASI (SNASTIKOM) 2023
Publisher : Unit Pengelola Jurnal Universitas Harapan Medan

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Abstract

Office stationery is a necessity that is indispensable in the world of offices and education because it has usefulness in helping and lightening when doing daily tasks in various types of work and circles. This research place sells various kinds of office stationery such as pens, books, pencils and various other types of equipment. Many piles of data that are not used effectively are a problem for Novacom office stationery shop owners. Because of the pile of data, a settlement or pattern formation is needed in order to assist shop owners in determining sales patterns and provide recommendations to shop owners what items are most often purchased by consumers. The algorithm that will be used as an analysis process for office stationery sales is the a priori algorithm to show the results that have met the determination of the pattern of buying office stationery based on sales to consumers
MACHINE LEARNING WITH LIGHTWEIGHT CNN (RESNET-18) FOR EARLY DETECTION OF RICE LEAF DISEASES Poningsih; Suhendra; Ahmad Zamsuri
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 4 (2026): JITK Issue May 2026
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i4.7358

Abstract

Rice leaf diseases such as blast and brown spot significantly threaten rice productivity, especially in agrarian countries like Indonesia. Manual diagnosis methods remain subjective, slow, and inconsistent across field conditions, highlighting the need for an automated and reliable detection system. This study presents a lightweight deep learning framework for the automatic classification of rice leaf diseases from image data. To assess its effectiveness, four Convolutional Neural Network (CNN) architectures ResNet-18, VGG-16, Inception V3, and MobileNetV2 were evaluated. The dataset, obtained from Kaggle, consists of three classes healthy, blast-infected, and brown spot with all images preprocessed through normalization and augmentation before being split into training and validation sets. Experimental results show that ResNet-18 achieves the best overall performance, with 96.94% accuracy, 100% precision, 95.45% recall, an F1-score of 96.18%, and an AUC of 1.0000. Compared to the other architectures, ResNet-18 demonstrates higher stability, stronger generalization, and lower overfitting tendencies while maintaining computational efficiency. The findings indicate that ResNet-18 is a promising lightweight model for practical deployment in mobile or IoT-based agricultural monitoring systems, supporting early disease detection and enhancing local food security efforts
Co-Authors Ahmad Zamsuri, Ahmad Aldo Daniel Purba Dedy Kristianto Lumbantobing Suhendra Sumarno Widodo Saputra