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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Fokus Elektroda (Energi Listrik, Telekomunikasi, Komputer, Elektronika dan Kendali)
Matalangi
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Computer Science & IT Electrical & Electronics Engineering Energy Engineering

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Implementasi Metode Double Exponential Smoothing Untuk Prediksi Hasil Panen Sayuran Kentang Billy Eden William Asrul; Matalangi; Kamaruddin
Jurnal Fokus Elektroda : Energi Listrik, Telekomunikasi, Komputer, Elektronika dan Kendali) Vol. 7 No. 3 (2022): Jurnal Fokus Elektroda Vol 7 No 3 2022
Publisher : Jurusan Teknik Elektro Fakultas Teknik Universitas Halu Oleo

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1195.536 KB) | DOI: 10.33772/jfe.v7i3.9

Abstract

   Potato plants have great potential as a source of carbohydrates for human needs. Potato yields are very influential in meeting food needs, so a system that is able to predict the amount of potato production is needed in order to be able to meet the production of potato harvests every year as a material for consideration and recommendations for the amount of potato production each year so that the need for the next market demand can be met. In addition, farmers can determine the amount of land area for planting potato vegetables. This study aims to implement the double exponential smoothing method in a decision support system for predicting potato crop yields, using this technique to help potato farmers/managers in recording crop yields per month and every year. Harvest data from the previous 6 years is used as training data, to find predictive data for the following year's harvest. From the results of the study, the best alpha value used to predict was 0.5 with an accuracy of 82.9%.
Comparative Analysis of CNN, MobileNetV2 and EffecientNetBO in Smart Farming System for Chili Leaf Disease Detection Arda, Abdul Latief; Syamsu Alam; Matalangi
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.v9i6.6709

Abstract

Chili leaf diseases greatly affect agricultural productivity, making early and accurate detection essential to support smart farming systems. This study presents a comparative analysis of three deep learning architectures—Convolutional Neural Network (CNN), MobileNetV2, and EfficientNetB0—for detecting chili leaf diseases using RGB images. The dataset consists of three main disease classes: Bacterial Spot, Curl Virus, and White Spot. Each model was trained and evaluated using accuracy, precision, recall, F1-score, macro AUC, and training time as performance metrics. Experimental results show that MobileNetV2 achieved the highest performance with 99% accuracy, 0.99 F1-score, and 0.99 macro AUC, although it required the longest training time of 115.12 seconds. CNN demonstrated competitive results with 96% accuracy and the shortest training time of 60 seconds, while EfficientNetB0 performed poorly with only 38% accuracy and an F1-score of 0.18. These findings highlight that model architecture, dataset characteristics, and training configuration significantly influence performance outcomes. This study contributes to the development of intelligent agricultural monitoring systems by identifying the most suitable deep learning architecture for real-time chili leaf disease detection in smart farming applications.
Co-Authors Abdul Latief Arda Billy Eden William Asrul Kamaruddin Syamsu Alam