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All Journal JITK (Jurnal Ilmu Pengetahuan dan Komputer)
Muji Ernawati
Nusamandiri University
Computer Science & IT

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TRANSFER LEARNING-BASED CLASSIFICATION OF BELL PEPPER LEAF DISEASES USING VGG16 AND EFFICIENTNETB3 ARCHITECTURES Siti Nurhasanah Nugraha; Evita Fitri; Muji Ernawati
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 3 (2026): JITK Issue February 2026
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i3.7913

Abstract

Diseases affecting pepper leaves can significantly reduce crop productivity and quality, while manual disease identification remains subjective, time-consuming, and prone to error. Therefore, an accurate automated classification system is required to support early disease detection. This study aims to evaluate and compare the performance of a conventional Convolutional Neural Network (CNN) with two transfer learning–based architectures, VGG16 and EfficientNetB3, for classifying pepper leaf images into healthy and bacterial spot classes, as well as to analyze the impact of applying a soft voting ensemble method on classification performance. The dataset was obtained from Kaggle and divided into training, validation, and test sets. Image preprocessing included resizing all images to 224×224 pixels and applying data augmentation to improve model generalization. Model performance was evaluated using accuracy, precision, recall, and F1-score metrics. The experimental results indicate that EfficientNetB3 outperforms the conventional CNN and VGG16 models. Furthermore, the application of the soft voting ensemble enhances prediction stability, achieving an accuracy of 99.68% on the test dataset with balanced precision and recall across both classes. These findings demonstrate that the integration of transfer learning and soft voting ensemble methods is an effective approach for image-based pepper leaf disease classification under the experimental conditions, and provides a basis for further validation using more diverse datasets.
ELECTRICITY CONSUMPTION PREDICTION AND INFLUENTIAL FACTORS ANALYSIS USING MACHINE LEARNING REGRESSION Evita Fitri; Siti Nurhasanah Nugraha; Muji Ernawati
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.v11i4.7914

Abstract

The increase in electricity demand in line with population growth and economic activity requires an accurate and reliable electricity consumption forecasting system. Short-term electricity consumption predictions are an important component in energy system planning and management, particularly to support grid stability and operational efficiency. This study aims to model electricity consumption predictions using a machine learning regression approach and analyze the factors that most influence electricity consumption based on historical data. The dataset used consists of smart meter data with a 30-minute time interval that has undergone data cleansing, data transformation, and feature engineering, including the formation of lag features and temporal features. Three regression algorithms were used, namely Linear Regression, Random Forest Regression, and Gradient Boosted Trees Regression. Model evaluation was performed using the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Coefficient of Determination (R²) metrics. The results show that Linear Regression provides the best performance on the test data with an RMSE value of 0.156, MAE of 0.125, and R² of 0.140, and demonstrates stable generalization capabilities. The analysis of influencing factors reveals that historical consumption variables, particularly Avg_Past_Consumption and electricity consumption lag features, are dominant factors in the prediction, while environmental variables contribute relatively less. These findings provide practical implications for short-term energy demand planning by enabling more accurate load estimation and supporting data-driven decision-making through interpretable electricity consumption patterns.
Co-Authors Evita Fitri Siti Nurhasanah Nugraha