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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

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

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.

OPTIMASI KINERJA LINEAR REGRESSION, RANDOM FOREST REGRESSION DAN MULTILAYER PERCEPTRON PADA PREDIKSI HASIL PANEN Evita Fitri; Siti Nurhasanah Nugraha
INTI Nusa Mandiri Vol. 18 No. 2 (2024): INTI Periode Februari 2024
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v18i2.5269

Rice yield prediction is a significant challenge in the context of climate uncertainty and farmland variation. Erratic weather factors, along with land differences, make this prediction more complex. This research aims to address these issues using a machine learning approach. The method used involves three machine learning models namely Linear regression, Random Forest Regression, and ANN with MultiLayer Perceptron algorithm as well as the evaluation matrix RMSE (Root Mean Squared Error), MAE (Mean Absolute Error) and MAPE (Mean Absolute Percentage Error). This research focuses on testing the accuracy of the three models in the face of uncertain seasonal conditions and variations in agricultural land. The results showed that the MultiLayer Perceptron prediction model gave the best results with an error value of 0.094. The random forest regression method ranks second with an error value of 0.510, followed by Linear regression with an error value of 0.281. The importance of outlier testing in the model development process can be seen from the significant improvement in the performance of the MultiLayer Perceptron model. This research contributes to the development of a more reliable and dependable rice yield prediction system, especially in the midst of uncertain climatic conditions. Machine learning models, particularly MultiLayer Perceptron, can be an effective solution to increase agricultural productivity and reduce risks associated with weather changes and land variations.

AUDIT SISTEM INFORMASI MANAJEMEN SEKOLAH MENGGUNAKAN FRAMEWORK COBIT 4.1 Andi Saryoko; Evita Fitri; Siti Nurhasanah Nugraha; Instianti Elyana; Faruq Aziz
INTI Nusa Mandiri Vol. 19 No. 1 (2024): INTI Periode Agustus 2024
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v19i1.5578

The School Management Information System (SIMS) has brought many benefits, even though it has been implemented, SMPIT Ajimutu Global Insani Bekasi faces several challenges and problems that require special attention, including limitations in IT Strategic Planning, Less Optimal IT Risk Management, Evaluation of Automation Solutions, Security Information Systems, IT Service Performance Measurement, IT Governance have not been fully implemented in their entirety. This article discusses the application of the COBIT 4.1 framework in conducting SIMS audits at SMPIT Ajimutu Global Insani Bekasi. This research aims to assess the suitability of the information system with the school's strategic objectives, identify strengths and weaknesses in its management, and provide recommendations for improvement. The methodology used includes evaluation of the four main domains in COBIT 4.1: Planning and Organization (PO), Acquisition and Implementation (AI), Delivery and Support (DS), and Monitoring and Evaluation (ME). The audit results show that although SIMS has provided significant benefits, there are several areas that require improvement, such as IT strategic plan documentation, risk management, evaluation of automation solutions, information system security, IT service performance measurement, and IT governance. Based on these findings, recommendations for improvement are provided which include improving documentation and communication, developing formal processes for risk management, routine evaluation of automation solutions, improving security policies, establishing more comprehensive performance metrics, and strengthening IT governance.

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