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All Journal JSIKTI (Jurnal Sistem Informasi dan Komputer Terapan Indonesia)
Ni Wayan Wardani
Okayama University
Computer Science & IT

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Enhancing Price Classification of Chili Using Gradient Boosting Machines Putu Sugiartawan; Ni Wayan Wardani
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 6 No 3 (2024): March
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33173/jsikti.233

Abstract

This study explores the application of Gradient Boosting Machines (GBM) to enhance the classification and prediction of chili prices. The research uses a comprehensive dataset collected from various sources, including local markets, online platforms, and agricultural databases, covering multiple attributes such as chili type, region, harvest season, weather conditions, and demand-supply dynamics. The GBM model outperforms traditional machine learning algorithms, achieving an accuracy of 87%, with a high area under the ROC curve (AUC) of 0.91. Feature importance analysis indicates that harvest season and region are the most significant factors influencing price variations. The findings suggest that the GBM model provides reliable price predictions and insights into price-driving factors, offering valuable tools for stakeholders in the agricultural market. The study emphasizes the need for broader data sources and advanced techniques, such as time-series forecasting and XGBoost, to further improve chili price prediction models. These insights can help optimize supply chain management, price forecasting, and decision-making for producers, traders, and policymakers.
Machine Learning Forecasting Techniques for Analyzing Tourist Arrivals in Bali Putu Sugiartawan; Ni Wayan Wardani
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 7 No 1 (2024): September
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33173/jsikti.243

Abstract

This study investigates the application of machine learning (ML) techniques for forecasting tourist arrivals in Bali, leveraging a dataset spanning from 1982 to 2024. The Random Forest model, along with Linear Regression and Decision Tree, was evaluated for its ability to handle the complexities of tourism data, characterized by seasonality and nonlinear patterns. Among the models tested, Random Forest achieved the best performance, with the lowest Mean Squared Error (MSE) and Mean Absolute Error (MAE), demonstrating its robustness in capturing both short-term fluctuations and long-term trends. The findings highlight the potential of ML techniques to improve forecasting accuracy compared to traditional methods, especially in managing seasonal variations and external disruptions like the COVID-19 pandemic. However, limitations in predicting unprecedented events underscore the need for integrating external variables, such as economic indicators and travel restrictions. Future research should focus on hybrid models, scenario-based forecasting, and real-time data integration to enhance adaptability and predictive accuracy. These advancements aim to support policymakers and stakeholders in optimizing resource allocation, designing marketing strategies, and fostering sustainable tourism development in Bali.
DenseNet121 and Transfer Learning for Lung Disease Classification from Chest X-Ray Images Putu Sugiartawan; Ni Wayan Wardani
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 8 No 2 (2025): December
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33173/jsikti.266

Abstract

Lung-related disorders, including pneumonia, are still among the primary causes of death and illness worldwide, particularly in areas where medical imaging facilities and trained radiologists are scarce. The manual assessment of chest X-ray (CXR) images demands significant time and is prone to subjective interpretation, limiting its scalability for mass screening and early disease identification. To overcome these challenges, this study introduces an automated classification approach utilizing the DenseNet121 convolutional neural network through transfer learning for the detection of lung diseases from CXR scans. The pretrained ImageNet weights were adopted to capture hierarchical visual features efficiently, while overfitting was mitigated using dropout and batch normalization layers. The dataset employed consisted of 1,880 training images and 235 testing images, equally distributed between Normal and Viral Pneumonia categories. Experimental evaluation revealed an overall classification accuracy of 97%, alongside precision, recall, and F1-score metrics of 0.97 each, indicating reliable and balanced model performance. These outcomes suggest that DenseNet121 offers a highly effective foundation for computer-aided diagnostic systems capable of differentiating between healthy and infected lungs with high precision. The proposed framework provides a scalable diagnostic tool suitable for healthcare environments with limited radiological expertise. Future improvements will include expanding toward multi-class disease classification, incorporating explainable artificial intelligence (XAI) techniques to enhance interpretability, and validating the system on larger, more diverse clinical datasets.
Co-Authors Putu Sugiartawan