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All Journal JSIKTI (Jurnal Sistem Informasi dan Komputer Terapan Indonesia)
Dewa, Hari Putra Maha
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Computer Science & IT

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Optimizing Chili Price Prediction Using Machine Learning Classification Antara, I Gede Made Yudi; Sugiartawan, Putu; Ardriani, Ni Nengah Dita; Dewa, Hari Putra Maha; Widya Dharma, I Gusti Ngurah Adi; Satya, I Putu Adnya
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 8 No 1 (2025): September
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33173/jsikti.214

Abstract

Optimizing chili price prediction is critical for agricultural stakeholders, enabling better decision-making in supply chain management, market strategies, and farming practices. This research focuses on leveraging machine learning classification models to improve the accuracy and reliability of chili price predictions. The research addresses the challenges of class imbalance, which often occurs due to the uneven representation of price fluctuations in datasets. Resampling techniques, including oversampling the minority class with Synthetic Minority Oversampling Technique (SMOTE) and undersampling the majority class, were employed to balance the dataset and enhance the model's sensitivity to less frequent price drops. Key predictive features such as weather conditions, market demand, transportation costs, and economic indicators were integrated into the models. Advanced classification algorithms like Random Forests and Gradient Boosted Trees were utilized, demonstrating their effectiveness in handling non-linear relationships and class imbalance. Regularization techniques and k-fold cross-validation were applied to prevent overfitting and ensure robust model performance across different data subsets.The results show significant improvements in precision, recall, and overall model accuracy, making the approach suitable for real-world applications. By optimizing machine learning models, this research provides actionable insights for stakeholders to manage price volatility effectively, supporting sustainable agricultural practices and market stability.
Implementation of the Simple Additive Weighting (SAW) Method in a Decision Support System for Tourist Destination Selection in North Bali Utama Giri, I Gede Andi; Dewa, Hari Putra Maha
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 6 No 1 (2023): September
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33173/jsikti.261

Abstract

North Bali boasts remarkable natural and cultural attractions, but the process of choosing the best tourist spots is still based on personal opinions and lacks consistency. Without a fair and organized way to evaluate options, visitors and those involved often depend only on trends or individual tastes, which hinders effective promotion and handling of destinations. This research seeks to create a Decision Support System (DSS) that employs the Simple Additive Weighting (SAW) technique to fairly evaluate and rank travel sites using various factors like ease of access, appeal, amenities, hygiene, expenses, fame, security, and crowd levels. The suggested framework combines these prioritized elements to generate an overall rating for each potential location. By examining data from ten well-known spots in North Bali, Pura Ulun Danu Beratan topped the list with a score of (0.8800), showing excellence in most assessed aspects. The findings show that the SAW approach can reliably aid complex choices in tourism oversight, ensuring clear and dependable rankings. This tool offers a flexible and expandable structure suitable for evaluating tourism in other areas. Upcoming efforts will focus on adding live data feeds and visitor input via online or app interfaces to boost the system's precision and ease of use.
Co-Authors Ardriani, Ni Nengah Dita I Gede Made Yudi Antara PUTU SUGIARTAWAN Satya, I Putu Adnya Utama Giri, I Gede Andi Widya Dharma, I Gusti Ngurah Adi