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Syopiansyah Jaya Putra
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Computer Science & IT

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Machine Learning-Based News Classification: Comparison of KNN Accuracy with Hyperparameter Tuning Muhamad Nur Gunawan; Nuryasin; Syopiansyah Jaya Putra; Sarah Arhami
Jurnal Informasi dan Teknologi 2025, Vol. 7, No. 2
Publisher : SEULANGA SYSTEM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60083/jidt.vi0.661

Abstract

This study aims to develop an automatic news text classification system using the K-Nearest Neighbor (KNN) algorithm with a hyperparameter tuning approach. Manual classification by editors is considered inefficient, so an accurate and lightweight automated approach is needed. News datasets were obtained through web scraping of bbc.com sites with five main categories, namely business, technology, entertainment, science, and health. This research follows the CRISP-DM methodology which consists of six stages: business understanding, data understanding, data preparation, modeling, evaluation, and deployment. Feature representation is done using TF-IDF and preprocessing includes stopword removal as well as pattern-based noise cleaning. Two experimental scenarios were performed: first, using complete data without balancing; Second, using more balanced undersampling data. Hyperparameter tuning was performed with k-value variations from 1 to 50 and validated with 5-fold cross-validation. The results showed that the model with balanced data and a value of k=11 produced an accuracy, precision, recall, and F1-score of 95%. The system was also successfully implemented into a Flask-based web application that can be used by news editors for real-time text classification. This study emphasizes the importance of parameter optimization and preprocessing in text classification and shows that simple algorithms such as KNN remain competitive if supported by good data processing.
Development of an Integrated Commodity Auction System Based on the English Auction Model for Transaction Transparency and Efficiency Syopiansyah Jaya Putra; Muhammad Akbar; Siti Saluiatu Rohmah
Jurnal Informasi dan Teknologi 2025, Vol. 7, No. 2
Publisher : SEULANGA SYSTEM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60083/jidt.vi0.662

Abstract

Commodity Auction Markets (PLK) serve as a platform for interactions between sellers and buyers to conduct transactions through a price-bidding system. Traditionally, these auctions are held offline to facilitate transactions between farmers, farmer groups, traders, businesses, and other commodity buyers. However, conventional auctions face multiple challenges, including inefficiencies in execution time, limited access to information for buyers, and high operational costs. This study aims to design and develop an Integrated e-Auction Market System (PLT) based on the English Auction model. The system supports the entire auction process from preparation, announcement, and bidding, to winner determination and transaction finalization. System development was conducted using the Rapid Application Development (RAD) approach for planning, analysis, design, and implementation phases. Unified Modeling Language (UML) tools were utilized to model system design components.The web-based PLT system successfully integrates all auction processes efficiently and transparently. It provides stakeholders with access to commodity catalogs, session schedules, and detailed transaction information, while enabling real-time online auctions. The implementation of this system is expected to broaden commodity marketing networks, increase local government revenues, and significantly reduce auction operational costs particularly venue rental expenses.
Co-Authors Muhamad Nur Gunawan Muhammad Akbar Nuryasin Sarah Arhami Siti Saluiatu Rohmah