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All Journal Jurnal Informatika Progres
Mohammad Nur Fitriyadi
Informatika, Universitas Teknologi Digital
Computer Science & IT

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SENTIMEN PUBLIK TERHADAP KONTROVERSI BYON COMBAT SHOWBIZ BERBASIS SUPPORT VECTOR MACHINE DAN NAIVE BAYES Moch. Akbar Ramdani; Mohammad Nur Fitriyadi; Mamok Mamok Andri Senubekti
PROGRESS Vol 18 No 2 (2026): September
Publisher : P3M STMIK Profesional Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56708/progres.v18i2.475

Abstract

The controversial match result of the sportainment event Byon Combat Showbiz Vol. 6 sparked massive debates on social media. The high volume of comments filled with slang and combat sports jargon makes public opinion mapping prone to subjective bias and Out-Of-Vocabulary (OOV) problems. This study aims to objectively classify netizens' sentiment polarity and compare the performance of Naïve Bayes and Support Vector Machine (SVM) algorithms. The extracted dataset of 1,081 documents was processed using Custom Dictionary-based Normalization to reduce linguistic noise. Features were extracted using TF-IDF weighting, while class imbalance was handled using the Synthetic Minority Over-sampling Technique (SMOTE) strictly on the training data to prevent data leakage. The results showed that public opinion was dominated by negative sentiments at 64.5%, rooted in criticism of the referee's technical regulations. Model evaluation proved that linear kernel-based SVM had the most optimal performance with an accuracy rate of 59.45%, outperforming Naïve Bayes which reached 58.99%. The use of a custom dictionary and strict test data separation in SVM proved effective in precisely mapping public sentiment on a complex syntactic dataset.
ANALISIS SENTIMEN DAN IDENTIFIKASI PRODUK TERLARIS PADA E-COMMERCE PAKAIAN ANAK MENGGUNAKAN K-NEAREST NEIGHBOR Siti Jatsiah; Ridha Adjie Eryadi; Mohammad Nur Fitriyadi
PROGRESS Vol 18 No 2 (2026): September
Publisher : P3M STMIK Profesional Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56708/progres.v18i2.476

Abstract

The rapid growth of the children's clothing E-Commerce industry requires businesses to understand consumer preferences through digital review data. This study aims to perform Sentiment Analysis of customer reviews and identify best-selling products at the Baju Anak Kece Online Store by utilizing Text Mining techniques. The primary challenge addressed is the volume of unstructured customer reviews, making it difficult for store owners to accurately determine satisfaction levels and purchasing patterns. The method employed in this research is the K-Nearest Neighbor (KNN) algorithm to classify customer reviews into three sentiment categories: Positive, Neutral, and Negative. Text preprocessing stages include case folding, cleaning, tokenizing, Stopword removal, and Stemming using the Sastrawi Library, followed by word weighting using the TF-IDF method. To illustrate the scale of the experiment, this study utilized a dataset of 326 customer reviews, which were validated and divided into an 80% training set and a 20% testing set. The results indicate that the KNN algorithm is capable of classifying customer sentiment with an Accuracy rate of 69.23%. These findings are integrated to identify best-selling products based on the dominance of positive sentiment volume, providing actionable marketing strategy recommendations for the store to optimize stock and enhance competitiveness in the digital market.
Co-Authors Mamok Andri Senubekti Moch. Akbar Ramdani Ridha Adjie Eryadi Siti Jatsiah