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All Journal Brilliance: Research of Artificial Intelligence
Zainil Abidin
Universitas Jambi, Indonesia
Decision Sciences, Operations Research & Management Mathematics Other

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Sentiment Analysis of Indonesian National Team in 2024 AFF Using Naive Bayes and KNN Rahmad Adrian; Reni Aryani; Zainil Abidin
Brilliance: Research of Artificial Intelligence Vol. 5 No. 2 (2025): Brilliance: Research of Artificial Intelligence, Article Research November 2025
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v5i2.7111

Abstract

Social media platforms like Twitter (now X) serve as key channels for public opinion on major events, including sports tournaments such as the AFF Cup, where sentiments reflect nationalism, criticism, and support. Prior studies have highlighted varying accuracies in sentiment classification for Indonesian football contexts, prompting comparisons of algorithms like Naive Bayes and K-Nearest Neighbors (KNN). This research aims to analyze public sentiment directions towards the Indonesian National Team during the 2024 AFF Cup and compare the performance of Naive Bayes and KNN algorithms. Data comprised 1,918 tweets collected from December 8, 2024, to January 8, 2025, reduced to 1,598 unique entries after preprocessing (cleaning, case folding, tokenizing, filtering, stemming). Sentiments were labeled as positive, negative, or neutral by linguistic experts. TF-IDF vectorized features, and SMOTE addressed class imbalance. Models were trained on 90:10 data splits and evaluated using accuracy, precision, recall, and F1-score, with visualizations including frequency diagrams and word clouds. Neutral sentiments dominated at 49.6%, followed by negative (27.3%) and positive (23.2%). Naive Bayes with SMOTE achieved 79.38% accuracy, outperforming KNN (50-53%). Word clouds revealed supportive terms in positives ("garuda", "semangat"), critical in negatives ("kalah", "pecat"), and factual in neutrals ("indonesia", "piala"). Naive Bayes proves more effective for this dataset, offering insights for team management. Future work should explore advanced models like SVM or BERT and expand data sources for broader generalization.
Comparison of SVM and KNN Methods for the Integratin of MyIndiHome into MyTelkomsel Application Harul Risina Siagian; Dedy Setiawan; Zainil Abidin
Brilliance: Research of Artificial Intelligence Vol. 5 No. 2 (2025): Brilliance: Research of Artificial Intelligence, Article Research November 2025
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v5i2.7234

Abstract

This study aims to analyze user sentiment toward the merger of the MyIndiHome application into the MyTelkomsel platform conducted by PT Telkom Indonesia. In the digital era, the integration of these two customer service applications represents a strategic step to create a unified digital ecosystem. However, this merger has also generated diverse user responses, reflected in various reviews on the Google Play Store. To analyze these opinions, 1,556 user reviews were collected using the web scraping technique. The preprocessing stage included cleaning, tokenizing, filtering, normalization, stemming, and the application of the Synthetic Minority Over-Sampling Technique (SMOTE) to address class imbalance. Two machine learning algorithms, Support Vector Machine (SVM) and K-Nearest Neighbor (KNN), were applied to classify sentiments into positive, negative, and neutral categories. The experimental results showed that SVM achieved higher accuracy (86.2% before SMOTE and 84.9% after SMOTE) compared to KNN (83.7% before SMOTE and 67.6% after SMOTE). These results indicate that SVM performs more effectively and consistently in handling high-dimensional text data than KNN. Therefore, SVM is considered a more reliable algorithm for sentiment classification in this context. The findings provide valuable insights for PT Telkom Indonesia in understanding user perceptions, improving service quality, and enhancing user experience following the digital integration of MyIndiHome into MyTelkomsel.
Application of Visual Data Mining for Visualization of UKBI Achievement Data Ketri genes Yolanda; Pradita Eko Prasetyo Utomo; Zainil Abidin
Brilliance: Research of Artificial Intelligence Vol. 5 No. 2 (2025): Brilliance: Research of Artificial Intelligence, Article Research November 2025
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v5i2.7409

Abstract

The current visualization of Adaptive Indonesian Language Proficiency Test (UKBI Adaptif) results in Jambi Province is suboptimal, often relying on static, basic charts, which hinders transparency and the effective formulation of evidence-based language policies. This research aims to address this critical gap by developing an interactive, data-driven system to analyze the language proficiency profile of UKBI participants in Jambi from 2021 to 2024. The research objective is to accurately map regional competence, identify hidden patterns, and provide actionable intelligence to the Jambi Language Center. The study adopts the Visual Data Mining (VDM) methodology, integrating interactive visualization with the K-Means clustering algorithm. This method allowed for the normalization and grouping of over 10,000 participant data points, with the optimal number of clusters determined by the Silhouette Score. The research results successfully established three distinct proficiency clusters, including a "Listening Struggler Group" dominated by non-education professions, exhibiting significantly low scores in the Listening section. Furthermore, geographical analysis revealed a disparity where Jambi City—the region with the highest participation—maintained an average proficiency at the lower boundary of the Intermediate category, while smaller regions like Muaro Jambi showed higher rates of Superior and Exceptional achievement. The conclusion is that the VDM-based interactive dashboard is a validated and effective tool that successfully provides micro-level insights, supporting the strategic allocation of resources and the design of targeted intervention programs to address specific skill weaknesses, such as listening comprehension.
Co-Authors Dedy Setiawan Harul Risina Siagian Ketri genes Yolanda Pradita Eko Prasetyo Utomo Rahmad Adrian Reni Aryani