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All Journal JUITA : Jurnal Informatika Jurnal Sistem Informasi dan Informatika (SIMIKA) Journal of Fuzzy Systems and Control (JFSC)
Alqis Rausanfita
Telkom University
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering

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IMBALANCED DATA HANDLING FOR OPTIMIZING RANDOM FOREST IN SENTIMENT ANALYSIS OF EAST JAVA GUBERNATORIAL ELECTION Rahma Putri Widyaiswari; Anisa Dzulkarnain; Alqis Rausanfita
Jurnal Sistem Informasi dan Informatika (Simika) Vol. 9 No. 1 (2026): Jurnal Sistem Informasi dan Informatika (Simika)
Publisher : Program Studi Sistem Informasi, Universitas Banten Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/simika.v9i1.4131

Abstract

Social media has become a strategic platform in conveying public opinion, especially at the moment of the Regional Head Election (Pilkada). The large amount of opinion data produced opens up opportunities for the application of sentiment analysis to map public perception. One of the main challenges in the classification of sentiment is the imbalance of distribution between classes, which can degrade the accuracy of the model, especially in recognizing minority classes. This study aims to analyze the impact of the application of data balancing techniques on the performance of the 2024 East Java Regional Election sentiment classification model using the Random Forest algorithm. The series of processes in the study include data preprocessing, manual sentiment labeling, text preprocessing, word weighting with TF-IDF, and model training on three data ratios, namely 90:10, 80:20, and 70:30. Each ratio was tested in three scenarios, namely no balancing (baseline), undersampling using the Tomek Links method, and oversampling using Borderline-SMOTE. Of all scenarios, Borderline-SMOTE gave the highest accuracy of 82.40% at an 80:20 ratio, an increase of 2.19% compared to the unbalanced condition at the same ratio. These results show that data balancing is able to improve the performance of the model in classifying sentiment more proportionally.
Optimalisasi Random Forest untuk Sentimen Pilkada Jawa Timur dengan Chi-Square dan Mutual Information Rahma Putri Widyaiswari; Anisa Dzulkarnain; Alqis Rausanfita
JUITA: Jurnal Informatika JUITA Vol. 13 Issue 3, November 2025
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v13i3.26778

Abstract

The rise of social media has transformed the way people express opinions, including in political contexts. In the 2024 East Java Gubernatorial Election, social media platform X became a major outlet for public sentiment toward the governor and deputy governor candidates. This study aims to analyse public sentiment toward three candidate pairs by categorizing the data into three sentiment classes: positive, negative, and neutral. Feature selection was conducted by combining Term Frequency-Inverse Document Frequency (TF-IDF) with Chi-Square and Mutual Information (MI) methods to improve feature quality. The Random Forest algorithm was employed as the primary classification model. In addition, several other algorithms were tested for comparison. The results indicate that the TF-IDF and Chi-Square combination with Random Forest achieved the highest accuracy of 82.07%. These findings highlight the importance of feature selection in improving model performance for sentiment classification. The study provides insights into public opinion that can serve as a reference for strategic decision-making in the political and public sectors.
Hybrid GA-GWO with Dual-Vector Encoding for Indonesian School Timetabling Akbar Muhammad Sadat; Alqis Rausanfita; Pima Hani Safitri
Journal of Fuzzy Systems and Control Vol. 4 No. 2 (2026): Vol. 4 No. 2 (2026)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jfsc.v4i2.418

Abstract

School timetabling is a complex combinatorial optimization problem that involves assigning subjects, teachers, and classes to predefined time slots while satisfying numerous institutional constraints. In many Indonesian junior high schools, scheduling is still performed using manual approaches, which are often time-consuming and prone to conflicts. Compared with university timetabling, school timetabling presents additional challenges due to fixed class groups, rigid subject allocations, teacher availability constraints, and institutional regulations. To address these challenges, this study proposes a hybrid optimization framework that combines a Guided Genetic Algorithm (GA) and Grey Wolf Optimizer (GWO) for the school timetable. The proposed framework incorporates dual-vector solution encoding to provide a structured representation of scheduling components and support efficient constraint handling during the optimization process. In addition, a majority-voting and guided mutation strategy is employed to enhance the balance between exploration and exploitation. The proposed method was evaluated using real-world scheduling data from an Indonesian junior high school consisting of 27 classes, 54 teachers, 13 subjects, and 36 time slots. Experimental results show that the proposed hybrid GA-GWO achieved a fitness improvement of 95.84%, reducing the fitness value from 16,120 to 670, compared with improvements of 89.83% and 94.31% obtained by Traditional GA and Guided GA, respectively. Although the proposed method required approximately 28 minutes of execution time, it produced the highest overall timetable quality among the evaluated approaches. These findings demonstrate that the integration of dual-vector encoding, majority voting, and guided mutation within a hybrid GA-GWO framework can effectively improve timetable optimization for real-world Indonesian school scheduling environments.
Co-Authors Akbar Muhammad Sadat Anisa Dzulkarnain Pima Hani Safitri Rahma Putri Widyaiswari