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All Journal Prosiding Amal Insani Foundation INOVTEK Polbeng - Seri Informatika
Asro
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Religion Humanities Computer Science & IT Economics, Econometrics & Finance Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Other

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Comparative Evaluation of Preprocessing Techniques in Twitter Sentiment Analysis for Indonesia’s 2024 Regional Elections Asro; Solihin
INOVTEK Polbeng - Seri Informatika Vol. 11 No. 1 (2026): February
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/tt65bb54

Abstract

The rapid expansion of social media has positioned Twitter as a critical platform for capturing public opinion during political events, including Indonesia’s 2024 Regional Elections. This study investigates the impact of preprocessing strategies and class balancing on the performance of sentiment analysis models applied to election-related tweets. An initial dataset of 9,096 tweets was collected and refined into 6,202 relevant entries from 2024–2025 through text cleaning, normalization, tokenization, and duplicate removal. Sentiment distribution analysis reveals a dominance of positive sentiment (58.4%), followed by negative (33.6%) and neutral (8.0%) expressions. Two classical machine learning classifiers—Naïve Bayes and Logistic Regression—were implemented using TF–IDF feature representation. To address class imbalance, the Synthetic Minority Oversampling Technique (SMOTE) was applied exclusively to the training data, and hyperparameter optimization was conducted using GridSearchCV. Model evaluation employed an 80/20 train–test split with accuracy, precision, recall, F1-score, and confusion matrices as performance metrics. Experimental results indicate that logistic regression combined with SMOTE and hyperparameter tuning achieved the highest accuracy of 93.08%, outperforming Naive Bayes. The findings confirm that carefully designed preprocessing pipelines and class balancing significantly enhance the reliability of sentiment classification in political social media analysis.
Strategi Pengembangan Bisnis Laundry Berbasis Online Asro; Istiharoh, Iis
Prosiding Amal Insani Foundation Vol. 2 (2023): PROSIDING NASIONAL
Publisher : Amal Insani Foundation

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Abstract

In the modern digital era, technology and social media are crucial in supporting businesses, including the laundry business. This business falls under the category of Micro, Small, and Medium Enterprises (MSMEs) that operate in the service sector. Xilaundry, located in Serang Regency, Banten Province, exemplifies how MSMEs utilize technology in their operations. Xilaundry uses social media platforms such as WhatsApp, Instagram, and Facebook for marketing and promotional strategies. Two payment methods are available for customers: conventional (pay on the spot) and digital (ATM, mobile banking, OVO, and Gopay). Xilaundry implements a SWOT analysis for its business strategy development. The results show that Xilaundry's business is feasible to run. Xilaundry's finances record a monthly turnover of IDR 6,570,800 and a cash expenditure of IDR 2,890,000. This generates a net profit of around IDR 2,743,800 per month. Thus, Xilaundry can generate a net income of approximately IDR 34,680,000 in a year. In the SWOT and social media context, Xilaundry can leverage its strength in using social media for marketing and promotion (strength). However, they must maintain service quality to avoid weaknesses in service (weakness). The opportunity that Xilaundry can take is the increase in social media and digital payment users (opportunity), while threats can come from competitors who also use social media in their marketing strategies (threat).
Co-Authors Istiharoh, Iis Solihin