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All Journal JOURNAL OF SCIENCE AND SOCIAL RESEARCH Journal of Computer System and Informatics (JoSYC) Jurnal Sistem Komputer dan Informatika (JSON) TIN: TERAPAN INFORMATIKA NUSANTARA Bulletin of Computer Science Research
Aidil Halim Lubis
Universitas Islam Negeri Sumatera Utara, Medan
Aerospace Engineering Arts Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Social Sciences Veterinary Other

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Journal : bulletin of computer science research

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Klasifikasi Tingkat Kepuasan Pengguna Produk Body Care Menggunakan Algoritma Decision Tree Nur Jannah Hasibuan; Aidil Halim Lubis
Bulletin of Computer Science Research Vol. 6 No. 4 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i4.1110

Abstract

The increasing competition in the body care industry encourages companies to understand customer satisfaction as a basis for improving product quality and service performance. However, analyzing user satisfaction often produces complex data that are difficult to process manually. This study aims to apply the Decision Tree algorithm to classify the satisfaction levels of body care product users based on user characteristics and product evaluations. The research data were collected through questionnaires distributed to 250 respondents, including attributes such as gender, age, frequency of use, product quality, price, service quality, and satisfaction level as the target variable. The research stages consisted of data preprocessing, attribute selection, data transformation, splitting data into training and testing datasets, and building a classification model using the Decision Tree algorithm. Model evaluation was carried out using a confusion matrix with accuracy, precision, recall, and F1-score metrics. The results showed that the model was able to classify user satisfaction into four categories: very satisfied, satisfied, fairly satisfied, and dissatisfied, with an accuracy of 58%, precision of 57%, recall of 57%, and F1-score of 57%. This study contributes to the implementation of data mining for customer satisfaction analysis in the body care industry and helps companies identify dominant factors influencing user satisfaction, particularly product quality and service quality. In addition, the findings are expected to serve as a reference for developing customer satisfaction analysis systems based on data mining in the beauty and body care industry.
Klasifikasi Persepsi Publik Terhadap Perang Dagang Amerika Serikat Menggunakan Algoritma Naïve Bayes Classifier Bunga Nurul Manisa; Aidil Halim Lubis
Bulletin of Computer Science Research Vol. 6 No. 4 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i4.1112

Abstract

The import tariff policy implemented by the President of the United States on April 2, 2025 triggered tensions in global trade and provoked various public reactions. Differences in public perceptions of the policy generated diverse opinions, including support, criticism, and neutral responses, making sentiment analysis necessary to understand public opinion trends more systematically. This study aims to classify public perceptions of the U.S. trade war through sentiment analysis of Twitter data using the Naïve Bayes Classifier (NBC) algorithm. The dataset consists of 2,000 tweets collected using the keywords “trade war” and “import tariff increase” during April 3–30, 2025. Six preprocessing stages were applied: cleaning, case folding, tokenizing, slangword normalization, stopword removal, and stemming to improve data quality and consistency. Automatic labeling was conducted using a lexicon-based method with the InSet dictionary, yielding sentiment distributions of 83.5% negative, 12.8% positive, and 3.8% neutral. Feature representation was performed using TF-IDF, followed by an 80:20 train-test split. To address class imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was applied. Experimental results show that the NBC model without SMOTE achieved an accuracy of 83.5% but exhibited bias toward the majority class. After applying SMOTE, the dataset became balanced with 1,335 samples per class. Although overall accuracy decreased to 76%, the Macro F1-Score improved from 0.30 to 0.45, indicating improved model performance in handling multi-class classification more fairly. Additionally, the model achieved a recall of 43% for the positive class and 13% for the neutral class, providing a more representative evaluation of public sentiment toward the U.S. trade war issue.
Co-Authors Bunga Nurul Manisa Ilka Zufria Ismail Mahfuddin Muhammad Luthfi Lubis Nur Jannah Hasibuan Rakhmat Kurniawan R Rakmat Kurniawan R Sarmila Sarmila Siti Ayu Hadisa