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All Journal Building of Informatics, Technology and Science
Kuncoro, Aneira Vicentiya
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Computer Science & IT

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Perbandingan Multi Algoritma Klasifikasi dan Tuning Parameter untuk Prediksi Ketergantungan Skincare Berbasis Streamlit Kuncoro, Aneira Vicentiya; Ni’mah, Laila Maulin; Faisal, Edi
Building of Informatics, Technology and Science (BITS) Vol 7 No 2 (2025): September 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i2.7897

Abstract

The use of skincare products in Indonesia has increased significantly along with the increasing public awareness of the importance of skincare, but this also raises indications of dependence behaviour that needs to be anticipated, especially in young age groups. This research aims to build a skincare dependency prediction system based on demographic, psychological and behavioural attributes collected through an online survey. In addition, a comparison of five classification algorithms-Decision Tree, Random Forest, Support Vector Machine, K-Nearest Neighbors, and Logistic Regression-was conducted to determine the best model that is most accurate and efficient in predicting the dependency tendency. The data obtained was processed through normalisation and categorical feature transformation with One-Hot Encoding, then evaluated using accuracy, precision, recall, and F1-score metrics. The results showed that the Decision Tree algorithm provided the best performance with accuracy reaching 87% and excellence in model interpretability. The model was then implemented in the form of an interactive web application based on Streamlit that allows users to make predictions independently and in real-time. The contribution of this research is the availability of a prediction system that supports education and wiser decision-making in the use of skincare, as well as opening up opportunities for the utilisation of machine learning technology for other issues.
Analisis Sentimen Pengguna X terhadap Kasus Korupsi Gula Tom Lembong Menggunakan Naïve Bayes, SVM, dan Random Forest Kuncoro, Aneira Vicentiya; Budiman, Fikri; Kurniawan, Defri
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8577

Abstract

The alleged sugar import corruption case involving Tom Lembong has become one of the most widely discussed public issues on social media, generating diverse reactions. This phenomenon illustrates how public opinion on legal issues is often influenced by perceptions of the public figures involved. This study aims to analyze public sentiment regarding the case on the social media platform X (formerly Twitter). The dataset consists of 1,802 tweets collected through a crawling process using the X API with the keyword “Tom Lembong.” The research stages include data cleaning, case folding, text normalization, tokenizing, stopword removal, stemming, sentiment labeling using a lexicon-based approach, and feature extraction with the Term Frequency–Inverse Document Frequency (TF-IDF) method. The prepared dataset was then tested using three classification algorithms: Naïve Bayes, Support Vector Machine (SVM), and Random Forest. The results show that the SVM algorithm achieved the highest accuracy (84%), followed by Random Forest (80%) and Naïve Bayes (76%). Based on the sentiment labeling results, positive sentiment dominated with 61%, while negative sentiment accounted for 39%. Although the analyzed issue concerns an alleged corruption case, the dominance of positive sentiment indicates that public opinion tends to focus on Tom Lembong’s personal image or public track record, which is viewed positively rather than on the substance of the legal allegations. These findings demonstrate the effectiveness of the SVM algorithm in analyzing high-dimensional text and provide insights into how public perception of legal issues can be influenced by image factors and the socio-political context on social media.
Co-Authors Edi Faisal Fikri Budiman Kurniawan, Defri Ni’mah, Laila Maulin