Bulletin of Data Science

Analisis Sentimen Ulasan Pengguna Aplikasi MyTelkomsel Menggunakan TF-IDF dan Random Forest

Rizki Ariyani — 2026-10-05

The development of digital services in the telecommunications sector encourages service providers to continuously improve the quality of applications used by customers. The MyTelkomsel application has become one of the main media for users to access various Telkomsel services, so that reviews provided by users on the Google Play Store can be an important source of information to determine the level of satisfaction and problems experienced by users. However, the increasing number of reviews makes the manual analysis process less effective, takes a long time, and has the potential to produce subjective assessments. This study aims to analyze the sentiment of MyTelkomsel application user reviews by applying Term Frequency–Inverse Document Frequency (TF-IDF) weighting as a feature extraction method and the Random Forest algorithm as a sentiment classification method. The research data was obtained through the process of scraping MyTelkomsel application user reviews on the Google Play Store. The research stages include data collection, text preprocessing consisting of cleaning, case folding, normalization, tokenization, stopword removal, and stemming, followed by TF-IDF weighting, sentiment labeling, classification process using Random Forest, and model evaluation using a confusion matrix, accuracy, precision, recall, and F1-score. The results showed that the Random Forest model with TF-IDF features achieved classification performance with an accuracy of 81.91%, a precision of 76.77%, a recall of 81.91%, and an F1-score of 79.11%. These results indicate that the combination of TF-IDF and Random Forest methods is capable of identifying sentiment patterns in user reviews with good performance. This study provides information on the trends in user opinions regarding the service quality of the MyTelkomsel application and can serve as evaluation material for developers in improving service quality and user experience.

Comparative Machine Learning Approaches for Classifying Student Stress Types

Chrisensia Abigail Gani — 2026-10-19

Student stress is a major mental health concern that may affect academic performance, psychological well-being, and quality of life. Early identification of stress types is important because different stress categories may require different intervention strategies. With advances in data-driven technologies, machine learning has emerged as a promising approach for identifying patterns in mental health data and supporting automated assessment. This study aims to classify student stress types using the Stress Indicators Dataset for Mental Health Classification, which contains 2,000 student responses. The dataset includes demographic information, psychological symptoms, academic challenges, physical health indicators, and social factors associated with stress. Before model development, preprocessing procedures included data cleaning, duplicate removal, filtering extreme age values, and exploratory data analysis (EDA). Feature engineering was also performed by creating three composite variables, namely symptom_score, academic_stress_score, and social_stress_score, to represent broader stress-related dimensions. Three classification algorithms, Logistic Regression, K-Nearest Neighbors (KNN), and Gaussian Naive Bayes, were developed and evaluated using confusion matrix analysis and Accuracy, Precision, Recall, and F1-Score metrics. The dataset was split into training and testing subsets using an 80:20 stratified split to preserve class distribution. Results indicate that Logistic Regression achieved the highest performance, with an accuracy of 98.10%, precision of 98.14%, recall of 98.10%, and F1-score of 97.96%. Feature engineering slightly improved KNN performance but did not enhance Logistic Regression or Gaussian Naive Bayes. These findings demonstrate the potential of machine learning as a supporting tool for student stress assessment and monitoring, enabling identification of stress categories.