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All Journal Jurnal Ilmiah METADATA JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Fitri Ayuning Tyas
Universitas Muhammadiyah Brebes
Religion Computer Science & IT Education Law, Crime, Criminology & Criminal Justice Social Sciences

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SENTIMENT ANALYSIS OF THE NATIONAL MANDATE PARTY (PAN) IN YOUTUBE COMMENTS USING THE TF-IDF AND COSINE SIMILARITY APPROACHES Tresna Yudha Prawira; Fitri Ayuning Tyas; Azhar Basir
Jurnal Ilmiah METADATA Vol. 8 No. 1 (2026): Edition January 2026
Publisher : LPPM YPITI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47652/metadata.v8i1.937

Abstract

This study analyzed public sentiment towards the National Mandate Party (PAN) on YouTube comments using the Term Frequency Inverse Document Frequency (TF-IDF) approach and cosine similarity. The aim of the study was to map the tendency of public opinion into three categories: positive, negative, and neutral, and to evaluate the performance of this method compared to a simple lexicon-based approach. Comment data is obtained through the YouTube Data API, then processed through the stages of text cleanup, normalization, stopword removal, and stemming using Sastrawi. Word representation is formed with TF-IDF, while sentiment classification is done by calculating vector similarity using cosine similarity. The results of the analysis showed that neutral sentiment dominated at 63.2%, followed by negative sentiment at 20.7%, and positive sentiment at 16.1%.  The TF-IDF model  is able to classify neutral and negative comments quite well, although some positive comments are difficult to distinguish from neutral comments. These findings suggest that discussions about PAN on YouTube tend to be informative rather than emotional, with a higher tendency to criticize than support. In conclusion, the TF-IDF and cosine similarity methods are effective in providing an overview of public opinion with low computational complexity, while being a lightweight alternative to deep learning-based methods.
Peningkatan Performa Classification and Regression Tree Menggunakan Bagging pada Diagnosis Penyakit Jantung Kokom Hera Fitriyana; Fitri Ayuning Tyas; Abdul Jamil
Jurnal Teknik Informatika dan Sistem Informasi Vol 12 No 1 (2026): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v12i1.12439

Abstract

Heart disease is one of the leading causes of death worldwide, necessitating fast and accurate diagnostic methods for effective prevention. One approach that can be used is data mining, particularly classification methods to analyze health data. The Classification and Regression Tree (CART) algorithm is known for its interpretability but has a drawback in terms of model stability against data variation. To address this issue, the Bootstrap Aggregating (Bagging) technique is applied to improve the model’s stability and accuracy. This study aims to implement and evaluate the effectiveness of the Bagging technique in enhancing the performance of the CART algorithm for heart disease diagnosis. The data used in this study consists of three datasets available on the Kaggle platform: Heart Disease, Heart Disease Cleveland, and Heart Disease Prediction. The model is built under two conditions: using default parameters and using parameters optimized through the Grid Search method. The research process includes data preprocessing (data type adjustment, handling missing values, and outlier detection), training of two types of classification models (single CART and CART with Bagging), and evaluation based on accuracy metrics. The results show that the application of the Bagging technique consistently improves the accuracy of the CART algorithm. Under default parameters, accuracy increased from 72.89% to 78% (Heart Disease), 81.89% to 85.78% (Heart Disease Cleveland), and 77.44% to 82.44% (Heart Disease Prediction). With tuned parameters, accuracy increased from 75% to 84% (Heart Disease), 77% to 83% (Heart Disease Cleveland), and remained at 83% (Heart Disease Prediction). Therefore, the Bagging technique is proven effective in enhancing the accuracy and stability of the CART model for heart disease diagnosis.
Co-Authors Abdul Jamil Azhar Basir Kokom Hera Fitriyana Tresna Yudha Prawira