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All Journal Jurnal Nasional Teknologi Informasi dan Aplikasinya
I Komang Arya Ganda Wiguna
Universitas Udayana
Computer Science & IT Engineering

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Analisis Sentimen Ulasan Aplikasi Loklok Menggunakan Metode Support Vector Machine (SVM) I Gusti Ngurah Adhiwangsa Devananda; Luh Arida Ayu Rahning Putri; I Komang Arya Ganda Wiguna
Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 4 No. 1 (2025): JNATIA Vol. 4, No. 1, November 2025
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JNATIA.2025.v04.i01.p09

Abstract

Rapid advances in digital technology have led to an increase in the amount of text data available online, including user reviews of mobile applications. The Loklok application, as a popular entertainment platform, is one source of review data that is rich in user opinions. This research focuses on performing sentiment analysis on user reviews of the Loklok application by employing the Support Vector Machine (SVM) algorithm alongside the Term Frequency-Inverse Document Frequency (TF-IDF) method for feature extraction. The review dataset was sourced from the Kaggle platform and underwent several text preprocessing steps, including data cleaning, tokenization, stopword elimination, and stemming. The evaluation results indicate that the SVM model, combined with TF-IDF, achieved an accuracy of 86%, a precision of 88%, a recall of 86%, and an F1-score of 87%. Classification performance tends to be better for positive sentiment classes compared to negative ones, due to data imbalance. This finding demonstrates that the combination of TF-IDF and SVM methods is effective in classifying user review sentiment and can serve as a foundation for decision-making in the development of digital applications.
Analisis Kualitas Air PAM Layak Minum dengan Metode Random Forest dan Decision Tree Stefani Kelin Martha Ampak; Anak Agung Istri Ngurah Eka Karyawati; I Komang Arya Ganda Wiguna
Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 3 No. 4 (2025): JNATIA Vol. 3, No. 4, Agustus 2025
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JNATIA.2025.v03.i04.p23

Abstract

Water is an important source of life for living things including humans. Human needs for water include water that is suitable for use in cooking, washing, and bathing activities that are clean and healthy, as well as water that is safe to drink. Drinking Water Companies (PAM) have a vital role in providing water that meets the standards of consumption eligibility. This study aims to analyze the quality of PAM water by utilizing the Random Forest method as a classification method. The data used include physical, chemical, and microbiological parameters of water. The use of the random forest method was chosen because of its ability to handle complex data and produce accurate predictions. The results of the study showed that the random forest model was able to classify water quality with a high level of accuracy and identify the parameters that most influence the eligibility of drinking water. This study is expected to help related parties in monitoring and improving the quality of PAM water so that it is in accordance with the established health standards.
​​Evaluasi KNN, SVM, dan Random Forest untuk Klasifikasi Leukemia Berdasarkan Citra Sel Darah​ Angelica Audeska Sali; I Ketut Gede Suhartana; I Komang Arya Ganda Wiguna
Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 4 No. 3 (2026): JNATIA Vol. 4, No. 3, Mei 2026
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JNATIA.2026.v04.i03.p01

Abstract

Leukemia is a type of cancer that affects the blood-forming system and requires early detection to improve patient outcomes. One of the primary indicators of leukemia is the presence of blast cells in blood smears. Manual detection by hematologists is time-consuming and requires specialized expertise, prompting the need for automated classification methods. This study evaluates and compares the performance of three machine learning algorithms like K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Random Forest for detecting leukemia blast cells from microscopic blood images. The dataset used consists of 15,000 labeled images classified as either normal or blast cells. Feature extraction involved RGB and HSV color histograms, along with texture features derived from the Gray-Level Co-occurrence Matrix (GLCM). Model performance was assessed using confusion matrices and evaluated through accuracy, precision, recall, and F1-score. Among the models tested, Random Forest achieved the highest accuracy at 86.31%, followed by SVM at 83.61% and KNN at 81.40%. These results indicate that Random Forest is the most effective model for automated detection of leukemia blast cells in this context 
Co-Authors Anak Agung Istri Ngurah Eka Karyawati Angelica Audeska Sali I Gusti Ngurah Adhiwangsa Devananda I Ketut Gede Suhartana Luh Arida Ayu Rahning Putri Stefani Kelin Martha Ampak