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All Journal Jurnal Buana Informatika Perspektif : Jurnal Ekonomi dan Manajemen Universitas Bina Sarana Informatika Jurnal Teknik Komputer AMIK BSI Jurnal Teknik Informatika UNIKA Santo Thomas Jurnal Manajemen Informatika Jurnal Riset Informatika EVOLUSI : Jurnal Sains dan Manajemen Jurnal Informatika dan Rekayasa Elektronik JATI (Jurnal Mahasiswa Teknik Informatika) Abditeknika - Jurnal Pengabdian Kepada Masyarakat Jurnal Teknik Informatika Unika Santo Thomas (JTIUST) Indonesian Journal Computer Science (ijcs) Abdi Teknoyasa Jurnal Aruna Mengabdi (ARMI) Jurnal Pengabdian Masyarakat Teknologi Informasi dan Informatika
Risa Wati - AMIK BSI Tasikmalaya Risa Wati - AMIK BSI Tasikmalaya
Universitas Bina Sarana Informatika
Computer Science & IT Decision Sciences, Operations Research & Management Education Engineering Environmental Science Languange, Linguistic, Communication & Media Public Health Social Sciences Other

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SENTIMENT ANALYSIS OF THREE-PERIOD POLEMICS USING K-NEAREST NEIGHBOR WITH TF-IDF WEIGHTING Siti Ernawati; Risa Wati
Jurnal Riset Informatika Vol. 4 No. 3 (2022): June 2022
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1114.484 KB) | DOI: 10.34288/jri.v4i3.160

Abstract

The issue of changing the presidential term which was originally 2 periods of government into 3 periods raises pros and cons in the community. Many 3-period hashtags have sprung up on social media twitter. So that conducted research on sentiment analysis of presidential election polemics 3 period. The purpose of the study was to produce the value of classification on the issue of presidential election change discourse into 3 periods using the K-NN method and whether the k-NN method proved to be well used for classifying text in the review of presidential election polemics 3 periods. Dataset totaling 1152 data, data is processed using Python and Jupyter Notebook as a text editor. The data is classified into positive reviews and negative reviews, then the data is divided into training data and test data with a ratio of 90:10. Weighting words using TF-IDF and sentiment classification using K-NN method. From the results of classification using the K-NN method obtained the highest accuracy when the value of k=17 and k = 18 with an accuracy of 85.3%. The results of the analysis of public sentiment to review the issue of discourse on the change of presidential term into 3 periods tend to be negative with a percentage of 21.26% positive sentiment and 78.74% negative sentiment.
SENTIMENT ANALYSIS OF MENTAL HEALTH REVIEWS USING MACHINE LEARNING ALGORITHMS Risa Wati; Siti Ernawati*
Jurnal Riset Informatika Vol. 8 No. 1 (2025): Desember 2025
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1342.133 KB) | DOI: 10.34288/jri.v8i1.422

Abstract

Mental health is a significant issue in the modern era due to lifestyle changes, social pressures, and technological advancements that introduce new challenges. These problems affect various aspects of life, including education, employment, social relationships, and overall quality of life. Technological development enables the use of machine learning to automatically classify large amounts of data. This study aims to analyze and compare the performance of Support Vector Machines (SVM), K-Nearest Neighbor (K-NN), Naïve Bayes (NB), Logistic Regression (LR), Decision Tree (DT), and Random Forest (RF) in sentiment classification on mental health issues, while simultaneously contributing to scientific development and supporting the understanding of public psychological conditions. The dataset used in this research was obtained from Kaggle and consists of 20,364 mental health–related reviews in .CSV format, processed using Google Colab with the Python programming language. The data were categorized into two groups—depression and suicidewatch—and then underwent preprocessing, data splitting into training and testing sets with an 80:20 ratio, and TF-IDF weighting. The results indicate that the SVM algorithm outperforms the other methods. Using an RBF kernel and a C parameter of 15, SVM achieved an accuracy of 72.09%, a precision of 72.11%, a recall of 72.09%, and an F1-score of 72.09%. This study not only provides scientific contributions but also supports efforts to better understand the psychological conditions experienced by society.
Co-Authors Ahmad Fauzi Armahwati, Armahwati Ernawati, Siti Fauzi Ahmad Muda Fitriansyah, Lambang Hadidinar Hani, Siti Hasanudin Hasanudin Hilda Rachmi Ilham Maulana Ilham Maulana Imam Nawawi Imam Nawawi, Imam Nurlaela, Siti Hani Nuzuliarini Nuris Pranata, Fitano Priyanti, Widi Intan Ratih Yulia Hayuningtyas Riani, Meifana Regita Silvy Nur Azizah Siti Ernawati Siti Ernawati* Siti Hani Nurlaela Widi Intan Priyanti