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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Edukasi dan Penelitian Informatika (JEPIN) JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Komputasi INOVTEK Polbeng - Seri Informatika Jurnal Teknoinfo JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Building of Informatics, Technology and Science Journal of Computer System and Informatics (JoSYC) Jurnal Teknik Informatika (JUTIF) Jurnal Informatika dan Rekayasa Perangkat Lunak Jurnal Data Mining dan Sistem Informasi Jurnal Teknologi dan Sistem Informasi Journal Social Science And Technology For Community Service KLIK: Kajian Ilmiah Informatika dan Komputer Bulletin of Data Science Jurnal Ilmiah Sistem Informasi Akuntansi (JIMASIA) Journal of Engineering and Information Technology for Community Service Malcom: Indonesian Journal of Machine Learning and Computer Science Jurnal Ilmiah Computer Science Jurnal Informatika: Jurnal Pengembangan IT Journal of Artificial Intelligence and Technology Information Journal of Information Technology, Software Engineering and Computer Science International Journal of Informatics and Data Science
Isnain, Auliya Rahman
Fakultas Teknik Dan Ilmu Komputer, Universitas Teknokrat Indonesia
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Social Sciences Other

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Journal : Jurnal Teknik Informatika (JUTIF)

 1 
COMPARISON OF RANDOM FOREST, SUPPORT VECTOR MACHINE AND NAIVE BAYES ALGORITHMS TO ANALYZE SENTIMENT TOWARDS MENTAL HEALTH STIGMA Elisa, Putri; Isnain, Auliya Rahman
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 1 (2024): JUTIF Volume 5, Number 1, February 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.1.1817

Abstract

Advances in technology, especially the internet, have significantly changed the way people communicate, including social media. Social media facilitates more effective and efficient online communication. Twitter has 18.45 million users in Indonesia by 2022. Discussion of mental health stigma on twitter, increased 17% in 2021 compared to the previous year. Lifestyle transformation, social pressures, and technological advancements have created new challenges in maintaining individual mental health. Discussions of mental health issues have become pros and cons on twitter. The tendency of twitter users in posting content can be known by means of sentiment analysis. Therefore, sentiment analysis can be used to classify comments and tweets related to mental health stigma into negative, positive and neutral. So, it is expected to provide a number of significant benefits in the aspect of managing mental health issues. The methods used to analyze sentiment towards mental health stigma are Random Forest, Support Vector Machine (SVM) and Naïve Bayes algorithms. Based on the research that has been done, it produces 3,095 data for the period 2020-2023. After preprocessing and labeling the data, 1,635 data (negative class), 633 data (positive class) and 208 data (neutral class) were obtained. The SVM model test results show an accuracy of 86.11%, the Random Forest model shows an accuracy of 82.55%, while the Naive Bayes model shows an accuracy of 78.19%. Therefore, it can be concluded that SVM has the best performance in classifying tweets containing mental health stigma.
SENTIMENT ANALYSIS OF PUBLIC OPINION ON THE RIGHT OF INQUIRY IN INDONESIA IN 2024 USING THE SUPPORT VECTOR MACHINE (SVM) METHOD Sebastian, Dicky Fernanda; Sulistiani, Heni; Isnain, Auliya Rahman
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 4 (2024): JUTIF Volume 5, Number 4, August 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.4.1968

Abstract

Research on the right of inquiry refers to public responses on twitter social media related to the 2024 elections. The right of inquiry is a right used in investigations. There are a lot of public opinions about the right of inquiry that are discussed on twitter social media that convey their various opinions or criticisms of government policies towards the 2024 elections. Based on Law No. 17/2014, the right of inquiry of the House of Representatives is regulated in Article 20A of the 1945 Constitution, which regulates the right of inquiry of the House of Representatives. Sentiment analysis is used in this research to determine the accuracy value of public opinion which is categorized into two, namely positive and negative sentiment. In this study, the SVM method is used to identify and find the results of public opinions or responses regarding the issue of the right of inquiry in Indonesia in 2024 which is being widely under the twitter social media platform, so it is necessary to analyze the sentiment. By using the support vector machine (SVM) algorithm and word weighting using TF-IDF (term frequency-inverse document frequency). Data collection using Google Collaboratory tools with the python programming language. The data used were 2,179 tweets with the keywords "inquiry right", "DPR inquiry right", "election inquiry right". The results obtained from the SVM process with an accuracy value of 77%, negative precision value 77%, positive precision value 77%, negative recall value 57%, positive recall value 89%, positive f1-score value 66%, negative f1-score value 82%. The data that has been tested and processed has an adequate accuracy value for SVM algorithm classification using confusion matrix calculation. The results of the research conducted have been effective with the SVM method.
Co-Authors Ade Dwi Putra Ade Surahman Adhie Thyo Priandika Ady Chandra Agung Saputra Agus Sihabuddin Ahmad Ari Aldino Aidil Akbar Alita, Debby Andi Nurkholis Andrian, Eka Aprian Nuriansah Aprilia, Ni Wayan Ayu Sekar Ari Wibowo Bagus Miftaq Hurohman Bakti Eka Putra Deby Sintia Amelia Dedi Darwis Dinda Ayu Prasticha Donaya Pasha Elisa, Putri Elvano Delisa Mega Fatriade Saputra Fenty ariany Fikri Hamidy Heni Sulistiani Ikbal Yasin Ikbal Yasin Indah Sari Jepi Supriyanto Jepi Supriyanto Lestari, Rohmah Dewi Lili Andraini Linda Yunita M Agung Saputra Marga, Nurman Satya Mesran, Mesran Muhammad Indigo Muhammad Pajar Kharisma Muhammad Pajar Kharisma Putra Nadia Apriliza Neneng Neneng Nirwana Hendrastuty Nurman Satya Marga Nuroji Nuroji Oktavia, Ingrid Permata Permata Priandika, Arjuna Priskilia Lovika Prita Dellia Qadhli Jafar Adrian Rido Febriansyah Rika Mersita Romadhona, Waldy Ryan Randy Suryono Sakti, Adam Indra Sanriomi Sintaro Saputra, Dany Eka Sebastian, Dicky Fernanda Setiawan, Samuel Budi Setiawansyah Setiawansyah Styawati Styawati Styawati, Styawati Taufiq Martiwansyah Very Hendra Saputra Wahyudi, Agung Deni Wang, Junhai Yasinta Ismi Yohanes Suyanto Yunita Yunita Yuri Rahmanto Zaenal Abidin Zai, Charles Zeafli Pebriawan Zulfa Nabila