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All Journal TESLA: Jurnal Teknik Elektro Logic : Jurnal Rancang Bangun dan Teknologi Jurnal Teknologi Informasi dan Terapan (J-TIT) Jurnal Teknik Elektro dan Komputasi (ELKOM) Among : Jurnal Pengabdian Masyarakat BIOS : Jurnal Teknologi Informasi dan Rekayasa Komputer Abdimas Awang Long: Jurnal Pengabdian dan Pemberdayaan Masyarakat Jurnal Publik: Jurnal Ilmiah Bidang Ilmu Administrasi Negara Logic : Jurnal Rancang Bangun Dan Teknologi J-ENSITEC (Journal of Engineering and Sustainable Technology) Indonesian Journal of Multidisciplinary on Social and Technology Jurnal Aplikasi Sistem Informasi dan Elektronika International Applied Science Journal Of Humanities Community Empowerment Jurnal Edukasi Pengabdian Masyarakat: EDUABDIMAS ABDIMASTEK Computing and Information System Journal Teknologi : Jurnal Ilmiah Sistem Informasi
Irawan, Dudi
Universitas Muhammadiyah Jember
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Biochemistry, Genetics & Molecular Biology Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mechanical Engineering Medicine & Pharmacology Social Sciences Other

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Journal : computing and information system journal

 1 
PUBLIC SENTIMENT ANALYSIS OF THE ANTAM GOLD CASE ON SOCIAL MEDIA X USING THE SUPPORT VECTOR MACHINE (SVM) METHOD Ahmad Raudhotul Hikmah; Nur Qodariyah Fitriyah; Dudi Irawan
Computing and Information System Journal Vol. 2 No. 1 (2026): Inovasi Teknologi Cerdas Berbasis AI, IoT, dan Data Mining di Era Digital
Publisher : IndoCompt Publisher

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Abstract

The development of social media, especially X (Twitter), has made it an important source for observing public opinion. One of the issues widely discussed is the Antam gold case related to the alleged misuse of official stamps and the circulation of illegal gold. This study analyzes public sentiment toward this issue by classifying tweets into positive, negative, and neutral categories. Data were collected through crawling using the keyword “kasus emas antam” during the period March 1–May 7, 2025, resulting in 716 tweets. The research process includes preprocessing, feature extraction using N-gram and TF-IDF, and classification using Support Vector Machine (SVM). Evaluation was carried out using a confusion matrix to calculate accuracy, precision, recall, and F1-score. The results show that the SVM model achieved an accuracy of 85%, precision of 91%, recall of 78%, and an F1-score of 80%, indicating good performance in classifying sentiment. This research is expected to provide an overview of public perception and serve as a reference in the application of sentiment analysis on other social issues.
EARLY DETECTION OF DIABETES MELLITUS TYPE 1, TYPE 2, AND GESTATIONAL DIABETES MELLITUS (GDM) USING WEBSITE-BASED CERTAINTY FACTOR Rayhan Ibrahim; Guruh Wijaya; Dudi Irawan
Computing and Information System Journal Vol. 2 No. 1 (2026): Inovasi Teknologi Cerdas Berbasis AI, IoT, dan Data Mining di Era Digital
Publisher : IndoCompt Publisher

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Abstract

The development of information technology plays an important role in supporting healthcare services, particularly in assisting the early detection of diseases. One chronic disease that requires early detection is Diabetes Mellitus, which consists of Type 1 Diabetes Mellitus, Type 2 Diabetes Mellitus, and Gestational Diabetes Mellitus (GDM). These three types of diabetes have similar early symptoms, making them difficult to recognize by the general public. Therefore, this study aims to design and develop a web based expert system to detect Type 1 Diabetes Mellitus, Type 2 Diabetes Mellitus, and Gestational Diabetes Mellitus (GDM) using the Certainty Factor method. This method is used to calculate the level of diagnostic confidence based on symptoms selected by users, referring to confidence weights provided by medical experts. The problem-solving process is carried out through Certainty Factor calculations using a weighted average approach to generate the percentage of diagnostic confidence. Based on testing and validation results through manual calculations and expert evaluation, the developed system achieved an accuracy rate of 87.10%. The results indicate that this expert system can be used as a supporting tool for the early detection of Diabetes Mellitus prior to further medical examination.
Co-Authors Adi Pratama Putra Aditia, Daffa Hibran Ahmad Raudhotul Hikmah Ari Eko Wardoyo Ari Eko Wardoyo Aurelia Agustin Sans Bahri, Mokh Hairul Bahri, Mokh. Hairul Brahma, Aji Brian, Muhammad Budi Satrio Bakti Daffa, Clarence Daffa Rizqullah Dodik Aris Setiawan Fauzan Nafazi Zul Hilmi Fitriana, Fitriana Fitriyah, Nur Qodariyah Ginanjar Abdurrahman Gugun Geusan Akbar Hairani Hairani Hakim, Muhammad Amirul Idris Mahmudi Iswahyudi Izzati Muhimmah Lintang Setyo Kurniawati M Aan Auliq Moh Khoirul Anam Mokh Hairul Bahri Mokh. Hairul Bahri Muhammad A'an Auliq Muhammad Ainul Wafi Muharom, Lutfi Ali Nogroho, Aji Brahma Nur Qodariyah Fitriyah Oktavianto, Hardian Qodariyah , Nur Rahman, Miftahur Rayhan Ibrahim Rhomdani, Rohmad Wahid Robo, Salahudin Saifudin, Ilham Sofia Ariyani Sulistyo, Henny Wahyu Sutikno Taufiq Timur Warisaji Tito Yuwono, Tito Umilasari, Reni Warisaji, Taufiq Timur Wijaya, Guruh Yuri Ariyanto Zainul Arifin ZAINUL ARIFIN