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All Journal Jurnal Informatika Lontar Komputer: Jurnal Ilmiah Teknologi Informasi TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Informatika Jurnal Informatika Proceeding International Conference on Information Technology and Business Dharmakarya : Jurnal Aplikasi Ipteks Untuk Masyarakat Scientific Journal of Informatics International conference on Information Technology and Business (ICITB) jurnal Teknologi Informasi Magister Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal CoreIT Indonesian Journal of Artificial Intelligence and Data Mining Jurnal SOLMA Jurnal TAM (Technology Acceptance Model) JMM (Jurnal Masyarakat Mandiri) Prosiding Seminar Nasional Darmajaya Building of Informatics, Technology and Science Jurnal Aisyah : Jurnal Ilmu Kesehatan Journal of Computer Networks, Architecture and High Performance Computing Bioscientist : Jurnal Ilmiah Biologi Jurnal Pengabdian kepada Masyarakat JUSTIN (Jurnal Sistem dan Teknologi Informasi) NEAR: Jurnal Pengabdian kepada Masyarakat SIENNA Jurnal TAM (Technology Acceptance Model)
Y. Suhendro
Darmajaya Informatics And Business Institute
Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Immunology & microbiology Languange, Linguistic, Communication & Media Library & Information Science Medicine & Pharmacology Nursing Public Health Social Sciences Transportation Veterinary Other

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Journal : Jurnal CoreIT

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Sentiment Analysis on Reviews of the Documentary Film "Dirty Vote" Using Lexicon-Based and Support Vector Machine Approaches Ramadhan, Apri; Irianto, Suhendro Yusuf
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 11, No 1 (2025): June 2025
Publisher : Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/coreit.v11i1.34603

Abstract

The general election (Pemilu) is a state agenda in Indonesia held every five years. During this democratic event, citizens have the right to freely and fairly choose their leaders. Rules and procedures related to elections are regulated under Law No. 7 of 2017 on General Elections. One of the provisions in this law is the electoral silence period. In the 2024 election, February 11–13, 2024, is designated as the electoral silence period. During this period, Article 287, Paragraph 5 of the Election Law states that print media, online media, social media, and broadcasting institutions are prohibited from disseminating news, advertisements, or any content that benefits or harms election participants. On February 11, 2024, during the silence period, a video titled "Dirty Vote" was uploaded on YouTube, drawing significant public attention. Its release during the silence period sparked controversy and prompted various opinions in the video’s comment section. Sentiment analysis is a suitable method to determine whether public opinions regarding the video are predominantly positive, negative, or neutral. This study utilized the Support Vector Machine (SVM) classification method with different kernels, including linear and non-linear (polynomial, RBF, and sigmoid). To accelerate labeling for large datasets, a Lexicon-Based approach was employed. The combination of SVM and Lexicon-Based methods demonstrated that the linear kernel outperformed others, achieving evaluation metrics of 91.1% accuracy, 91.1% recall, 90.9% precision, and 90.8% F1-score.
Optimizing Student Depression Prediction Using Particle Swarm Optimization and Random Forest Effendi, Mukhammad Khoirul; -, Sriyanto; Irianto, Suhendro Yusuf; Fauzi, Chairani; Vitriani, Yelfi
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 11, No 1 (2025): June 2025
Publisher : Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/coreit.v11i1.35954

Abstract

Student mental health is a growing concern due to increasing academic pressure, social demands, and economic factors affecting their well-being. Depression, a common issue among students, significantly impacts academic performance and overall quality of life. Therefore, early detection and accurate prediction of student mental health conditions are essential to provide timely interventions. This study aims to improve the accuracy of depression prediction among university students by integrating Particle Swarm Optimization (PSO) for feature selection with Random Forest (RF) as the classification model. The dataset used is the Student Depression Dataset from Kaggle, consisting of 27,900 respondents with 18 features related to demographic, academic, and psychological factors. Data preprocessing includes handling missing values, normalization, categorical encoding, and feature selection using PSO. The model is trained and evaluated using 10-Fold Cross-Validation. Experimental results show that PSO-optimized Random Forest outperforms the standard Random Forest model. The optimized model achieves an accuracy of 84.08%, precision of 82.79%, recall of 77.79%, and an AUC-ROC score of 0.912, improving classification performance. These findings demonstrate that PSO effectively enhances feature selection, leading to better classification accuracy. This study contributes to the development of a more accurate and efficient machine learning model for detecting student depression. By optimizing feature selection, this approach reduces computational complexity while maintaining high predictive performance. Future research can explore hybrid optimization techniques such as Genetic Algorithm (GA) or Differential Evolution (DE) to further enhance model generalization across different datasets.
Co-Authors - Nurjoko - Winarsih -, Meiliza -, Nisar A.A. Ketut Agung Cahyawan W Adimas, Adimas Agung Pradana Agus Rahardi Agus Rahardi, Agus Ahmad Rofi’i Ali, Rionaldi Andani, Mia Andreas Perdana anggalia wibasuri Anuar Sanusi Apri Ramadhan, Apri ARDIANSYAH ARDIANSYAH Arman Suryadi Karim, Arman Suryadi Armansyah Armansyah Aziz, RZ. Abdul Dian Sari Dona Yuliawati Dwi Handoko Effendi, Mukhammad Khoirul Eko Hendrawan Endang Astitin Enrico Findley Farduwin, Alhada Fauzi, Chairani Fitria - Fitria - Fitria Fitria handoyo widi nugroho Hendra Kurniawan Herlina Herlina Herwanto, Riko Ida Ayu Puspita Sari Indera Irianto . Irwanto, Ika Krisdiyanto, Wawan Lukmanul Hakim M Said Hasibuan Muhammad Galih Ramaputra Muktiawan, Danang Ade Ningsiah Nisar - Nursiyanto Ochi Marshella Febriani Onno W. Purbo Rahmansyah, Ferdian Ramadhan, M Fitrah Rian Yunandar Ribut Yulianto Rio Kurniawan Roffi, Ahmad Safitri, Egi Septiawan, Yuda Sken Jian Tao Sri Karnila Sri Karnila Sri Karnila Sri Lestari Sriyanto Sriyanto Sriyanto Sriyanto Sumbogo, Fransiskus Dibyo Sushanty Saleh Triloka, Joko Vitriani, Yelfi Wahyu Nugroho Wenni Puspa Sari Yudiansyah, Lepi Astra