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All Journal JSI: Jurnal Sistem Informasi (E-Journal) Scientific Journal of Informatics Jurnal Kajian Informasi & Perpustakaan SMATIKA Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Abdimas Dewantara CAPITAL: JURNAL EKONOMI DAN MANAJEMEN Jurnal Abdi Insani REMIK : Riset dan E-Jurnal Manajemen Informatika Komputer Jurnal Teknik Informatika (JUTIF) Journal of Soft Computing Exploration Journal of Advances in Information Systems and Technology Scientific Journal of Informatics Ulil Albab Journal of Advances in Information Systems and Technology Indonesian Journal of Mathematics and Natural Sciences Recursive Journal of Informatics
Budiman, Kholiq
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Religion Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Social Sciences Other

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Sentiment Analysis of Presidential Candidates in 2024: A Comparison of the Performance of Support Vector Machine and Random Forest with N-Gram Method Muhammad Rizki Ramadhan; Kholiq Budiman
Recursive Journal of Informatics Vol. 3 No. 1 (2025): March 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/rji.v3i1.8385

Abstract

Abstract. This paper conducts a sentiment analysis of presidential candidates in Indonesia's 2024 election using Twitter data. Utilizing the "Indonesia Presidential Candidate’s Dataset, 2024" from Kaggle, containing 8555 Twitter entries, sentiment was categorized as positive or negative. Preprocessing techniques cleaned and normalized the data, followed by labeling with the VADER lexicon. This study contributes insights into public sentiment towards presidential candidates and the effectiveness of machine learning algorithms for political sentiment analysis. Purpose: This study aims to analyze public sentiment towards presidential candidates in Indonesia's 2024 election using the N-Gram method. By employing Support Vector Machine and Random Forest algorithms, we compare their performance in sentiment analysis. Utilizing the "Indonesia Presidential Candidate’s Dataset, 2024" from Kaggle, containing 8555 Twitter data entries, we seek to provide insights into the electorate's perceptions and preferences, contributing to a deeper understanding of the political landscape during this crucial period. Methods/Study design/approach: The study uses Support Vector Machine (SVM) and Random Forest algorithms for sentiment analysis on a dataset of 8555 tweets about Indonesia’s 2024 presidential candidates. SVM, paired with TF-IDF, and Random Forest, paired with N-Gram, are used for feature extraction. The data is labeled using the Vader lexicon. Result/Findings: The study compared Support Vector Machine (SVM) with TF-IDF and Random Forest with N-Gram methods in analyzing public sentiment towards Indonesia's 2024 presidential candidates. Results showed Random Forest with N-Gram achieved 85% accuracy, outperforming SVM with TF-IDF at 82%. Novelty/Originality/Value: This study provides insights into sentiment analysis applied to the 2024 Indonesian presidential election, enhancing understanding of public sentiment dynamics. Comparing SVM with TF-IDF and Random Forest with N-Gram contributes to the field, suggesting avenues for future research such as integrating contextual information or social network analysis for deeper insights into political opinion trends.
Improving Pantun Generator Performance with Fine Tuning Generative Pre-Trained Transformers Achmat Sodikkun; Kholiq Budiman
Recursive Journal of Informatics Vol. 3 No. 2 (2025): September 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/ge6xey51

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Purpose: The study aims to address the challenges in generating high-quality pantun, an important element of Indonesian cultural heritage. Traditional methods struggle with limited vocabulary, variation, and consistency in rhyme patterns. This research seeks to enhance the performance of a pantun generator by applying fine-tuning techniques to the Generative Pre-trained Transformers (GPT) model, coupled with post-processing, and validated by linguistic experts. Methods/Study design/approach: The research involves fine-tuning the GPT model using a dataset of Indonesian pantun. The methodology includes dataset collection, data pre-processing for cleaning and adjustment, and hyperparameter optimization. The effectiveness of the model is evaluated using perplexity and rhyme accuracy metrics. The study also incorporates post-processing to refine the generated pantun further. Result/Findings: The study achieved a best perplexity value of 14.64, indicating a strong predictive performance by the model. Post-processing significantly improved the rhyme accuracy of the generated pantun to 89%, a substantial improvement over previous studies by Siallagan and Alfina, which only achieved 50%. These results demonstrate that fine-tuning the GPT model, supported by appropriate hyperparameter settings and post-processing techniques, effectively enhances the quality of generated pantun. Novelty/Originality/Value: This research contributes to the development of generative applications in Indonesian, particularly in the context of cultural preservation. The findings highlight the potential of fine-tuning GPT models to improve language generation tasks and provide valuable insights for creative and educational applications. The validation by experts ensures that the generated pantun adheres to established writing standards
Optimization of Machine Learning Model using Grid and Random Search Algorithms for Predicting Student Dropout Hakim, M. Faris Al; Wahyuni, Siti; Budiman, Kholiq; Marianti, Aditya; Susilo, Bambang Eko; Widiarti, Nuni; Sukaesih, Sri; Rifaatunnisa, Rifaatunnisa
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.3.5627

Abstract

Student dropout is a serious problem that can affect the quality of education and operational efficiency of higher education institutions. Early prediction of potential students who will dropout is essential to develop appropriate intervention strategies, so as to increase graduation rates and reduce the negative impact on academic continuity. A better model for student dropout prediction becomes an objective of this research. The method used in this research is to improve the performance of machine learning models through the selection of optimal hyperparameters. The research methodology consists of several stages, including data preprocessing, handling imbalanced data, model training, and performance evaluation. There are three machine learning models used in this research, namely XGBoost, AdaBoost, and Random Forest. The selection of optimal hyperparameter values is carried out using the Random Search and Grid Search methods. Model evaluation is conducted using k-fold cross-validation and multiple evaluation metrics, including accuracy, precision, recall, and F1-score. As part of the important results, the combination of XGBoost and Random Search produced the best performance with 91.18% accuracy, indicating that hyperparameter optimization significantly improves predictive performance. The findings of this research explicitly contribute to the field of informatics, particularly educational data mining, and provide insights for educational institutions to identify high-risk dropout students more accurately.
Co-Authors Achmat Sodikkun Aditya Marianti Afif Amirullah Ahmad Mundzir Aji Purwinarko, Aji Akbar, Bintang Faisal Akhmad Munawar Alamsyah - Alfat Mutoriq Ali Djamhuri Alya Aulia Nurdin Amin Pujiati Amin Retnoningsih Amirullah, Afif Arif Widiyatmoko, Arif Ariolin, Anantha Revoislami Asharinnisa Salsabila Astagina, Paramesti Bambang Eko Susilo Bambang Sugiantoro Dianti, Erika Noor Efdika, Muhamad Fadil Efrilianda, Devi Ajeng Fattah, Ahmad Maulana Malik Fitri Arum Sasi, Fitri Arum Habibah, Berlian Ummu Hakim, M. Faris Al Hartono, Rudi Haryanti, Unik Slamet Izdiharsant, Almaas Karmesti, Danissa Wirna Khoirunnisa, Oktaria Mahargjo Hapsoro Adi Muhammad Rizki Ramadhan Muhammad Wildan Yasykur Mundzir, Ahmad Nugroho, Anan Nuni Widiarti Nurdin, Alya Aulia Nurul Huda Agustiani Pasha, Rasikh Khalil Pertiwi, Dwika Ananda Agustina Pramesti, Pricilia Intan Pricilia Intan Pramesti Putra Perdana, Muhamad Richardo, Hizkia Natanael Rifaatunnisa, Rifaatunnisa Riza Firmansyah Sarmini Satrio Ardiansyah, Adi Siti Wahyuni Slamet Kafrawi Sri Sukaesih suprih murdyantara Unik Slamet Haryanti Usman, Sahda Armandiva Wahyu Hardyanto Widi Widayat Widi Widayat wiyanto, Agung Yahya Nur Ifriza Yogiana Marta Dewi