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All Journal JSI: Jurnal Sistem Informasi (E-Journal) Jurnal Teknologi Informasi dan Ilmu Komputer Cyberspace: Jurnal Pendidikan Teknologi Informasi Journal of Electrical Engineering and Computer (JEECOM) Brilliance: Research of Artificial Intelligence ITQAN: Jurnal Ilmu-ilmu Kependidikan Heca Journal of Applied Sciences Transcendent Journal of Mathematics and Applications J-SIGN (Journal Of Informatics, Information System, And Artificial Intelligence) Infolitika Journal of Data Science PESARE: Science and Engineering Service Journal The Indonesian Journal of Computer Science
Misbullah, Alim
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Religion Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Library & Information Science Mathematics Medicine & Pharmacology Physics Public Health Social Sciences Other

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Journal : Infolitika Journal of Data Science

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Optimizing Geothermal Power Plant Locations in Indonesia: A Multi-Objective Optimization on The Basis of Ratio Analysis Approach Rahman, Isra Farliadi; Misbullah, Alim; Irvanizam, Irvanizam; Yusuf, Muhammad; Maulana, Aga; Marwan, Marwan; Dharma, Dian Budi; Idroes, Rinaldi
Infolitika Journal of Data Science Vol. 2 No. 1 (2024): May 2024
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/ijds.v2i1.184

Abstract

As the global energy landscape shifts towards sustainable sources, geothermal energy emerges as a pivotal renewable resource, particularly in regions with abundant geothermal potential like Indonesia. This study focuses on Mount Seulawah in Aceh Province, a region rich in geothermal resources, to optimize the selection of geothermal power plant (GPP) sites using the Multi-Objective Optimization on the Basis of Ratio Analysis (MOORA) method. Our approach integrates environmental, technical, and accessibility criteria, including distance to settlements, land slope, proximity to fault lines and heat sources, and road access. By employing a structured decision matrix and applying MOORA, we systematically evaluated and ranked potential sites based on their suitability for GPP development. The results highlight the site at Ie Brôuk as the most optimal due to its minimal environmental impact and superior geological and accessibility conditions. This study not only contributes to the strategic deployment of geothermal resources in Indonesia but also provides a replicable model for other regions with similar geothermal potentials, emphasizing the importance of a balanced and informed approach to renewable energy site selection.
Performance Assessment of Machine Learning and Transformer Models for Indonesian Multi-Label Hate Speech Detection Bagestra, Ricky; Misbullah, Alim; Zulfan, Zulfan; Rasudin, Rasudin; Farsiah, Laina; Nazhifah, Sri Azizah
Infolitika Journal of Data Science Vol. 2 No. 2 (2024): November 2024
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/ijds.v2i2.235

Abstract

Hate speech, characterized by language that incites discrimination, hostility, or violence against individuals or groups based on attributes such as race, religion, or gender, has become a critical issue on social media platforms. In Indonesia, unique linguistic complexities, such as slang, informal expressions, and code-switching, complicate its detection. This study evaluates the performance of Support Vector Machine (SVM), Naive Bayes, and IndoBERT models for multi-label hate speech detection on a dataset of 13,169 annotated Indonesian tweets. The results show that IndoBERT outperforms SVM and Naive Bayes across all metrics, achieving an accuracy of 93%, F1-score of 91%, precision of 91%, and recall of 91%. IndoBERT's contextual embeddings effectively capture nuanced relationships and complex linguistic patterns, offering superior performance in comparison to traditional methods. The study addresses dataset imbalance using BERT-based data augmentation, leading to significant metric improvements, particularly for SVM and Naive Bayes. Preprocessing steps proved essential in standardizing the dataset for effective model training. This research underscores IndoBERT's potential for advancing hate speech detection in non-English, low-resource languages. The findings contribute to the development of scalable, language-specific solutions for managing harmful online content, promoting safer and more inclusive digital environments.
Co-Authors . Zulfan Aflah, Tsani Akbar, Mufid Arie Budiansyah Asma Liza, Lia At-Tharfi’in, Khairunnisa Azhar, Fauzul Bagestra, Ricky Basrul, Basrul Candra, Rudi Arif Deviani, Rini Dharma, Dian Budi Farsiah, Laina Ghazi Mauer Idroes Hanifiah, Jessy Hisam, Muhammad Aizal Hanafi Bin Fazli Husaini Husaini Husaini Husaini Husaini Husni Husni Ilham, Dirja Nur Irvanizam, Irvanizam Kusumo, Fitranto Lala, Andi Majid, Basrul Abdul Martiwi Sukiakhy, Kikye Marwan Marwan Maulana, Aga Muhammad Rusdi Muhammad Subianto Muhammad Yusuf Mursyida, Waliam MUSLIM MUSLIM Muzailin, Muzailin N. Nazaruddin Nazhifah, Sri Azizah Nizamuddin Rahman, Isra Farliadi Rasudin, Rasudin Rinaldi Idroes SAFIRA Saifullah Sani, Muhammad Saputra, Kurnia sukiakhy, kikye martiwi Teuku Rizky Noviandy Yunardi, Dalila Husna Zahnur