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All Journal International Journal of Electrical and Computer Engineering Tekno : Jurnal Teknologi Elektro dan Kejuruan Teknologi dan Kejuruan: Jurnal teknologi, Kejuruan dan Pengajarannya Jurnal Inovasi Teknologi Pendidikan International Journal of Advances in Intelligent Informatics Proceeding of the Electrical Engineering Computer Science and Informatics JOIN (Jurnal Online Informatika) Briliant: Jurnal Riset dan Konseptual JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Journal of Information Technology and Computer Science INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Knowledge Engineering and Data Science Jurnal Penelitian Pendidikan IPA (JPPIPA) JOURNAL OF APPLIED INFORMATICS AND COMPUTING Pendas : Jurnah Ilmiah Pendidikan Dasar Cetta: Jurnal Ilmu Pendidikan ILKOM Jurnal Ilmiah at-tamkin: Jurnal Pengabdian kepada Masyarakat SENTIA 2016 SENTIA 2015 MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Karinov TRIDARMA: Pengabdian Kepada Masyarakat (PkM) Edunesia : jurnal Ilmiah Pendidikan Letters in Information Technology Education (LITE) Jurnal Teknik Informatika (JUTIF) Journal of Applied Data Sciences Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI) Decode: Jurnal Pendidikan Teknologi Informasi Emerging Information Science and Technology Bulletin of Community Engagement Journal of Education Research Jurnal Pustaka AI : Pusat Akses Kajian Teknologi Artificial Intelligence Jurnal Sistem Informasi Triguna Dharma (JURSI TGD) JUSIFOR : Jurnal Sistem Informasi dan Informatika Jurnal Ekonomi, Bisnis dan Pendidikan (JEBP) Energy: Jurnal Ilmiah Ilmu-ilmu Teknik
Didik Dwi Prasetya
Universitas Negeri Malang
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry 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 Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Other

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Educational Data Mining: Multiple Choice Question Classification in Vocational School Sucipto Sucipto; Didik Dwi Prasetya; Triyanna Widiyaningtyas
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 23 No. 2 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v23i2.3499

Abstract

Data mining on student learning outcomes in the education sector can overcome this problem. This research aimed to provide a solution for selecting quality multiple choice questions (MCQ) using the results of students’ mid-semester exams in vocational high schools using a Data Mining approach. The research method used was the Cross-Industry Standard Process for Machine Learning (CRISP-ML) model. Steps to assess the accuracy of analyzing the difficulty level of questions based on student profile data and midterm test results. The data used in this research were the findings of basic computer tests on mid-term exams in mathematics disciplines at vocational high schools. This research used several classification algorithms, including SVM, Naive Bayes, Random Forest, Decision Three, Linear Regression, and KNN. The results of evaluating the classification
Student Flowchart Automated Evaluation for Scalable Assessment in Introductory Programming Usman Nurhasan; Didik Dwi Prasetya
Jurnal Penelitian Pendidikan IPA Vol 11 No 12 (2025): December
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v11i12.13594

Abstract

This study evaluates the Automated Flowchart Assessment Tool (AFAT) to overcome limitations in semantic sensitivity and layout robustness prevalent in existing tools. Through a quantitative analysis of 312 student submissions, AFAT demonstrated superior diagnostic performance with a Micro-F1 score of 0.92 and substantial inter-rater agreement (Fleiss' Kappa = 0.88), supporting the hypothesis of expert-level accuracy. Key findings reveal that AFAT significantly enhances operational efficiency, reducing evaluation time by 61.2% (averaging 1.87 minutes per flowchart) while decreasing inter-rater variability by 28%. Generalized Linear Model (GLM) analysis confirmed significant time savings, particularly in high-complexity sessions (Wald χ² = 87.44, p < 0.001). Beyond technical efficiency, this research contributes to applied science education by providing a scalable framework for computational science literacy, enabling the rigorous assessment of algorithmic thinking within integrated STEM curricula. These results substantiate AFAT’s potential for large-scale deployment as a robust tool for automated scoring in formal educational settings
DRE!: A RULE-BASED SOFTWARE REQUIREMENT EXTRACTION FRAMEWORK FOR INDONESIAN DOCUMENTS Maulana, Moh. Zulfiqar Naufal; Adrian, Ahmad Reza; Adhyaksa, Fathan Alfariel; Prasetya, Didik Dwi; Ardiansyah, Jevri Tri; Adha, Hanif Rifai
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 11, No 1 (2026)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v11i1.9357

Abstract

Extracting software requirements from descriptive documents is a crucial yet challenging phase in software engineering, par-ticularly when performed manually. This research proposes DRE!, a framework for automatically extracting functional requirements from Indonesian-language system descriptions. The employed methodology applies a concise workflow that begins with data acquisition, involving a system description and a predefined list of actors. The process continues with preprocessing, word dependency parsing for grammatical analysis, actor detection featuring a previous-sentence refer-ence mechanism, and requirement construction using an ac-tor-centric template. The workflow concludes with a similarity check (cosine similarity) for redundancy elimination. The framework was evaluated on eight software description da-tasets. The evaluation results demonstrate promising perfor-mance, with F1-Scores on seven of the eight datasets ranging from 0.76 to 0.88. Peak performance was recorded on the "EduLearn" dataset (F1-Score 0.88). However, an anomaly was identified in the "LogiWare" dataset (F1-Score 0.52), which was attributed to a high rate of False Positives. This finding indicates that DRE! is effective in extracting require-ments, but its performance is highly influenced by the clarity and linguistic style of the source documents.
Comparative Study of Random Forest and Ordinal Regression in Concept Map Quality Assessment: The Role of TF-IDF, BERT, and SMOTE-based Balancing Rismayanti, Nurul; Prasetya, Didik Dwi; Widiyaningtyas, Triyanna; Hirashima, Tsukasa
ILKOM Jurnal Ilmiah Vol 17, No 3 (2025)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v17i3.2906.336-345

Abstract

Automatic assessment of concept map quality is an important challenge in the field of education, particularly in evaluating students' conceptual understanding objectively and efficiently. This study aims to compare the performance of two machine learning algorithms, namely Random Forest and Ordinal Regression, in classifying the quality of concept maps. The evaluation was conducted on three approaches to text feature representation: Term Frequency-Inverse Document Frequency (TF-IDF), Bidirectional Encoder Representations from Transformers (BERT), and a combination of both (TF-IDF + BERT). Additionally, this study compares the performance of the models under two dataset conditions: original data and data balanced using the Synthetic Minority Over-sampling Technique (SMOTE), to address the class imbalance that often occurs in educational data. The data used consists of a collection of propositions from students' concept maps that have been labeled with ordinal scores based on quality. Text representation is extracted using the TF-IDF and BERT approaches, and then used as input to build the classification model. Performance evaluation was conducted using the metrics of Accuracy, Precision, Recall, F1-score, Cohen’s Kappa, and MAE. The results show that the Ordinal Regression model with TF-IDF representation combined with SMOTE achieved the best performance, with an accuracy of 0.8777, an F1-score of 0.8773, and a Cohen’s Kappa of 0.7701. These results indicate that classical feature representations like TF-IDF remain effective in limited data scenarios, and that the SMOTE technique successfully improved the model's performance by reducing bias towards the majority class. This research contributes to the development of an automatic concept map assessment system and suggests optimal classification strategies for educational datasets with ordinal and imbalanced characteristics
An Epistemological Approach to Explainable Automated Assessment of Open Concept Map Propositions Using SHAP Ciptaningrum, Mega Satya; Patmanthara, Syaad; Prasetya, Didik Dwi
ENERGY: JURNAL ILMIAH ILMU-ILMU TEKNIK 2025: ENERGY: JURNAL ILMIAH ILMU-ILMU TEKNIK (Special Issue on Engineering Paradigm 2025 Edition)
Publisher : Universitas Panca Marga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51747/energy.si2025.255

Abstract

Concept mapping is widely recognized as an effective method for supporting meaningful learning and critical thinking because it allows teachers to assess students’ underlying knowledge structures. However, evaluating concept maps and providing feedback remain challenging, as these processes are time-consuming, increase teachers’ workload, and can reduce instructional efficiency. To address this issue, this study applies Transformer-based architectures, which rely on large-scale pre-training and task-specific fine-tuning, to develop an automated assessment system for concept maps. In addition, Explainable Artificial Intelligence (XAI) is integrated through the SHAP (SHapley Additive exPlanations) framework to generate interpretable explanations of the model’s scoring decisions. Using Transformer models such as BERT and DeBERTa, SHAP values are computed at the token level to show how individual words within each proposition contribute to the final score. The results indicate that tokens with positive SHAP values increase scores in line with correct rubric indicators, whereas negative values reduce them. Tokens that consistently show positive contributions in high-scoring outputs reflect stable and faithful model reasoning. Overall, the findings demonstrate that combining Transformer-based assessment with SHAP explanations improves epistemic transparency by aligning the model’s internal reasoning with expert evaluation criteria, thereby supporting more reliable, interpretable, and trustworthy automated feedback in concept mapping-based learning.
Minangkabau Language Stemming: A New Approach with Modified Enhanced Confix Stripping Fadhli Almu'iini Ahda; Aji Prasetya Wibawa; Didik Dwi Prasetya; Danang Arbian Sulistyo; Andrew Nafalski
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 3 (2025): June 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i3.6511

Abstract

Stemming is an essential procedure in natural language processing (NLP), which involves reducing words to their root forms by eliminating affixes, including prefixes, infixes, and suffixes. The employed method assesses the efficacy of stemming, which differs according to language. Complex affixation patterns in Indonesian and regional languages such as Minangkabau pose considerable difficulties for traditional algorithms. This research adopts the enhanced fixed-stripping method to tackle these issues by integrating linguistic characteristics unique to Minangkabau. This study has three phases: data acquisition, pseudocode development, and algorithm execution. Testing revealed an average accuracy of 77.8%, indicating the algorithm's proficiency in managing Minangkabau’s intricate morphology. Nevertheless, constraints persist, particularly with irregular affixation patterns. Possible improvements could include adding more datasets, improving the rules for handling affixes, and using machine learning to make the system more flexible and accurate. This study emphasizes the significance of customized solutions for regional languages and provides insights into the advancement of NLP in various linguistic environments. The findings underscore the progress made in processing Minangkabau text while also emphasizing the need for further research to address current issues.
Co-Authors Abdul Wafi Achmad Afif Irwansyah Adha, Hanif Rifai Adhyaksa, Fathan Alfariel Adi Wahyu Wardani Adrian, Ahmad Reza Ahmad Fajruddin Syauqi Ahmad Yusuf Setiawan Ainun Nur Baiti Aji P Wibawa Aji Prasetya Wibawa Akbar, Asna Isyarotul Andika Dwiyanto, Felix Andrew Nafalski Anik Nur Handayani Anjar Dwi Rahmawati Ardiansyah, Jevri Tri Arifiati Fitri Anggraini Aripriharta - Aryo Pinandito Ashar, Muhammad Azhar Ahmad Smaragdina Bagaskoro Biantoro, Yudhi Bintang Romadhon Cakir, Gulsun Kurubacak Ciptaningrum, Mega Satya Danang Arbian Sulistyo Denis Eka Cahyani Dwi Widiyasari Dyah Ayu Langening Tyas Ella Amelia Widodo F.ti Ayyu Sayyidul Laily Fadhli Almu’iini Ahda Fadli Hidayat, M. Noer Fatrisna Salsabila, Reni Firdaus, Nabilah Zakiyah Salmaa Gradiyanto Radityo Kusumo Hafid, Ahmad Hairani Hairani Hakkun Elmunsyah Hanifah Muslimah Az-Zahra, Hanifah Muslimah Haq, Salsabila Thifal Nabil Hariyanto Hariyanto Hayashi, Yusuke Hirashima, Tsukasa I Nyoman Gede Arya Astawa Ilham Ari Elbaith Zaeni Intan Sulistyaningrum Sakkinah Iskandar Syah, Abdullah Kalifatullah, M. Ajie Khoirul Anwar KHOIRUL ANWAR Kusumo, Gradiyanto Radityo Laily, F.ti Ayyu Sayyidul Lalu Ganda Rady Putra Langlang Gumilar Lismi Animatul Chisbiyah Luqman Affandi Lutfi Budi Ilmawan, Lutfi Budi M. Ajie Kalifatullah Marsono Marsono Marsono Marsono Maskur Maskur Mayadi, Mayadi Mega Oktaviana Moh. Nur Zamzami Moh. Zainul Falah Moh. Zulfiqar Naufal Maulana Muhammad Arief Nugroho Muhammad Aris Ichwanto muhammad hafiizh, muhammad Muhammad Jauharul Fuady Muhammad Mushawwir Muhammad Zaki Wiryawan Muhammad Zidni Ridlo Mukhamad Angga Gumilang Muladi Nadiah Alma Ratnaduhita Nadindra Dwi Ariyanta Nafalski, Andrew Nanscy Evi Wardani Natalina Wahyu Siswijayanti Nena Erviana Nunung Nurjanah Nur Hidayat, Wahyu Nuryakin, Mokhamad Perkasa, Gigih Prasetya, Luhur Adi Prasetyo, Muchamad Wahyu Prihandicha, Adiftya Bayu Putro, Maulana Nur Antoro Ratnaduhita, Nadiah Alma Reni Fatrisna Salsabila Reo Wicaksono Ridlo, Muhammad Zidni Rismayanti, Nurul Rofiudin, Amir Ryan Kurniawan Samodra, Joko Setiadi Cahyono Putro Setiawan, Ahmad Yusuf Setyani, Ida Agus Shafelbilyunazra, Alvalen Sigit Perdana Siti Sendari Sofiya Anggraini Sri Sumanti, Endang Sucipto Sucipto Sucipto Sucipto Sulistyo, Danang Arbian Syaad Patmanthara Syaichul Fitrian Akbar Syamsul Arifin Triyanna Widiyaningtyas Triyanna Widyaningtyas Triyanna Widyaningtyas, Triyanna Tsukasa Hirashima Tsukasa Hirashima Tsukasa Hirashima Tuwoso Usman Nurhasan Usman Nurhasan Utomo Pujianto Wahfi, Muhammad Fikri Wahyu Sakti Gunawan Irianto Wahyu Styo Pratama Wahyu Tri Handoko Wahyudi, Erlik Prasetyo Wardani, Adi Wahyu Wibawa, Aji P Wibisono Sukmo Wardhono, Wibisono Sukmo Widiyanti Widiyanti, Widiyanti Yana Andayani Yusril Imamuddin Zainul Falah, Moh.