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All Journal JUTI: Jurnal Ilmiah Teknologi Informasi Journal of Information Systems Engineering and Business Intelligence Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Indonesian Journal of Artificial Intelligence and Data Mining JSAI (Journal Scientific and Applied Informatics) Walisongo Journal of Information Technology Brilliance: Research of Artificial Intelligence Abdimas Indonesian Journal Jurnal Pengabdian Masyarakat DIGIMAS: Jurnal Transformasi Digital Masyarakat
Narandha Arya Ranggianto
Universitas Jember
Religion Agriculture, Biological Sciences & Forestry Computer Science & IT Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Engineering Environmental Science Industrial & Manufacturing Engineering Library & Information Science Mathematics Social Sciences Other

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MULTI-DOCUMENT SUMMARIZATION USING A COMBINATION OF FEATURES BASED ON CENTROID AND KEYWORD Narandha Arya Ranggianto; Diana Purwitasari; Chastine Fatichah; Rizka Wakhidatus Sholikah
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 21, No. 2, July 2023
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v21i2.a1195

Abstract

Summarizing text in multi-documents requires choosing important sentences which are more complex than in one document because there is different information which results in contradictions and redundancy of information. The process of selecting important sentences can be done by scoring sentences that consider the main information. The combination of features is carried out for the process of scoring sentences so that sentences with high scores become candidates for summary. The centroid approach provides an advantage in obtaining key information. However, the centroid approach is still limited to information close to the center point. The addition of positional features provides increased information on the importance of a sentence, but positional features only focus on the main position. Therefore, researchers use the keyword feature as a research contribution that can provide additional information on important words in the form of N-grams in a document. In this study, the centroid, position, and keyword features were combined for a scoring process which can provide increased performance for multi-document news data and reviews. The test results show that the addition of keyword features produces the highest value for news data DUC2004 ROUGE-1 of 35.44, ROUGE-2 of 7.64, ROUGE-L of 37.02, and BERTScore of 84.22. While the Amazon review data was obtained with ROUGE-1 of 32.24, ROUGE-2 of 6.14, ROUGE-L of 34.77, and BERTScore of 85.75. The ROUGE and BERScore values outperform the other unsupervised models.
Hybrid Reinforcement and Evolutionary Learning Model for Adaptive Pathway Optimization In Computer Networks Education Anggraeni, Sherly Rosa; Wahyudi, Dian Julianto; Silviariza, Waode Yunia; Ro’is, Rachmy Rosyida; Ranggianto, Narandha Arya
Indonesian Journal of Artificial Intelligence and Data Mining Vol 8, No 3 (2025): November 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v8i3.38398

Abstract

This paper introduces a Hybrid Reinforcement and Evolutionary Learning Model developed to optimize adaptive learning pathways in computer network education. Traditional uniform curricula often struggle to accommodate diverse learner profiles, resulting in knowledge gaps across hierarchical concepts such as OSI layers, routing protocols, and security mechanisms. The proposed model integrates Deep Knowledge Tracing (DKT) with Long Short Term Memory (LSTM) networks for real-time estimation of learners’ knowledge states, Proximal Policy Optimization (PPO) for dynamic sequential content selection, and a Genetic Algorithm Particle Swarm Optimization (GA–PSO) hybrid for global pathway refinement under constraints such as prerequisites and time limits. The model was evaluated using real learner data from an e-learning platform and achieved an average final mastery score of 0.867, quiz accuracy of 0.822, and an F1-score of 0.880 for path recommendations outperforming baseline models such as static curricula (0.740 mastery) and DKT+PPO (0.824 mastery) by 5–17%. Ablation studies validated the synergistic contribution of each component, with the GA–PSO module enhancing optimization efficiency by approximately 10%. Overall, these findings demonstrate that the proposed model offers superior personalization, learning efficiency, and adaptability, marking a significant advancement in AI-driven education for computer networks.
Co-Authors Afandi, Khoirunnisa Akbar Pandu Segara Atmoko, Rizky Baihaqi, Yustri Budi Yuswanto, Istatuk Chastine Fatichah Damar Novtahaning Dian Julianto Wahyudi Diana Purwitasari Dwi Wijonarko Isti Fadah Kartiko, Erik Yohan M. Habibullah Arief Martiana Kholila Fadhil Muhammad Andryan Wahyu Saputra Rizka Wakhidatus Sholikah Rizka Wakhidatus Sholikah, Rizka Wakhidatus Ro’is, Rachmy Rosyida RR. Ella Evrita Hestiandari Sherly Rosa Anggraeni Vika Octaviani Waode Yunia Silviariza Yudha Alif Auliya