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All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Joglo Jurnal Ilmu Komputer dan Agri-Informatika Journal of the Indonesian Mathematical Society Telematika : Jurnal Informatika dan Teknologi Informasi Scientific Journal of Informatics Jurnal Kajian Informasi & Perpustakaan Abdimas UNWAHAS : Jurnal Pengabdian Masyarakat UNWAHAS SEMAR Jurnal Teknik Informatika (JUTIF) Jurnal Abdi Masyarakat Indonesia Prosiding Konferensi Nasional PKM-CSR Jurnal Pengabdian dan Pemberdayaan Masyarakat Indonesia Journal of Computing Theories and Applications Scientific Journal of Informatics International Journal of Information Systems and Technology (IJOINT) Journal of Soft Computing Exploration
Ristu Saptono
Sebelas Maret University
Agriculture, Biological Sciences & Forestry Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Medicine & Pharmacology Nursing Public Health Social Sciences

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Rethinking the role of three-star ratings: Handling inconsistency in indonesian tourism reviews using cost-sensitive learning XGBoost Muhammad Nur Hikmah Ramadhan; Akhmad Syaifuddin; Ristu Saptono
Journal of Soft Computing Exploration Vol. 7 No. 3 (2026): September 2026
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v7i3.159

Abstract

Indonesian people often give contrary online reviews, for instance ratings that do not always match the actual feelings. This can make it difficult for tourism sector, such as Solo Safari, to handle complaints and improve service quality. Consequently, this study has formulated a rating prediction model using the XGBoost algorithm with a dataset of 2,047 reviews that have been relabeled. The best model, the FastText-XGBoost with Cost-Sensitive Learning, signify that it performs quite well with an average difference the prediction is only 0.04 points from the actual rating. However, the results are not optimal because the meaning of the review text still feels fuzzy even though being relabeled. This problem arises because reviewers tend to position sentiment very positively or negatively in the moderate category, so the perimeters between classes become less clear. This study then proposed an extreme class restructuring by simplifying the category by removing the 3-star rating. This method can increase the accuracy of the model to 0.9440 and clarify the category limits. Therefore, the model can be used on the service dashboard to help Solo Safari management respond to critical feedback faster.
Content-Based Recommendation System for Non-Textbook using TF-IDF, Cosine Similarity, and Educational Level Filtering Arif Rohmadi; Ristu Saptono; Brilyan Hendrasuryawan; Bambang Widoyono; Akhmad Syaifuddin
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 4 (2026): JUTIF Volume 7, Number 4, August 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.4.5425

Abstract

Non-textbook educational resources refer to books designed to enrich readers’ knowledge, insights, and skills, serving as complementary materials to formal textbooks. These may include fiction, non-fiction, biographies, self-help books, and other supplementary literature. However, the broad range of available non-textbooks targeting diverse educational levels often presents challenges for students in selecting materials that are appropriate to their academic stage. This study aims to develop a content-based recommendation system capable of recommending non-textbooks based on the reader's educational level. The recommendation process employs Term Frequency–Inverse Document Frequency (TF-IDF) for feature extraction and Cosine Similarity to calculate semantic similarity between user search queries and book content. To improve relevance, a filtering mechanism based on education level is introduced prior to feature extraction. Experimental results show that applying this filtering process significantly improves the recommendation's performance, yielding an average precision of 100%. In contrast, models without the filtering process achieve only 50% precision. These findings highlight the effectiveness of contextual filtering in improving the accuracy of non-textbook recommendation systems.  
Evaluating Disruption Recovery across Pareto-Representative Solutions in Multi-Objective University Course Timetabling Hanifah Salsabila Ryadi; Ristu Saptono; Muhammad Fahmy Nadhif
Journal of Computing Theories and Applications Vol. 4 No. 1 (2026): JCTA 4(1) 2026
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.17110

Abstract

Multi-objective university course timetabling produces a Pareto front of trade-off solutions, but ultimately, one timetable must be selected for deployment. Existing studies commonly base this choice on objective values, robustness criteria, or stakeholder preferences, while the downstream recovery implications of selecting different Pareto representatives remain insufficiently understood. This study proposes a deployment-oriented evaluation framework to examine whether the selected Pareto-representative timetable affects recovery after calendar-based disruptions. The framework keeps the original optimization objectives fixed, selects the best-f1, best-f2, and compromise timetables from each run, projects them onto an academic calendar with actual dates and holidays, and evaluates them under five disruption scenarios using greedy direct rescheduling followed by target-meeting fulfillment repair. The framework is evaluated using institutional scheduling and calendar data from the odd and even semesters of the 2025/2026 academic year, which provide a natural contrast in room-slot occupancy and disruption exposure. Weekly timetables are generated using NSGA-II by minimizing a weighted soft-constraint penalty (f1) and average room-capacity waste (f2), with the experiments repeated across five independent random seeds. No representative timetable produced a consistent recovery advantage under the tested setting: differences in the direct rescheduling success rate were small relative to seed-to-seed variation, even when the representative timetables differed substantially in their class placements. The larger contrast occurred between semesters. The denser odd semester, with approximately 91% room-slot occupancy, directly recovered about one-third of affected meetings, whereas the less dense even semester, with approximately 52% occupancy, directly recovered nearly all affected meetings. Both semesters achieved 100% target-meeting fulfillment after repair. Overall, the stronger observed contrast occurred between semester settings and was more consistent with differences in structural slack than with the selected representative category.
Co-Authors Abdul Aziz Adam Asshidiq Afrizal Doewes, Afrizal Akhmad Syaifuddin Akhmad Syaifuddin Anggoro, Rizal Dwi Anindita Putri Kirani Arif Rohmadi Arissa Aprilia Nurcahyani, Arissa Aprilia Balgis Balgis Bambang Widoyono Brilyan Hendrasuryawan Budianti Wiboworini Esti Suryani Esti Suryani Galih Dian Hutama, Galih Dian Getut Pramesti Hanifah Salsabila Ryadi Haryono Setiadi Hendra, Brilyan Hendrasuryawan, Brilyan Hestin Mulyasari Ibrahim, Muhammad Syafiq Inge Najwa Aqiilah Joko Susilo Kartika Permatasari Suryajaya, Kartika Permatasari M Rizky Wijaya, M Rizky Mantrini I. Hapsari Meianto Eko Sulistyo Meianto Eko Sulistyo Meiyanto Eko Sulistyo Meiyanto Eko Sulisyo Mudhi Handayani, Eko Muhammad Fahmy Nadhif Muhammad Nur Hikmah Ramadhan Nur Shobriana Trihabsari Pringgo Widyo Putu Suriyasa Respatiwulan, Respatiwulan Rini Anggrainingsih Sari Widya Sihwi Sarngadi Palgunadi Shaifudin Zuhdi Syaifuddin, Akhmad Umi Salamah Umi Salamah Wachid Daga Suryono Widoyono, Bambang Wiharto Wiharto Wiharto Wiharto Wiharto Wiharto Wihidayat, Endar Wiranto Wisnu Widiarto