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All Journal Jurnal Pengabdian UntukMu NegeRI JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Komputer Terapan IKRA-ITH Informatika : Jurnal Komputer dan Informatika Dinamisia: Jurnal Pengabdian Kepada Masyarakat INTECOMS: Journal of Information Technology and Computer Science Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) JTIM : Jurnal Teknologi Informasi dan Multimedia Indonesian Journal of Electrical Engineering and Computer Science Majalah Ilmiah UPI YPTK Jurnal J-PEMAS JUSTIN (Jurnal Sistem dan Teknologi Informasi) Proceeding Applied Business and Engineering Conference Jurnal INFOTEL
Muhammad Ihsan Zul
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Religion Humanities Automotive Engineering 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 Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

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Journal : Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)

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Sistem Pakar Untuk Mendiagnosis Penyakit Stroke Hemoragik dan Iskemik Menggunakan Metode Dempster Shafer Jansen Kanggeraldo; Rika Perdana Sari; Muhammad Ihsan Zul
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 2 No 2 (2018): Agustus 2018
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (464.858 KB) | DOI: 10.29207/resti.v2i2.268

Abstract

Stroke is a disease that associated with bloodstreams to the brain. Usually, stroke is caused by the presence of broken blood vessels or obstructed by a blood clot. According to basic health research data by Health Research and Development Agency of Indonesia Ministry of Health (2013), stroke has become one of the deadliest diseases in Indonesia. One effort made to prevent stroke is to create a system that can diagnose stroke. Based on Indraswari's (2015) research, it was found out that stroke can be diagnosed by risk factor criterion. However, to get the data, the patient must check to the hospital or laboratory first To overcome these problems, the authors create an expert system that can diagnose stroke without having to consult directly with the doctor. This expert system adopts the expertise of a neurologist. The result of this system diagnosis’ is the type of desease and the percentage of the probability value of stroke. After the black box testing, it was found that all system functionality has been met. Then, based on the white box testing results, the value of cyclomatic complexity after the optimization of the program code is 8, it shows the program code of Dempster Shafer method is simple program code without much risk. The level of expert system accuracy is 97% so that the system can be used as an alternative for patients to make a diagnosis of stroke
Student-Generated User Story Quality: A Study on Practitioner and ChatGPT Evaluation Zul, Muhammad Ihsan; Yasin, Suhaila Mohd.; Sahid, Dadang Syarif Sihabudin
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 5 (2025): October 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i5.6950

Abstract

Evaluating the quality of student-generated user stories is important in software engineering education, but only a limited number of industry practitioners can assist. The integration of generative AI can facilitate this process. To do so, the INVEST quality evaluation framework is widely recognized for assessing user story quality; however, prior research has not explored its use in conjunction with generative AI. This study investigated ChatGPT's ability to evaluate user stories using the INVEST framework. This study compares two ChatGPT-based evaluation approaches with those of experienced practitioners, focusing on student-generated user stories. Discrepancies between ChatGPT and practitioner evaluations were measured using Mean Absolute Deviation (MAD), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). Statistical significance was tested using the Mann-Whitney U Test. The results indicate that ChatGPT’s 1st approach yielded lower discrepancies than practitioner evaluations. Moreover, significance testing showed no statistically significant differences between the ChatGPT and practitioner results for the two INVEST criteria- Independent and Estimable. These findings suggest that the 1st approach can assist in the evaluation process, although practitioners must ensure comprehensive and accurate evaluations. ChatGPT can provide preliminary evaluations in educational contexts, enabling students to receive formative feedback and allowing educators to streamline evaluation processes. Although practitioner validation is still required, their role may shift toward verifying AI-generated results, thus reducing the overall workload and accelerating quality evaluation
Co-Authors Agus Urip Ari Wibowo Anggy Trisnadoli Anwar, MHD Ardianto Wibowo Arifin Renaldo , Zainal Atiya Karimah Baitul Atiq Chandra, Felly Dadang Syarif Sihabudin Sahid Dzaky Kurniawan Hamid Azwar Istianah Muslim Jacky Efendi Jansen Kanggeraldo Jeni Aprilia Juni Nurma Sari Karimah, Atiya Luqman Hakim M Arif Fadly Ridha Maksum Rois Adin Saf Maksum Rois Adin Saf Marrina Marrina Meliza Putriyanti Zifi memen akbar Mohd. Yasin, Suhaila Muhammad Fauzan Delfani Muhammad mahrus zain Nasha Hikmatia A.E. Okta Riveranda Panjaitan, Yoel Rahma Mawaddah Febriani Rahmat Suhatman Retno Tri Wahyuni Rika Perdana Sari, Rika Perdana Risya Listya Rizalni Rizalni, Risya Listya Satria Perdana Arifin Situmorang, Hartanto Styorini, Wiwin Surya, Ibnu Susianti , Elva Susianti, Elva Tri Wahyuni , Retno Wawan Yunanto Yasin, Suhaila Mohd.