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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) CommIT (Communication & Information Technology) Proceeding of the Electrical Engineering Computer Science and Informatics Jurnal Komputer Terapan Syntax Literate: Jurnal Ilmiah Indonesia JUTEI (Jurnal Terapan Teknologi Informasi) Sebatik Technomedia Journal INTECOMS: Journal of Information Technology and Computer Science Jurnal Sistem Cerdas Jurnal Pendidikan dan Konseling Syntax Idea Jurnal Informasi dan Teknologi TEPIAN Journal of Economics and Business UBS Buletin Poltanesa Co-Value : Jurnal Ekonomi, Koperasi & Kewirausahaan Jurnal Indonesia Sosial Teknologi Innovative: Journal Of Social Science Research Data : Journal of Information Systems and Management ITEJ (Information Technology Engineering Journals)
Jarot S. Suroso
Pradita University
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Public Health Social Sciences Other

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Journal : sebatik

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Risk Assessment and Detection of Fraudulent Claims in Insurance Systems with Machine Learning Approaches Sutardiman, Mario; Arditya, Dyah Ayu; Suroso, Jarot S
Sebatik Vol. 28 No. 2 (2024): December 2024
Publisher : STMIK Widya Cipta Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46984/sebatik.v28i2.2522

Abstract

Fraudulent insurance claims pose a significant challenge to the sustainability and efficiency of insurance systems, resulting in substantial financial losses and eroding trust between insurers and policyholders. The complexity and volume of modern data make traditional fraud detection methods, such as manual assessments, increasingly ineffective. This study investigates the application of machine learning approaches, including Random Forest and Artificial Neural Networks (ANN), to detect fraud in insurance claims. Using a structured methodology, models were trained on historical claim data and evaluated using metrics such as accuracy, F1-score, recall, and precision. The Random Forest algorithm achieved an accuracy of 94%, while the ANN demonstrated superior performance on controlled datasets. Feature importance analysis identified key predictors, including claim amount and submission frequency, offering actionable insights for fraud prevention strategies. The integration of machine learning into claims management systems provides a scalable, accurate, and cost-effective solution to combating fraud. Future research will focus on testing with larger datasets and exploring hybrid approaches to enhance robustness and adaptability.
The Effectiveness of Using Quizizz in Improving Learners' Motivation and Engagement in Learning Heriyanto, Heriyanto; Cahyadi, Achmad; Suroso, Jarot S
Sebatik Vol. 28 No. 2 (2024): December 2024
Publisher : STMIK Widya Cipta Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46984/sebatik.v28i2.2526

Abstract

This research aims to evaluate the effectiveness of using Quizizz in enhancing learners' motivation and engagement in the learning process. The study seeks to provide educators with insights into the benefits and positive impacts of incorporating gamification in teaching. The materials for this study include academic journal literature related to the use of Quizizz in education. Literature sources were obtained from academic databases such as Google Scholar and Mendeley. This research is limited to literature published within the last five years to ensure the relevance and currency of the findings. It focuses on studies discussing the use of Quizizz in educational contexts. Based on the analysis of several studies, it can be concluded that Quizizz effectively enhances students' motivation and engagement in learning.
Student Perceptions of ChatGPT Integration in the Digital Age Ruyat, Ruyat; Jatiwahyuni, Lucia; Suroso, Jarot S
Sebatik Vol. 28 No. 2 (2024): December 2024
Publisher : STMIK Widya Cipta Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46984/sebatik.v28i2.2533

Abstract

The digital era presents new opportunities in education, including the integration of artificial intelligence (AI) technologies like ChatGPT. This study explores students' perceptions of using ChatGPT in face-to-face learning through a literature review approach. The analysis draws on various reliable sources, including scientific journals, books, and relevant research reports. The findings suggest that ChatGPT is beneficial for helping students understand materials, complete tasks, and enhance their learning motivation. Additionally, it is considered user-friendly due to its simple interface and quick response times. However, several challenges were identified, including limited digital literacy, the risk of receiving inaccurate information, and the potential misuse of the tool as a shortcut, bypassing deeper engagement with learning materials. The study concludes that ChatGPT holds significant potential to support face-to-face learning, especially when used responsibly under teacher guidance. To maximize its effectiveness, efforts should focus on improving students' digital literacy, establishing clear usage policies, and providing structured guidance during the learning process.
Co-Authors Andrea, Jasmine Aubrey Arditya, Dyah Ayu Arif Kurniawan Arif Kurniawan ARIF KURNIAWAN Ayesha Ayub Syed Barisan Budiman, Thomas Cahyadi, Achmad Daniel Nugraha Dimas Firdaus Edoeardo Setiawan Emanuel Anggit Kristian Nugroho Fatchan, Hitori A. Ferdata Adhitya Winarno Putra Ghifari Yusuf, Mohammad Gondomulio, Timothy Gunawan Wang Handono Warih Hitori A. Fatchan Ida Ayu Putu Sri Widnyani Isman Hidayat Jasmine Aubrey Andrea Jatiwahyuni, Lucia Johan Gerry Fernando Kofal Hilkiah Lasewa Maria Veronika Markus Adrian Milytia Christabella Tumengkol Mohammad Ghifari Yusuf Muhammad Azhari Marpaung Muhammad Rashief Nico Yonatan Wicaksana Nugraha, Arief Nurul Jamilah, Eva Pradana, Adhi Priambodo, Nugroho Yoga Puja Hanifah Puja Hanifah Puspa Riri Agustiana Retnowardhani, Astari ruyat, ruyat Sutardiman, Mario Syed, Ayesha Ayub Thomas Budiman Wicaksana, Nico Yonatan Wiemas N. G., Kanka Wilson