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All Journal Jurnal Teknik Informatika TEKNO Knowledge Engineering and Data Science BAREKENG: Jurnal Ilmu Matematika dan Terapan JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Teknik Elektro dan Komputer
Steven Ray Sentinuwo
Sam Ratulangi University
Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Library & Information Science Mathematics Mechanical Engineering Physics Transportation

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Debtor Eligibility Prediction Using Deep Learning with Chatbot-Based Testing Noviania, Reski; Sela, Enny Itje; Latumakulita, Luther Alexander; Sentinuwo, Steven R.
Knowledge Engineering and Data Science Vol 7, No 2 (2024)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um018v7i22024p128-138

Abstract

Predicting debtor eligibility is essential for effective risk management and minimizing bad credit risks. However, financial institutions face challenges such as imbalanced data, inefficient feature selection, and limited user accessibility. This study combines Recursive Feature Elimination (RFE) and Deep Learning (DL) to improve prediction accuracy and integrates a chatbot interface for user-friendly testing. RFE effectively identifies critical features, while the DL model achieves a validation accuracy of 97.62%, surpassing previous studies with less comprehensive methodologies. The chatbot's novel design not only ensures accessibility but also enhances user engagement through flexible input options, such as approximate values, enabling non-experts to interact seamlessly with the system. For financial institutions, this chatbot-based testing approach offers practical benefits by streamlining debtor evaluation processes, reducing dependency on manual assessments, and providing consistent, scalable, and efficient solutions for credit risk management. It allows institutions to handle inquiries outside business hours, ensuring a continuous service flow. Furthermore, the system’s flexibility supports better customer interaction, increasing trust and transparency. By combining advanced machine learning with accessible interfaces, this study offers a scalable solution to improve the precision and practicality of debtor eligibility assessments, making it a valuable tool for modern financial institutions.
APPLICATION OF THE FUZZY TOPSIS METHOD FOR LECTURER CERTIFICATION ASSESSMENT Raintung, Stephanie Marceline; Latumakulita, Luther A.; Paat, Franky; Karim, Irwan; Sentinuwo, Steven; Islam, Noorul
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 3 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss3pp1747-1764

Abstract

Lecturer Certification (Serdos) is the method of granting educational certificates to lecturers as a formal verification of the speaker's recognition as an expert at a higher level of teaching. In Lecturer Certification, there is an Assessment of Lecturers' Self-Statements in Higher Education Tridharma Performance (PDD-UKTPT), which is divided into three Assessment Elements, namely Teaching, Research and Publication of Scientific Work and Community Service (PkM). The study focuses on teaching assessment. Sam Ratulangi University is one of the Universities Organizing Educator Certification for Lecturers (PTPS) in 2023. The Lecturer Certification assessment at Sam Ratulangi University does not describe the specific assessment range or include the importance weight of each criterion. Thus, this research aims to apply the Fuzzy TOPSIS method as an alternative in the assessment, which determines the importance and weight of each criterion and provides a description of the specific assessment range for each criterion to overcome uncertainty in the evaluation to provide clear guidelines for Serdos assessors in conducting the assessment. The research results regarding lecturer suitability decisions in assessing the Teaching Element. Therefore, it is found that Fuzzy TOPSIS can be used as an assessment method in Lecturer Certification, and it is better suited to handle the uncertainty issues often encountered in lecturer certification assessments. The result of this study provides an excellent accuracy of 100% compared with the manual method.
DOLPHIN DETECTION USING AN ENHANCED LIGHTWEIGHT YOLO ARCHITECTURE Ludja, Febriyanti; Lintong, Robby Moody; Sumarauw, Florensce; Sambul, Alwin M.; Sentinuwo, Steven R.; Putro, Muhamad Dwisnanto
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 3 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i3.9169

Abstract

Dolphin detection plays an important role in marine ecosystem monitoring, species conservation, and behavioral analysis. However, visual identification in underwater environments faces challenges such as light refraction, water turbidity, and dynamic sea conditions. This study proposes a deep learning-based dolphin detection approach by modifying the YOLOv8 architecture to produce a lightweight yet accurate model. The modifications include reducing the number of channels in the backbone and neck, as well as simplifying the SPPF block, thereby reducing the model parameters from 3.01 million to 1.83 million and the computational complexity from 8.2 GFLOPs to 7.2 GFLOPs. A specialized dolphin dataset consisting of 5,493 labeled images, collected from underwater and surface conditions, was developed to train and evaluate the model. Experimental results show that the proposed model achieves 67.1% mAP@50 and 45.8% mAP@50–95, outperforming YOLOv8-Nano and other lightweight YOLO variants. Additionally, the model demonstrates better runtime efficiency, with a latency of 49.2 ms and 20.38 FPS, making it suitable for real-time implementation on resource-constrained devices. Overall, this research presents a more efficient and accurate dolphin detection solution, while also providing a specialized dataset that can support further research in the field of computer vision-based marine conservation.
Co-Authors Agustinus Jacobus Alan Stevenres Bentelu, Alan Stevenres Alexander, Luisan William Alicia A. E. Sinsuw Alicia Sinsuw Alwin M. Sambul, Alwin M. Alwin Melkie Sambul Ambat, Mentari Putri Ando, M Tasyrik Andre Timothy Kapugu Antameng, Gabriella S. Arie Lumenta Auliawati Buchari, Auliawati Bahar, Jasinda Bayu Sy. Kurniawan, Bayu Sy. Brave A. Sugiarso Brave A. Sugiarso Brave Sugiarso, Brave Deiby Tineke Salaki Dringhuzen J. Mamahit Enny Itje Sela Fadli Umafagur, Fadli Hans Wowor Hasan, Olivia Hera Wulanratu Wulur, Hera Wulanratu Ilhammad Maulana Ani, Ilhammad Islam, Noorul Jimmy Robot Jinifer Rori, Jinifer John, Sumual David Julio Nari Kaawoan, Yuliani Y.I Karim, Budianto Karim, Irwan Kasema, Lady O. Kasenda, Lorenzo M. Kulung, Andri Linda Jayanti, Linda Lintong, Robby Moody Lolaroh, Stefanie M.E. Lombok, Rizky Dwi Putra Sani Lontaan, Agnestasia A.S. Ludja, Febriyanti Luther Latumakulita Mananoma, Yosua mandolang, arthur Mangamba, Yunifer Martina K. E. T. Dundu, Martina K. E. T. Martoyo, Ika M.H. Mathindas, Rivaldo Rendy Monica Kumaat, Monica Muhamad Z. Buchari, Muhamad Z. Nancy Tuturoong Noorul Islam Noviania, Reski Octavian Lantang Oktavian A. Lantang Paat, Franky Paputungan, Adiwarman P. Pinrolinvic D.K. Manembu Putro, Muhamad Dwisnanto Raintung, Stephanie Marceline Roberto Rengkung, Roberto Ruindungan, Dirko G.S. Rumetor, Josua Jovan Rumondor, Aryando G. Runtuwene, Steven Runtuwene, Syalom Veninda sambul, alwin Sambul, Alwin Melkie Sandy Laurentius Lumintang Sary D. E. Paturusi Sasoeng, Arief A. Sherwin R.U.A Sompie Staal, Nofli K. Stanley D.S Karouw, Stanley D.S Stanley D.S. Karouw Stanley Karouw Sumarauw, Florensce Sumolang, Billy B. Supit, Josua Waraney Takasana, Evangelista M. Tanjung, Yudhi Pratama Tjoanapessy, Nathasya Tompoh, Jos Forman Umboh, Wisnu W. A. Virginia Tulenan Wenno, William Dave Wowor, Novita E. Xaverius B. N. Najoan, Xaverius B. N. Xaverius B.N. Najoan Xaverius Najoan Yaulie Deo Y. Rindengan Yonna Kaburuan, Yonna