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All Journal Jurnal Teknik Informatika TEKNO Sinergi 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
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing 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.
Organizational Alignment and IT Governance Using COBIT 2019 Goals Cascade: Keselarasan Tujuan Organisasi dan Tata Kelola Teknologi Informasi Menggunakan Goals Cascade COBIT 2019 Turangan, Bravo; Sentinuwo, Steven Ray; Kaunang, Sumenge Tangkawarouw G.
Jurnal Teknik Elektro dan Komputer Vol. 14 No. 3 (2025): Jurnal Teknik Elektro dan Komputer
Publisher : Universitas Sam Ratulangi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35793/jtek.v14i3.65580

Abstract

Abstract — Information technology (IT) governance is a crucial factor in ensuring that IT investments are aligned with organizational objectives, improve operational efficiency, and mitigate risks. This study focuses on the Regional Education Office of North Sulawesi Province, which plays a strategic role in improving the quality of secondary and special education services. The Strategic Plan for 2025–2029 explicitly supports the provincial government’s mission to improve human resource quality through equitable and quality education. However, the 2024 SPBE evaluation revealed significant weaknesses in governance and management domains, indicating that IT implementation remains suboptimal and prone to information system fragmentation. This study aims to map the organization’s vision, mission, and objectives into the Goals Cascade mechanism of the COBIT 2019 framework. A  mixed methods approach, combining qualitative and quantitative analysis was applied through the identification of stakeholder drivers, stakeholder needs, enterprise goals, alignment goals, and governance and management objectives. The novelty of this research lies in the comprehensive application of the COBIT 2019 Goals Cascade to determine priority IT governance objectives in the regional education sector. The results indicate a primary focus on objectives supporting application integration, management information quality, and acceleration of digital transformation. The study concludes that COBIT 2019 provides an effective foundation for formulating aligned IT governance strategies supporting the 2025–2029 Strategic Plan. Keywords — COBIT 2019; Goals Cascade; IT Governance; Digital Transformation in Education   Abstrak — Tata kelola teknologi informasi (TI) merupakan faktor krusial untuk memastikan investasi TI selaras dengan tujuan bisnis, meningkatkan efisiensi operasional dan memitigasi risiko. Penelitian ini berfokus pada Dinas Pendidikan Daerah Provinsi Sulawesi Utara yang memiliki peran strategis dalam peningkatan mutu pendidikan menengah dan layanan khusus. Rencana Strategis Dinas Pendidikan Daerah Provinsi Sulawesi Utara Tahun 2025–2029 secara eksplisit mendukung misi pemerintah daerah dalam meningkatkan kualitas sumber daya manusia melalui pendidikan berkualitas yang merata. Namun, hasil  evaluasi Sistem Pemerintahan Berbasis Elektronik (SPBE) Provinsi Sulawesi Utara tahun 2024 menunjukkan kelemahan signifikan pada domain tata kelola dan Manajemen SPBE, yang mengindikasikan implementasi TI belum optimal dan berpotensi menimbulkan fragmentasi sistem informasi. Penelitian ini bertujuan memetakan visi, misi dan tujuan organisasi ke dalam mekanisme Goals Cascade pada framework COBIT 2019. Metode penelitian yang digunakan adalah metode campuran atau pendekatan kualitatif dan kuantitatif melalui tahapan identifikasi stakeholder drivers, stakeholder needs, enterprise goals, alignment goals dan governance and management objectives. Kebaruan penelitian ini terletak pada penerapan Goals Cascade COBIT 2019 secara menyeluruh untuk mengidentifikasi prioritas objektif tata kelola TI di sektor pendidikan daerah. Hasil penelitian menunjukkan perlunya fokus pada objektif yang mendukung integrasi aplikasi, peningkatan kualitas informasi manajemen, dan percepatan transformasi digital. Simpulan penelitian menegaskan bahwa COBIT 2019 efektif sebagai dasar perumusan strategi tata kelola TI yang selaras dengan Renstra 2025–2029. Kata Kunci: COBIT 2019; Goals Cascade; Tata Kelola Teknologi Informasi; Transformasi Digital Pendidikan.
An effective and efficient vehicle detection using ER-EMA-YOLOv10n Kutika, Imanuel; Lahimade, Vicky Nolant Setyanto; Todingan, Tomi Heri Julius; Prasetya, Hebron; Sentinuwo, Steven Ray; Putro, Muhamad Dwisnanto
SINERGI Vol 30, No 1 (2026)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2026.1.017

Abstract

Vehicle detection plays a key role in automating traffic analysis, a field that continues to advance rapidly. Vision-based systems identify vehicle types and sizes, but achieving high accuracy and efficiency remains a challenge. Reliable real-world deployment requires optimized models that balance performance and computational cost. YOLOv10n, the most efficient version of the YOLO family, offers a solid foundation for lightweight feature extraction. To improve its detection performance, this study proposes an enhanced version of YOLOv10n by incorporating a scale-aware attention mechanism. We proposed the Expanded Refinement Efficient Multi-Scale Attention (ER-EMA) module, which enhances feature encoding by capturing vehicle characteristics across multiple receptive fields. ER-EMA consists of two core components: the Expanded Converted Inverted Block (ECIB) and the Convolutional Refinement Block (CRB). These components use diverse convolutional kernels to extract and refine multi-frequency spatial features. Integrating ER-EMA into the YOLOv10n framework produces a more compact and accurate detection model. Experimental results show that the proposed model increases mAP@50 by 1%, while reducing the number of parameters by 0.1M and computation by 0.1 GFLOPS on the Vehicle-COCO dataset. On the UA-DETRAC benchmark, it achieves a 4% improvement in mAP@50:95, with a reduction of 0.2M in parameters and 0.4 GFLOPS in computational efficiency—outperforming the original YOLOv10n and prior methods in both performance and computational efficiency.
Aligning University Vision–Mission using COBIT 2019 Goals Cascade Palar, Syalomita; Sentinuwo, Steven Ray; Rumagit, Arthur
Jurnal Teknik Elektro dan Komputer Vol. 15 No. 1 (2026): Jurnal Teknik Elektro dan Komputer
Publisher : Universitas Sam Ratulangi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35793/jtek.v15i1.65961

Abstract

Abstract — A university's strategic objectives need to be systematically aligned with InformationTechnology (IT) governance to ensure IT truly supports the achievement of the institution's vision and mission. An effective method to achieve this goal is by using the COBIT 2019 framework. The approach taken is a two-stage mapping process. The first stage involves translating the university's Vision and Mission into COBIT's Enterprise Goals (EG). Then, in the second stage, each EG is mapped to the relevant IT Alignment Goals (AG). This step-by-step mapping process serves as a bridge connecting the university's high-level strategic direction with specific IT management activities and objectives. The result of this method is the creation of a clear and measurable connection between the university's strategic objectives and the main focus areas of IT governance, namely Value, Risk, and Performance. This connection shows how each university objective is influenced or supported by IT governance efforts. The implications are significant, as the mapping results become a strong foundation for developing the university's IT roadmap. Additionally, it helps the institution prioritize which COBIT management and governance domains should be optimized first to align with the institution's most pressing strategic needs. Thus, IT transforms from merely a supporting function into a strategic driver for the university. Key words — COBIT 2019; Enterprise Goals; Alignment Goals; IT Governance; Higher EducationStrategy; Strategic Mapping.
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 Arthur Rumagit 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. Kaunang, Sumenge Tangkawarouw G. Kulung, Andri Kutika, Imanuel Lahimade, Vicky Nolant Setyanto 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 Palar, Syalomita Paputungan, Adiwarman P. Pinrolinvic D.K. Manembu Prasetya, Hebron 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 Todingan, Tomi Heri Julius Tompoh, Jos Forman Turangan, Bravo 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