Article Per Year (5 Year)
p-Index From 2021 - 2026
2.808
P-Index
This Author published in this journals
All Journal BERKALA FISIKA JURNAL SISTEM INFORMASI BISNIS YOUNGSTER PHYSICS JOURNAL Jurnal Sains dan Teknologi Jurnal Ilmiah Kursor Jurnal Teknologi Informasi dan Ilmu Komputer Register: Jurnal Ilmiah Teknologi Sistem Informasi Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Journal of Physics and Its Applications Jurnal Sisfokom (Sistem Informasi dan Komputer) Journal of Information Systems and Informatics Jurnal Fisika Indonesia JINAV: Journal of Information and Visualization Journal of Applied Data Sciences Jurnal Algoritma Journal of Emerging Science and Engineering Scientific Journal of Informatics
Catur Edi Widodo
Departemen Fisika, Fakultas Sains Dan Matematika, Universitas Diponegoro, Semarang
Astronomy Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Library & Information Science Materials Science & Nanotechnology Mathematics Physics

Published : 42 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search
Journal : Scientific Journal of Informatics

 1 
Optimization of Coronary Heart Disease Risk Prediction Using Extreme Learning Machine Algorithm (Case Study: Patients of Dr. Soeselo Hospital) Iswanti, Arie; Isnanto, R. Rizal; Widodo, Catur Edi
Scientific Journal of Informatics Vol. 12 No. 2: May 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i2.24746

Abstract

Purpose: Coronary heart disease (CHD) is the leading cause of death globally, with 17.8 million deaths reported by the WHO in 2021. Early detection remains a major challenge due to low public awareness and dependence on manual diagnostic procedures. These limitations necessitate the development of automated and accurate predictive models. This study aims to construct a CHD risk prediction model using the Extreme Learning Machine (ELM) algorithm. The research addresses a critical limitation in existing models, namely, poor performance on minority classes (CHD stages 2–4), caused by data imbalance. To overcome this, oversampling techniques such as Synthetic Minority Oversampling Technique (SMOTE) and Adaptive Synthetic Sampling (ADASYN) are applied. The objective is to improve classification performance, particularly in high-risk categories, and to enhance the model’s generalisation capability for real-world implementation. Methods: This research implements the Extreme Learning Machine (ELM) algorithm to achieve optimal prediction results. The data used in this study as the initial database of patients consists of gender, age, height, weight, whether they have diabetes or not, the number of cigarettes consumed daily, and blood pressure. The data will be the main component in building the heart disease prediction system. The prediction classes are: no heart disease, stage 1 heart disease, stage 2 heart disease, stage 3 heart disease, and stage 4 heart disease. The total number of dataset are 521 data points, with 70% of the training data amounting to 364 patients, and 30% of the test data amounting to 157 patients. The data collection process uses patient data from RSUD Dr. Soeselo, Tegal Regency, Central Java, for the years 2023 and 2024. Result: The research successfully developed and evaluated an Extreme Learning Machine (ELM) algorithm for Coronary Heart Disease (CHD) risk prediction using patient data from Dr. Soeselo Hospital. The model achieved an overall accuracy of 82% on the dataset of 157 patients, demonstrating a promising capability for automated risk assessment. Novelty: This predictive model can be utilised in the medical field to facilitate the early detection of heart disease or other risks. This model will soon be introduced in hospitals in the Tegal Regency and City area, Central Java.
Strategy Improvement for IT Governance in Library Services Using an Adaptation of ITIL 4 and FitSM : A COBIT 2019 Evaluation Idrus, Nurul Asyikin; Widodo, Catur Edi
Scientific Journal of Informatics Vol. 12 No. 4: November 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i4.34197

Abstract

Purpose: Effective management of IT services in public libraries is critical to maintaining service continuity, optimizing resources, and ensuring user satisfaction. However, current practices face persistent challenges in workforce capability, operational consistency, and problem resolution. This study evaluates IT service governance and develops improvements across four objectives: APO07 (Managed Human Resources), DSS01 (Managed Operations), DSS02 (Managed Service Requests and Incidents), and DSS03 (Managed Problems). Methods/Study design/approach: An integrated approach was applied by combining COBIT 2019 and ITIL 4 with FitSM to fit the scale of public libraries. A capability assessment established the baseline, followed by a gap analysis to identify weaknesses. Adaptive procedures were then formulated by retaining the comprehensive guidance of ITIL 4 and integrating the lightweight, practical principles of FitSM, while excluding elements unsuited for small-scale organizations. Result/Findings: The assessment showed that all four objectives are at capability level 1 and have not reached the “fully achieved” benchmark. Gaps were identified in human resource development, operational standardization, and incident and problem handling. The proposed improvements introduce standardized processes to strengthen workforce capability, stabilize daily operations, and accelerate service request and problem resolution. Novelty/Originality/Value: This study offers a practical governance improvement model tailored for public libraries and similar small-scale public sector organizations. The integration of ITIL 4 and FitSM provides a structured yet simplified framework that supports process standardization and service quality enhancement, addressing the limitations of applying COBIT 2019 in isolation.
Co-Authors Agusta Rakhmat Taufani Agustiyar Agustiyar Aprilia Ayu Andarinny Arif Setiawan Aris Puji Widodo Aryasa, Jiyestha Aji Dharma Budi Warsito Choirul Anam AM Diponegoro Didik Supriyadi, Didik eka rahmawati Eko Sediyono ellya helmud, ellya Faisal Akbar Fatkhur Rohman Figur Humani Fitra Nur Asri, Muh Fitro, Achmad H Hadiyanto Hadiyanto Hadiyanto Hadyan Arifianto Husen Nasrullah Armin I Gusti Bagus Wiksuana Idrus, Nurul Asyikin Irwan Agus Saputro Isnain Gunadi Iswanti, Arie Jatmiko Endro Suseno K. Sofjan Firdausi Koesuma, Sorja Kusworo Adi Kuworo Adi Labib Mustofa, Refo Labib Mustofa, Tarno Maratullatifah, Yulaikha Muchammad Azam, Muchammad Mustafid Mustafid Novettralita, Ucky Pradestha Oky Dwi Nurhayati Oky Dwi Nurhayati Otong Saeful Bachri R. Rizal Isnanto Rachmat Gernowo Rahmatika, Yuni Ramanda, Febri Retno Hapsari, Angga Ayu Rizky Ayomi Syifa Romadhon, Zainur Royani, Noorhanida Saitem Saitem Salsabila Naqiyah Sri Mulyani Tri Retnaningsih Soeprobowati Tutur Urip Wahyu Adi Wibowo Wibowo, Mochamad Agung Zainal Bachrudin