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All Journal Jupiter Aisyah Journal of Informatics and Electrical Engineering Jurnal Teknik Informatika (JUTIF) Jurnal Algoritma Finger : Jurnal Ilmiah Teknologi Pendidikan Jurnal Pengabdian Masyarakat Bangsa Jurnal Pendidikan Teknologi Informasi (J-Diteksi) Jurnal Rekayasa Perangkat Lunak
Ikna Awaliyani
Pendidikan Teknologi Informasi, Universitas Aisyah Pringsewu, Indonesia
Agriculture, Biological Sciences & Forestry Arts Humanities 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 Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Medicine & Pharmacology Nursing Physics Public Health Social Sciences Other

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Journal : Jurnal Teknik Informatika (JUTIF)

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A User-Driven E-Audit System for Improving Transparency and Efficiency in Regional Government Supervision Aminudin, Nur; Hidayat, Nurul; Feriyanto, Dwi; Mukaromah, Hafsah; Septasari, Dita; Awaliyani, Ikna
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.4.5145

Abstract

Internal audit processes in regional government institutions often face challenges such as time inefficiency, low transparency, and poorly digitized documentation. This study aims to develop an E-Audit system to enhance the effectiveness of internal supervision in a regional inspectorate environment. Employing a user-centered design approach and a structured system development methodology, this research involved key roles—auditors, technical controllers, and follow-up teams—throughout the design and testing stages. The developed system integrates three core phases of the audit process—planning, reporting, and follow-up—into a single, modular, and interactive digital platform. Implementation results indicate a significant improvement in audit efficiency, with a reduction of more than 50% in process duration compared to manual methods. The system also enhances documentation consistency through digital audit trails, role-based dashboards, and automatic reporting features. User acceptance testing revealed a high level of satisfaction, with users highlighting the system’s ease of use, increased accuracy, and alignment with daily audit tasks. Additionally, user feedback emphasized the need for integrated notification features and inter-unit communication tools, indicating readiness for more advanced digital transformation. Overall, this study provides practical value as a model for digital audit implementation at the regional government level while contributing to the advancement of Computer Science through the application of software engineering principles and information systems to support digital government oversight. The developed E-Audit model can serve as a reference for designing real-time collaborative public auditing systems relevant to the development of information systems engineering and computational governance.
Optimizing Type 2 Diabetes Classification with Feature Selection and Class Balancing in Machine Learning Wantoro, Agus; Yuliana, Aviv Fitria; Andini, Dwi Yana Ayu; Awaliyani, Ikna; Caesarendra, Wahyu
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.4.5166

Abstract

Type 2 Diabetes (T2DM) is a crucial factor in patient survival and treatment effectiveness. Errors in diabetes detection lead to disease severity, high costs, prolonged healing time, and a decline in service quality. Additionally, a major challenge in developing Machine Learning (ML)-based detection decision support systems is the class imbalance in medical data as well as the high feature dimensionality that can affect the accuracy and efficiency of the model. This research proposes an approach based on feature selection (FS) and handling class imbalance to improve performance in type 2 diabetes. Several feature selection techniques such as Information Gain (IG), Gain Ratio (GR), Gini Decrease (GD), Chi-Square (CS), Relief-F, and FCBF can perform feature selection based on weighting ranking. Furthermore, to address the imbalanced class distribution, we utilize the Synthetic Minority Over-Sampling Technique (SMOTE). ML classification models such as Support Vector Machine (SVM), Gradient Boosting (GB), Tree, Neural Network (NN), Random Forest (RF), and AdaBoost were tested and evaluated based on the confusion matrix including accuracy, precision, recall, and time. The experimental results show that the combination of strategies for handling imbalanced classes significantly improves the predictive performance of ML algorithms. In addition, we found that the combination of feature selection techniques IG+AdaBoost consistently demonstrates optimal performance. This study emphasizes the importance of data preprocessing and the selection of the right algorithms in the development of machine learning-based T2DM detection systems. Accurate detection can reduce the severity of disease, lower treatment costs, speed up the healing process, and improve healthcare services.
Digital Landscape and Behavior in Indonesia 2024: A National Survey Analysis of Internet Penetration, Cybersecurity Risks, and User Segmentation Using K-Means Clustering and Logistic Regression Aminudin, Nur; Hidayat, Nurul; Feriyanto, Dwi; Septasari, Dita; Awaliyani, Ikna
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.5117

Abstract

Digital transformation in Indonesia reveals significant disparities in internet access, digital behavior, and cybersecurity vulnerabilities. This study analyzes the digital landscape using national survey data from 8,720 respondents across 38 provinces. This research employs a quantitative approach, utilizing chi-square tests, logistic regression for risk analysis, and K-Means clustering for user segmentation, supported by Principal Component Analysis (PCA) for dimensionality reduction. The results show a national internet penetration rate of 79.5%, with significant disparities across regions and socio-economic segments. Logistic regression analysis reveals that higher education, greater income, and the use of fixed broadband are negatively correlated with cybersecurity risks. Furthermore, K-Means clustering identifies three distinct user profiles: 'Digital Savvy', 'Pragmatic Users', and the 'Vulnerable Segment', each with unique characteristics regarding digital access and literacy. This research provides a critical empirical basis for understanding digital transformation in a developing nation. The findings underscore the necessity of data-driven, segmented policies to foster digital inclusion and enhance national cybersecurity, offering actionable insights for policymakers and service providers.
Co-Authors Abdul Aziz, RZ Agus Wantoro Andini, Dwi Yana Ayu Ariyanti, Septika Asadi, Shima Aviv Fitria Yulia Dita Septasari Dwi Feriyanto Mukaromah, Hafsah Nur Aminudin Nurul Hidayat nurwahid, rizky12 Ulfa Isni Kurnia Wahyu Caesarendra Yudha Pratama, Rendy