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All Journal Equivalent: Jurnal Ilmiah Sosial Teknik
Dyah Lestari Widaningrum
Universitas Bina Nusantara
Economics, Econometrics & Finance Electrical & Electronics Engineering Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Social Sciences

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Development of SDLC Framework for Financial Technology Project Management in Banking Institutions Evania Raihan; Dyah Lestari Widaningrum
Equivalent: Jurnal Ilmiah Sosial Teknik Vol. 8 No. 1 (2026): Equivalent: Jurnal Ilmiah Sosial Teknik
Publisher : Politeknik Siber Cerdika Internasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59261/jequi.v8i1.269

Abstract

Background: The rapid expansion of agent banking in Indonesia has increased the need for reliable and timely delivery of financial technology applications. The choice of Software Development Life Cycle (SDLC) methodology plays a critical role in project schedule performance, yet empirical comparisons particularly involving hybrid approaches within regulated banking environments remain limited. Objective: This study analyzes the impact of SDLC methods on project time performance in IT application development, with particular attention to the testing phase as a recurrent source of delay. Method: A quantitative comparative design was applied to 18 internal IT projects conducted in 2025 using Waterfall and Agile methodologies across Android-based EDC applications, mobile apps, and web systems. Independent-samples t-tests were used to examine differences in Schedule Performance Index (SPI), actual project duration, and delay. A phase-selective Hybrid SDLC was subsequently implemented in the EDC Android v1.0.5 project, applying Agile practices in testing while maintaining Waterfall in other phases. Result: Statistically significant differences were found between Agile and Waterfall across all time performance indicators, with Agile demonstrating superior schedule outcomes. The hybrid implementation further improved performance, reflected by an SPI greater than 1 and reduced testing duration. Conclusion: A phase-selective Hybrid SDLC provides an effective strategy to enhance schedule performance, particularly when testing is a bottleneck. The findings support selective SDLC adoption based on phase-specific characteristics. Limitations include the single-institution scope and absence of cost and quality metrics, suggesting directions for future multi-institutional research.
Intelligent Remaining Useful Life Prediction of Power Transformers Using Machine Learning Fanni Mudarris Kurniawan; Dyah Lestari Widaningrum
Equivalent: Jurnal Ilmiah Sosial Teknik Vol. 8 No. 4 (2026): Equivalent: Jurnal Ilmiah Sosial Teknik
Publisher : Politeknik Siber Cerdika Internasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59261/jequi.v8i4.440

Abstract

Background: Power transformers play an integral role in power generation and transmission. Transformer failures can lead to service interruptions, reduce system reliability, and incur significant failure-related costs. Efficient transformer maintenance requires accurate condition assessment and effective maintenance decision-making. Objective: The purpose of this paper is to develop a machine-learning-based decision-support framework for power transformer maintenance that integrates health index estimation, Remaining Useful Life (RUL) prediction, and Life Cycle Cost (LCC) evaluation. Methods: The framework draws on multimodal monitoring data from 166 oil-immersed power transformers collected over a ten-year period. Six machine learning models (XGBoost, Random Forest, Support Vector Regression, Convolutional Neural Networks, Ridge Regression, and K-Nearest Neighbors) were evaluated using various feature sets. Results: Among the evaluated models, XGBoost achieved the best performance under the basic feature configuration, with an RMSE of 0.285 years, an MAE of 0.199 years, and an R² of 0.956. The robustness evaluation showed that the model retained satisfactory predictive performance under moderate feature reduction. Based on the Life Cycle Cost assumptions applied in this study, the predictive maintenance scenario was estimated to reduce the total cost of the transformer fleet by approximately 47%. Conclusion: The proposed approach combines accurate RUL prediction with technical and economic analyses to provide a practical decision-making tool for transformer asset management.
Co-Authors Evania Raihan Fanni Mudarris Kurniawan