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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Teknik Industri Terintegrasi (JUTIN)
Ismawan, Andika Noor
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Computer Science & IT Decision Sciences, Operations Research & Management Energy Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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Analisis Perbandingan Strategi Reuse Service dalam Implementasi Service-Oriented Architecture (SOA) untuk Efisiensi Proyek Teknologi Informasi Ismawan, Andika Noor; Hamdani, Bagus Rosyid; Ramadan, Sahri; Egnel, Unique Scovi; Komul, Theodora Maria Putri
Jurnal Teknik Industri Terintegrasi (JUTIN) Vol. 8 No. 3 (2025): July
Publisher : LPPM Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jutin.v8i3.46348

Abstract

The rapid advancement of digital technology necessitates the adoption of efficient and sustainable system development strategies. This study aims to conduct a comparative analysis of four reuse service approaches within the framework of Service-Oriented Architecture (SOA), namely API & Service Standardization, Microservices Architecture, API-First Development, and Enterprise Service Bus (ESB). The research method employed is a literature review of recent scholarly sources, along with an evaluation of the effectiveness of each approach in terms of time efficiency, cost, and resource utilization. The findings indicate that each strategy possesses distinct characteristics and contextual advantages, with the API-First approach deemed the most flexible in supporting service integration and modular system development. Based on these findings, it is recommended that the selection of reuse strategies be aligned with organizational needs and the complexity of the systems being developed. 
Hybrid Machine Learning Predictive Model for Resource Allocation Optimization and Project Risk Management Ismawan, Andika Noor; Wahyu, Sawali
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 1 (2026): Article Research January 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i1.15773

Abstract

IT project management faces critical challenges related to inaccurate resource allocation estimation and project risk assessment, which complicates decision-making and threatens project performance. Although machine learning techniques have been widely adopted in this domain, existing studies predominantly rely on single models or simple ensemble strategies, limiting their ability to capture heterogeneous interactions among organizational, technical, and risk-related factors. This study proposes a hybrid machine learning–based decision support framework that integrates feature-level representation learning and probabilistic decision fusion. Gradient Boosting is reconceptualized as a feature selection and nonlinear interaction modeling mechanism, while Artificial Neural Networks generate latent feature embeddings representing complex project characteristics. These representations are fused through a Naive Bayes classifier to produce calibrated probabilistic predictions, supported by a weighted fusion strategy with F1-score–based threshold optimization to improve stability under imbalanced risk conditions. Experimental evaluation is conducted using 5,997 synthetic IT project records from PT Anugerah Nusa Teknologi. Model performance is evaluated using accuracy, precision, recall, F1-score, and ROC-AUC metrics. Compared to standalone Gradient Boosting, Artificial Neural Network, and Naive Bayes models, the proposed hybrid framework consistently demonstrates superior predictive performance, achieving an accuracy of 0.85, an F1-score of 0.8485, and a ROC-AUC of 0.9050. Theoretically, this study contributes to project management research by demonstrating that IT project outcomes are more effectively modeled through multi-perspective learning rather than isolated predictors. Practically, the proposed framework provides actionable decision support to assist project managers in optimizing resource allocation and prioritizing risk mitigation under uncertainty.
Co-Authors Egnel, Unique Scovi Hamdani, Bagus Rosyid Komul, Theodora Maria Putri Ramadan, Sahri Wahyu, Sawali