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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) BISMA (Bisnis dan Manajemen)
Daniel Siahaan
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Computer Science & IT Engineering Social Sciences

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Pengaruh Likuiditas Dan Kualitas Aset terhadap Profitabilitas pada Bank Umum Nasional (Studi pada Bursa Efek Indonesia Periode 2010-2014) Daniel Siahaan; Nadia Asandimitra
BISMA (Bisnis dan Manajemen) Vol. 9 No. 1 (2016)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (289.716 KB) | DOI: 10.26740/bisma.v9n1.p1-12

Abstract

This study exmines the factors that affect the profitability of the national commercial banks. This study used data 32 commercial banks that go pulic in Indoesia Stock Exchange period 2010-2014. Sampling was done by purposive sampling method. This study used multiple linear regression analysis using SPSS 20. Independent variable of this study is Liquidity (LDR) and Asset Quality (NPL).The results of the study explain that Liquidity is a positive effects on Profitability. Liquidity be a measure of the success a bank to obtain high profitability. The lower the LDR reflects the bank has not been able to optimize third-party funds (DPK), which will be distributed to customers as a credit. Asset Quality negatively affect Profitability. High-Quality assets in a bank means more credit problems experienced by banks and will result in losses in the bank.
Enhancing Agile Defect Prediction with Optimized Machine Learning and Feature Selection Faiq Dhimas Wicaksono; Daniel Siahaan
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 4 (2025): August 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i4.6713

Abstract

In Agile software development, efficient defect prediction is crucial because of the rapid and iterative nature of the delivery. Conventional methods that rely on source code or commit logs often fail to capture the critical contextual signals necessary for early bug detection. This study proposes a hybrid machine learning framework that leverages enriched contextual features from Jira issue tickets and combines them with optimized feature selection techniques. Various classification models, including Random Forest, XGBoost, CatBoost, SVM, and Transformer, are employed to predict defects. To further enhance model performance, metaheuristic-based feature selection methods such as the Bat Algorithm (BA) and Particle Swarm Optimization (PSO) are applied to reduce dimensionality and improve predictive relevance. Experimental results show that Random Forest with BA optimization achieves the highest performance, with an F1-score of 0.83 and an AUC-ROC of 0.86, outperforming other models. While the Transformer model does not surpass tree-based algorithms in all metrics, it shows high recall and competitive F1-scores, making it suitable for high-sensitivity applications. These findings highlight the importance of integrating optimized machine learning models and feature selection techniques to improve model robustness, reduce computational complexity, and meet the needs of Agile development. This approach supports software teams in prioritizing quality assurance tasks, reducing long-term maintenance costs, and optimizing defect management processes.
Co-Authors Faiq Dhimas Wicaksono Nadia Asandimitra Haryono