Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal JUTI: Jurnal Ilmiah Teknologi Informasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) BISMA (Bisnis dan Manajemen)
Daniel Siahaan
Unknown Affiliation
Computer Science & IT Dentistry Education Engineering Library & Information Science Social Sciences

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
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.
SISTEM PEMBANGKIT ANOTASI PADA ARTIKEL BERGAMBAR DENGAN PENDEKATAN KONTEKSTUAL Diana Purwitasari; Dian Saputra; Esti Yuniar; Umi Laili Yuhana; Daniel Siahaan
JUTI: Jurnal Ilmiah Teknologi Informasi Vol 9, No 1, Januari 2011
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (553.833 KB) | DOI: 10.12962/j24068535.v9i1.a64

Abstract

Development of E-learning sites and their materials make it is necessary to help users finding the desired materials. Context-based search engine will help users for the finding task. However that kind of searching can only be done for learning materials that have been semantically signed or annotated. Annotation is given for the article’s content or the article’s image within. There are many constraints for manually providing annotations to the learning articles such that automatic metadata or annotation generating method is needed. This paper discusses about annotation generating system with two subsystems: annotation recommender for learning material using contextual analysis and image metadata generator. The methods for contextual analysis are Latent Semantic Analysis (LSA) and WordNet-lexical dictionary usage. Our experimental results showed that subsystems can be used to generate annotation for articles and images in the articles though we have not done combination of two subsystems.
Co-Authors Dian Saputra Diana Purwitasari Esti Yuniar Faiq Dhimas Wicaksono Nadia Asandimitra Haryono Yuhana, Umi Laili