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All Journal JIEET (Journal of Information Engineering and Educational Technology) Insyst : Journal of Intelligent System and Computation
Setiawan, Boy
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Computer Science & IT Engineering

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A Review of Sentiment Analysis Applications in Indonesia Between 2023-2024 setiawan, boy
JIEET (Journal of Information Engineering and Educational Technology) Vol. 8 No. 2 (2024)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jieet.v8n2.p71-83

Abstract

The landscape of sentiment analysis applications in Indonesia is on the rise with the many published papers on the subject over the years. The need to predict sentiment coincides with the rise of social media and how the public uses it to express sentiments toward an interesting topic. The lack of tools for working with the Indonesian language has brought the invention of libraries to tackle the difficulty and uniqueness of the language on various topics from diverse data sources. The introduction of Sarkawi as a stemmer helps researchers overcome dimensionality problems commonly found with text processing, and boosts the performance of machine learning (ML) models. Using InSet as a lexicon dictionary capable of performing sentiment prediction has started gaining popularity for automatic labeling. The development of IndoBERT, an advanced neural network (NN) large language model (LLM) specifically trained from a large Indonesian text corpus capable of more than sentiment analysis, has gained traction both for automatic labeling and prediction models. Although the majority of research revolves around Naïve Bayes (NB), State Vector Machine (SVM), and K-Nearest Neighbor (KNN) the future of sentiment analysis applications in Indonesia could be heading towards a more advanced deep learning architecture. Finally, this study is intended as a basis for future research in the applications of sentiment analysis in Indonesia and the development of the language.
Multi View Natural Network for Cross-Project Software Defect Prediction Setiawan, Boy; Subekti, Agus
Intelligent System and Computation Vol 7 No 1 (2025): INSYST: Journal of Intelligent System and Computation
Publisher : Institut Sains dan Teknologi Terpadu Surabaya (d/h Sekolah Tinggi Teknik Surabaya)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52985/insyst.v7i1.436

Abstract

Software Defect Prediction (SDP) plays a critical role in software engineering by enabling early identification of potentially defective modules, to assist developers and testers in prioritizing testing and inspection efforts to improve software quality and reliability. Driven by rapidly changing business requirements, defect prediction models have become increasingly essential in quality assurance workflows. Traditional approaches to SDP focused on Within-Project Defect Prediction (WPDP), where models are trained on historical data from the same project and effective under sufficient data conditions. This challenge motivates the adoption of Cross-Project Defect Prediction (CPDP), which leverages data from different projects. However, CPDP faces notable challenges including datasets distributional differences and class imbalance, which can degrade prediction performance and bias. To address these issues, recent studies have proposed data transformation, resampling, and domain adaptation techniques. In this study, we explore a multi-view learning approach using Neural Networks (NN) to enhance generalization and performance in CPDP scenarios. By leveraging multiple views of the same dataset—generated through concatenation of heterogeneous software metrics, imputation for missing values, normalization using Box-Cox transformation, and embedding-based feature transformation—we aim to construct a robust Multi-View Neural Network (MVNN). This architecture enables the integration of diverse information while mitigating the limitations of single-view learning in CPDP. Our method preserves more in-formation compared to conventional approaches that rely only on shared features. Experimental validation using benchmark SDP repositories demonstrates the competitiveness of our approach, offering improved performance over existing CPDP models and highlighting the potential of multi-view learning in defect prediction tasks.
Multi View Neural Network for Software Effort Estimation Prediction Setiawan, Boy; Subekti, Agus
Intelligent System and Computation Vol 7 No 2 (2025): INSYST: Journal of Intelligent System and Computation
Publisher : Institut Sains dan Teknologi Terpadu Surabaya (d/h Sekolah Tinggi Teknik Surabaya)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52985/insyst.v7i2.442

Abstract

Software Effort Estimation (SEE) is a critical challenge in software project management, dating back to the early years of software engineering. Accurate estimation of the effort required for software development is essential for project planning, resource allocation, and risk management. Incorrect effort estimates can result in poor resource distribution, cost overruns, missed deadlines, and even complete project failure. This issue is increasingly urgent today as software systems are deeply embedded in almost every product and service, amplifying the need for reliable and accurate predictions. Over the years, several methods for SEE have been proposed, ranging from algorithmic models to expert judgment. More recently, machine learning (ML) approaches such as Case-Based Reasoning (CBR), Support Vector Machines (SVM), Decision Trees (DT), and Neural Networks (NN) have gained attention for their ability to model complex, nonlinear relationships inherent in SEE tasks. In this study, we propose a novel approach based on multi-view learning with NN (MVNN), which leverages multiple views from existing datasets, thus improving performance and generalization, particularly when the available data is small and scarce. The effectiveness of the MVNN model is validated through empirical comparisons with existing SEE models, demonstrating its potential to enhance SEE accuracy and improve prediction reliability.
Co-Authors Agus Subekti