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All Journal JURIKOM (Jurnal Riset Komputer) Building of Informatics, Technology and Science Bulletin of Computer Science Research
Syaeful Machfud
Universitas Pamulang, Tangerang Selatan
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Evaluasi Komparatif Algoritma Decision Tree, Random Forest, dan XGBoost untuk Software Defect Prediction Menggunakan Dataset NASA Software Metrics Okky Prasetia; Syaeful Machfud
Building of Informatics, Technology and Science (BITS) Vol 8 No 1 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v8i1.10268

Abstract

Software Defect Prediction (SDP) has become an important approach for identifying software modules that are likely to contain defects during the early stages of software development. However, the performance of prediction algorithms remains highly dependent on dataset characteristics, and no single algorithm has consistently demonstrated superior performance across different datasets. In addition, the use of synthetic datasets in SDP research still requires empirical validation to ensure that their characteristics remain representative of benchmark datasets such as the NASA Metric Data Program (NASA MDP). Therefore, this study aims to compare the performance of Decision Tree, Random Forest, and XGBoost using the Playground Series Season 3 Episode 23 dataset, a synthetic dataset developed based on the characteristics of the NASA MDP dataset. Prior to model training, the dataset underwent preprocessing, including missing value imputation, label encoding, and feature standardization. Model performance was evaluated using 10-fold stratified cross-validation with Accuracy, F1-Score, and AUC-ROC as the primary evaluation metrics. The experimental results indicate that ensemble learning methods achieved competitive performance compared with the single-classifier approach. Random Forest achieved the highest Accuracy of 0.8144, while XGBoost obtained the highest AUC-ROC score of 0.7929, indicating strong capability in distinguishing between defective and non-defective software modules on the evaluated dataset. Furthermore, feature importance analysis identified Lines of Code (LOC), Cyclomatic Complexity, Halstead Volume, IOCode, and branchCount as the most influential factors affecting software defect prediction. Based on the experimental results obtained from the selected dataset, the ensemble learning approach demonstrated competitive predictive performance and may be considered a promising alternative for developing Software Defect Prediction models. Nevertheless, the selection of the most appropriate algorithm should remain dependent on dataset characteristics and specific implementation requirements
Klasifikasi Gender Berbasis Citra Wajah Menggunakan Clustering Dan Deep Learning Okky Prasetia; Syaeful Machfud; Perani Rosyani; Bobi Agustian
Bulletin of Computer Science Research Vol. 5 No. 4 (2025): June 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i4.581

Abstract

Gender classification based on facial images is a significant challenge in the field of computer vision, especially when dealing with unstructured data sourced from social media platforms. This study proposes an integrated approach combining facial image preprocessing, clustering methods, and deep learning to enhance the accuracy of gender classification. The dataset used was obtained from a Big Data Competition and consists of male and female face images sourced from Instagram. Preprocessing was performed using OpenCV for face detection and cropping. Subsequently, the data were clustered using K-Means and DBSCAN algorithms to reduce noise and redundancy. Gender classification was then conducted using a sequential learning model based on Inception_v3, enhanced with Agglomerative Clustering for feature refinement. The evaluation of the system demonstrated strong performance with an accuracy of 92.97%, F1-score of 0.89556, precision of 0.97727, and recall of 0.83069. These results confirm that the integration of clustering techniques and deep learning significantly improves the effectiveness of gender classification based on facial images, especially for open-source and non-curated datasets.
Implementasi Entreprises Resource Planning Berbasis Web dan Mobile Menerapkan Metode SCRUM Muhammad Syahdan; Nanang Nanang; Suryaningrat Suryaningrat; Syaeful Machfud
Bulletin of Computer Science Research Vol. 5 No. 5 (2025): August 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i5.711

Abstract

Companies operating in the property sector have highly complex business processes involving multiple divisions, such as engineering, marketing, legal, and finance. However, many property companies still manage their data manually and in a fragmented manner, leading to various risks such as data entry errors, communication failures, and other inefficiencies. This study aims to implement an integrated web- and mobile-based Enterprise Resource Planning (ERP) system to support and streamline business processes in a property company, making them more efficient and effective. The development methodology used is Agile, with data collected through interviews, observation, and documentation studies. The system was developed using web and mobile technologies to provide users with flexible access. The implementation results show that the developed ERP system is capable of supporting and improving business processes in the property sector, facilitating real-time data tracking, and increasing operational efficiency. With this system, the company no longer needs to rely on manual data recording and can improve the accuracy of decision-making. This research demonstrates that a digitally based ERP system can be a strategic and effective solution for property companies in facing the challenges of the modern era.
Klasifikasi Spesies Suara Burung Menggunakan YAMNet dan Random Forest untuk Konservasi Alam Syaeful Machfud; Simon Simarmata; Nur Rofiq
JURIKOM (Jurnal Riset Komputer) Vol. 13 No. 1 (2026): Februari 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i1.9443

Abstract

Monitoring and identification of bird species is an important aspect of biodiversity conservation, but manual identification methods based on direct observation and expert listening still have limitations in terms of time, cost, and subjectivity. Other challenges arise due to variations in the quality of sound recordings, the presence of environmental noise, and the similarity in vocalization patterns between species that make it difficult to automate the classification process. This study aims to develop an automatic classification system of bird species based on acoustic signals by combining the YouTube Audio Event Network (YAMNet) model and the Random Forest algorithm. YAMNet is utilized to extract spectral log-Mel features that represent the frequency and temporal characteristics of bird sounds, while Random Forest is used as a classifier to determine species based on those features. The dataset used is the Sound of 114 Species of Birds till 2022, which includes species variation, recording duration, and complex acoustic conditions. The results showed that the features produced by YAMNet were able to form separation between species visually through Principal Component Analysis (PCA), although there was still overlap in species with similar vocalization characteristics. Evaluation using the confusion matrix shows that some species can be classified with a high degree of accuracy, while misclassification occurs mainly in species with similar frequency patterns. Receiver Operating Characteristic (ROC) analysis yields Area Under Curve (AUC) values of up to 0.98 in certain species, indicating the model's excellent discriminating ability. These findings suggest that the integration of YAMNet and Random Forest has the potential to be an efficient and reliable solution to support automated bird species identification systems in nature conservation.
Co-Authors Bobi Agustian Muhammad Syahdan Nanang Nanang Nur Rofiq Okky Prasetia Perani Rosyani Simon Simarmata Suryaningrat Suryaningrat