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JURIKOM (Jurnal Riset Komputer)
Published by Sekolah Tinggi Manajemen Informatika dan Komputer Budi Darma
ISSN : 2407389X     EISSN : 27157393     DOI : http://dx.doi.org/10.30865/jurikom.v7i1.1927
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering
JURIKOM (Jurnal Riset Komputer) membahas ilmu dibidang Informatika, Sistem Informasi, Manajemen Informatika, DSS, AI, ES, Jaringan, sebagai wadah dalam menuangkan hasil penelitian baik secara konseptual maupun teknis yang berkaitan dengan Teknologi Informatika dan Komputer. Topik utama yang diterbitkan mencakup: 1. Teknik Informatika 2. Sistem Informasi 3. Sistem Pendukung Keputusan 4. Sistem Pakar 5. Kecerdasan Buatan 6. Manajemen Informasi 7. Data Mining 8. Big Data 9. Jaringan Komputer 10. Dan lain-lain (topik lainnya yang berhubungan dengan Teknologi Informati dan komputer)
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 1,025 Documents
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Eksplorasi Teknik Pre-Processing Berbasis eXtreme Gradient Boosting (XGBoost) pada Serangan DDoS Nur Faiz, Muhammad; Sari, Laura; Imam Riadi; Arif Wirawan Muhammad; Sukma Aji
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 6 (2025): Desember 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i6.9380

Abstract

Distributed Denial of Service (DDoS) attacks represent a critical threat to modern network security, particularly within Internet of Things (IoT) environments characterized by large-scale and heterogeneous traffic patterns. The primary challenges in detecting such attacks involve class imbalance, irrelevant features, and noise within the data, all of which can degrade the performance of machine learning-based detection models. This study evaluates the impact of a pre-processing pipeline—comprising the Synthetic Minority Over-sampling Technique (SMOTE), correlation-based feature selection, and advanced feature selection methods—on the performance of the XGBoost algorithm in detecting DDoS attacks using the CIC-IoT2023 dataset. Experimental results indicate that the XGBoost model trained on RAW data achieves exceptionally high performance, with an accuracy of 0.999983, precision of 0.985531, recall of 0.961390, and an F1-score of 0.999983. However, after applying the pre-processing techniques, all metrics experienced a decline, with accuracy decreasing to 0.958899, precision to 0.865729, recall to 0.748332, and the F1-score to 0.959158. The reduction in recall suggests a higher number of undetected attacks, whereas the drop in precision indicates an increase in false alarms. Nevertheless, the F1-score remaining above 0.95 demonstrates that the model continues to perform effectively overall. These findings reveal that pre-processing does not always lead to performance improvements, especially when the raw dataset is already relatively clean and balanced. This study provides deeper insights into how SMOTE, feature selection, and noise injection influence the generalization of XGBoost on IoT traffic, and emphasizes that the effectiveness of pre-processing is highly dependent on dataset characteristics and the intended application context of intrusion detection systems.
Analisis Perbandingan Metode Random Forest, XGBoost, dan Logistic Regression Untuk Klasifikasi Deteksi Dini Penyakit Diabetes Novriansyah Afqi Nur Akmal Fauzi; Fikri Budiman
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 6 (2025): Desember 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i6.9392

Abstract

Diabetes Mellitus is a chronic disease with a continuously increasing prevalence, posing serious challenges to public health and contributing significantly to the global economic burden. The often non-specific nature of early symptoms increases the risk of delayed diagnosis, highlighting the need for accurate early detection approaches to support clinical decision-making. This study aims to analyze and compare the performance of three machine learning algorithms Logistic Regression, Random Forest, and XGBoost in classifying diabetes risk based on several clinical parameters, including age, body mass index (BMI), blood pressure, glucose level, and HbA1c. The dataset used in this research was obtained from the Diabetes Prediction Dataset, consisting of 100,000 records. The research process involved handling missing data, applying One-Hot Encoding to categorical variables, normalizing numerical features, and addressing class imbalance using the Synthetic Minority Over-sampling Technique (SMOTE). Model performance was evaluated using Accuracy, Precision, Recall, F1-Score, and ROC-AUC metrics to provide a comprehensive assessment. The experimental results indicate that XGBoost achieved the best performance, with an accuracy of 96.88% and a ROC-AUC value of 98.00%. Meanwhile, Random Forest attained an accuracy of 95.68% with an F1-Score of 74.76%, while Logistic Regression recorded an accuracy of 88.96% and the highest recall value of 89.12%. These findings suggest that ensemble learning methods, particularly boosting approaches, are more effective in improving the accuracy of diabetes and non-diabetes classification. The primary contribution of this study lies in providing a multi-metric comparative analysis that can serve as a reference for selecting the most effective machine learning model in the development of medical decision support systems for early diabetes detection.
Pemodelan WebGIS untuk Prediksi dan Analisis Spasial Cakupan ASI Eksklusif di Kota Padang Rahmad Mulia, Jefri; Ahmad Afif; Donal Ortega
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 6 (2025): Desember 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i6.9395

Abstract

Penelitian ini mengembangkan model Sistem Informasi Geografis (SIG) berbasis web untuk prediksi dan analisis spasial cakupan ASI eksklusif pada bayi di bawah enam bulan di Kota Padang. Model ini dirancang untuk mengintegrasikan pemetaan spasial dengan analisis prediktif sehingga dapat memberikan gambaran menyeluruh mengenai kondisi cakupan ASI eksklusif pada tingkat wilayah. Analisis spasial dilakukan dengan memanfaatkan data historis tahun 2023–2024 untuk memetakan distribusi cakupan pada tingkat kecamatan dan pusat pelayanan kesehatan. Hasil pemetaan ini menggambarkan variasi spasial antarwilayah dan memberikan informasi awal mengenai area dengan cakupan tinggi maupun rendah.Pendekatan Monte Carlo Simulation diterapkan untuk menghasilkan model prediksi berdasarkan data tahun 2024. Hasil simulasi menunjukkan tingkat akurasi sebesar 98,48%, yang mengindikasikan kesesuaian sangat tinggi dengan data aktual tahun 2024. Tingkat akurasi tersebut menunjukkan bahwa model memiliki kapabilitas yang baik dalam merepresentasikan kondisi nyata dan dapat diandalkan sebagai alat analisis prediktif untuk tahun-tahun berikutnya. Temuan ini juga menegaskan potensi metode probabilistik dalam mendukung analisis kesehatan masyarakat yang bersifat dinamis.Integrasi hasil prediksi ke dalam platform SIG berbasis web memungkinkan visualisasi interaktif dan akses data secara real-time. Kehadiran sistem ini diharapkan dapat meningkatkan efektivitas pengambilan keputusan dan perumusan kebijakan berbasis data dalam upaya peningkatan cakupan ASI eksklusif di Kota Padang, terutama bagi pemangku kepentingan di bidang kesehatan masyarakat. Kata Kunci: Analisis_Spasial; ASI_Eksklusif; Sistem_Informasi_Geografis_(SIG); Simulasi_Monte _Carlo; Kota_Padang
Analisis Performa Algoritma Naïve Bayes dan SVM Menggunakan Python Pada Ulasan Sentimen Game Roblox Dia Komalla; Alam, RG Guntur; Wijaya, Ardi
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 6 (2025): Desember 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i6.9396

Abstract

The imbalance of user reviews in the Roblox game creates accuracy challenges in sentiment classification, where the number of positive reviews significantly exceeds negative ones, causing the model to struggle particularly in identifying negative sentiment. This study aims to compare the performance of the Naïve Bayes and Support Vector Machine algorithms in classifying sentiment on imbalanced data. The research was conducted through several stages, including web scraping, pre-processing, automatic labeling using CNN, data splitting, model training, and performance evaluation using a Confusion Matrix. The findings reveal that Naïve Bayes tends to classify most samples as positive, resulting in very high recall for the positive class, reaching 0.995–0.997, but poor performance on the negative class, leading to consistent imbalance across all test ratios. In contrast, SVM achieves higher accuracy and more stable performance, with a Macro-F1 score of 0.740–0.769 and an AUC-PR of 0.936–0.942. The performance differences between the two models are statistically significant, with p-values of 0.001 and 0.0004, indicating that SVM is more effective in identifying both majority and minority classes. However, in terms of computational efficiency, Naïve Bayes is superior, requiring only 0.003–0.016 seconds of training time. Therefore, SVM is considered more reliable and robust for sentiment analysis on imbalanced data such as Roblox game reviews, whereas Naïve Bayes is more suitable when processing speed is the priority.
Penggunaan Algoritma Stacking Classifier Pada Sistem Deteksi Risiko Kardiovaskular Imam Bari Setiawan; Maulida Ayu Fitriani; Elindra Ambar Pambudi; Muhammad Hamka
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 6 (2025): Desember 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i6.9402

Abstract

Cardiovascular disease is a leading cause of global death. However, the complexity of medical data often makes conventional models fail to capture hidden patterns, resulting in suboptimal predictive performance. This study evaluates the effectiveness of a hybrid model that integrates K-Modes Clustering with the Stacking Classifier algorithm and tests whether the model's complexity can provide significant performance improvements compared to a single model. The methodology involves data preprocessing including outlier handling, clinical feature engineering, and cluster feature extraction using K-Modes (K=2). The Stacking Classifier architecture is built using five optimized heterogeneous base-learners (CatBoost, Decision Tree, MLP, SVC, Logistic Regression) and XGBoost as a meta-learner, validated through Stratified 5-Fold Cross-Validation. The results showed that although K-Modes effectively mapped clinically valid risk categories, the Stacking Classifier model (87.99% accuracy and 95.89% ROC-AUC) was not able to surpass the performance of the best single model, namely CatBoost (88.03% accuracy and 95.90% ROC-AUC). The most significant finding lies in the computational time efficiency, where the Stacking Classifier algorithm required 560 times longer computational time (7587.7686 seconds) than CatBoost (13.4635 seconds) without providing a commensurate performance improvement. This indicates that Boosting-based algorithms are able to capture complex patterns without requiring additional ensemble layers, so that an optimized single model is more recommended for real-world implementations by providing the best balance between prediction accuracy and computational time efficiency.

Page 103 of 103 | Total Record : 1025

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