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J-KOMA : Jurnal Ilmu Komputer dan Aplikasi
Published by Universitas Negeri Jakarta
ISSN : 26204827     EISSN : 26204827     DOI : -
Core Subject : Science,
Computer Science & IT
J-KOMA is an open access journal, with core focus in two aspect: computer science general and information technology. All copyrights are retained by each respective author, but we hold publishing right. Currently, this journal has E-ISSN :2620-4827 published by LIPI which made it as a national journal.
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 53 Documents
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Implementing Rasch Model for Modern Test Evaluation Using Python Prototype Piliyang, Yusriizal; Arafiyah, Ria; Rahayu, Wardani
J-KOMA : Jurnal Ilmu Komputer dan Aplikasi Vol 8 No 1 (2025): J-KOMA : Jurnal Ilmu Komputer dan Aplikasi
Publisher : Universitas Negeri Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/j-koma.v8i1.04

Abstract

This study explores the implementation of the Rasch Model for modern test evaluation using a custom Python prototype, validated against Winsteps software. Focusing on dichotomous exam data from a significant sample, the research estimates participant ability and item difficulty with high precision, achieving standard errors below 0.30. The model identifies misfitting items, such as Item I5 with an outfit mean square of 1.45, enhancing test design reliability. Item Characteristic Curves (ICC) and Item Information Functions (IIF) support the efficacy of Computer Adaptive Testing (CAT) across varying ability levels. Results demonstrate the prototype's consistency with Winsteps (correlation = 0.98), affirming its potential as a flexible tool for educational assessment. Limitations include the command-line interface and the need for larger datasets, suggesting future improvements in scalability and usability. This work advances modern testing practices, offering a foundation for adaptive and fair assessment systems.
Robust Anomaly Detection in Network Traffic Using Bagging with Majority Voting Ensemble Sultan Ilham Seftiansyah, Muhammad; Chairunnas, Andi; Yanti, Yusma
J-KOMA : Jurnal Ilmu Komputer dan Aplikasi Vol 8 No 1 (2025): J-KOMA : Jurnal Ilmu Komputer dan Aplikasi
Publisher : Universitas Negeri Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/j-koma.v8i1.03

Abstract

Anomaly detection in computer networks is a crucial aspect of ensuring system security and availability. One of the most common and disruptive threats is Distributed Denial of Service (DDoS) attacks, which can overload servers and compromise service continuity. Traditional Intrusion Detection Systems (IDS) often struggle to detect sophisticated and evolving attack patterns, leading to reduced detection performance. This research proposes the use of ensemble learning with Bagging and Majority Voting to enhance anomaly detection. The dataset used in this study was CIC-DDoS2019, consisting of 33,066 rows and 88 features, processed through data cleaning, label encoding, and normalization. Three base classifiers—Decision Tree, Random Forest, and XGBoost—were integrated using Bagging with Majority Voting. Experiments were conducted with different train-test split ratios of 70:30, 75:25, 80:20, and 90:10. The results showed that the 70:30 split achieved the best performance with an accuracy of 93.58%, an F1-score of 90.51%, and the fastest evaluation time of 142.86 seconds. Additional tests on spam and phishing datasets confirmed the robustness of the Bagging approach, achieving accuracy above 96%. These findings demonstrate that Bagging with Majority Voting can effectively improve IDS performance and provide a reliable solution for detecting various types of cyberattacks.
Evaluation of TF-IDF Extraction Techniques in Sentiment Analysis of Indonesian-Language Marketplaces Using SVM, Logistic Regression, and Naive Bayes Budi Lestari, Verra; Apriansyah Hutagalung, Carli
J-KOMA : Jurnal Ilmu Komputer dan Aplikasi Vol 8 No 1 (2025): J-KOMA : Jurnal Ilmu Komputer dan Aplikasi
Publisher : Universitas Negeri Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/j-koma.v8i1.05

Abstract

This study evaluates the application of TF-IDF feature extraction in sentiment analysis of Indonesian-language marketplace product reviews using Logistic Regression, Naïve Bayes, and Support Vector Machine (SVM) algorithms. The dataset, sourced from Kaggle, comprises 831 reviews (385 positive, 446 negative), processed through preprocessing steps including text cleaning, tokenization, stopword removal, and stemming. The data was split into 80% training and 20% testing sets. Results show that Logistic Regression with TF-IDF achieved the highest performance, with 90.4% accuracy, 91.8% precision, 90.4% recall, and 90.9% F1-measure, outperforming Naïve Bayes (87.4% accuracy) and SVM (89.8% accuracy). Logistic Regression effectively captures linear relationships in TF-IDF features, while Naïve Bayes struggles with emotional context, and SVM requires complex parameterization. TF-IDF is efficient for explicit reviews but limited in handling complex semantic contexts like sarcasm. This study confirms that Logistic Regression combined with TF-IDF is the most effective approach for sentiment analysis of Indonesian marketplace reviews, with recommendations for future exploration of methods like word embedding.

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