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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 60 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.
Outlier Handling in Applied Regression: Performance Comparison Between Least Trimmed Squares and Maximum Likelihood-Type Estimators Oktarina, Cinta Rizki; Andini Setyo Anggraeni; Muhammad Arib Alwansyah; Reza Pahlepi
J-KOMA : Jurnal Ilmu Komputer dan Aplikasi Vol 8 No 02 (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/JKOMA.082.01

Abstract

Poverty analysis often relies on regression models whose performance can deteriorate in the presence of outliers, leading to biased estimates and unreliable conclusions. This study aims to evaluate the effectiveness of robust regression methods compared with Ordinary Least Squares (OLS) when modeling poverty levels across 154 regions in Sumatra. Four socioeconomic indicators were used as predictors, and outlier detection was conducted using the DFFITS approach. After identifying deviations from normality and the presence of influential observations, two robust estimation techniques M-estimation and Least Trimmed Squares (LTS) were applied to improve model stability. The results show that while all predictors significantly influence poverty, the LTS estimator provides the most accurate and robust performance, yielding the smallest Mean Squared Error (MSE) and an R-squared value of 53.37%. These findings demonstrate that LTS is better suited than OLS and M-estimation for handling data contamination and offers a more reliable approach for modeling poverty determinants
Handling Missing Data in Bivariate Gamma Generation Data Using the Random Forest Method Arib, Muhammad Arib Alwansyah; Ramya, Ramya Rachmawati
J-KOMA : Jurnal Ilmu Komputer dan Aplikasi Vol 8 No 02 (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/JKOMA.082.02

Abstract

Missing data is a common problem in data analysis that can reduce the quality and accuracy of study results if not handled properly. This study aims to evaluate the performance of the Random Forest (RF) imputation method at various levels of missing value proportions, namely 5%, 10%, 15%, and 20%. The data used are Bivariate Gamma data of 200 observations with two variables, generated using RStudio software. Evaluation of imputation performance is carried out by considering the correlation value between the imputed data and the original data, the p-value as an indicator of the significance of the difference, and the error measures Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE).
Forest Fire Clustering in Indonesia Using the Clustering Large Applications (CLARA) Method Arib, Muhammad Arib Alwansyah; Ridya, Ridya Destriani; Sigit, Sigit Nugroho; Nurul, Nurul Hidayati
J-KOMA : Jurnal Ilmu Komputer dan Aplikasi Vol 8 No 02 (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/JKOMA.082.03

Abstract

Clustering is a process of grouping, observing or grouping classes that have similar objects. One clustering method that handles large amounts of data is clustering large applications (CLARA). This research aims to identify groups of forest fires in Indonesia using the CLARA method and to determine the characteristics of forest fires and the locations of forest fire occurrence points in Indonesia. The data used is hot spot data totaling 3,265 events, which can be obtained from the NASA LANCE–FIRM MODIS Active Fire website. The variables used to group forest fire events are latitude, longitude, brightness, frp and confidence. So by grouping 3,265 hot spot data by determining the optimum cluster using the Shilhoutte index and Dunn index values, the optimum cluster results were obtained, namely 2 clusters
Bahasa Inggris Arib, Muhammad Arib Alwansyah; Viola, Viola Oktamelisa; Sigit, Sigit Nugroho; Etis, Etis Sunandi
J-KOMA : Jurnal Ilmu Komputer dan Aplikasi Vol 8 No 02 (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/JKOMA.082.04

Abstract

Clustering is a data grouping method applied to identifies groups formed by combining elements that have the same characteristics. One of the clustering methods that can be used is the K-Medoids method known as Partitioning Around Medoids (PAM). This study aims to obtain grouping and determine the characteristics of the results of grouping regencies/cities in the Sumatra Region based on the percentage of poverty using the K-medoids cluster method. The data used are poverty data per district/city totaling 154 in the Sumatra Region with the variables used being the expected length of schooling, average length of schooling, open unemployment rate, and percentage of poor population. The results obtained in this study are that districts/cities in the Sumatra Region have 2 optimum clusters as seen from the silhouette index value and davies-bouldin index value
Utilizing KNN for Estimating Lignin in Rice Bran through Color Imagery with PCA Preprocessing Aziz Kustiyo; Triadi Sutrisno, Rijal
J-KOMA : Jurnal Ilmu Komputer dan Aplikasi Vol 8 No 02 (2025): J-KOMA : Jurnal Ilmu Komputer dan Aplikasi
Publisher : Universitas Negeri Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Feed is essential for enhancing livestock production, particularly in maintaining animal health and stamina. Rice bran is commonly used as animal feed; however, its quality can decline when mixed with other ingredients, such as rice husks. The addition of rice husks to rice bran increases the levels of crude fiber and lignin, which are difficult for livestock to digest and can lead to health issues. This mixing can be assessed by estimating the lignin content through the phloroglucinol dye reaction. This study aimed to estimate the lignin content in a mixture of rice bran and rice husks using the dye reaction and the resulting color images. The images were captured using the red-green-blue (RGB) color model. A feature extraction technique called principal component analysis (PCA) was employed on each RGB component. The results from the PCA were subsequently classified using the k-Nearest Neighbor (KNN) algorithm. The findings indicated that the red (R) color component yielded the highest classification accuracy of 77.27%.
Prediksi Risiko Putus Sekolah Menggunakan Long Short-Term Memory Berdasarkan Data Prestasi Akademik Arafiyah, Ria
J-KOMA : Jurnal Ilmu Komputer dan Aplikasi Vol 8 No 02 (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/JKOMA.082.06

Abstract

Student dropout is a critical issue affecting academic quality and institutional performance in higher education. Dropout behavior usually emerges gradually through declining academic performance across semesters. Therefore, time-series modeling is essential to capture such temporal patterns. This study proposes a Long Short-Term Memory (LSTM) model to predict student dropout risk based on semester-wise academic data. The dataset consists of 385 undergraduate students from the Computer Science program, FMIPA, represented by Grade Point Average (GPA) and credit load (SKS) over eight semesters. Student status is converted into a binary label: dropout and non-dropout. To address class imbalance, Synthetic Minority Over-sampling Technique (SMOTE) is applied. Experimental results show that the proposed LSTM model achieves a recall of 1.00 for the dropout class, indicating that all dropout cases are successfully detected. Although the precision remains relatively low due to false positives, the model demonstrates strong potential as a basis for academic monitoring and early intervention systems.
THE COMPUTER GRAB HOLDING CONTROL SYSTEM ON A GSU (GRAB SHIP UNLOADER) CRANE: THE COMPUTER GRAB HOLDING CONTROL SYSTEM ON A GSU (GRAB SHIP UNLOADER) CRANE Alimuddin2, Alimuddin
J-KOMA : Jurnal Ilmu Komputer dan Aplikasi Vol 8 No 02 (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/JKOMA.082.07

Abstract

Grab Ship Unloader (GSU) is one of the main pieces of equipment in bulk material handling operations at industrial ports, where the reliability of the control system plays a crucial role in ensuring operational efficiency and safety. One of the critical subsystems in a GSU is the grab holding control system, which regulates the opening, holding, and closing processes of the grab during material handling. This internship report aims to analyze the grab holding control system of the GSU crane at PT Krakatau Bandar Samudra, with a particular focus on the Programmable Logic Controller (PLC)-based control logic and the implementation of ladder diagrams. The research methods include direct field observation, literature review, and discussions with field supervisors and academic advisors. The analysis focuses on the operating principles of the control system, operating modes (manual, automatic, and high-speed), safety interlocks, and the utilization of sensors such as encoders, load cells, and motor torque feedback. The results indicate that the grab holding control system is designed with a well-structured logic that processes position and torque signals to determine safe and stable hold, close, and open commands. The PLC ladder diagram plays a significant role in managing automatic cycles, speed control through ramp steps, and preventing motion conflicts. With this control system, grab operations on the GSU crane can be carried out more precisely, efficiently, and safely. This internship report is expected to serve as a reference for the development, maintenance, and improvement of GSU crane control systems in industrial port environments.

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