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Jurnal Ilmu Komputer dan Sistem Komputer Terapan (JIKSTRA)
Published by Universitas Harapan Medan
ISSN : 2715887X     EISSN : 27472485     DOI : https://doi.org/10.35447/jikstra.v6i2
Core Subject : Science,
Computer Science & IT
A journal managed by the Informatics Engineering and Information Systems study program at Universitas Harapan Medan (UNHAR), this journal discusses science in the field of Informatics and information systems, as a forum for expressing research results both conceptually and technically related to informatics. Jikstra is published twice a year, namely in April and October, the first issue in the April 2019 edition. JIkstra in the peer review process uses blind peer review.
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 74 Documents
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Rancang Bangun Aplikasi Kuis Mobile Berbasis Android Untuk Pembelajaran Ips Di Kelas 5 Sd Dengan Metode Waterfall Mellia Cristanty Sinuhaji
Jurnal Ilmu Komputer dan Sistem Komputer Terapan (JIKSTRA) Vol. 7 No. 2 (2025): Edisi Oktober
Publisher : Universitas Harapan Medan

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Abstract

Social Science (IPS) learning in 5th grade elementary schools often uses methods that are less interactive and less engaging for students, affecting the effectiveness of learning. This research aims to design and develop a mobile quiz application based on Android using the Waterfall method to support IPS learning for 5th grade students. The research scope includes needs analysis, design, implementation, testing, and maintenance of the quiz application. The Waterfall method is used because it provides structured and systematic stages in application development. The results show that the quiz application can increase students' interest and understanding of IPS material through an interactive and enjoyable learning method. In conclusion, the mobile-based quiz application can serve as an effective alternative learning media for 5th grade students. Suggestions include further feature development and content expansion to optimize the application's use in the learning process.
Media Pembelajaran Sistem Pengoperasian Simulasi Perakitan PC Berbasis Augemented Reality Ayulestari, Raniyah; Inayah , Suci
Jurnal Ilmu Komputer dan Sistem Komputer Terapan (JIKSTRA) Vol. 8 No. 1 (2026): Edisi April
Publisher : Universitas Harapan Medan

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Abstract

Learning Media PC assembly simulation operating system based on Augmented Reality PC assembly simulation based on Augmented Reality (AR) is an innovative learning media to understand computer operating systems. By utilizing AR technology, users can learn about components and the PC assembly process interactively and in-depth. Main Features of AR Simulation 3D Visualization Displays PC components such as Motherboard, CPU, RAM, and storage devices in three dimensions. Users can rotate and zoom in on objects to see their details. Interactive Guide Provides clear step-by-step instructions for each assembly stage using text to explain the function of each component. The assembly process simulation allows users to perform virtual assembly by combining components in real-time. Conclusion Augmented Reality-based PC assembly simulation is an effective learning media to understand computer operating systems. With interactive features and attractive visualizations, this simulation not only enhances students' understanding but also prepares them for real-world practice. The use of AR in education opens up new opportunities for more innovative and engaging learning methods
Evaluasi Recursive Feature Elimination Untuk Klasifikasi Kanker Payudara Menggunakan Berbagai Algoritma Machine Learning Syarifah Yusnaini Putri; Sayuti Rahman; Nia Ramadani; Novalia Aprianti Ginting; Layla Syalsyadilla; Dedi Agustriaman Zebua
Jurnal Ilmu Komputer dan Sistem Komputer Terapan (JIKSTRA) Vol. 8 No. 1 (2026): Edisi April
Publisher : Universitas Harapan Medan

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Abstract

Early detection of breast cancer requires classification models that are not only accurate but also efficient and interpretable. This study evaluates the effect of Recursive Feature Elimination (RFE) on the performance of several machine learning algorithms for breast cancer classification. The dataset used is the Wisconsin Diagnostic Breast Cancer (WDBC) dataset from the UCI Machine Learning Repository, consisting of 569 samples and 30 numerical features. The research stages include data preprocessing, removal of non-informative attributes, feature standardization using StandardScaler, train-test splitting with an 80:20 ratio, feature selection using Logistic Regression-based RFE, and training and testing of 11 classification algorithms. Model performance was evaluated using accuracy, precision, recall, F1-score, confusion matrix, and Receiver Operating Characteristic (ROC) curve. The results show that before feature selection, Support Vector Machine, Logistic Regression, and Voting Classifier achieved the highest accuracy of 98.25%. After applying RFE, the accuracy of these models decreased slightly to 97.37%, while the number of features was reduced from 30 to 15. Several algorithms, including Nearest Centroid, Naïve Bayes, and AdaBoost, showed improved accuracy after RFE. These findings indicate that RFE does not always improve the best model accuracy, but it can produce a more compact, efficient, and interpretable classification model.
Penanganan Ketidakseimbangan Data Pada Klasifikasi Penyakit Campak Menggunakan Kombinasi Smote Dan Xgboost Novita Ranti Muntiari; Kharis Hudaiby Hanif; Muliyadi; Mufida
Jurnal Ilmu Komputer dan Sistem Komputer Terapan (JIKSTRA) Vol. 8 No. 1 (2026): Edisi April
Publisher : Universitas Harapan Medan

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Abstract

Data imbalance is one of the main challenges in developing disease classification models, as it can cause algorithms to recognize the majority class more dominantly and perform less optimally in detecting positive cases. This study aims to analyze the application of the combination of Synthetic Minority Over-sampling Technique (SMOTE) and XGBoost in measles disease classification. The data used consisted of 1,000 records with clinical features including age, immunization history, fever, cough, runny nose, conjunctivitis, skin rash, and measles status. The research data were divided into two subsets, namely 80% for the model training process and 20% for testing. The SMOTE technique was applied to the training data to address class distribution imbalance, while the XGBoost algorithm was used to build the classification model. Model performance was then evaluated using a confusion matrix and the metrics of accuracy, precision, recall, and F1-score. The results showed that XGBoost without SMOTE achieved an accuracy of 94.0%, precision of 83.3%, recall of 50.0%, and F1-score of 62.5%. After applying SMOTE, the performance improved, with an accuracy of 97.0%, precision of 79.2%, recall of 95.0%, and F1-score of 86.4%. These results indicate that the combination of SMOTE and XGBoost is more effective in improving the detection capability of positive measles cases in imbalanced data..

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