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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 86 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 Raniyah Ayulestari; Suci Inayah
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..
Rancang Bangun Sistem Informasi Pengelolaan Surat Berbasis Web (Studi Kasus: Kelurahan Jatiasih) Mawar Anggraini Prabowo; Eko Aziz Apriadi; Ribut Julianto; Agus Komarudin
Jurnal Ilmu Komputer dan Sistem Komputer Terapan (JIKSTRA) Vol. 8 No. 1 (2026): Edisi April
Publisher : Universitas Harapan Medan

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Abstract

This study aims to design and develop a web-based information system for managing incoming and outgoing correspondence at the Jatiasih Urban Village Office. The main problem faced is that correspondence recording is still carried out manually using a logbook, which may lead to data loss, difficulty in retrieving archives, delays in document management, and disorganized record keeping. The system development method used in this study is the waterfall model, which consists of requirements analysis, system design, implementation, and testing stages. The system was developed using the PHP programming language and MySQL database to support integrated processes for recording, storing, searching, managing, and reporting correspondence data. System testing was conducted using the black-box testing method on eight main functions, including admin login, incoming letter input, outgoing letter input, letter data editing, letter data deletion, letter file uploading, incoming letter report printing, and outgoing letter report printing. The test results show that all functions operate according to user requirements, with a success rate of 100%. Therefore, the developed information system can help make correspondence management at the Jatiasih Urban Village Office more structured, faster, more accurate, and more efficient.
Pendeteksian Anomali Pada Daun Tanaman Jambu Biji Menggunakan Isodata Cluster Nadila Harianti; Yunita Sari; Mufida Khairani
Jurnal Ilmu Komputer dan Sistem Komputer Terapan (JIKSTRA) Vol. 7 No. 2 (2025): Edisi Oktober
Publisher : Universitas Harapan Medan

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Abstract

The purpose of this study is to develop a disease identification system for guava leaves using the ISODATA Clustering method. Diseases affecting guava plants may reduce plant quality and productivity; therefore, an automatic identification system is required. The ISODATA method was selected because it can automatically organize image data through cluster creation, merging, and splitting processes. The system was developed using Visual Studio and utilized guava leaf images as input data. The research stages included image acquisition, pixel extraction, clustering using ISODATA, and disease identification based on color pattern grouping. The results indicate that the ISODATA Clustering method is capable of grouping leaf color patterns and supporting the identification of guava leaf diseases. The developed system classifies leaves into several categories, including leaf blight, leaf spot, rust disease, and healthy leaves. This system is expected to assist farmers in early disease detection and decision-making for prevention and treatment.
Multimodal Learning Menggunakan Efficientnetv2 Dan Distilbert Untuk Deteksi Website Ilegal Dan Implementasinya Pada Browser Extension Sahal Maghfud; Ulfa Khaira; Akhiyar Waladi
Jurnal Ilmu Komputer dan Sistem Komputer Terapan (JIKSTRA) Vol. 8 No. 1 (2026): Edisi April
Publisher : Universitas Harapan Medan

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Abstract

To address the ineffectiveness of domain blocking for illegal content such as online gambling, pornography, and piracy in Indonesia due to VPNs or new domains, this study proposes a Chromium extension based on multimodal learning. It fuses visual features using EfficientNetV2 M, text using multilingual DistilBERT, and HTML structure via early fusion into a vector of 2,187 dimensions, which is then classified by an MLP into four categories namely normal, online gambling, pornography, and piracy. Using a completely novel dataset consisting of 16,224 samples, the text and image combination achieved the best performance with an accuracy of 88.74%, a Macro F1 Score of 0.8275, and a Macro Recall of 0.812. For real world robustness, the extension utilizes all three modalities comprising text, image, and HTML, successfully classifying 36 of 40 unseen websites. Misclassifications occurred only in the digital piracy category due to limited training data and high visual similarity to legitimate websites..  
Perancangan Aplikasi Augmented Reality Media Pembelajaran Pengenalan Monumen Nasional Berbasis Android Menggunakan Sdk Vuforia Alwi Alfisyahri
Jurnal Ilmu Komputer dan Sistem Komputer Terapan (JIKSTRA) Vol. 7 No. 2 (2025): Edisi Oktober
Publisher : Universitas Harapan Medan

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Abstract

The development of information and communication technology has significantly contributed to innovations in educational media, one of which is the use of Augmented Reality (AR). AR is a technology capable of combining virtual objects with the real environment in real-time, thereby enhancing students’ engagement and understanding of learning materials. This research aims to design an Android-based Augmented Reality application as an interactive learning medium for introducing the National Monument (Monas) by utilizing the Vuforia Software Development Kit (SDK). The research method consists of several stages, including needs analysis, design, implementation, and testing. The needs analysis focused on identifying learning materials about Monas, including its main parts, historical background, and symbolic values. In the design stage, an interactive multimedia development model was applied by integrating 3D objects of Monas, informational texts, and audio explanations. The application was implemented using Unity 3D as the game engine combined with Vuforia SDK for marker recognition and 3D object rendering. The testing results indicate that the application can detect markers stably, display a detailed 3D model of Monas, and provide users with comprehensible educational information. A limited trial conducted with a group of students revealed increased learning motivation and understanding of the National Monument, as the interactive and visual-based presentation was more engaging compared to conventional media. Therefore, this Android-based Augmented Reality application for Monas recognition can serve as an innovative, effective, and technology-driven learning medium that creates a more enjoyable and meaningful learning experience.
Perancangan Aplikasi Augmented Reality Statis Sebagai Media Pembelajaran Bagian Tumbuhan Hermafrodit Untuk Siswa Sekolah Dasar Rivan Rubenzky; Munjiat Setiani Asih
Jurnal Ilmu Komputer dan Sistem Komputer Terapan (JIKSTRA) Vol. 7 No. 2 (2025): Edisi Oktober
Publisher : Universitas Harapan Medan

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Abstract

Science learning in elementary schools still often uses conventional media such as textbooks, lectures, and two-dimensional images. This condition can make it difficult for students to understand materials that require clear visual representation, such as the parts of hermaphroditic plants. This study aims to design and develop a static Augmented Reality (AR) application based on Android as an interactive learning medium for introducing the parts of hermaphroditic plants to elementary school students. The development method used in this study is the Luther multimedia development method, which consists of concept, design, material collecting, assembly, testing, and distribution stages. The application was developed using Unity 3D and Vuforia SDK with a marker-based tracking method. The 3D objects used in this application are lily and tulip flowers. The results of this study show that the application can display 3D objects of hermaphroditic plants when the smartphone camera is directed at the marker. The application also provides information menus and quiz features to support the learning process. Based on the results, this application can be used as an alternative learning medium that is more visual, interactive, and easy to use in helping elementary school students understand the parts of hermaphroditic plants.
Tren Penelitian Dan Struktur Pengetahuan Explainable Artificial Intelligence Untuk Pemodelan Prediktif : Bibliometric Review Eka Rahayu; Boni Oktaviana; Mufida Khairani; Arie Rafika Dewi
Jurnal Ilmu Komputer dan Sistem Komputer Terapan (JIKSTRA) Vol. 8 No. 1 (2026): Edisi April
Publisher : Universitas Harapan Medan

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This study aims to analyze research trends and knowledge structures in explainable artificial intelligence (XAI) for predictive modeling through a bibliometric review approach. Data were obtained from Scopus metadata in BIB format exported on June 30, 2026. The initial search strategy yielded 343 documents, which were then filtered based on article document type, English language, and journal source, resulting in 177 articles analyzed for the period 2015-2026. The analysis was conducted using a bibliometric approach through mapping publication productivity, journals and primary authors, influential documents, country contributions, keyword co-occurrence, trend topics, thematic maps, and thematic evolution. The results show that XAI research for predictive modeling experienced a strong acceleration after 2023, indicating a shift in focus from predictive models that are solely accuracy-oriented to models that are transparent, explainable, and accountable. The intellectual structure of this field is interdisciplinary, with contributions from computer science, education, health, energy, environment, geospatial, industry, materials, and engineering. The dominant themes center on machine learning, data mining, forecasting, interpretability, SHAP, LIME, and deep learning, while emerging themes focus on counterfactual explanation, causality-aware forecasting, physics-informed learning, transformers, and graph neural networks. This study identifies five key gaps: method, data, application, theory, and evaluation. The primary contribution of this research is to provide a systematic mapping of the developments, intellectual actors, dominant themes, emerging themes, and future research agendas of XAI to build more accurate, transparent, auditable, and accountable predictive modeling

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