Jurnal Komtika (Komputasi dan Informatika)
Aims Jurnal Komtika (Komputasi dan Informatika) is a scientific journal published by the Faculty of Engineering, Universitas Muhammadiyah Magelang and is Accredited by the Ministry for Research, Technology, and Higher Education (RISTEKDIKTI)(No:200/M/KPT/2020). It is a medium for researchers, academics, and practitioners interested in Computer Science and wish to channel their thoughts and findings. Our concept of Informatics includes technologies of information and communication as well as results of research, critical, and comprehensive scientific study which are relevant and current issues covered by the journals. Jurnal Komtika publishes regular research articles. We encourage researchers to publish their theoretical and empirical results in as much detail as possible. For theoretical papers, full details of proofs must be provided so that the results can be checked; for experimental papers, full experimental details must be given so that the results can be reproduced. Additionally, electronic files or software regarding the full details of the calculations, experimental procedure, etc., can be deposited along with the publication as “Supplementary Material”. Scope Jurnal Komputasi dan Informatika (Komtika) focuses on various issues, but not limited in the field of: Software Development: Software development process, Requirements analysis, Software design, Software construction, Software deployment, Software maintenance, Programming team, Open-source model Mathematics of Computing: Discrete mathematics, Mathematical software, Information theory Theory of computation: Model of computation, Computational complexity Human Computer Interaction: Interaction design, Social computing, Ubiquitous computing, Visualization, Accessibility, User Interface Study, User Experience Study Applied Computing: E-commerce, Enterprise software, Electronic publishing, Cyberwarfare, Electronic voting, Video game, Word processing, Operations research, Educational technology, Document management. Machine Learning: upervised learning, Unsupervised learning, Reinforcement learning, Multi-task learning Graphics: Animation, Rendering, Image manipulation, Graphics processing unit, Mixed reality, Virtual reality, Image compression, Solid modeling Information System: Database management system, Information storage systems, Enterprise information system, Social information systems, Geographic information system, Decision support system, Process control system, Multimedia information system, Data mining, Digital library, Computing platform, Digital marketing, World Wide Web, Information retrieval
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<![CDATA[Pengelompokkan Abstrak Jurnal Ilmiah Menggunakan Term Frequency-Inverse Document Frequency dan K-Means ]]>
<![CDATA[Aprianti, Nadia Wati]]>;
<![CDATA[Herman Yuliansyah]]>;
<![CDATA[Muhammad Kunta Biddinika]]>
<![CDATA[ Jurnal Komtika (Komputasi dan Informatika) Vol. 10 No. 1 (2026) ]]>
Publisher : <![CDATA[Universitas Muhammadiyah Magelang ]]>
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DOI: 10.31603/komtika.v10i1.15516
<![CDATA[ The rapid growth of scientific publications in Indonesia has created a need for text analysis methods capable of automatically clustering articles based on content similarity and research themes. This study aims to implement a combination of Term Frequency Inverse Document Frequency (TF-IDF) and the K-Means in the process of grouping scientific journal abstracts in the field of informatics. The research data consist of 1,200 scientific journal abstracts manually collected from the official SINTA (Science and Technology Index) portal for the 2023”“2024 publication period, covering various levels of national journal accreditation. The study employs an unsupervised machine learning approach consisting of several stages, including text preprocessing, TF-IDF weighting, clustering using K-Means, and result evaluation using the Silhouette Score and Davies”“Bouldin Index (DBI) metrics. The TF-IDF weighting process produced 3,000 of the most informative terms, dominated by keywords such as data, method, result, and system, reflecting the research characteristics in the field of informatics. The clustering process generated four main clusters with a Silhouette Score of 0.0121 and a DBI value of 8.3996, indicating that the model was able to identify initial thematic similarities among abstracts. The Word Cloud visualization revealed variations in research topic focus across clusters, including algorithm testing, data model development, system applications, and methodological implementation. This study contributes to the development of a national framework for scientific text analysis that can be utilized for research topic mapping, inter-institutional collaboration, and data-driven research policy formulation.]]>
<![CDATA[Pengembangan Prototype Kotak Obat Otomatis Berbasis IoT untuk Pemantauan dan Pengingat Konsumsi Obat ]]>
<![CDATA[Radika, Putu Dendy Kayoana]]>;
<![CDATA[Putra, Made Adi Paramartha]]>;
<![CDATA[Permana, Putu Trisna Hady]]>
<![CDATA[ Jurnal Komtika (Komputasi dan Informatika) Vol. 10 No. 1 (2026) ]]>
Publisher : <![CDATA[Universitas Muhammadiyah Magelang ]]>
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DOI: 10.31603/komtika.v10i1.16096
<![CDATA[The smart medicine box is a modern solution designed to assist users in managing their medication schedules consistently and on time. This study aims to design an automated reminder and medicine dispensing system based on the Internet of Things (IoT) using an ESP32 microcontroller. The system is connected to a Firebase Realtime Database to store and display data in real-time through a web interface built with Next.js. The device is equipped with a stepper motor, I2C LCD, buzzer, and a manual button that functions as a mechanism for refilling medicine. Testing was conducted using the black-box method to ensure all features functioned properly. The implementation results show that the system successfully performs medicine time reminders, automatic dispensing, and history logging. It is expected that this system can be further developed to enhance user convenience, improve adherence to medication schedules, and ensure reliable remote monitoring through the website platform.]]>
<![CDATA[Analisis Dampak SMOTE terhadap Feature Importance pada Klasifikasi Data Migraine menggunakan Random Forest dan Extra Trees ]]>
<![CDATA[Leidiyana, Henny]]>
<![CDATA[ Jurnal Komtika (Komputasi dan Informatika) Vol. 10 No. 1 (2026) ]]>
Publisher : <![CDATA[Universitas Muhammadiyah Magelang ]]>
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DOI: 10.31603/komtika.v10.i1.16684
<![CDATA[This study analyzes the impact of the Synthetic Minority Over-sampling Technique (SMOTE) on model performance and feature importance in the classification of migraine patients using Random Forest (RF) and Extra Trees (ET) algorithms. Evaluation was conducted based on recall and F1-Score for the minority class, as well as Permutation Importance analysis. The results indicate that ET, especially when combined with SMOTE (ET + SMOTE), delivers the best performance for the minority class. ET + SMOTE achieved an average F1-Score of 0.7000 and an average recall of 0.8041 using 11 optimal features, indicating better feature efficiency. The application of SMOTE significantly affected the ranking of important features. Although SMOTE improved detection for some minority classes, its impact was not always consistent and occasionally reduced performance on other minority classes. This study concludes that SMOTE alters feature contributions and model interpretability, as well as enhances performance on certain minority classes, particularly when combined with ET.]]>
<![CDATA[Perbandingan XGBoost, Random Forest, dan MLP untuk Klasifikasi Kesiapan Atlet ]]>
<![CDATA[Wardana, Bintang Putra]]>;
<![CDATA[Santoso, Cahyono Budy]]>
<![CDATA[ Jurnal Komtika (Komputasi dan Informatika) Vol. 10 No. 1 (2026) ]]>
Publisher : <![CDATA[Universitas Muhammadiyah Magelang ]]>
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DOI: 10.31603/komtika.v10.i1.16850
<![CDATA[Athlete readiness classification is critical for optimizing training load and preventing overtraining-related injuries. This study develops and compares three machine learning algorithms XGBoost, Random Forest, and MLP Neural Network to classify athlete readiness into three ordinal categories: Lower, Middle, and Upper. The dataset comprises 1153 instances with nine multidimensional features encompassing physiological parameters (training duration, intensity, interval days, points, recovery) and psychological indicators (mental score, athlete category, consistency). Data preprocessing involved label encoding for categorical variables and standard scaling for numerical features, followed by a stratified 80:20 train-test split. Model performance was evaluated using weighted precision, recall, F1-score, confusion matrix, and one-vs-rest ROC-AUC curves with 5-fold cross-validation. Results indicate that XGBoost achieved the highest predictive performance (F1-score: 0.93, AUC: 0.99), followed by Random Forest (F1-score: 0.91, AUC: 0.98) and MLP Neural Network (F1-score: 0.85, AUC: 0.94). Feature importance analysis revealed that mental score, training intensity, and consistency were the strongest predictors of readiness status. The proposed framework offers a robust, data-driven decision support tool for sports practitioners, enabling objective readiness monitoring and dynamic training adjustments. Future work will focus on real-time wearable integration and automated hyperparameter optimization]]>
<![CDATA[Perbandingan Kinerja Algoritma Machine Learning pada Sentimen #KaburAjaDulu dengan Penanganan Ketidakseimbangan Data Menggunakan SMOTE ]]>
<![CDATA[Sumahesa, Alisha]]>;
<![CDATA[Santoso, Cahyono Budy]]>
<![CDATA[ Jurnal Komtika (Komputasi dan Informatika) Vol. 10 No. 1 (2026) ]]>
Publisher : <![CDATA[Universitas Muhammadiyah Magelang ]]>
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DOI: 10.31603/komtika.v10.i1.16851
<![CDATA[The phenomenon of labor migration abroad has become a widely discussed social issue on social media, particularly through the hashtag #KaburAjaDulu on the X platform. This study aims to analyze public sentiment toward the phenomenon using machine learning classification methods with the implementation of Synthetic Minority Over-sampling Technique (SMOTE) to address data imbalance. The research was conducted using 1,750 data collected from the X platform through several stages, including data collection, text preprocessing, sentiment labeling, TF-IDF weighting, SMOTE implementation, and classification using Support Vector Machine (SVM), Naive Bayes, Random Forest, and Logistic Regression algorithms. Model evaluation was carried out using accuracy, precision, recall, and f1-score metrics. The results show that the implementation of SMOTE significantly improved classification performance. Logistic Regression achieved the best performance with an accuracy of 91.36%, followed by Random Forest at 90.30%, Support Vector Machine at 88.89%, and Naive Bayes at 82.89%. These findings indicate that Logistic Regression has the best capability in recognizing sentiment patterns within unstructured social media data. This study proves that data balancing using SMOTE plays an important role in improving sentiment classification performance and in understanding public opinion regarding social phenomena developing in digital media.]]>
<![CDATA[Komparasi Kinerja ResNet50 dan MobileNetV2 pada Klasifikasi Citra Multi-Domain ]]>
<![CDATA[Herlawati, Herlawati]]>;
<![CDATA[Hendharsetiawan, Andy Achmad]]>;
<![CDATA[Priatna, Wowon]]>;
<![CDATA[Dzulqiyana, Afina Putri]]>;
<![CDATA[Rahmadanti, Regita Ari]]>
<![CDATA[ Jurnal Komtika (Komputasi dan Informatika) Vol. 10 No. 1 (2026) ]]>
Publisher : <![CDATA[Universitas Muhammadiyah Magelang ]]>
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DOI: 10.31603/komtika.v10.i1.16926
<![CDATA[This study aims to compare the performance of the Convolutional Neural Network (CNN) architectures ResNet50 and MobileNetV2 in multi-domain image classification, namely herbal leaf images and facial skin undertone images. The herbal plant dataset consists of 11 classes of leaf images, while the skin undertone dataset includes warm, cool, and neutral categories obtained from secondary and primary data sources. The research stages include image preprocessing, model training using transfer learning, and performance evaluation using accuracy, precision, recall, F1-score, and confusion matrix. The results show that ResNet50 achieved better performance than MobileNetV2 in both classification domains. In herbal plant classification, ResNet50 achieved an accuracy of 95.00%, while MobileNetV2 obtained 94.09%. In facial skin undertone classification, ResNet50 achieved an accuracy of 89.3% and a weighted F1-score of 0.893, whereas MobileNetV2 achieved an accuracy of 68.3% and a weighted F1-score of 0.684. These findings indicate that ResNet50 is more effective and stable than MobileNetV2 for multi-domain image classification.]]>
<![CDATA[Integrasi Fitur Notifikasi Pemesanan untuk Meningkatkan Pelayanan Pengguna pada E-Commerce Batik Wistara ]]>
<![CDATA[Sutanto, Aqila Murodah]]>;
<![CDATA[Rizky Parlika]]>;
<![CDATA[Teguh Sutanto]]>
<![CDATA[ Jurnal Komtika (Komputasi dan Informatika) Vol. 10 No. 1 (2026) ]]>
Publisher : <![CDATA[Universitas Muhammadiyah Magelang ]]>
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DOI: 10.31603/komtika.v10.i1.15484
<![CDATA[Batik Wistara is a Micro, Small, and Medium Enterprise (MSME) that sells various types of fabrics both offline and through its e-commerce website. In practice, the admin often struggles to keep up with online orders because most of the work is still centered on offline sales. As a result, some orders are noticed late, which slows down the response to customers and affects service quality. To help overcome this problem, this study develops an automatic notification system that connects directly to WhatsApp and Telegram using an API. With this system, every new order is sent to the admin in real time, allowing them to immediately see and respond to incoming transactions. The development process follows the waterfall method, starting from identifying needs, designing the API flow, implementing the system, and finally testing its performance. The results show that this feature makes the admin’s workflow more efficient because online orders are no longer dependent on manually checking the dashboard. Notifications arrive instantly, helping the admin maintain service consistency and avoid missed orders. This study also offers practical insight for other MSMEs that want to strengthen their e-commerce operations by adopting API-based notification solutions]]>
<![CDATA[Prediksi Risiko Depresi Pada Remaja Berdasarkan Frekuensi Penggunaan Media Sosial Yang Tinggi Menggunakan SMOTE dan Algoritma Naive Bayes ]]>
<![CDATA[Margi Santoso, Dian]]>;
<![CDATA[Kusnia, Ulfah]]>;
<![CDATA[Arif Faizin]]>
<![CDATA[ Jurnal Komtika (Komputasi dan Informatika) Vol. 10 No. 1 (2026) ]]>
Publisher : <![CDATA[Universitas Muhammadiyah Magelang ]]>
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DOI: 10.31603/komtika.v10.i1.17504
<![CDATA[The increased use of social media among adolescents has been identified as a suspected contributing factor to the emergence of mental health issues, including depression. However, identifying depression risk in adolescents remains challenging, primarily due to data imbalanced, which can affect the performance of classification models. This study aims to build a classification model for adolescent depression risk based on social media usage frequency using the naive bayes algorithm, and compare classification performance before and after applying SMOTE based on accuracy, precision, recall, and F1-Score. This study utilized the Teen Mental Health Dataset obtained from Kaggle. The research stages include data pre-processing, feature and target separation, splitting the data into training and testing sets with an 80:20 ratio. Applying the SMOTE method to the training data, and classification using the naïve bayes algorithm. Model evaluation was conducted using a confusion matrix. The test result showed that the model without SMOTE achieved an accuracy of 99%, precision of 100%, recall of 50%, and an F1-Score 67%. Meanwhile, the model with the application of SMOTE achieved an accuracy 97%, precision of 50%, recall of 33%, and an F1-Score of 40%. Based on the research results, the naïve bayes algorithm is capable of providing excellent classification performance in prediction the risk of depression in adolescents. The application of SMOTE successfully balanced the class distribution in the training data, but did not provide a significant performance improvement compared to the model without SMOTE. Therefore, the naïve bayes model without SMOTE is the best approach for the dataset used in this study.]]>
