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Jurnal Komtika (Komputasi dan Informatika)
Published by Universitas Muhammadiyah Magelang
ISSN : 25802852     EISSN : 2580734X     DOI : https://doi.org/10.31603/komtika
Core Subject : Science, Engineering,
Computer Science & IT Engineering
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
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 152 Documents
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Integrasi Fitur Notifikasi Pemesanan untuk Meningkatkan Pelayanan Pengguna pada E-Commerce Batik Wistara Sutanto, Aqila Murodah; Rizky Parlika; Teguh Sutanto
Jurnal Komtika (Komputasi dan Informatika) Vol. 10 No. 1 (2026)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/komtika.v10.i1.15484

Abstract

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
Prediksi Risiko Depresi Pada Remaja Berdasarkan Frekuensi Penggunaan Media Sosial Yang Tinggi Menggunakan SMOTE dan Algoritma Naive Bayes Margi Santoso, Dian; Kusnia, Ulfah; Arif Faizin
Jurnal Komtika (Komputasi dan Informatika) Vol. 10 No. 1 (2026)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/komtika.v10.i1.17504

Abstract

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.

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