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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A Comparative Analysis of Univariate and Multivariate LSTM Models for Nokia (NOK) Stock Price Prediction
Saputra, Roni;
Martanto, Martanto;
Dana, Raditya Danar
Jurnal Komtika (Komputasi dan Informatika) Vol. 9 No. 2 (2025)
Publisher : Universitas Muhammadiyah Magelang
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DOI: 10.31603/komtika.v9i2.15152
Predicting stock prices is a challenging yet crucial task in financial markets. This research aims to compare the performance of two Long Short-Term Memory (LSTM) neural network models for forecasting the closing price of Nokia Corporation (NOK) stock: a univariate model using only historical closing prices and a multivariate model incorporating open, high, low, close, and volume (OHLCV) data. Utilizing historical daily data from 2015 to 2025, both models were trained to predict the next day's price based on the previous 60 days. The models' accuracy was rigorously evaluated using three key metrics: Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). The findings revealed a decisive outcome. The univariate LSTM model consistently outperformed its multivariate counterpart across all evaluation metrics. It achieved an MAE of 0.0591, an RMSE of 0.0887, and a MAPE of 1.39%, while the multivariate model recorded higher values of 0.0623, 0.0934, and 1.45%, respectively. This study concludes that for NOK stock prediction, a simpler model with fewer features proved to be more effective. The additional data points in the multivariate model did not enhance predictive accuracy and may have introduced noise, suggesting that the historical pattern of closing prices alone is a more powerful predictor for this particular asset.
Monitoring dan Pemberian Pakan Ikan Lele Otomatis berbasis Internet of Things (IoT) di Tambak Good's Lele
Putra, Nyoman Adi Andrian Kusuma;
Paramartha Putra, Made Adi;
Noviyanti Kusuma, Ni Putu
Jurnal Komtika (Komputasi dan Informatika) Vol. 9 No. 2 (2025)
Publisher : Universitas Muhammadiyah Magelang
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DOI: 10.31603/komtika.v9i2.15171
Budidaya ikan lele merupakan sektor potensial dalam memenuhi kebutuhan konsumsi masyarakat. Namun, metode pemberian pakan manual sering menyebabkan ketidakteraturan dan memicu kanibalisme, yang menurunkan produktivitas. Tambak Good’s Lele di Batubulan, Sukawati, Gianyar, masih menggunakan metode manual sehingga diperlukan sistem otomatis untuk meningkatkan efisiensi. Pengembangan sistem ini memanfaatkan microcontroller ESP32 dan dilengkapi dengan berbagai sensor seperti sensor suhu (DS18B20), sensor pH, turbidity sensor, ultrasonic, dan loadcell. Sistem ini mampu memantau kualitas air serta mendeteksi tinggi dan berat pakan dalam wadah. Ketika kondisi terdeteksi sesuai, mekanisme pemberian pakan akan diaktifkan secara otomatis menggunakan motor servo dan motor DC. Data hasil pemantauan ditampilkan melalui LCD 20x4 I2C serta dikirimkan ke antarmuka website yang dapat diakses melalui perangkat seperti laptop atau smartphone. Hasil akhir dari proyek ini adalah sebuah sistem yang terintegrasi dan dapat bekerja secara otomatis serta manual melalui antarmuka website. Sistem ini memungkinkan pengawasan dan pemberian pakan ikan secara tepat waktu dan efisien. Selain itu, sistem ini juga diharapkan dapat membantu meningkatkan produktivitas tambak dan mendukung pengembangan teknologi di sektor perikanan berbasis IoT