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
Articles
144 Documents
<![CDATA[Monitoring dan Pemberian Pakan Ikan Lele Otomatis berbasis Internet of Things (IoT) di Tambak Good's Lele ]]>
<![CDATA[Putra, Nyoman Adi Andrian Kusuma]]>;
<![CDATA[Paramartha Putra, Made Adi]]>;
<![CDATA[Noviyanti Kusuma, Ni Putu]]>
<![CDATA[ Jurnal Komtika (Komputasi dan Informatika) Vol 9 No 2 (2025) ]]>
Publisher : <![CDATA[Universitas Muhammadiyah Magelang ]]>
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DOI: 10.31603/komtika.v9i2.15171
<![CDATA[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]]>
<![CDATA[Implementasi Algoritma Random Forest Berbasis Machine Learning Untuk Prediksi Klon Kopi Unggul ]]>
<![CDATA[Febriansyah, Febriansyah]]>;
<![CDATA[Nurmaleni, Nurmaleni]]>
<![CDATA[ Jurnal Komtika (Komputasi dan Informatika) Vol 9 No 2 (2025) ]]>
Publisher : <![CDATA[Universitas Muhammadiyah Magelang ]]>
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DOI: 10.31603/komtika.v9i2.15227
<![CDATA[The significant increase in coffee prices in recent years has not been matched by optimized production, particularly in major coffee-producing regions such as Pagar Alam City. One of the main challenges is farmers’ limited capacity to determine the most suitable coffee clone for their environmental conditions. This study aims to develop an intelligent system based on machine learning to predict superior coffee clones that can improve productivity and support food security. The Random Forest algorithm was applied using the CRISP-DM framework, consisting of business understanding, data understanding, data preparation, modelling, evaluation, and deployment stages. The dataset comprised environmental variables (altitude, rainfall, soil pH, soil type, pest resistance, and production) and coffee clone labels (Clone1–Clone4). Experimental results indicate that the model achieved an average accuracy of approximately 75% under 5-fold cross-validation, with altitude and rainfall identified as the most influential factors in clone selection. The predictive system was implemented in Python and can be further developed into web- or mobile-based applications. This study demonstrates the potential of artificial intelligence in optimizing coffee production, enhancing farmers’ welfare. Kenaikan harga kopi yang signifikan dalam beberapa tahun terakhir belum diimbangi dengan produksi yang optimal, terutama di wilayah penghasil kopi utama seperti Kota Pagar Alam. Salah satu tantangan utama adalah keterbatasan kemampuan petani dalam menentukan klon kopi yang paling sesuai dengan kondisi lingkungannya. Penelitian ini bertujuan untuk mengembangkan sistem cerdas berbasis machine learning guna memprediksi klon kopi unggul yang dapat meningkatkan produktivitas dan mendukung ketahanan pangan. Algoritma Random Forest diterapkan dengan menggunakan kerangka kerja CRISP-DM, yang meliputi tahap pemahaman bisnis, pemahaman data, persiapan data, pemodelan, evaluasi, dan penerapan. Dataset yang digunakan mencakup variabel lingkungan seperti ketinggian, curah hujan, pH tanah, jenis tanah, ketahanan terhadap hama, serta data produksi, dengan label klon kopi (klon1–klon4). Hasil eksperimen menunjukkan bahwa model yang dibangun mencapai rata-rata akurasi sekitar 75% menggunakan metode 5-fold cross-validation, dengan ketinggian dan curah hujan teridentifikasi sebagai faktor paling berpengaruh dalam pemilihan klon. Sistem prediksi ini diimplementasikan menggunakan Python dan dapat dikembangkan lebih lanjut menjadi aplikasi berbasis web atau mobile. Penelitian ini menunjukkan potensi kecerdasan buatan dalam mengoptimalkan produksi kopi, meningkatkan kesejahteraan petani, serta memperkuat ketahanan pangan nasional.]]>
<![CDATA[Optimasi Gradient Boosted Trees dalam Memprediksi Minat Nasabah untuk Berlangganan Pinjaman Berjangka ]]>
<![CDATA[Achmad, Refi Riduan]]>;
<![CDATA[Zulfariansyah, Muhammad]]>
<![CDATA[ Jurnal Komtika (Komputasi dan Informatika) Vol 9 No 2 (2025) ]]>
Publisher : <![CDATA[Universitas Muhammadiyah Magelang ]]>
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DOI: 10.31603/komtika.v9i2.15259
<![CDATA[This research focuses on optimizing the design parameters of Gradient Boosted Trees (GBT) to predict customer interest in subscribing to term loans. The study highlights the importance of tuning parameters such as the number of trees, tree depth, and learning rate to enhance the predictive accuracy of GBT. Through this optimization, the model aims to provide more precise insights into customer behavior, aiding financial institutions in making informed decisions and improving operational efficiency. The research compares GBT with other algorithms like Decision Trees and Random Forests, utilizing metrics such as accuracy, precision, recall, and AUC. The results indicate that GBT, with optimal parameter settings, outperforms the other models in predicting customer interest. The study concludes that GBT is an effective tool for market segmentation and can significantly contribute to more accurate predictions in financial services, ultimately helping companies develop better-targeted marketing strategies.]]>
<![CDATA[Segmentasi Berbasis Data Time Series Penjualan Produk Kopi Menggunakan Algoritma K-Means ]]>
<![CDATA[Anggaini, Meri]]>;
<![CDATA[Herlawati, Herlawati]]>;
<![CDATA[Purnomo, Rakhmat]]>
<![CDATA[ Jurnal Komtika (Komputasi dan Informatika) Vol 9 No 2 (2025) ]]>
Publisher : <![CDATA[Universitas Muhammadiyah Magelang ]]>
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DOI: 10.31603/komtika.v9i2.15336
<![CDATA[Coffee shops are businesses in the Food and Beverage (F&B) sector that contribute 7.15% to Indonesia's economy. The high demand for coffee has led to increasingly fierce competition. Kanae Coffee & Space in Bekasi faces challenges in maintaining customer loyalty and managing unpredictable demand. This study aims to apply the K-Means algorithm to cluster coffee products based on time series sales data, using the 6-step CRISP-DM methodology. The number of clusters was determined using the elbow method and confirmed with a silhouette coefficient of 0.5916 (good structure). The analysis resulted in five clusters with distinct characteristics: Cluster 0 (very low demand, stable trend, very high price), Cluster 1 (very high demand but sharply declining trend, very low price), Cluster 2 (moderately high demand, moderately stable trend, moderate price), Cluster 3 (moderate demand, slowly declining trend, moderately high price), and Cluster 4 (low demand, stable trend, moderately low price). These segmentation results are expected to serve as the basis for more effective marketing strategies and product management.]]>
