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[Implementasi Support Vector Machine dan Resampling dalam Analisis Ulasan Pengguna Google Maps ]]>
<![CDATA[Khultsum, Umi]]>;
<![CDATA[Rahmawati, Eka]]>;
<![CDATA[Rahmawati, Annida]]>;
<![CDATA[Annajib, Barra Rifki]]>;
<![CDATA[Anggita, Christina Yuli]]>
<![CDATA[ Jurnal Komtika (Komputasi dan Informatika) Vol 9 No 2 (2025) ]]>
Publisher : <![CDATA[Universitas Muhammadiyah Magelang ]]>
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DOI: 10.31603/komtika.v9i2.14813
<![CDATA[The development of information technology has driven the increasing use of digital services such as Google Maps, which functions not only as a navigation tool but also as a platform for users to provide reviews. These reviews serve as an important data source for sentiment analysis; however, they are often unstructured and contain noise. This study aims to conduct sentiment analysis using the Support Vector Machine (SVM) model with the application of resampling techniques to address data imbalance issues in user reviews of the Google Maps application. A total of 1,000 recent reviews were collected through a scraping process, followed by data cleaning (lowercasing, stopwords removal, stemming, and lemmatization) and data preprocessing. The SVM model combined with resampling techniques was then implemented and evaluated using accuracy, precision, and recall metrics. The results indicate that the SVM model achieved an accuracy of 81%, with a weighted average precision of 0.79, recall of 0.81, and F1-score of 0.76. These findings demonstrate that applying resampling techniques to SVM yields good performance in sentiment classification. The study is expected to contribute to the development of sentiment analysis methods using the SVM model with resampling in the context of Google Maps reviews.]]>
<![CDATA[Desain Sistem Gamifikasi Digital Berbasis Octalysis dan MDA Framework untuk Promosi Pariwisata Kota Surakarta ]]>
<![CDATA[Agustina, Candra]]>;
<![CDATA[Rahmawati, Eka]]>;
<![CDATA[Permata, Afifta Ilham]]>;
<![CDATA[Laily, Isnaini Mufidatul]]>
<![CDATA[ Jurnal Komtika (Komputasi dan Informatika) Vol 9 No 2 (2025) ]]>
Publisher : <![CDATA[Universitas Muhammadiyah Magelang ]]>
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DOI: 10.31603/komtika.v9i2.14820
<![CDATA[Tourism plays a strategic role in driving local economic growth and preserving culture. However, the City of Surakarta still faces challenges such as the low length of stay of tourists and the lack of connectivity between destinations. This study aims to design a digital gamification strategy as an innovative approach to enhance the competitiveness of regional tourism. The research method used is mixed methods, combining qualitative and quantitative approaches. Data were collected through field observations, in-depth interviews with tourism stakeholders, surveys distributed to tourists, and trials of web- or mobile-based gamification applications. Qualitative data were analyzed using a thematic approach, while quantitative data were examined using descriptive and inferential statistics. The theoretical foundations applied are the Octalysis Framework and the MDA Framework (Mechanics–Dynamics–Aesthetics) to design game elements such as points, badges, challenges, and leaderboards in tourism promotion. The expected outcome is the development of a contextual and applicable digital gamification strategy model that can increase tourist engagement, extend the length of stay, and more evenly distribute economic benefits across the tourism ecosystem. This research contributes both academically and practically, serving as a guideline for local governments and tourism industry players to adopt interactive and sustainable digital strategies.]]>
<![CDATA[Pendekatan Hibrida Statistik dan Machine Learning untuk Peramalan Jumlah Kunjungan Turis ]]>
<![CDATA[Leidiyana, Henny]]>;
<![CDATA[Nurajizah, Siti]]>
<![CDATA[ Jurnal Komtika (Komputasi dan Informatika) Vol 9 No 2 (2025) ]]>
Publisher : <![CDATA[Universitas Muhammadiyah Magelang ]]>
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DOI: 10.31603/komtika.v9i2.14909
<![CDATA[Tourist arrival forecasting is a crucial aspect of planning and decision-making in the tourism sector. Accurate predictions are essential to anticipate surges or declines in visitor numbers, design effective marketing strategies, and manage resources efficiently. This study proposes a hybrid forecasting approach that integrates traditional statistical methods with machine learning algorithms to improve the accuracy of tourist arrival forecasts. Five forecasting models are implemented: ARIMA as a representative of traditional statistical models; Random Forest and Extreme Gradient Boosting (XGBoost) as machine learning models; a simple hybrid model, which combines ARIMA and XGBoost predictions through simple averaging; a weighted hybrid model, which merges the two models using performance-based weights; and a stacking hybrid model, which utilizes a meta-model to optimize prediction combinations. Given that the dataset exhibits significant pattern changes, or structural breaks, particularly during the COVID-19 pandemic, this study employs a rolling window backtesting approach for model evaluation. This method allows the models to be tested progressively across normal, crisis, and recovery periods, providing a realistic assessment of their performance under dynamic conditions. Model performance is evaluated using three key metrics: RMSE, MAE, dan MAPE. The results demonstrate that the stacking hybrid model consistently achieves the lowest RMSE across all test periods, highlighting its ability to capture complex trends and extreme fluctuations caused by COVID-19 Keywords: Rolling Window Backtesting, Weighted Hybrid, Weighted Hybrid.]]>
<![CDATA[Perbandingan Metode Reduksi Noise pada Citra Digital Berbasis Website ]]>
<![CDATA[Salsabilla, Dea]]>;
<![CDATA[Wahyusari, Retno]]>
<![CDATA[ Jurnal Komtika (Komputasi dan Informatika) Vol 9 No 2 (2025) ]]>
Publisher : <![CDATA[Universitas Muhammadiyah Magelang ]]>
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DOI: 10.31603/komtika.v9i2.14966
<![CDATA[Citra digital memiliki peran penting dalam penyampaian informasi visual, namun kualitasnya sering menurun akibat gangguan berupa noise. Penelitian ini membahas tiga jenis noise utama, yaitu salt and pepper, gaussian, dan speckle, yang direduksi menggunakan tiga metode filter, yaitu median, gaussian, dan bilateral. Pengujian dilakukan dengan menerapkan setiap filter terhadap citra uji yang terkontaminasi berbagai jenis noise untuk mengevaluasi performa berdasarkan nilai Mean Squared Error (MSE) dan Peak Signal-to-Noise Ratio (PSNR). Hasil menunjukkan bahwa filter median secara konsisten memberikan performa terbaik pada ketiga jenis noise, dengan nilai MSE terendah sebesar 7,007 dan PSNR tertinggi 39,675 dB pada noise salt and pepper, MSE 72,046 dan PSNR 29,554 dB pada noise gaussian, serta MSE 60,917 dan PSNR 30,283 dB pada noise speckle. Sementara itu, filter gaussian dan bilateral menghasilkan nilai yang relatif dekat, namun tidak melampaui performa filter median. Kombinasi beberapa filter juga tidak selalu meningkatkan kualitas citra secara signifikan. Dengan demikian, dapat disimpulkan bahwa filter median merupakan metode reduksi noise paling efektif secara umum dalam mempertahankan kualitas citra digital terhadap berbagai jenis noise.]]>
<![CDATA[Analisis Sentimen Ulasan Pengguna Indonesia terhadap Platform Pembelajaran Digital Ruangguru Menggunakan Algoritma Leksikal Multilingual ]]>
<![CDATA[Rullyana, Gema]]>;
<![CDATA[Triandari, Rizki]]>
<![CDATA[ Jurnal Komtika (Komputasi dan Informatika) Vol 9 No 2 (2025) ]]>
Publisher : <![CDATA[Universitas Muhammadiyah Magelang ]]>
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DOI: 10.31603/komtika.v9i2.15110
<![CDATA[Penelitian ini bertujuan untuk mengidentifikasi sentimen pengguna terhadap Ruangguru, salah satu platform pembelajaran digital paling populer di Indonesia. Metode yang digunakan bersifat kualitatif dengan pendekatan analisis sentimen menggunakan Orange Data Mining. Data yang dianalisis terdiri dari 1.913 ulasan pengguna aplikasi Ruangguru yang dikumpulkan dari Google Play Store dalam rentang waktu 1 Januari 2024 hingga 1 Januari 2025. Proses analisis mencakup tahapan praproses, analisis frekuensi kata, visualisasi, serta klasifikasi berdasarkan polaritas sentimen. Kata-kata yang paling sering muncul antara lain “bagus”, “belajar”, dan “aplikasi”, yang menunjukkan fokus pengguna pada kinerja aplikasi dan pengalaman belajar. Hasil penelitian menunjukkan bahwa 1.091 ulasan (57,02%) diklasifikasikan sebagai positif, mencerminkan kepuasan terhadap fitur, kualitas konten, dan kemudahan penggunaan. Sementara itu, 679 ulasan (35,49%) bersifat netral, dan hanya 143 ulasan (7,47%) bersifat negatif. Visualisasi word cloud, scatter plot, dan histogram sentimen memperkuat temuan tersebut, dengan distribusi emosi yang didominasi oleh joy dan surprise. Temuan ini menyiratkan bahwa mayoritas pengguna memiliki pengalaman yang positif secara kognitif maupun emosional saat menggunakan aplikasi Ruangguru. Implikasi dari temuan ini menggarisbawahi pentingnya bagi pengembang aplikasi pembelajaran digital untuk mempriotaskan optimalisasi performa teknis dan peningkatan fitur interaktif berbasis pengalaman pengguna. Hal tersebut tidak hanya krusial untuk menjaga tingkat kepuasan pengguna yang telah terbentuk, tetapi juga esensial dalam membangun retensi dan loyalitas jangka panjang dalam ekosistem pembelajaran digital yang semakin kompetitif.]]>
<![CDATA[A Comparative Analysis of Univariate and Multivariate LSTM Models for Nokia (NOK) Stock Price Prediction ]]>
<![CDATA[Saputra, Roni]]>;
<![CDATA[Martanto, Martanto]]>;
<![CDATA[Dana, Raditya Danar]]>
<![CDATA[ Jurnal Komtika (Komputasi dan Informatika) Vol 9 No 2 (2025) ]]>
Publisher : <![CDATA[Universitas Muhammadiyah Magelang ]]>
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DOI: 10.31603/komtika.v9i2.15152
<![CDATA[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.]]>
<![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.]]>
