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JURIKOM (Jurnal Riset Komputer)
Published by Sekolah Tinggi Manajemen Informatika dan Komputer Budi Darma
ISSN : 2407389X     EISSN : 27157393     DOI : http://dx.doi.org/10.30865/jurikom.v7i1.1927
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering
JURIKOM (Jurnal Riset Komputer) membahas ilmu dibidang Informatika, Sistem Informasi, Manajemen Informatika, DSS, AI, ES, Jaringan, sebagai wadah dalam menuangkan hasil penelitian baik secara konseptual maupun teknis yang berkaitan dengan Teknologi Informatika dan Komputer. Topik utama yang diterbitkan mencakup: 1. Teknik Informatika 2. Sistem Informasi 3. Sistem Pendukung Keputusan 4. Sistem Pakar 5. Kecerdasan Buatan 6. Manajemen Informasi 7. Data Mining 8. Big Data 9. Jaringan Komputer 10. Dan lain-lain (topik lainnya yang berhubungan dengan Teknologi Informati dan komputer)
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 10 Documents
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Search results for , issue "Vol. 12 No. 5 (2025): Oktober 2025" : 10 Documents clear
Perbandingan Model Decision Tree dan Random Forest untuk Penentuan Kesesuaian Lahan Budidaya Cabai dan Terong Amir, Astiah; Fachruddin, Fachruddin; Idris, Fadli; Safriani, Meylis; Saefuddin, Reskiana; Nasution, Indera Sakti; Sanusi, Sanusi; Arisma Siregar, Mawaddah Putri
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 5 (2025): Oktober 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i5.8672

Abstract

Kabupaten Aceh Barat memiliki potensi besar dalam budidaya tanaman hortikultura seperti cabai dan terong, meskipun karakteristik tanah gambut dengan tingkat keasaman tinggi dan variabilitas lingkungan menjadi tantangan utama. Penentuan kesesuaian lahan yang akurat memerlukan analisis berbagai variabel seperti pH tanah, kelembaban tanah dan udara, curah hujan, serta tekstur tanah. Penelitian ini bertujuan mengembangkan model klasifikasi kesesuaian lahan menggunakan algoritma Decision Tree dan Random Forest untuk tanaman cabai dan terong di wilayah tersebut. Data lingkungan dan karakteristik tanah dianalisis menggunakan kedua metode tersebut untuk mengevaluasi performa klasifikasi. Hasil penelitian menunjukkan bahwa algoritma Random Forest unggul dengan akurasi mencapai 99% pada klasifikasi lahan cabai, serta nilai precision dan recall yang lebih tinggi dibandingkan Decision Tree. Untuk klasifikasi lahan terong, kedua algoritma menunjukkan performa sempurna dengan akurasi dan metrik evaluasi mencapai 1.00 tanpa kesalahan klasifikasi. Keunggulan Random Forest terletak pada kemampuannya menangani variabel input yang kompleks dan mengurangi risiko overfitting melalui ensemble pohon keputusan, sehingga menghasilkan prediksi yang lebih stabil dan andal. Dengan demikian, Random Forest sangat cocok digunakan dalam sistem klasifikasi kesesuaian lahan berbasis data lingkungan di Aceh Barat, mendukung pengambilan keputusan budidaya yang lebih optimal dan berkelanjutan. Penelitian ini memberikan kontribusi penting dalam penerapan teknologi machine learning untuk meningkatkan efisiensi dan hasil produksi pertanian di wilayah dengan karakteristik tanah gambut yang menantang.Abstract. Kabupaten Aceh Barat has great potential for cultivating horticultural crops such as chili peppers and eggplants, despite the challenges posed by peat soil characteristics with high acidity levels and environmental variability. Accurate land suitability determination requires analysis of various variables such as soil pH, soil and air moisture, rainfall, and soil texture. This study aims to develop land suitability classification models using Decision Tree and Random Forest algorithms for chili and eggplant crops in the region. Environmental data and soil characteristics were analyzed using both methods to evaluate classification performance. The results show that the Random Forest algorithm outperforms with an accuracy of up to 99% in chili land classification, as well as higher precision and recall values compared to Decision Tree. For eggplant land classification, both algorithms demonstrated perfect performance with accuracy and evaluation metrics reaching 1.00 without any misclassification. The advantage of Random Forest lies in its ability to handle complex input variables and reduce the risk of overfitting through ensemble decision trees, resulting in more stable and reliable predictions. Therefore, Random Forest is highly suitable for use in land suitability classification systems based on environmental data in West Aceh, supporting more optimal and sustainable cultivation decision-making. This study makes an important contribution to the application of machine learning technology to improve agricultural efficiency and production outcomes in regions with challenging peat soil characteristics.
Implementasi Algoritma C4.5 pada Analisis Faktor Risiko Penyakit Jantung Koroner Yestina, Elsa Adinda; Prakoso, Bakhtiyar Hadi; Selviyanti, Erna; Suyoso, Gandu Eko Julianto
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 5 (2025): Oktober 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i5.8852

Abstract

Coronary heart disease is a non-communicable disease due to the process of atherosclerosis resulting in blockage or narrowing of the coronary blood vessels which causes reduced oxygen supply to the heart. In the morbidity and mortality data at RSD dr. Soebandi Jember from 2020-2024, there were fluctuations. In terms of age, the majority of sufferers are productive aged between 15-64 years. The impact that has occurred is an increade in cases of coronary heart disease which is getting higher and decreasing productivity at a productive age. The purpose of this studywas to analyze risk factors for coronary heart disease based on medical records of inpatients using the C4.5 algorithm at RSD dr. Soebandi Jember. This research uses the C4.5 algorithm method with RapidMiner tools and impelents k-fold cross validation. The selection of the C4.5 algorithm is because is produces classification basen on the gain ratio value so that the classification results can be analyzed for risk factor attributes. Determination of risk factors is known in the decision results in the form of classification rules. The implementation k-fold cross validation produces the highest accuracy at k=8, namely accuracy of 86,09%, precision of 82,63%, and recall of 91,39%. Based on the results, diabetes mellitus is the most influential risk factor for coronary heart disease because it has the highest gain ratio value. Other risk factors are physical inactivity, gender, obesity, hugh blood pressure, age, and smoking. Suggestions for dr. Soebandi Hospital are to improve Communication, Information, Education (IEC), especially for diabetes mellitus patients because it affects coronary heart disease.
Analysis of the K-Nearest Neighbor (KNN) Algorithm for Gender Classification Based on Voice Characteristics Hutagalung, Bintang; Sriani, Sriani
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 5 (2025): Oktober 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i5.9117

Abstract

The gender recognition system based on voice still faces challenges such as dependence on MFCC (Mel Frequency Cepstral Coefficients) features, which are not yet able to fully represent the complexity of human voice patterns. To overcome this, this study uses 20 voice characteristics and the K-Nearest Neighbor (KNN) algorithm because it is non-parametric, capable of handling non-linear relationships between features, and works intuitively by grouping data based on similarity of distance in the feature space, making it suitable for voice patterns that are not always linearly distributed. The purpose of this study is to analyze and develop a KNN model in classifying gender based on voice characteristics. Based on testing 50 variations of K values using K-Fold Cross Validation and Euclidean Distance, the evaluation results at K = 3, 5, and 7 showed average accuracies of 0.9740, 0.9700, and 0.9712. K = 3 was selected as the optimal parameter because it produced the highest accuracy. The results show that testing on 634 test data samples using K = 3 produced 619 correct predictions and 15 incorrect predictions, with an accuracy of 98% (0.9740), as well as precision, recall, and F1-score for the Female class of 0.98, 0.97, and 0.98, while for the Male class they were 0.97, 0.98, and 0.98.
Perbandingan Kinerja SVM, Random Forest dan XGBoost pada Aplikasi Access by KAI Menggunakkan ADASYN Epriyanti, Nadia; Meiriza, Allsela; Yunika Hardiyanti, Dinna
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 5 (2025): Oktober 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i5.9139

Abstract

The rapid growth of digital applications has heightened the need to understand user perceptions more thoroughly, particularlythrough sentiment analysis of user-generated reviews. In practice, sentiment classification often faces challenges related to class imbalance, especially when neutral reviews are significantly fewer than positive or negative ones. This imbalance can limit a model’s ability to accurately detect all sentiment categories. This study examines the comparative performance of three machine learning algorithmsSupport Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) by applying the Adaptive Synthetic Sampling (ADASYN) technique to address class imbalance. This study differs from previous similar research by conducting a simultaneous comparative analysis of three algorithms using the ADASYN method in the context of Access by KAIapplication reviews, which has not been examined in prior studies. Experimental results indicate that after implementing ADASYN, model accuracies reached 75.17% for SVM, 84.06% for RF, and 83.17% for XGBoost. Although accuracy slightly decreased after oversampling, the F1-scores for the neutral class improved to 0.13 (SVM), 0.05 (RF), and 0.14 (XGBoost). Before applying ADASYN, the models achieved accuracies of 85.88% (SVM), 85.13% (RF), and 85.37% (XGBoost), but they were unable to effectivelyrecognize neutral sentiments, with F1-scores of 0.00 for SVM and RF, and 0.03 for XGBoost. These findings suggest that ADASYN enhances model sensitivity to neutral sentiment, with XGBoost demonstrating the most consistent and robust performance in sentiment classification for the Access by KAIapplication.
Perbandingan Kinerja LSTM, Random Forest, dan SVR Berbasis Knowledge Discovery untuk Prediksi Harga Beras Sumatera Selatan Bahri, Cheisya Andini; Tania, Ken Ditha
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 5 (2025): Oktober 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i5.9140

Abstract

Rice is a primary staple food in Indonesia, particularly in South Sumatra Province. In February 2024, BBC News Indonesia reported that the price of premium rice surged to Rp18,000 per kilogram, marking the highest price in the country’s history. To anticipate and predict similar spikes in the future, this study applies a Knowledge Discovery approach and compares three machine learning models: LSTM, Random Forest, and SVR. The approach follows the stages of data selection, cleaning, transformation, modeling, and evaluation to uncover hidden patterns in historical data. The dataset, obtained from the official PIHPS Nasional website, consists of 1,412 daily rice price records from January 2020 to May 2025. Model performance was evaluated using MAPE, MAE, and RMSE metrics. The findings indicate that the SVR model outperformed LSTM and Random Forest, delivering the most accurate results. For the Super Quality II rice category, SVR achieved a MAPE of 0.00 percent, MAE of 40.93, and RMSE of 52.54. SVR also consistently produced the lowest prediction errors in other categories, such as Low Quality I (MAE 59.39) and Medium Quality I (MAE 38.92). This research is expected to serve as a foundation for developing machine learning–based food price monitoring systems to support more responsive policies and maintain rice price stability in the future.
Public Opinion Sentiment Analysis of the Brain Drain Phenomenon on Social Media X Using the Naive Bayes Classifier Algorithm Hidayati, Risma; Hasugian, Abdul Halim
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 5 (2025): Oktober 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i5.9131

Abstract

The brain drain phenomenon is an important issue in Indonesia due to the increasing number of young professionals choosing to work abroad, which reduces the quality of human resources within the country. This study aims to analyze public opinion toward the brain drain phenomenon through the X (Twitter) social media platform and classify public sentiment using the Naive Bayes Classifier algorithm. Data were collected through a web crawling process within the last two years, resulting in 1,170 relevant Indonesian-language tweets. The preprocessing stage included cleaning, case folding, tokenizing, normalization, stopword removal, and stemming to produce clean and structured data. Word weighting was performed using the Term Frequency–Inverse Document Frequency (TF-IDF) method to measure the significance of each term. The findings show that public opinion is divided into two main sentiments: positive and negative. Positive sentiment reflects the perception that working abroad offers career advancement and experience, while negative sentiment expresses concern about the loss of skilled human resources. The classification model achieved a high level of accuracy in categorizing sentiment data. This research contributes to understanding public perceptions and provides a foundation for developing strategic policies to address the brain drain issue in Indonesia
Analisis Prediktif Ketahanan Pangan Berbasis Data Spasial Dengan Metode Random Forest Dan Cellular Automata Di Provinsi Nusa Tenggara Timur Butar-Butar, Yulia Shafira; Opim Salim Sitompul; Amalia Amalia
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 5 (2025): Oktober 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i5.9234

Abstract

Food security remains a key concern in sustainable development, especially in regions like East Nusa Tenggara (NTT) that are prone to drought and land conversion. This study aims to explore future food security in NTT by applying spatial data and predictive models to forecast conditions in 2030. Two main approaches were used: the Cellular Automata–Artificial Neural Network (CA–ANN) model to simulate land cover changes, and the Random Forest Regressor to predict rice productivity using environmental variables such as NDVI, land surface temperature, rainfall, elevation, and slope. The CA–ANN model showed strong spatial accuracy at 87.6%, with results indicating a decrease in cropland in several areas. The Random Forest model performed well with an R² of 0.90 and RMSE of 1.74, highlighting elevation and temperature as key drivers of productivity. By 2030, projections suggest a rice deficit of 221,000 tons, equivalent to more than 790 billion kilocalories. These findings underscore the urgency for local governments to adopt data-driven approaches when planning for sustainable food security in the years ahead.
The Aplikasi Model Text Area Based Image Selective Encryption Menggunakan YoloV3, Arnold's Catmap dan AES Pada Pengamanan Konten Teks Pada Citra Digital Riza, Ferdy; Azhari, Mulkan; Zulherry, Andi
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 5 (2025): Oktober 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i5.9261

Abstract

sensitive content in digital images. This research proposes a selective encryption model based on text area detection in digital images, integrating object detection using You Only Look Once version 3 (YOLOv3), Arnold's Cat Map transformation, and the Advanced Encryption Standard (AES) algorithm. The model automatically identifies and selects areas containing text in the image using YOLOv3, applies Arnold's Cat Map for spatial disorganization, and then encrypts the transformed result with AES to ensure data security. System performance is evaluated through visual quality analysis using PSNR (Peak Signal-to-Noise Ratio) and SSIM (Structural Similarity Index) parameters, as well as encryption and decryption processing time. The test results show that this approach can maintain the integrity of non-text areas while providing strong protection for sensitive text areas without compromising efficiency or overall visual quality. This model has the potential to be applied in the context of securing digital documents, visual identities, and other sensitive data in images.
Optimalisasi Promosi dan Pemasaran Kebaya Secara Digital Berbasis Web Melalui Teknik SEO dan Penggunaan Chatbot AI pada Butik XYZ Ayuningsih, Ekatri; Husna, Asmaul; Meuzaki, Ryezi; Panggabean, Jan Kevin
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 5 (2025): Oktober 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

This study aims to optimize the promotion and marketing of kebaya digitally by applying Search Engine Optimization (SEO) techniques and the use of AI Chatbot in a web-based application. The main problem faced by Butik Putri is the rapid development of digital technology that pushes kebaya entrepreneurs to adapt their promotional and marketing strategies online. The advancement of digital technology and intence business competition drives growth in Indonesia’s fashion Industry, especially kebaya products, thus making digital marketing crucial to reach a broader and modern market. Butik Putri is a brand engaged in kebaya fashion. Currently, Butik Putri experiences limitations in digital marketing, which a restricted to the WhatsApp platform and lacks optimal service responsiveness. The proposed solution is to develop a web-based application by implementing Search Engine Optimization (SEO) strategies and AI Chatbot. This study aims to design and build a promotional website a modern, responsive interface that supports customer interaction through the application of Tailwind CSS and AI Chatbot integration. Additionally, Search Engine Optimization (SEO) techniques are applied to improve website visibility on Search Engines. The research method uses a qualitative approach, involving the Software Development Life Cycle (SDLC) method with prototype and flowchart models. The results show that the implementation of a Tailwind CSS-based website with AI Chatbot features can increase the volume of kebaya sales through both digital platforms and physical stores, strengthen the brand, and enhance real-time responsiveness and interaction with website visitors by providing 24-hour interactive service
Analisis Website Butik XYZ Menggunakan Google Search Console dan Google Analytics Ayuningsih, Ekatri; Husna, Asmaul; Meuzaki, Ryezi; Panggabean, Jan Kevin
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 5 (2025): Oktober 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Butik Putri is a business in the fashion sector that sells its products through a website. A website is an application that contains various types of multimedia documents such as text, images, sound, animation, and video, used to provide information and introduce a product to the public broadly. To optimally increase the number of website visitors, an evaluation needs to be conducted by analyzing the website’s performance and visitor characteristics. The purpose of this study is to analyze the performance of the website and the characteristics of visitors to the Butik Putri website. The method used is a descriptive method. The research population consists of website visitors on the site Butik Putri from June to September 2025, totaling 620 people. Data collection techniques use secondary data from Google Search Console (GSC) and Google Analytics (GA). The results show that the website’s performance on search engine averages 2,2. The highest keyword search is “Butik Putri”. Indonesia is the country that accesses the website the most. Visitor characteristics increased with a total of 620 visitors. The most used browser is Chrome at 61,11%, the operating systems is Windows at 55,42%, and the device used is desktop at 61,11%. The website’s performance improved after adding Google Search Console (GSC) to the Butik Putri website. Along with that, visitor characteristics from Google Analytics (GA) data also increased but then decreased in the following month. To keep visitors continually increasing, website content must be continuously updated.

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