cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Mesran
Contact Email
mesran.skom.mkom@gmail.com
Phone
+6282161108110
Journal Mail Official
jurikom.stmikbd@gmail.com
Editorial Address
STMIK Budi Darma Jalan Sisingamangaraja No. 338 Simpang Limun Medan - Sumatera Utara
Location
Kota medan,
Sumatera utara
INDONESIA
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 44 Documents
 1   2   3   4   5 
Search results for , issue "Vol. 12 No. 4 (2025): Agustus 2025" : 44 Documents clear
Identifikasi Penyakit Diabetes Mellitus Menggunakan Algoritma Support Vector Machine dan Random Forest Agusti, Anggi Renata; Fauzi, Ahmad; Baihaqi, Kiki Ahmad; Rohana, Tatang
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 4 (2025): Agustus 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i4.8686

Abstract

Diabetes mellitus is a chronic metabolic disease that is increasingly common in Indonesia, estimated to affect more than 10.8 million people in 2020. This disease needs to be recognized early to prevent serious complications that can increase morbidity and mortality. By comparing the two methods, this study was conducted to determine whether one approach shows a better level of accuracy and to develop a classification model based on patient data. The research data was provided by the Anggadita Health Center which includes demographic data, lifestyle, and health assessment results from 1001 patients. One of the research steps is data pre-processing to evaluation. SVM and RF modeling can evaluate models using accuracy, precision, recall, and F1-score metrics. Based on the test results, the Random Forest algorithm showed the best performance with an accuracy of 99%, precision of 99%, recall of 100%, and F1-score of 99%, while SVM got an accuracy of 91%, precision of 0.93%, recall of 0.91%, and F1-score of 0.92%. This shows how well Random Forest separates patients with and without diabetes. This study is expected to be one of the references in obtaining information for making medical decision support systems so that health workers can be faster and more accurate in diagnosing diabetes mellitus.
Perbandingan MobileNetV2, DenseNet121, InceptionV3, dan Xception pada Klasifikasi Citra Panel Surya Bersih dan Berdebu Nugroho, Aswin Mulyo; Mustafidah, Hindayati; Fitriani, Maulida Ayu; Supriyono, Supriyono
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 4 (2025): Agustus 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i4.8688

Abstract

The buildup of dust on solar panels can greatly diminish energy output, lower system efficiency, and raise operational expenses. A productive way to tackle this problem is to utilize image classification through Convolutional Neural Network (CNN) techniques. This study examines the classification capabilities of four CNN models, namely MobileNetV2, DenseNet121, InceptionV3, and Xception, using transfer learning. These models leverage pre-trained weights from large datasets such as ImageNet to accelerate convergence and improve generalization. The dataset of images utilized in this research is obtained from Kaggle and includes pictures of both clean and dusty solar panels. The dataset was divided into training, validation, and testing subsets using a stratified approach to ensure balanced class distribution across all subsets. During training, class weighting was used to address potential class imbalance. The models were developed using TensorFlow with multi-GPU support, optimized using the AdamW optimizer, and fine-tuned to enhance performance. Model evaluation was conducted using accuracy, precision, recall, and F1-score metrics. Among all the architectures evaluated, the Xception model achieved the best performance with an accuracy of 90.52%, outperforming MobileNetV2 with an accuracy of 87.92%, DenseNet121 with 89.78%, and InceptionV3 which achieved 87.73%. These results indicate that modern CNN-based models can effectively recognize relevant visual patterns to detect dust on solar panels.
Implementasi Algoritma XGBoost dengan Walk Forward Validation untuk Prediksi Harga Emas Antam Hisyam, Mochammad; Fitri, Zahratul; Aidilof, Hafizh Al Kautsar
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 4 (2025): Agustus 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i4.8693

Abstract

Accurate gold price prediction is crucial in supporting financial and investment decision-making. This study aims to develop and optimize a daily gold price prediction model using the Extreme Gradient Boosting (XGBoost) algorithm based on historical price data and technical indicators. The model was constructed to predict two types of prices, namely "Close" and "Buyback" prices in IDR/gram. Optimization was carried out using Bayesian Optimization to obtain the best hyperparameter combinations. The model was evaluated using a Walk Forward Validation (WFV) approach with a 14-day sliding window and two main evaluation metrics: Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). The results show that the model provides excellent predictive performance, with an average RMSE of 15,431.92 and MAPE of 1.03% for Close price, and RMSE of 15,382.64 and MAPE of 1.15% for Buyback price. The prediction visualizations indicate that the model consistently follows the actual price trend. Feature importance analysis reveals that technical indicators such as RSI, EMA, and MACD significantly contribute to the model. The success of this study demonstrates that an optimized XGBoost model can serve as a reliable approach for gold price forecasting and opens opportunities for developing more advanced predictive models in future research.
Optimalisasi Metode RBFNN Dengan Fuzzy C-Means Dalam Prediksi Import Barang Konsumsi Indonesia Budiastawa, I Dewa Gede; Sunarya, I Made Gede; Wirawan, I Made Agus
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 4 (2025): Agustus 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i4.8711

Abstract

Prediction or forecasting is an action that aims to find out future events based on indicators that influence an event. Consumer goods are products or goods purchased by people or households that are intended for direct consumption in the sense that they are not for further production purposes. Based on this, serious handling is needed to maintain the state of the Indonesian economy, especially in the industrial sector. Predicting the value of consumer goods imports is a step in finding out the value of consumer goods imports in the next period so that the government has a reference in determining policies. In this study, the prediction of the value of consumer goods imports was carried out based on factors that influence the value of consumer goods imports based on research in the field of economics. This study uses the Radial Basis Function Neural Network (RBFNN) method using a combination of clustering methods, namely Fuzzy C-Means Clustering to improve method performance. The RBFNN method is the best method used in predicting future data based on previous research and the FCM method is a clustering method that is able to overcome ambiguity in the prediction process. This study proves that the Fuzzy C-Means method is effective in optimizing the performance of the Radial Basis Function Neural Network method with a comparison of MAPE values in each combination, namely RBFNN - FCM 15.73%, RBFNN - K-Means 16.87% and RBFNN - Random centroid 17.70%. The learning rate parameter is directly proportional to the RBFNN - FCM model where the greater the learning rate, the better the model performance, indicating that the model does not need to do in-depth learning to recognize data patterns. In contrast to the fuzzification parameter which increases accuracy when the fuzzification value is lowered, indicating that the model does not require a very vague approach to recognize data patterns. The best architecture is 8 - 4 - 1 with a fuzzification parameter value of 1.5, a learning rate of 0.3 and a threshold error of 0.3 produced by a combination of RBFNN and FCM.
Pengembangan Model Klasifikasi Aritmia Pada Lansia Menggunakan Algoritma Support Vector Machine (SVM) Berbasis Data EKG Rizki, Muhammad; Sinaga, Alfrendo; Mendrofa, Fide Moses; Sinaga, Bram Dimpos Fajar; Prabowo, Agung
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 4 (2025): Agustus 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i4.8719

Abstract

Arrhythmia is one of the most dangerous heart rhythm disorders, especially for the elderly, due to degenerative changes in cardiac structure and function. This study aims to develop an electrocardiogram (ECG)-based arrhythmia classification model for the elderly using the Support Vector Machine (SVM) algorithm. Data were collected from three nursing homes with a total of 184 subjects aged 50–75 years using the Smart Holter ECG 5-lead device. The research stages included ECG signal acquisition, signal preprocessing (baseline correction and Butterworth filter), physiological feature extraction (PR, QRS, QT, RR intervals, ST segment, heart rate, R/S ratio), and data labeling by cardiologists. The model was trained and tested using a hold-out approach with an 80:20 ratio and class stratification. Evaluation results showed high performance with 96.36% accuracy on the training set and 94.57% accuracy on the testing set. The Area Under Curve (AUC) reached 0.99 in micro-average and 0.98–1.00 for each class. This research confirms that SVM is effective for arrhythmia classification in the elderly and has potential as an accurate and efficient diagnostic tool
Implementasi Algoritma Support Vector Machine (SVM) dan Random Forest Untuk Klasifikasi Penyakit Hipertensi Berdasarkan Data Kesehatan Azhaar, Siti Alia; Mudzakir, Tohirin Al; Novita, Hilda Yulia; Faisal, Sutan
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 4 (2025): Agustus 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i4.8744

Abstract

One of the most common non-communicable diseases causing death in Indonesia is hypertension. At one community health center, the prevalence of hypertension is quite high. Based on examination results, more than 1,000 patients are diagnosed with hypertension each year. The issue faced at this health center is the lack of structured data classification for hypertensive and normal patients. The objective of this study is to compare the performance of the Support Vector Machine (SVM) and Random Forest (RF) algorithms in creating a hypertension classification model based on health examination data from the Anggadita Health Center. Data from 2,500 patients was collected and preprocessed, including handling missing values, removing duplicate data, transforming data using label encoding, and dividing the data into training and testing sets. The SVM method applied a Radial Basis Function (RBF) kernel, while the RF consisted of 100 decision trees. Evaluation was conducted using a confusion matrix to calculate accuracy, precision, recall, and F1-score. The results showed that the SVM method achieved an accuracy of 93%, precision of 0.96 (Normal) and 0.90 (Hypertension), and F1-scores of 0.94 and 0.92. Meanwhile, the RF model showed superior performance with an accuracy of 96%, precision of 0.97 (Normal) and 0.95 (Hypertension), and F1-scores of 0.97 and 0.95, respectively. Thus, the Random Forest algorithm performs better in classifying hypertension data and can be implemented as a tool to assist healthcare institutions in managing patient data.
Analisis Performansi Model Machine Learning dalam Klasifikasi Penyakit Diabetes Tipe 2 Hidayatulloh, Ryan; Prabowo, Wahyu Aji Eko
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 4 (2025): Agustus 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i4.8747

Abstract

Type 2 Diabetes Mellitus is a chronic disease that develops gradually and can lead to serious complications—such as heart disease, kidney failure, and blindness—if not detected early. This study aims to evaluate and compare the performance of four machine learning algorithms—Logistic Regression, Random Forest, Multilayer Perceptron, and Deep Neural Network—in predicting the risk of type 2 diabetes based on medical data. The analysis uses the Pima Indians Diabetes dataset, which contains 9.538 patient records and 16 predictor variables. We split the data into training and testing sets using an 80:20 ratio. During training, we performed hyperparameter tuning using Grid Search combined with cross-validation. To evaluate model performance, we applied several metrics, including accuracy, precision, recall, F1-score, Mean Squared Error (MSE), Root Mean Squared Error (RMSE), R², and an analysis of overfitting. The results indicate that the Random Forest model outperformed the others, achieving 100% accuracy, zero classification errors, near-zero prediction error values, and no signs of overfitting. Logistic Regression also performed well, though slightly below the Random Forest. In contrast, the Multilayer Perceptron and Deep Neural Network models showed mild overfitting and higher false negative rates. Based on these findings, we recommend the Random Forest model as the most reliable option for early prediction systems in type 2 diabetes mellitus.
Perbandingan Proses Klasterisasi Data Menggunakan K-Means Clustering dan Agglomerative Hierarchical Clustering Hartono, Budi; Lusiana, Veronica; Al Amin, Imam Husni
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 4 (2025): Agustus 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i4.8766

Abstract

Large amounts of data require good processing and analysis. One of the data analysis techniques is data clustering, which is grouping data into several groups or data clusters based on the similarity of data characteristics. This study observed the clustering process and cluster results using the K-Means and Agglomerative Hierarchical Clustering (AHC) algorithms or methods. Clustering was carried out using three different amounts of data, namely 10 (A10 data), 30 (B30 data), and 60 (C60 data), with choices of two, three, and four clusters. The experimental results obtained were that the A10 data cluster was the same, but the C60 data was different. Both methods provide the same cluster results, namely in the number of cluster members and their data numbers; conversely, different cluster results are obtained if there are differences in the number of cluster members. The B30 data cluster results for three clusters are the same, while for two and four clusters they are different. The results of this study are expected to provide a better understanding of the data clustering process and can be a basis for selecting a more appropriate clustering method.
Klasifikasi Kesehatan Mental Mahasiswa Menggunakan Light Gradient Boosting Machine Dan Analisa Fitur Menggunakan SHAP Wibowo, Ditto Ridhwan; Umbara, Fajri Rakhmat; -, Ridwan Ilyas
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 4 (2025): Agustus 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i4.8771

Abstract

The mental health of college students is an important issue as many do not receive treatment despite needing it. According to the Association of University and College Counseling Center Directors 95% of college students experience an increase in psychopathology. This study uses the Light Gradient Boosting Machine algorithm to classify the mental health of college students based on a dataset that has a total of 61.794 rows and 16 columns. Light Gradient Boosting Machine is an implementation of Gradient Boosting Decision Tree which has two strategies namely gradient-base one-side sampling (GOSS) and leaf-wise growth. The accuracy results obtained using LightGBM reached 67% where the data used had been balanced using the class_weight parameter and the ADASYN technique. In addition, the research was analyzed to find the most contributing features using the SHAP (SHapley Additive exPlanations) method with the results obtained there are 6 features that have the highest contribution value including Country, treatment, mental_health_interview, family_history, Gender, dan self_employed.
Sistem Rekomendasi Film Menggunakan Data User-End dan Knowledge Graph Convolutional Network pada Dataset MovieLens 1 M Yanuar, Muhammad Rizki; Umbara, Fajri Rakhmat; -, Agus Komarudin
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 4 (2025): Agustus 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i4.8772

Abstract

Traditional recommendation systems such as Collaborative Filtering and Content-Based Filtering often fail to provide relevant recommendations due to their limitations in handling sparsity and cold-start problems. This study proposes a Knowledge Graph Convolutional Network (KGCN) model enriched with user demographic data from the MovieLens 1M dataset to address these issues. The primary focus of the research is to demonstrate that the Importance Sampling technique is significantly superior to Uniform Sampling in effectively training the model. After hyperparameter tuning, the optimal model configuration achieved peak performance with an AUC score of 0.8798 and NDCG@10 of 0.9719. These results demonstrate that the proposed approach is effective in building an accurate, personalised recommendation system capable of addressing sparsity and cold-start issues.

Page 3 of 5 | Total Record : 44

 1   2   3   4   5 

Filter by Year

2025 2025


Reset
Filter By Issues
All Issue Vol. 12 No. 5 (2025): Oktober 2025 Vol. 12 No. 4 (2025): Agustus 2025 Vol 12, No 3 (2025): Juni 2025 Vol. 12 No. 3 (2025): Juni 2025 Vol 12, No 2 (2025): April 2025 Vol. 12 No. 2 (2025): April 2025 Vol 12, No 1 (2025): Februari 2025 Vol. 12 No. 1 (2025): Februari 2025 Vol 11, No 6 (2024): Desember 2024 Vol. 11 No. 6 (2024): Desember 2024 Vol. 11 No. 5 (2024): Oktober 2024 Vol 11, No 5 (2024): Oktober 2024 Vol 11, No 4 (2024): Augustus 2024 Vol. 11 No. 4 (2024): Augustus 2024 Vol. 11 No. 3 (2024): Juni 2024 Vol 11, No 3 (2024): Juni 2024 Vol 11, No 2 (2024): April 2024 Vol. 11 No. 2 (2024): April 2024 Vol 10, No 3 (2023): Juni 2023 Vol 10, No 2 (2023): April 2023 Vol 10, No 1 (2023): Februari 2023 Vol 9, No 6 (2022): Desember 2022 Vol 9, No 5 (2022): Oktober 2022 Vol 9, No 4 (2022): Agustus 2022 Vol 9, No 3 (2022): Juni 2022 Vol 9, No 2 (2022): April 2022 Vol 9, No 1 (2022): Februari 2022 Vol 8, No 6 (2021): Desember 2021 Vol 8, No 5 (2021): Oktober 2021 Vol 8, No 4 (2021): Agustus 2021 Vol 8, No 3 (2021): Juni 2021 Vol 8, No 2 (2021): April 2021 Vol 8, No 1 (2021): Februari 2021 Vol 7, No 6 (2020): Desember 2020 Vol. 7 No. 5 (2020): Oktober 2020 Vol 7, No 5 (2020): Oktober 2020 Vol 7, No 4 (2020): Agustus 2020 Vol 7, No 3 (2020): Juni 2020 Vol 7, No 2 (2020): April 2020 Vol 7, No 1 (2020): Februari 2020 Vol 6, No 6 (2019): Desember 2019 Vol 6, No 5 (2019): Oktober 2019 Vol 6, No 4 (2019): Agustus 2019 Vol 6, No 3 (2019): Juni 2019 Vol 6, No 2 (2019): April 2019 Vol 6, No 1 (2019): Februari 2019 Vol 5, No 6 (2018): Desember 2018 Vol 5, No 5 (2018): Oktober 2018 Vol 5, No 4 (2018): Agustus 2018 Vol 5, No 3 (2018): Juni 2018 Vol 5, No 2 (2018): April 2018 Vol 5, No 1 (2018): Februari 2018 Vol 4, No 5 (2017): Oktober 2017 Vol 4, No 4 (2017): Agustus 2017 Vol 3, No 6 (2016): Desember 2016 Vol 3, No 5 (2016): Oktober 2016 Vol 3, No 4 (2016): Agustus 2016 Vol 3, No 1 (2016): Februari 2016 Vol 2, No 6 (2015): Desember 2015 More Issue