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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 897 Documents
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Deteksi Serangan ICMP Flood pada Internet of Things dengan Feature Selection dan Machine Learning Harid, Harid; Kurniabudi, Kurniabudi; Harris, Abdul
JURIKOM (Jurnal Riset Komputer) 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.v12i4.8554

Abstract

IoT devices have played an important role in driving DDoS attacks, and are a threat to IoT networks. One of them is the ICMP Flood attack. To overcome attacks on IoT, one of them uses an Intrusion Detection System (IDS). However, on the other hand, IDS has challenges in handling the complexity of high-dimensional data. One of the suggested solutions to overcome the problem of data dimensions is the use of feature selection techniques. The Forward Selection feature selection technique is used to eliminate irrelevant features. This study compares the performance of the Random Forest and SVM algorithms. For experimental purposes, this study used the CICIoT2023 dataset, which represents IoT traffic. The use of Forward Selection obtained 11 selected features that will be used in the machine learning process using the Random Forest and SVM methods. Feature selection affects the computation time or processing time, because the fewer features used, the more the system's workload in carrying out the classification process. The test results show that the use of feature selection improves the performance of random forest with an accuracy of 100%. Meanwhile, the SVM model gets better accuracy by using feature selection with the highest accuracy of 99.4508% in the supplied test set test.
Penerapan Convolutional Neural Network Dan DenseNet121 untuk Identifikasi Penyakit Daun Jagung Di Daerah Toba Hutapea, Oppir; Ford Lumban Gaol; Takuro Matsuo
JURIKOM (Jurnal Riset Komputer) 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.v12i4.8646

Abstract

Corn is one of the most important agricultural commodities in the Toba region of North Sumatra. However, its productivity is often reduced due to foliar diseases that appear prior to harvest. The three most commonly observed leaf diseases include leaf spot, blight, and rust. To support early detection efforts among local farmers, this study proposes an image-based classification system employing the Convolutional Neural Network (CNN) algorithm and the DenseNet121 model as a transfer learning approach. The primary objective of this research is to automatically identify the type of disease affecting corn leaves using image data, thereby enabling farmers to promptly implement appropriate countermeasures. A series of experiments were conducted to evaluate various model configurations, including different activation functions (ReLU and Tanh), adjustments to learning rates, and the tuning of other hyperparameters such as optimizers and preprocessing methods (normalization, rotation augmentation, zooming, and contrast adjustments). The results demonstrate that DenseNet121, when trained with an optimal learning rate of 0.001, achieved the highest accuracy of 97%, outperforming the custom-built CNN model which attained an accuracy of 95%. The combination of effective preprocessing techniques and hyperparameter tuning significantly contributed to the improved performance of the models. This study highlights the potential of image-based plant disease detection technologies in agriculture, particularly in aiding real-time decision-making, enhancing land management efficiency, and supporting increased corn yield.
Implementasi Algoritma Apriori Untuk Pola Pembelian Bundling Pada UMKM Berbasis Website Fahriza Hasibuan, Alya putri; Triase, Triase
JURIKOM (Jurnal Riset Komputer) 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.v12i4.8663

Abstract

The rapid development of the digital era encourages the utilization of information technology to enhance the efficiency and competitiveness of MSMEs, including Bumie Belepot. Problems in the sales system, which is still manual and lacks a bundling feature, have led to unstable transactions and long queues. This study aims to develop a website-based system that applies bundling strategies to increase the number of transactions and improve customer satisfaction. The Apriori method is used to analyze customer purchasing patterns based on transaction data from January 2024 to January 2025, with a minimum support threshold of 60%. The analysis results identified five itemset 3 combinations with strong correlations between menu items, such as Bumie Belepot – Mie Ayam Bakar – Lemon Tea and Mie Ayam Bakar – Teh Manis – Lemon Tea, which are considered potential bundling options. Unlike previous studies, this research not only focuses on analyzing association patterns but also develops a website-based system equipped with bundling features, order management, and sales data reports. The system design utilizes UML modeling to describe the website's structure and workflow in detail. With this approach, the research is expected to improve operational efficiency, customer satisfaction, and the competitiveness of Bumie Belepot in the culinary industry.
Perbandingan MobileNetV2, DenseNet121, InceptionV3, dan Xception pada Klasifikasi Citra Panel Surya Bersih dan Berdebu Nugroho, Aswin Mulyo; Mustafidah, Hindayati; Fitriani, Maulida Ayu; Supriyono, Supriyono
JURIKOM (Jurnal Riset Komputer) 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
JURIKOM (Jurnal Riset Komputer) 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
JURIKOM (Jurnal Riset Komputer) 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
JURIKOM (Jurnal Riset Komputer) 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
JURIKOM (Jurnal Riset Komputer) 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
JURIKOM (Jurnal Riset Komputer) 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.
Sistem Pendukung Keputusan Untuk Menentukan Kelayakan Penerima Bantuan Langsung Tunai Menggunakan Metode AHP-Topsis Simatupang, Aidil Akbar; Hasugian, Abdul Halim
JURIKOM (Jurnal Riset Komputer) 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.v12i4.8833

Abstract

Social inequality and inaccuracy in aid distribution are still challenges in the Direct Cash Assistance (BLT) program, especially at the village level such as Bandar Selamat Village, North Labuhan Batu Regency. The process of determining BLT recipients which is still manual and subjective poses a risk of injustice and inefficiency. This study formulates the problem: how to develop an objective and targeted decision support system (DSS) for the selection of BLT recipients. The purpose of this study is to design and implement a DSS based on the Analytical Hierarchy Process (AHP) method and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) which can increase the accuracy and efficiency of aid recipient selection. The method used is Research and Development (R&D), with data collection techniques through interviews and observations, as well as comprehensive system testing. The results show that from 110 household head data, the system is able to identify 69 families eligible to receive assistance with a preference value ? 0.6. Employment and home conditions are the dominant criteria in determining eligibility. The system is proven to be consistent (CR = 0.0298 <0.1) and is able to simplify the decision-making process. This research provides real benefits in improving transparency, accountability, and effectiveness of social assistance distribution at the village level through a data and technology-based approach.

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