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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 998 Documents
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Identifikasi Berita Palsu di Portal Media Online Menggunakan Model IndoBERT dan LSTM Kamal, Angga Mochamad; Chrisnanto, Yulison Herry; Yuniarti, Rezki
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 3 (2025): Juni 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i3.8660

Abstract

The rapid spread of political fake news on Indonesian online media portals poses serious threats to public trust and democratic stability. The main research problem is the limitation of existing models in handling the complexity of Indonesian political narratives containing local idioms and long text structures. The proposed solution employs a hybrid IndoBERT-LSTM model with ensemble stacking approach using logistic regression meta-learner to optimize fake news detection. IndoBERT is selected to capture Indonesian language nuances, while LSTM handles sequential dependencies in long articles. The research objective is to develop an accurate detection system for political fake news by leveraging the complementary strengths of both models. The dataset comprises 32,218 political articles from credible portals (Kompas, CNN Indonesia, Tempo, Detiknews, Viva) and Turnbackhoax.id validation from September 2021 to December 2024. Research results demonstrate that ensemble stacking achieves superior performance with F1-score 0.9544, accuracy 95.41%, and AUC-ROC 0.9936, outperforming standalone IndoBERT (F1: 0.9542) and LSTM (F1: 0.9417). Error analysis identifies 4.59% error rate with 134 false positives and 88 false negatives, particularly in long articles (average 2,739 characters). This model has potential for integration into fact-checking platforms for real-time detection of Indonesian political fake news.
Implementasi Algoritma Apriori Untuk Pola Pembelian Bundling Pada UMKM Berbasis Website Fahriza Hasibuan, Alya putri; Triase, Triase
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.v12i4.8663

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.
Performance Comparison Between ResNet50 and MobileNetV2 for Indonesian Sign Language Classification Daviana, Feriska Putri; Aryanti, Aryanti; Anugraha, Nurhajar
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 3 (2025): Juni 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i3.8667

Abstract

Hearing impairment was considered a significant barrier to understanding verbal communication. Therefore, an alternative communication medium in the form of sign language was required to bridge interactions between Deaf and hearing individuals. One of the sign languages used in Indonesia was the Indonesian Sign Language (BISINDO). The advancement of deep learning technology provided a great opportunity to develop an effective and accurate BISINDO alphabet classification system. This research was conducted to evaluate and compare the performance of two Convolutional Neural Network (CNN) architectures, namely ResNet50 and MobileNetV2, in classifying BISINDO alphabet images consisting of 26 classes from A to Z. Model training wa carried out over 100 epochs and was analyzed using metrics such as training and validation accuracy, precision, recall, F1-score, and confusion matrix. The training process used a dataset that was divided into 80% training data and 20% validation data, and include image preprocessing steps such as resizing and rescaling. The evaluation results showed that ResNet50 achieved 86.42% training accuracy and 98.64% validation accuracy with 98.80% precision, 98.69% recall, 98.57% F1-score, and 31 misclassifications. In contrast, MobileNetV2 showed superior performance with 99.99% training accuracy, 99.65% validation accuracy, 99.69% precision, 99.65% recall, 99.61% F1-score, and only 8 misclassifications. Based on these results, MobileNetV2 was recommended as a more effective and efficient architecture for BISINDO alphabet image classification compared to ResNet50.
Penerapan Algoritma K-Means Clustering untuk Segmentasi Kepadatan Penduduk Berbasis GIS Putri, Rizki Amelia; Safwandi, Safwandi; Fitri, Zahratul
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 3 (2025): Juni 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i3.8668

Abstract

This study aims to develop a clustering system using the K-means algorithm to analyze demographic data of sub-districts from 2020 to 2023. The system is designed to cluster sub-districts based on variables such as population size, population percentage, population density, and gender ratio. The clustering results reveal different grouping patterns each year, reflecting the dynamics of demographic data over time. Evaluation using the Davies-Bouldin Index (DBI) indicates that the clustering results are of reasonably good quality, with DBI values of 1.1492 in 2020, 0.6859 in 2021, 1.2470 in 2022, and 0.6805 in 2023. The best DBI value was recorded in 2023 at 0.6805, demonstrating that the clustering results in that year were the most optimal compared to other years. The system also facilitates Users with interactive map visualizations, supporting better data analysis and decision-making processes. This research is expected to contribute to the management of demographic data and support more accurate data-driven policy-making.
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.
Klasifikasi Kunyit dan Temulawak dengan VGG16 dan Fuzzy Tsukamoto Berbasis Android Setyawan, Muhammad Rizki; Bahari Putra, Fajar Rahardika; Ilham, Ahmad; Suseno, Dimas Adi
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 3 (2025): Juni 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i3.8696

Abstract

Indonesia has a very rich biodiversity, including various medicinal plants that are highly financially beneficial and health-promoting. Among these medicinal plants, temulawak and turmeric are the two most popular rhizomes widely used in traditional medicine as well as the herbal industry. However, because the shape and color of these two plants are very similar, it is often difficult to distinguish between them, especially for laypeople and new industry workers. This research developed an Android-based application that can effectively and accurately distinguish between temulawak and turmeric to address this issue. For this application, the Convolutional Neural Network (CNN) architecture of the VGG-16 model is used along with the Tsukamoto fuzzy method as an additional layer. The trials conducted on the developed model using test data showed an accuracy rate of 0.97, a recall value of 0.98, and an F1 score of 0.97. Meanwhile, the blackbox testing shows that this application functions stably without technical issues, making it ready for use. Additionally, blackbox testing shows that the system can function stably without any issues, making it suitable for real-world use
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.
Analisis Klusterisasi Stunting Pada Balita Menggunakan Algoritma K-Medoids Untuk Mengidentifikasi Faktor Dominan Saragih, Leonardo; Pasaribu, Nanda Sabrina; Harefa, Novi Karlianti; Tajrin, Tajrin
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 3 (2025): Juni 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i3.8713

Abstract

Indonesia has a very rich biodiversity, including various medicinal plants that are highly financially beneficial and health-promoting. Among these medicinal plants, temulawak and turmeric are the two most popular rhizomes widely used in traditional medicine as well as the herbal industry. However, because the shape and color of these two plants are very similar, it is often difficult to distinguish between them, especially for laypeople and new industry workers. This research developed an Android-based application that can effectively and accurately distinguish between temulawak and turmeric to address this issue. For this application, the Convolutional Neural Network (CNN) architecture of the VGG-16 model is used along with the Tsukamoto fuzzy method as an additional layer. The trials conducted on the developed model using test data showed an accuracy rate of 0.97, a recall value of 0.98, and an F1 score of 0.97. Meanwhile, the blackbox testing shows that this application functions stably without technical issues, making it ready for use. Additionally, blackbox testing shows that the system can function stably without any issues, making it suitable for real-world use.

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