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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 24 Documents
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Search results for , issue "Vol. 12 No. 3 (2025): Juni 2025" : 24 Documents clear
Klasifikasi Kanker Payudara Berbasis Deep Learning Menggunakan Vision Transformer dengan Teknik Augmentasi Data Citra Ardiyansyah, Muhamad Salman; Umbara, Fajri Rakhmat; Melina, Melina
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.v12i3.8619

Abstract

Breast cancer ranks among the leading causes of death in women worldwide. Early detection through mammographic image analysis plays a crucial role in increasing survival rates. However, manual interpretation of mammograms requires expert knowledge and is prone to errors. This study aims to develop a breast cancer classification model using mammography images based on the Vision Transformer (ViT) architecture without employing transfer learning. The dataset used is the Digital Database for Screening Mammography (DDSM), consisting of two categories: benign and malignant. To address class imbalance, undersampling and data augmentation techniques (flipping, rotation, cropping, and noise injection) were applied. All images were normalized and resized to 224×224 pixels to match the ViT input requirements. The model was trained for five epochs with a batch size of 16. Evaluation on the test data was conducted using seven metrics: accuracy, precision, recall, F1-score, Matthews Correlation Coefficient (MCC), Cohen’s Kappa Score, and Area Under the Curve (AUC). The results show that the model achieved an accuracy of 92.50%, precision of 90.48%, recall of 95.00%, F1-score of 92.68%, MCC of 85.11%, Kappa Score of 85.00%, and AUC of 95.75%. These findings indicate that the Vision Transformer is highly effective for mammographic image classification and holds potential as a reliable tool for automated breast cancer diagnosis support.
Analisis Sentimen Komunitas Counter-Strike 2 (CS2) Menggunakan Support Vector Machine (SVM) Riyadi, Saiful Faris; Chrisnanto, Yulison Herry; Abdillah, Gunawan
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.v12i3.8620

Abstract

Counter-Strike 2 (CS2) is a game that has received a lot of enthusiasm from the gaming community since its release. User reviews on the Steam platform are the main source for understanding community sentiment towards this game. This study aims to analyze sentiment towards CS2 reviews using the Support Vector Machine (SVM) method. Data was collected through the Apify platform, then cleaned through processes such as tokenization, stopword removal, and lemmatization. Text features were converted into numerical values using Term Frequency-Inverse Document Frequency (TF-IDF) to be used in the SVM model. The SVM model was used to classify review sentiment into three categories: positive, neutral, and negative. Evaluation was conducted by measuring accuracy, confusion matrix, and classification reports. In the evaluation results, the SVM model using the One-vs-Rest (OVR) approach showed that the model without SMOTE produced an accuracy of 81.95%. After applying the Synthetic Minority Over-sampling (SMOTE) technique to the training data to balance the distribution between classes, the model accuracy increased slightly to 82.18%. This study provides valuable insights for game developers in understanding players' opinions about CS2. Additionally, this study demonstrates the potential of SVM in text-based sentiment analysis on user review platforms.
Fuzzy Tsukamoto Untuk Merekomendasikan Pembelian Barang Berdasarkan Data Penjualan Purba, Siti Aisyah; Sriani, Sriani
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.v12i3.8631

Abstract

The development of the sports industry, especially in futsal shoe sales, requires an inventory management strategy that is able to anticipate fluctuations in market demand. The main problem in this study is how to overcome the uncertainty of futsal shoe demand caused by variables such as trends, competition seasons, and changing consumer preferences. This study aims to develop a fuzzy logic-based purchase recommendation system using the Fuzzy Tsukamoto method to improve stock management efficiency. This study uses a quantitative approach with fuzzy method stages consisting of fuzzification, rule formation, inference, and defuzzification. The tools used are MATLAB software that supports the creation of fuzzy inference systems and graphic modeling. The study was conducted at the Pasifik Club Sports Sibolga Futsal Shoe Store by processing 1,098 historical sales data. The results of the study showed that the system built was able to recommend futsal shoe purchases with good accuracy, indicated by the Mean Absolute Percentage Error (MAPE) value of 15%. This system not only provides a popularity value for each shoe variant, but also helps stores avoid excess or shortage of stock. Thus, the Fuzzy Tsukamoto method is proven to be feasible to be used as a decision-making tool in retail inventory management.
Implementasi Model ARIMA untuk Peramalan Reorder Point dalam Supply Chain Management Alexandra, Andrea Cellista; Hartomo, Kristoko Dwi
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.v12i3.8639

Abstract

This research analyzes the patterns and trends of reorder points in inventory management over a two-year period (2023-2024), utilizing weekly time series data generated from daily data resampling. The ARIMA (Autoregressive Integrated Moving Average) method was applied to forecast future reorder point values. An Augmented Dickey-Fuller (ADF) stationarity test revealed that the initial data was non-stationary but became stationary after a single differencing operation. Parameter identification using Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) plots indicated that the ARIMA(1,1,1) model was the best choice, based on the lowest Akaike Information Criterion (AIC). Model accuracy was evaluated using Mean Absolute Percentage Error (MAPE), yielding a value of 0.02%, signifying an excellent level of prediction accuracy. Consequently, the ARIMA model is demonstrated to be reliable for forecasting reorder points, supporting more precise decision-making in inventory management.
Prediksi Risiko Kesehatan Mental Mahasiswa Menggunakan Klasifikasi Naive Bayes Sumantri, Fithra Aditya; Chrisnanto, Yulison Herry; Melina, Melina
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.v12i3.8648

Abstract

The mental health of university students is a growing concern as academic, emotional, and social pressures contribute to increased psychological risks. This study aims to classify mental health risk levels Low, Medium, and High among students using the Naïve Bayes classification algorithm. A dataset consisting of 1,000 entries and 11 key variables was utilized, covering academic, psychological, and behavioral factors. The preprocessing stage included data cleaning, label encoding, normalization, and rule-based labeling to determine the target classes. Model training and testing were conducted using stratified data splitting to preserve class distribution. The initial model achieved a classification accuracy of 88,67%, with macro average F1-score of 0.87 and weighted average F1-score of 0.88. Grid Search optimization with k-fold cross-validation was applied but showed no significant improvement, indicating the model was already in optimal configuration. Furthermore, probabilistic analysis revealed that Sleep Quality and Study Stress Level were the most influential features in predicting mental health risks. The findings suggest that Naïve Bayes is effective for multi-class classification with interpretable results. This research contributes to early detection efforts and offers a foundation for targeted interventions in university mental health management.
Implementasi Metode Recurrent Neural Network Untuk Prediksi Kejang Pada Penderita Epilepsi Berdasarkan Data Electroenephalogram Febiyane, Raisya; Chrisnanto, Yulison Harry; Abdillah, Gunawan
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.v12i3.8656

Abstract

Epilepsy is a chronic neurological disorder that causes patients to experience recurrent seizures. Seizures are one of the main symptoms of epilepsy, requiring medical treatment and close monitoring. A major challenge in epilepsy management is the difficulty in predicting when seizures will occur. Electroencephalogram (EEG) can detect seizures as it contains physiological information about brain neural activity. This study aims to predict seizures using a Recurrent Neural Network (RNN) method based on EEG data. Deep Learning is a branch of Machine Learning that uses artificial neural networks to solve problems involving large datasets. The data used in this research is the Epileptic Seizure Recognition dataset obtained from Kaggle. It consists of patient ID attributes, 178 numerical attributes representing EEG signals, and a label y indicating conditions during the recording, including eyes open, eyes closed, healthy brain, tumor location, and seizure activity. The deep learning model tested is a Recurrent Neural Network (RNN) designed to learn patterns in the data. Performance evaluation was conducted using metrics including accuracy, precision, recall, and F1-Score. Based on the application of the RNN method and testing using EEG data, the best condition was achieved with a three-layer Long Short-Term Memory architecture and optimal training parameters, resulting in a seizure prediction accuracy of 98.6%. This result demonstrates that the model is capable of effectively and efficiently predicting the likelihood of seizure occurrences.
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.
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.
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

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