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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 1,069 Documents
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Analisis Komparatif Model Random Forest dan XGBoost untuk Klasifikasi Penyakit Jantung Berbasis Data Klinis Zunus Saputra, Angga Guardi; Fikri Budiman
JURNAL RISET KOMPUTER (JURIKOM) Vol. 13 No. 1 (2026): Februari 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i1.9427

Abstract

Heart disease remains a major global health challenge due to its increasing prevalence, highlighting the need for accurate and reliable early diagnostic systems. This study aims to analyze and compare the performance of Random Forest (RF) and XGBoost algorithms for heart disease classification, and to identify the most suitable model for data-driven clinical decision support. Addressing a research gap in ensemble learning studies, this research conducts a comprehensive comparative evaluation using the UCI Heart Disease Dataset. The proposed methodology includes data preprocessing, feature encoding, normalization, class imbalance handling using the Synthetic Minority Oversampling Technique (SMOTE), and hyperparameter optimization based on RandomizedSearchCV. Model performance is evaluated using accuracy, precision, recall, F1-score, ROC-AUC, and Matthews Correlation Coefficient (MCC), supported by Feature Importance analysis. The results demonstrate that both ensemble models achieve strong predictive performance, with consistently high F1-scores above 0.88. XGBoost exhibits superior overall performance, achieving the highest F1-score of 0.8995 and Precision of 0.8785, making it more effective in minimizing False Positive predictions. In contrast, Random Forest shows superior sensitivity, with the highest Recall of 0.9510 and ROC-AUC of 0.9582, along with better cross-validation stability. These findings indicate that the selection of heart disease classification algorithms should be aligned with specific clinical objectives, and the results of this study are expected to contribute to the development of effective machine learning–based clinical decision support systems.
Penerapan Metode YOLOv8 untuk Deteksi Jalan Berlubang Berbasis Object Detection Rafli Muhammad Fauzi; Ramadhan Putra Al Kariim; Nurul Latifah; Hilda Yulia Novita
JURNAL RISET KOMPUTER (JURIKOM) Vol. 13 No. 1 (2026): Februari 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i1.9441

Abstract

Road infrastructure damage, particularly potholes, is a critical issue that contributes to an increased risk of traffic accidents in Indonesia. This condition highlights the need for a road monitoring system that is capable of operating quickly, accurately, and continuously. This study aims to develop and evaluate an automated pothole detection system based on computer vision using the YOLOv8 (You Only Look Once version 8) method, a deep learning algorithm designed for real-time object detection. Dataset collection was conducted through two approaches, namely acquiring data from the Kaggle platform and capturing real-world road images directly using a smartphone camera to enhance data diversity and represent actual road conditions. All collected images were annotated using the Roboflow platform to generate labeled data suitable for model training. The YOLOv8 model was trained using a total of 345 images, consisting of 241 training images, 69 validation images, and 35 testing images. The training results indicate that the model achieved a mean Average Precision (mAP) of 93%, with a precision of 88% and a recall of 82%, demonstrating strong detection and localization performance. Furthermore, real-time testing using a smartphone camera showed that the system achieved an accuracy of over 85% under real-world conditions. These findings demonstrate that YOLOv8 has strong potential as an efficient and reliable automated pothole detection system, supporting road infrastructure maintenance and enhancing road user safety
Klasifikasi Spesies Suara Burung Menggunakan YAMNet dan Random Forest untuk Konservasi Alam Syaeful Machfud; Simon Simarmata; Nur Rofiq
JURNAL RISET KOMPUTER (JURIKOM) Vol. 13 No. 1 (2026): Februari 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i1.9443

Abstract

Monitoring and identification of bird species is an important aspect of biodiversity conservation, but manual identification methods based on direct observation and expert listening still have limitations in terms of time, cost, and subjectivity. Other challenges arise due to variations in the quality of sound recordings, the presence of environmental noise, and the similarity in vocalization patterns between species that make it difficult to automate the classification process. This study aims to develop an automatic classification system of bird species based on acoustic signals by combining the YouTube Audio Event Network (YAMNet) model and the Random Forest algorithm. YAMNet is utilized to extract spectral log-Mel features that represent the frequency and temporal characteristics of bird sounds, while Random Forest is used as a classifier to determine species based on those features. The dataset used is the Sound of 114 Species of Birds till 2022, which includes species variation, recording duration, and complex acoustic conditions. The results showed that the features produced by YAMNet were able to form separation between species visually through Principal Component Analysis (PCA), although there was still overlap in species with similar vocalization characteristics. Evaluation using the confusion matrix shows that some species can be classified with a high degree of accuracy, while misclassification occurs mainly in species with similar frequency patterns. Receiver Operating Characteristic (ROC) analysis yields Area Under Curve (AUC) values of up to 0.98 in certain species, indicating the model's excellent discriminating ability. These findings suggest that the integration of YAMNet and Random Forest has the potential to be an efficient and reliable solution to support automated bird species identification systems in nature conservation.
Implementasi Algoritma CNN dengan Arsitektur MobileNet untuk Klasifikasi Citra Daun Herbal Fida Maisa Hana; Agung Prihandono; Agung Bakti; Nuril Lutvi Azizah; Imam Prayogo Pujiono
JURNAL RISET KOMPUTER (JURIKOM) Vol. 13 No. 1 (2026): Februari 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i1.9444

Abstract

Indonesia's diversity is very rich, one of which is that Indonesia has herbal plants which people believe are natural medicines for curing diseases. Herbal leaves show different variations in size and shape for each type, indicating that each leaf has special characteristics, shape, texture and size. Researchers used one of the Deep Learning methods, namely Convolutional Neural Network (CNN) for classifying herbal leaves. The field of image classification has found CNNs to be quite effective for image classification. CNN is a type of neural network with convolutional layers that has the ability to automatically extract important features from images. MobileNet is a CNN structure created by Google. MobileNet has advantages in efficient use of computing resources. Specifically, in the MobileNet network model, an attention module was added to improve the model's ability to extract more detailed image features, and dropout technology was added to prevent overfitting. This research method includes image preprocessing, training a convolutional neural network-based model, and evaluating its performance using accuracy, precision, recall, and F1 score metrics. Training was conducted for 20 epochs, and testing was conducted using data separated from the training data. The evaluation results show that the MobileNet model has the ability to extract visual features and produce herbal leaf image classification with an accuracy rate of 97.50% and precision, recall, and F1 scores of 98% each. The proposed model can be used in mobile-based herbal leaf identification applications due to its high performance and lightweight architecture. The stable accuracy curve at the final epoch indicates that the model does not experience significant overfitting and is able to generalize well to the test data
Klasifikasi Komentar Toksik Berbahasa Indonesia di Media Sosial Berbasis Fine-Tuning IndoBERT Luqman Nur Hakim; Fida Maisa Hana; Widya Cholid Wahyudin
JURNAL RISET KOMPUTER (JURIKOM) Vol. 13 No. 1 (2026): Februari 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i1.9449

Abstract

Social media has become a primary platform for Indonesian society to interact and exchange information online. However, freedom of expression in digital spaces is often misused through the use of harsh, offensive, and hateful language. This study aims to develop a toxic comment classification model for the Indonesian language using the IndoBERT architecture through a fine-tuning process. IndoBERT was selected for its capability to understand bidirectional semantic context and its pretraining on a Bahasa Indonesia corpus, making it suitable for handling informal language styles, abbreviations, and common code-mixing phenomena in social media texts. The dataset used in this study is the Indonesian Abusive and Hate Speech Twitter Text, consisting of 12,942 entries 11,647 for training and 1,295 for validation. The research was conducted online using Google Colaboratory with GPU acceleration. The research stages included data preprocessing, tokenization, model training, and evaluation using precision, recall, F1-score, and confusion matrix as metrics. Evaluation results show that the fine-tuned IndoBERT model achieved high performance, with an average precision of 0.8842, recall of 0.884, F1-score of 0.883, and accuracy of 0.8834. These results indicate balanced performance across classes and strong model stability in detecting both toxic and non-toxic comments. This study contributes to the development of an automated Indonesian-language content moderation system, which can be deployed as a comment detection module via API. Although limited to Twitter data and binary classification, this model has the potential to be extended toward multi-class and cross-platform classification in supporting safer and healthier digital spaces in Indonesia.
Analisis Sentimen Multi-Platform Media Sosial pada Program Makan Bergizi Gratis Menggunakan Ensemble IndoBERT-SVM Muhammad Bisri Mustofa; Ifnu Wisma Dwi Prastya; Sahri, Sahri
JURNAL RISET KOMPUTER (JURIKOM) Vol. 13 No. 1 (2026): Februari 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i1.9460

Abstract

Sentiment analysis of public policy on social media faces significant challenges due to linguistic heterogeneity across platforms and limitations of single models in capturing the diversity of opinion expressions. Previous studies tend to employ single-platform and single-model approaches that potentially generate representational bias and accuracy degradation of up to 12–15% when applied to different platform contexts. This study aims to develop a soft voting-based ensemble model that integrates Support Vector Machine (SVM) and IndoBERT to analyze public sentiment toward the Free Nutritious Meal (MBG) Program across multiple platforms, and to evaluate the effectiveness of the ensemble approach compared to single models in addressing variations in linguistic characteristics of digital platforms. The research dataset consists of 7,500 comments from X, TikTok, and YouTube collected from January 6 to September 28, 2025, processed through informal Indonesian language preprocessing, lexicon-based labeling, and stratified split division. Results demonstrate that SVM performs optimally on TikTok (accuracy 98.1%, macro F1 98.0%) but weakly on the neutral class in X (F1 51.0%), while IndoBERT excels in handling pragmatic ambiguity in X (neutral F1 74.0%) despite slightly declining on TikTok (macro F1 93.0%). The ensemble model produces the most balanced performance with accuracies of 92.53% (YouTube), 95.73% (TikTok), 92.53% (X), and macro F1 scores of 85.07%, 94.33%, 84.92% respectively. The contributions of this research include the development of a multi-platform sentiment analysis approach that addresses single-platform bias, improved classification generalization capability across heterogeneous digital ecosystems, and provision of evidence-based evaluation instruments for improving government policy implementation and communication.
Analisis dan Implementasi Singular Value Decomposition (SVD) untuk Sistem Rekomendasi Anime Berbasis Collaborative Filtering Ginting, Muhammad Daffa; Muhammad Ikhsan
JURNAL RISET KOMPUTER (JURIKOM) Vol. 13 No. 1 (2026): Februari 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i1.9462

Abstract

The rapid growth of digital platforms has significantly increased the number of available anime titles, making it difficult for users to choose content that matches their preferences. In addition, the diversity of genres and content characteristics accessible to different age groups highlights the need for a system that can provide recommendations more accurately and efficiently. This study aims to develop an anime recommendation system based on collaborative filtering by applying the truncated Singular Value Decomposition (SVD) method to address the data sparsity problem in the user–item matrix. The dataset was collected from the AniList platform and consists of 30 users, 100 anime titles, and 1,230 explicit rating records. The evaluation was conducted using a hold-out scheme with an 80% training set (992 ratings) and a 20% testing set (248 ratings). Prediction performance was measured using an RMSE of 1.6464 and an MAE of 1.2370, while the quality of Top-N recommendations was assessed using Precision@10. The experimental results indicate that the best configuration was achieved at k = 4, which produced the lowest prediction error on the test set and generated relevant recommendations. The average Precision@10 obtained was 0.1897, meaning that approximately 18.97% of the Top-10 recommendations provided by the system were considered relevant.
Supervised Machine Learning Algorithms untuk Klasifikasi Penyakit Jantung Riadi, Denny Fajar; Prabowo, Wahyu Aji Eko
JURNAL RISET KOMPUTER (JURIKOM) Vol. 13 No. 1 (2026): Februari 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i1.9481

Abstract

Heart disease is one of the leading causes of death worldwide, requiring accurate predictive methods to support early detection and clinical decision making. This study aims to analyze and compare the performance of three supervised machine learning algorithms, namely K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Random Forest (RF), in classifying heart disease using the Cleveland Heart Disease dataset consisting of 303 patient records with 13 clinical features. The research stages include data preprocessing, splitting the dataset into 80% training data and 20% testing data, model training, and hyperparameter optimization using GridSearchCV with 5-fold cross-validation. After optimization, prediction was performed using test data followed by performance evaluation to assess generalization ability. Model performance was evaluated using accuracy, precision, recall, F1-score, AUC-ROC, and confusion matrix. The results show that KNN and Random Forest achieved the highest accuracy of 90.16%. The KNN model obtained a recall value of 1.0000, indicating perfect sensitivity in detecting positive cases, while Random Forest demonstrated a more balanced performance between precision and recall with the highest AUC value of 0.9481. Based on these findings, KNN is considered the most suitable model for medical screening purposes, as it successfully detected all positive heart disease patients without producing false negatives. This study is expected to serve as a reference for implementing clinical databased machine learning as a decision support tool for early heart disease detection.
Analisis Perbandingan Kinerja Arsitektur CNN untuk Klasifikasi Penyakit Tuberkulosis pada Citra Rontgen Thoraks Maulana, Isyeh Rafi; Prabowo, Wahyu Aji Eko
JURNAL RISET KOMPUTER (JURIKOM) Vol. 13 No. 1 (2026): Februari 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i1.9482

Abstract

Tuberculosis (TB) is a chronic infectious disease and one of the leading causes of mortality worldwide. Conventional diagnostic processes are often hampered by high costs and technical complexity; consequently, Chest X-Ray (CXR) examinations combined with Artificial Intelligence (AI)-based computer detection systems have emerged as a more efficient alternative. This study aims to comparatively analyze the effectiveness of three fundamental CNN architectures AlexNet, ZFNet, and ResNet18 in detecting TB from chest X-ray images. The research methodology employs the Knowledge Discovery in Databases (KDD) framework on a public CXR dataset. Hyperparameter optimization was implemented using a Grid Search strategy integrated with 5-Fold Cross-Validation to systematically identify the optimal configuration. Experimental results indicate a significant positive correlation between architectural depth and diagnostic performance. Based on the optimal parameters identified through Grid Search specifically a learning rate of 0.0001 and a batch size of 32 the ResNet18 model demonstrated superior performance, achieving 99.28% scores for Accuracy, and 100% for precision, recall, F1-score and AUC-ROC. The superiority of ResNet18 lies in its residual learning mechanism, which effectively addresses the vanishing gradient problem and facilitates the extraction of complex pathological features. The combination of ResNet18 with Grid Search optimization demonstrates that the synergy of modern architecture and systematic tuning yields a highly reliable Computer-Aided Detection (CAD) system, surpassing the results of previous studies
Analisis Komparasi Arsitektur Deep Learning untuk Klasifikasi Penyakit Daun Cabai Sitohang, Bramudya Toguando; Prabowo, Wahyu Aji Eko
JURNAL RISET KOMPUTER (JURIKOM) Vol. 13 No. 1 (2026): Februari 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i1.9483

Abstract

The productivity of chili (Capsicum annuum L.) in Indonesia faces significant challenges due to leaf diseases, which are estimated to reduce harvest yields by up to 35%. Conventional detection methods relying on visual observation often lack accuracy due to the high visual similarity between disease symptoms. This study focuses on a comparative evaluation of three leading Deep Learning architectures VGG16, ResNet50, and InceptionV3 in classifying six types of chili leaf diseases using a public dataset. The research implements a high-resolution image strategy (512 x 512 pixels) to maximize the extraction of disease texture features. The methodology employs a Transfer Learning approach with a standardized hyperparameter tuning scheme. Experimental results indicate that the use of high-resolution images significantly impacts model accuracy. The VGG16 architecture achieved the best performance with a testing accuracy of 99.83% and an F1-Score of 1.00, outperforming ResNet50 (99.75%) and InceptionV3 (84.00%). Confusion Matrix analysis demonstrates that VGG16 possesses superior stability in distinguishing disease classes with high visual similarity, such as Bacterial Spot and Cercospora. The study concludes that architectures preserving deep spatial information, such as VGG16, are more effective for high-resolution image-based plant disease diagnosis compared to more complex architectures that perform aggressive feature compression.

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