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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 1,025 Documents
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A Analisis Tingkat Kepuasan Konsumen Pada Pelayanan PT. AXZ Furniture Di Media Internet Menggunakan Metode VADER dan ARM Laksmi, Ida Ayu; I Made Candiasa; Putu Hendra Suputra
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 6 (2025): Desember 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i6.9322

Abstract

Bali Art Furniture is one of the companies in Bali that exports furniture and home decoration products. During its operation, this company utilises various internet media platform services such as websites, Facebook Marketplace, Instagram, WhatsApp Business, Pinterest, and Google to communicate online with consumers. As the company has grown over time, it has recruited many employees to increase its capacity to provide services. The company has never conducted a systematic evaluation of customer service satisfaction, either internally or externally. Based on this phenomenon, a customer satisfaction analysis was conducted using customer comment data. Customer service satisfaction was evaluated using the VADER and ARM methods as a basis for comparing the effectiveness of these methods. Based on the analysis of the two methods, the VADER method produced an accuracy of 34%, while the ARM method produced an accuracy of 64%. The evaluation results using the confusion matrix of the VADER model showed that positive comments were more recognisable by the system than negative and neutral comments, as seen from the positive recall value of 0.90, which was greater than the negative and neutral recall values. Meanwhile, the evaluation results using the ARM method showed that neutral comments were more recognisable by the system than positive and negative comments, as seen from the neutral recall value of 0.88, which was greater than the positive and negative recall values. Thus, the highest accuracy results in the ARM model became the guideline in making recommendation results.
Perancangan Sistem Pelayanan Klinik Gigi Berbasis Web dan Mobile dengan Integrasi Sistem Antrian Digital menerapkan Algoritma First Come First Served (FCFS) Yusuf, Fitria Dwi Handayani; Aji, Adam Sekti
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 6 (2025): Desember 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i6.9324

Abstract

Efficient and responsive dental clinic services are essential for improving patient satisfaction and ensuring streamlined healthcare delivery. However, many independent dental clinics still rely on manual procedures for patient registration, scheduling, and queue management, which often lead to long waiting times, unpredictable service flow, and a high risk of administrative errors. These issues negatively impact the overall quality of service and reduce operational effectiveness. This study aims to design and implement a web- and mobile-based dental clinic service system integrated with a digital queuing mechanism to address these challenges. The system was developed using Flutter as the patient-facing mobile interface, Flask as the backend service responsible for managing business processes, and Supabase as the real-time database and user authentication platform. Key features of the system include patient registration and login, service booking, automated scheduling based on reservation time or arrival order, real-time queue monitoring, electronic medical record management, and integrated digital payments to facilitate transactions. Based on functional and performance testing, the system was able to reduce service processing time by up to 80% compared to manual methods, minimize data entry errors, and significantly improve the accuracy of queue management. User feedback also indicated that the system provides a more convenient and informative experience, as patients can easily monitor their queue position through the mobile application. Overall, the proposed system effectively enhances service efficiency, data accuracy, and the quality of dental clinic operations, while also demonstrating strong potential for further development and scalability across larger healthcare facilities
Pengembangan Sistem Deteksi Plagiarisme Dokumen Jurnal Berbasis Bidirectional Encoder Representations from Transformers Dan Cosine Similarity Ariyanto, Cahya Yoga; Aji, Adam Sekti
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 6 (2025): Desember 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i6.9325

Abstract

The development of digital technology has had a significant impact across various fields, including education and the management of scientific documents. The ease of access to online journals has introduced a new challenge—an increase in the potential for plagiarism. To address this issue, an automated system capable of detecting document similarity quickly and accurately is required. This study aims to develop a plagiarism detection system based on Cosine Similarity and Bidirectional Encoder Representations from Transformers (BERT). The research stages include text preprocessing, word weighting using Term Frequency–Inverse Document Frequency (TF-IDF), Cosine Similarity computation, BERT model training, and model performance evaluation. The results show that integrating BERT with TF-IDF significantly improves performance compared to using BERT alone. Based on the experiments, the BERT model with TF-IDF achieved the highest accuracy of 0.9621 in a 10:90 data split scenario, with a precision of 0.8141, recall of 0.7302, and F1-score of 0.8022. Meanwhile, the BERT model without TF-IDF only achieved an accuracy of 0.8529. The application of Cosine Similarity with a threshold value of 0.6 also proved effective in identifying plagiarized and non-plagiarized documents. These findings demonstrate that combining BERT and TF-IDF enhances the accuracy of plagiarism detection systems by simultaneously capturing semantic context and word weighting.
Perancangan Aplikasi e-Lades sebagai Sistem Pelayanan Administrasi Masyarakat Berbasis Website di Desa XYZ menggunakan Metode Rapid Application Development Demonius Sarumaha; Junaidi
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 6 (2025): Desember 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i6.9341

Abstract

Public administration services in Desa Amplas are still conducted manually, resulting in delays, lack of transparency. This study aims to design and develop the e-Lades (Electronic Village Service) web-based application to improve efficiency, effectiveness, and accountability of services. The development method used is Rapid Application Development (RAD), allowing fast iterations based on user feedback. The research stages include requirements analysis, system design, development, testing, implementation, and evaluation. The main features include online administrative document registration, population data management, application status tracking, and WhatsApp-based automatic notifications. Trial results show an increase in service processing speed by up to 60% compared to the manual system, and an 85% increase in community satisfaction. This application is expected to serve as a model for digitizing village administrative services that can be replicated in other areas
Credit Card Fraud Detection Using Ensemble Variation: Logistic Regression, Support Vector Classifier and Random Forest Salibana, Chlyfen Richard; Ema Utami
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 6 (2025): Desember 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i6.9343

Abstract

Credit card fraud is a significant threat in the financial industry, causing significant financial losses annually, posing a challenge to both businesses and the financial sector. This requires research and development to identify fraud models that significantly improve over time. The purpose of this research is to develop a machine learning (ML)-based credit card fraud detection system with an ensemble approach to address the challenges of imbalanced data in digital financial transactions. The method used includes four main stages: data collection; SMOTE; Hyperparameter Tuning; and model evaluation. The dataset used is from Kaggle Credit Card Fraud Detection, which has a very low fraud proportion (0.17%). The increase in data volume was carried out using SMOTE on the training data. Three main models (Logistic Regression, Support Vector Classifier, Random Forest) and ensembles (hard and soft voting) were tested with hyperparameter tuning for optimal results. Random Forest performed best with an F1-Score of 0.8482 and an ROC-AUC of 0.9684. This model was able to detect 84% of fraudulent transactions with high precision, surpassing other models in handling imbalanced data. The combined advantages of RF and SMOTE are effective for fraud detection which is relevant for real-time systems in the financial sector.
Perbandingan Kinerja Algoritma C4.5 dan Naive Bayes Dalam Klasifikasi Data Penjualan Buku PT. XYZ Erfina Rianty; Kurnia Budi; Effiyaldi
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 6 (2025): Desember 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i6.9345

Abstract

Book sales data is an important component in supporting marketing strategies and managerial decision-making. The objective of this research is to evaluate and compare the effectiveness of the C4.5 and Naive Bayes in processing book sales data at PT. Sonpedia Publishing Indonesia. The dataset used consists of 299 book sales records, processed using RapidMiner software with two validation methods, namely Split Data (80:20) and 10-fold cross validation. Experimental results reveal that the C4.5 algorithm with the split data method obtained an accuracy 88.33%, precision 94.29%, recall 86.84%, and F-Score 90.41%. Using 10-Fold Cross Validation, the performance decreased with an accuracy 86.60%, precision of 92.53%, recall 85.64%, and F-Score 88,99%. In contrast, the Naïve Bayes algorithm demonstrated better and consistent performance. With the Split Data method (80:20), it obtained an accuracy 90.00%, precision 90.00%, recall 94.74%, and an F-Score 92.31%. Furthermore, its performance improved with 10-Fold Cross Validation, achieving an accuracy 91.29%, precision 92.63%, recall 93.62%, and F1-Score of 93.10%. These findings suggest that naive bayes produces more consistent and accurate classification results compared to C4.5. The research is intended to act as a guide for the development of book sales prediction systems that support the effetiveness and efficiency of bussiness decision making.
Penerapan Arsitektur Deep Learning EfficientNetB0 Berbasis Citra Digital untuk Meningkatkan Kinerja Sistem Klasifikasi Sampah Organik, Anorganik, dan B3 Alya Salsabila, Anggita; Muljono, Muljono
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 6 (2025): Desember 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i6.9360

Abstract

Waste management in Indonesia remains a major challenge due to increasing waste volumes and the low efficiency of manual sorting processes at Landfills (TPA). This study aims to improve the performance of an automated waste classification system for three categories: organic, inorganic, and hazardous and toxic waste (B3) using deep learning-based computer vision technology. The proposed method is the EfficientNetB0 architecture with a transfer learning approach, whose performance is compared with four other pre-trained architectures (VGG-16, InceptionV3, MobileNetV2, and ResNet50). The dataset used consists of 7,003 valid images collected from public sources and manual acquisition after a data cleaning process. The dataset is divided into 70% as training data, 20% as validation data, and 10% as test data. Data augmentation and class balancing strategies are used to increase variation and overcome data imbalance between classes. Training is conducted in two stages: Feature Extraction and Fine-Tuning, with consistent hyperparameters for a fair comparison. Performance evaluation is performed using accuracy, precision, recall, and f1-score metrics. The test results show that EfficientNetB0 managed to achieve the best performance with an accuracy rate of 96.87%. Modern architectures like EfficientNetB0 have proven capable of extracting complex features with good computational efficiency, thereby holding the potential for use in AI-based automatic waste sorting systems to support more effective and sustainable waste management.
Pengembangan Sistem Pendukung Keputusan Berbasis Machine Learning untuk Prediksi Kinerja Dosen Menggunakan Data Historis Evaluasi Pembelajaran Z, Ismail Abdurrozzaq; Widaningrum, Ida; Litanianda, Yovi
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 6 (2025): Desember 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i6.9363

Abstract

Lecturer performance evaluation is a crucial component in efforts to improve the quality of higher education. However, traditional evaluation methods still face various challenges, such as subjective assessments, a lack of consistent standards, and lengthy decision-making processes. These conditions highlight the need for a more measurable, accurate, and data-driven evaluation mechanism, particularly in the context of ongoing digital transformation. This study aims to design and develop a lecturer performance prediction system using a machine learning (ML) approach within a Decision Support System (DSS) framework. The research approach involves processing historical lecturer data covering aspects of Teaching (including student evaluation scores, instructional innovation, and attendance levels), Research (number of publications, H-index, and participation in academic conferences), Community Service, and other administrative activities. Predictive models were developed and compared using several machine learning algorithms, namely Random Forest, Support Vector Machine (SVM), Multilayer Perceptron (MLP), and XGBoost. Experimental results show that Random Forest achieved an accuracy of 88.0%, SVM 85.0%, and MLP 87.0%, while XGBoost demonstrated the best performance with an accuracy of 92.0%, precision of 91.0%, recall of 90.0%, and an F1-score of 91.0%. Based on these results, XGBoost was selected as the primary model for the DSS. In addition, the system is equipped with a rule-based module that generates follow-up recommendations based on the model’s prediction results. All system components are implemented in an interactive dashboard using the Streamlit framework, enabling users to input data, monitor prediction outcomes, and obtain decision recommendations in a fast and data-driven manner.
Rekomendasi Klasifikasi Dan Desain Otomatis Menu Restoran Kopi XYZ Berbasis Web Menggunakan Metode Naïve Bayes Hidayah, Adinda Fita; Hasugian, Abdul Halim
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 6 (2025): Desember 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i6.9364

Abstract

Abstract The advancement of information technology has accelerated digital transformation in the culinary industry, particularly in menu management, which demands fast, structured, and error-minimized processes. In many MSMEs, menu classification and design activities are still performed manually, resulting in inefficiencies. This study developed a web-based system capable of automatically classifying menu categories using the Multinomial Naïve Bayes algorithm and generating menu designs automatically. The dataset consists of 148 menu items covering food, beverages, and snacks. The features used include text-based menu names processed using TF-IDF, as well as numerical price attributes. The data were split into 80% training and 20% testing portions. The results show that the Multinomial Naïve Bayes model achieved the best performance with an accuracy of 93.24%, a precision of 0.92, a recall of 0.93, and an F1-score of 0.92. These values demonstrate the model’s ability to consistently recognize word patterns representing menu categories. The system also successfully generated menu template designs automatically based on the classification results. This research contributes to the application of data mining in the culinary sector and supports MSMEs in improving the effectiveness of menu management.
Prediksi Harga Rumah Menggunakan Algoritma Regresi Linier,Random Forest, Dan Gradient Boosting Akhmadi, Akhmadi; Budiman, Fikri
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 6 (2025): Desember 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i6.9369

Abstract

House price prediction is a crucial issue in the property sector because it is influenced by various interrelated factors, such as building characteristics and environmental conditions. Accurate prediction using conventional approaches is often difficult and can lead to errors in decision-making. Therefore, this study aims to develop and compare the performance of house price prediction models using three machine learning algorithms: Linear Regression, Random Forest, and Gradient Boosting. The dataset used is the Home Value Insights Dataset on Kaggle, which consists of 1,000 houses with eight main attributes. The research stages include data pre-processing, dividing training and test data, model training, parameter optimization using GridSearchCV, and performance evaluation based on Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Coefficient of Determination (R²) metrics using the 10-Fold Cross Validation method. The test results show that Linear Regression provides the best performance with an R² value of 0.8539 and a lower prediction error rate than Random Forest and Gradient Boosting. Although the ensemble model shows competitive results, increasing model complexity does not result in a significant increase in accuracy, so Linear Regression is considered the simplest, most efficient, and most easily interpreted approach for house price prediction systems on datasets with characteristics that tend to be linear.

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