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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
Performance Comparison Between ResNet50 and MobileNetV2 for Indonesian Sign Language Classification Daviana, Feriska Putri; Aryanti, Aryanti; Anugraha, Nurhajar
JURIKOM (Jurnal Riset Komputer) 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.
Optimasi Algoritma K-Nearest Neighbors pada Prediksi Penyakit Diabetes Arfiah, Sitti; Wajidi, Farid; Nur, Nahya
JURIKOM (Jurnal Riset Komputer) 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.v12i3.8615

Abstract

Diabetes mellitus is a chronic disease characterized by high blood sugar levels due to metabolic system disturbances, specifically related to insulin production or effectiveness. If left untreated, it can lead to serious complications. Early and accurate detection is crucial for timely medical intervention. This research aimed to improve the accuracy of a diabetes classification system using the K-Nearest Neighbors (KNN) algorithm. An initial KNN model with imbalanced data (without SMOTE) and no GridSearchCV achieved only 83% accuracy. While seemingly good, its performance for the positive class was low (precision 80%, recall 69%, F1-score 74%), indicating bias towards the negative class due to data imbalance. To address this, several steps were implemented: data preprocessing (handling missing data and feature normalization), hyperparameter optimization using GridSearchCV, and data balancing with SMOTE. After these improvements, the KNN model showed significant performance gains, with accuracy reaching 94%. Performance for the positive class greatly improved (precision 90%, recall 98%, F1-score 94%), and for the negative class (precision 98%, recall 89%, F1-score 93%). These results demonstrate that combining preprocessing, model optimization, and class balancing effectively enhances the KNN algorithm's ability to detect diabetes more accurately and robustly, proving that machine learning with proper data processing can aid in developing medical decision support systems for early diabetes diagnosis.
Penerapan Word2Vec dan SVM dengan Hyperparameter Tuning untuk Deteksi Phishing Wicaksana, Hilman Singgih; Huda, Khairul
JURIKOM (Jurnal Riset Komputer) 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.v12i3.8729

Abstract

The advancement of information technology in today’s digital age takes place very rapidly from one time to another. This phenomenon is accompanied by increasing cybersecurity threats like phishing. Phishing links are often designed with uniform resource locator (URL) structures that appear convincing and are difficult to distinguish from genuine links. This research proposes a word-to-vector (Word2Vec) and Support Vector Machine (SVM) approach with hyperparameter tuning where Word2Vec is a word embedding technique used to create a word vector representation of a particular URL, SVM is used as a machine learning (ML) approach used in this research, and hyperparameter tuning is used as a technique to find the best combination of parameters to produce an optimal SVM model in detecting phishing. The purpose of this research is to compare the performance between SVM and XGBoost models that have been optimized and deploy ML models into a prediction system using the Streamlit framework to detect phishing based on input made by users in the form of certain URLs. The findings of this study indicated that the SVM model performed very well compared to the XGBoost model, with precision, recall, f1-score, and accuracy values of about 99.84% for SVM. On the other hand, the XGBoost model recorded precision, recall, f1-score, and accuracy values of about 99.70% each. Thus, the SVM model is the optimal model to detect phishing precisely and accurately.
Implementasi Item-Based Collaborative Filtering Dalam Sistem Pemesanan Online Pada UMKM Berbasis Website Ikhsan, Ramadhani Al; Harahap, Aninda Muliani
JURIKOM (Jurnal Riset Komputer) 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.v12i3.8568

Abstract

This study designs a web-based online menu ordering system for the UMKM Solo Fried Chicken (SFC), located on Jl. Binjai KM.10.5, Paya Geli, Sunggal District, Deli Serdang Regency. The system is built using PHP and MySQL, with a responsive design to allow customers to place orders anytime and anywhere. The applied technology aims to address several issues previously faced by UMKM SFC, such as slow and inefficient manual ordering processes, long queues, and order recording errors that affect service quality. Additional problems include the lack of available menu information, the absence of a recommendation system to assist customers in choosing menus, and the unavailability of a digital system for recording transactions and sales reports. The main problem addressed in this study is how to build a web-based online ordering system that not only simplifies transactions but also accurately recommends menus based on customer preferences. As a solution, this research implements the Item-Based Collaborative Filtering method to recommend menus based on the purchasing patterns of other customers with similar preferences. Based on the calculations, the system recommends the top three most relevant menus for each main menu item, such as Combo Original Paha for Ayam Paha, and Kidz 3, which frequently appears as a recommendation due to its similarity with many customers' preferences. This system is expected to improve operational efficiency, reduce errors, accelerate service, and provide a more personalized ordering experience. Key features developed in the system include online ordering, menu recommendations, sales reports, and transaction recording, which are visualized through a Use Case Diagram and Flowmap
Peramalan Penjualan Semen Menggunakan Metode Single Moving Average dan Double Moving Average Yuliana, Nur Lutfi; Santi, Nirma Ceisa; Mahmudah, Nur
JURIKOM (Jurnal Riset Komputer) 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.v12i3.8537

Abstract

UD. Kurnia Makmur is a shop that sells various building materials including cement. Previously, the shop UD. Kurnia Makmur still had difficulty in determining the amount of cement stock that should be provided, UD. Kurnia Makmur often also experiences shortages or excess stock due to the rise and fall of inconsistent market demand. Therefore, a forecasting method is needed that can help make better decisions in estimating the amount of cement stock that must be provided. The method used to predict cement stock in this study is the single moving average and double moving average methods. The purpose of this forecasting study is for the company to know the amount that must be provided according to consumer demand for cement sales and to know the accuracy between the single moving average and double moving average methods. Because the previous journal showed that both methods produced a MAPE of less than 10% where, it can be interpreted that if the MAPE is less than 10% then the forecast is very good. In calculating the accuracy of this study using MAD (Mean Absolute Percentage Error) and MAPE (Mean Absolute Percentage Error) using Microsoft Excel as the calculation tool. After being calculated using Microsoft Excel, the results obtained in the study were MAD of 30.72 and MAPE of 2.0% for the single moving average, while the double moving average produced MAD of 19.0 and MAPE of 1.24%.
Rainfall Prediction Using Attention-Based LSTM Architecture Romadhani, Ahmad; Santoso, Irwan Budi; Crysdian, Cahyo
JURIKOM (Jurnal Riset Komputer) 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.v12i3.8727

Abstract

This study addresses the challenge of accurately predicting rainfall in regions with complex climate dynamics, such as Malang Regency, East Java. It evaluates the performance of a Long Short-Term Memory (LSTM) model enhanced with the Bahdanau Attention Mechanism, comparing it with a Standard LSTM model in forecasting daily rainfall based on historical weather parameters including average temperature, relative humidity, sunshine duration, and wind speed. Using daily data from BMKG covering 2000 to 2023, both models underwent a structured machine learning process including data preprocessing, feature selection, model training, and evaluation. The Attention-Based LSTM consistently outperformed the Standard LSTM, particularly in handling rainfall anomalies, achieving an MSE of 0.00800 and RMSE of 0.08948, compared to 0.00817 and 0.09039 respectively for the Standard LSTM. These results demonstrate that integrating Bahdanau Attention improves the model’s focus on relevant temporal features, enhancing prediction accuracy and robustness. The architecture consisting of two LSTM cells combined with the attention mechanism effectively captures complex sequential patterns that the standard model tends to overlook. This research highlights the potential of attention mechanisms in time series weather prediction, contributing to more reliable early warning systems, adaptive agricultural strategies, and disaster risk reduction frameworks. Future work could explore hybrid models or incorporate additional weather features to further improve performance and generalization.
Klasifikasi Kanker Payudara Berbasis Deep Learning Menggunakan Vision Transformer dengan Teknik Augmentasi Data Citra Ardiyansyah, Muhamad Salman; Umbara, Fajri Rakhmat; Melina, Melina
JURIKOM (Jurnal Riset Komputer) 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.
Implementasi Model ARIMA untuk Peramalan Reorder Point dalam Supply Chain Management Alexandra, Andrea Cellista; Hartomo, Kristoko Dwi
JURIKOM (Jurnal Riset Komputer) 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.
Identifikasi Berita Palsu di Portal Media Online Menggunakan Model IndoBERT dan LSTM Kamal, Angga Mochamad; Chrisnanto, Yulison Herry; Yuniarti, Rezki
JURIKOM (Jurnal Riset Komputer) 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.
Aplikasi Mobile Kesehatan Maternal Menggunakan Haversine Formula Untuk Pencarian Layanan Terdekat Khairunnisyah, Siti; Triase, Triase
JURIKOM (Jurnal Riset Komputer) 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.v12i3.8550

Abstract

Health facilities are important facilities used to provide health services to the community. Types of public health facilities include hospitals, health centers, clinics, pharmacies, poskesdes, and pustu. Knowing the location of the nearest health facility is very important, especially in emergency situations. One effective solution to access such information is through location-based mobile applications. This study aims to build a mobile application that can help users, especially pregnant women and postpartum mothers, in finding the nearest maternal health services in Bandar District by utilizing the Haversine Formula. This formula is used to calculate the distance between the user's location and health facilities based on geographic coordinates. This application also presents an ordered list of health facilities based on the closest distance and can be filtered based on the type of service needed. Functional testing is done using the black box method which shows that all application features run well according to the design. Interim results show that the application is able to display accurate data about the position and distance of maternal health services efficiently, and get positive feedback from early users who state that the application is very helpful in urgent conditions and for routine control needs.

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