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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 998 Documents
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Peramalan Penjualan Semen Menggunakan Metode Single Moving Average dan Double Moving Average Yuliana, Nur Lutfi; Santi, Nirma Ceisa; Mahmudah, Nur
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.v12i3.8537

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%.
Sentiment Analysis of Youtube Comments on Indonesian Presidential Candidates in 2024 using Naïve Bayes Classifier Mahfudza, Nurbaiti; Ihksan, Muhammad
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 2 (2025): April 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i2.8538

Abstract

The 2024 Indonesian presidential election is one of the most talked about topics on various social media platforms, including YouTube. The comments that appear on political-themed videos can reflect public opinion towards presidential candidates. This research aims to conduct sentiment analysis of YouTube comments related to Indonesian presidential candidates in 2024 using the Naïve Bayes Classifier method. This method was chosen due to its ability to classify text data effectively and efficiently. Data was collected from a number of relevant Kompas tv videos on YouTube, then text preprocessing stages such as data cleaning, tokenization, and stemming were performed. Next, the data was classified into three sentiment categories, namely positive, negative, and neutral. The research shows that the Naïve Bayes model is able to classify sentiment with sufficient accuracy.  This finding can provide an overview of public perceptions of each presidential candidate as well as input for interested parties in the fields of politics and public communication. The results of this study show that the naïve bayes classifier algorithm can analyze with an accuracy of 61 % in the evaluation process using confusion matrix. The results of this study indicate that the naïve bayes classifier algorithm can be an effective alternative for analyzing the sentiment of YouTube comments on presidential candidates.
Design Development of the JekNyong Application Using the Design Thinking Method Pambudi, Wendri Tri; Wardhana, Ariq Cahya
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.v12i3.8539

Abstract

The JekNyong application is a platform that allows the people of Banyumas Regency to sell household waste for recycling. However, the application’s user adoption rate remains low, with only 3.33% of families using it and a rating of 3.4 on the Google Play Store. This is due to an unintuitive interface design and limited feature accessibility. An initial usability test showed a task success rate of 75%, a time-based efficiency of 0.0206 goals/second, and a System Usability Scale (SUS) score of 63. The design development process followed the Design Thinking methodology through the stages of empathy, problem definition, ideation, prototyping, and testing. Several improvements were made to navigation, feature accessibility, and app layout. The second round of testing revealed significant improvements: the task success rate increased to 95.83%, time-based efficiency rose to 0.0382 goals/second, and the SUS score jumped to 86. These results indicate that the design improvements successfully enhanced the application's effectiveness and efficiency in accessing features.
Implementasi Deep Neural Network untuk Prediksi Harga Saham PT Bank Central Asia Tbk Almasah, Muhammad Rakha; Prabowo, Wahyu Aji Eko
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 2 (2025): April 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i2.8544

Abstract

Stock price prediction is one of the main challenges in financial market analysis. This study develops a Deep Neural Network (DNN) model using TensorFlow to predict the stock price of PT Bank Central Asia Tbk (BBCA) based on historical stock price data, the Jakarta Composite Index (IHSG), and the USD/IDR exchange rate. The model is optimized through hyperparameter tuning using KerasTuner with the Hyperband method, allowing for more efficient exploration of hyperparameter combinations. The tuning process yielded the best model configuration with a learning rate of 0.001269, four hidden layers, and the application of Batch Normalization, L2 kernel regularization, and dropout in each hidden layer.The model was evaluated using various batch sizes (4, 16, 32, 64, and 128) with Mean Squared Error (MSE) and Mean Absolute Error (MAE) as performance metrics. The results show that batch size 128 provides the best performance, with MSE of 0.0094 and MAE of 0.0490 on the test data, indicating high accuracy and good generalization on unseen data. The best model was then implemented as an API using Flask, deployed to Google Cloud Run, and integrated with a Flutter-based mobile application. Testing confirmed that the API can handle requests quickly using TensorFlow and produce accurate predictions.Thus, this study successfully developed a DNN-based BBCA stock price prediction system that can be applied to data-driven investment decision-making.
Identifikasi Polaritas Sikap Pengguna Aplikasi X terhadap Coretax di Indonesia Menggunakan Algoritma Naïve Bayes Prasilda, Dina Rahma; Yuniarti, Wenty Dwi; Handayani, Maya Rini; Umam, Khothibul
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.v12i3.8548

Abstract

The Core Tax Administration System (Coretax) was launched by the Directorate General of Taxes (DGT) in January 2025 as a technology-based integrated tax system. While its initial goal was to improve tax efficiency and compliance, Coretax faced technical challenges, including system errors, slow processing speed, and criticism from the public. The main platform used to address these challenges is the X app (formerly known as Twitter). This research aims to understand the public's views and responses to Coretax's services by analyzing user sentiment patterns seen on social media. The research identifies the polarity of user attitudes by utilizing natural language processing (NLP) and Naïve Bayes algorithms, applied to a dataset of 1,628 tweets collected between January and March 2025. The analyzed data reflects a wide range of public reactions that include both positive and negative opinions towards the Coretax implementation, both in terms of functionality and ease of use. The results show that the model has an accuracy rate of 93.07%, a precision value of 95%, a recall value of 96%, and an F1-Score value of 96%. The results of this study are expected to be able to provide precise mapping related to changes in public opinion towards Coretax, so that it can be a valuable source of information for application developers, policy makers in the field of taxation, and analysis in the technology sector in responding to the needs and expectations of society in the digital era.
Aplikasi Mobile Kesehatan Maternal Menggunakan Haversine Formula Untuk Pencarian Layanan Terdekat Khairunnisyah, Siti; Triase, Triase
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.v12i3.8550

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.
Deteksi Serangan ICMP Flood pada Internet of Things dengan Feature Selection dan Machine Learning Harid, Harid; Kurniabudi, Kurniabudi; Harris, Abdul
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.v12i4.8554

Abstract

IoT devices have played an important role in driving DDoS attacks, and are a threat to IoT networks. One of them is the ICMP Flood attack. To overcome attacks on IoT, one of them uses an Intrusion Detection System (IDS). However, on the other hand, IDS has challenges in handling the complexity of high-dimensional data. One of the suggested solutions to overcome the problem of data dimensions is the use of feature selection techniques. The Forward Selection feature selection technique is used to eliminate irrelevant features. This study compares the performance of the Random Forest and SVM algorithms. For experimental purposes, this study used the CICIoT2023 dataset, which represents IoT traffic. The use of Forward Selection obtained 11 selected features that will be used in the machine learning process using the Random Forest and SVM methods. Feature selection affects the computation time or processing time, because the fewer features used, the more the system's workload in carrying out the classification process. The test results show that the use of feature selection improves the performance of random forest with an accuracy of 100%. Meanwhile, the SVM model gets better accuracy by using feature selection with the highest accuracy of 99.4508% in the supplied test set test.
Analisis Sentimen Publik Terkait Kekerasan Seksual di Indonesia dengan Algoritma Naïve Bayes dan SVM Nalista, Ni Made Naila; Mandenni, Ni Made Ika Marini; Suarjaya, I Made Agus 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.v12i3.8556

Abstract

Semakin meningkatnya kasus kekerasan seksual yang terjadi di Indonesia, dan media sosial merupakan ruang bagi masyarakat Indonesia untuk mengekspresikan pendapat. The increasing number of sexual violence cases in Indonesia, along with the role of social media as a space for the public to express their opinions, forms the basis for this research. The study aims to classify various types of public sentiment expressed on X (formerly Twitter) and Instagram comments by applying two algorithms for comparison: Naïve Bayes and SVM. Several processes carried out, including data collection from social media, data preprocessing, manual labeling, and the implementation of both algorithms on the processed dataset. The data sources utilized are posts written in Indonesian on X (Twitter) and Instagram, focusing on issues of sexual violence in Indonesia. The sentiment analysis results were grouped into three main categories: positive, negative, and neutral. The outcomes show that SVM achieved an accuracy of 82.17% using an 80:20 data split without applying GridSearch for optimization. The SVM results outperformed those of Naïve Bayes, which achieved an accuracy of 78.92%. This investigation leads to the conclusion that SVM is more optimal in analyzing public sentiment related to sexual violence in Indonesia compared to Naïve Bayes. The sentiment analysis results from social media regarding sexual violence in Indonesia show that the majority of sentiments are neutral, with the dataset being dominated by informative content, case reports without emotional expression, and off-topic comments
Implementasi Item-Based Collaborative Filtering Dalam Sistem Pemesanan Online Pada UMKM Berbasis Website Ikhsan, Ramadhani Al; Harahap, Aninda Muliani
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.v12i3.8568

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
Klasterisasi Data Stunting Pada Balita Di Puskesmas Xyz Dengan Menggunakan Metode Mixture Modelling Delianda, Anggun; Asrianda, Asrianda; 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.v12i3.8580

Abstract

This research is motivated by the high prevalence of stunting in Indonesia, reflecting nutritional imbalances in early childhood. To address this issue, an information technology approach is employed to identify at-risk infant groups. The analyzed data consists of anthropometric information, including height, weight, and age of infants, collected from the Peusangan Health Center. The applied method is the Gaussian Mixture Model (GMM) with the Expectation-Maximization algorithm to cluster the data into two groups: "Potential Stunting" and "Not Stunting." The research results indicate that several Posyandu and villages have notably high potential stunting rates, such as Posyandu Bungong Seulanga (141 infants) and Pante Gajah village (116 infants), with a higher prevalence among male infants (34.67%) and those aged 52–60 months (24.18%). Model evaluation using a confusion matrix on 1,465 data points showed a True Positive of 958 (65.36%), False Negative of 4 (0.27%), False Positive of 503 (34.33%), and True Negative of 0 (0%), with an accuracy of 65.36% and an error rate of 34.64%. However, a previous accuracy test on 1,665 data points only achieved 34.55%, indicating unsatisfactory individual prediction performance. In conclusion, Mixture Modelling is effective for clustering and identifying at-risk groups but lacks accuracy in individual predictions, with a bias toward the "Potential Stunting" class that requires improvement in future research.

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