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INDONESIA
Jurnal Riset Informatika
Published by KresnaMedia Publisher
ISSN : 26561743     EISSN : 26561735     DOI : -
Core Subject : Science,
Computer Science & IT
Jurnal Riset Informatika, merupakan Jurnal yang diterbitkan oleh Kresnamedia Publisher. Jurnal Riset Informatika, berawal diperuntukan menampung paper-paper ilmiah yang dibuat oleh peneliti dan dosen-dosen program studi Sistem Informasi dan Teknik Informatika.
Arjuna Subject : -
Articles 432 Documents
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COMPARATIVE MACHINE LEARNING ALGORITHMS FOR YOUTUBE SENTIMENT ANALYSIS ON DPR DEMONSTRATION 2025 USING LEXICON Samsudin, Syafri; Abdul Chamid, Ahmad; Jazuli, Ahmad
Jurnal Riset Informatika Vol. 8 No. 1 (2025): Desember 2025
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v8i1.470

Abstract

The high volume of public comments on YouTube regarding the DPR Demonstrasion August 2025, which reached 43,910 raw data, presents a significant challenge in conducting efficient sentiment analysis. Time and cost limitations in manual labeling for large-scale datasets are a major obstacle in the development of predictive models. This study aims to address this problem by proposing a hybrid approach that integrates Lexicon-Based auto-labeling with a comparative evaluation of five Machine Learning algorithms. The research methodology included a text preprocessing stage that generated 40,097 unique comments, feature extraction using TF-IDF, and data sharing with an 80:20 ratio. The performance of the Support Vector Machine algorithm was comprehensively compared to Random Forest, Decision Tree, K-Nearest Neighbors, and Naive Bayes. The results of the experiment showed that the SVM model recorded the most superior performance with an accuracy of 96.5% and a weighted F1-Score of 0.966. This score significantly outperformed other benchmarking algorithms, where Random Forest came in second place with 89.2% accuracy, followed by Decision Tree at 85.6%, KNN at 84.6%, and Naive Bayes at the lowest with 84.0%. These findings validate that the integration of Lexicon-Based labeling with SVM classification is a highly accurate, robust, and efficient solution for handling sentiment analysis on large-scale social media data in Indonesia.
The Implementation of MOORA Methods to Support the Refinement of Decision Priority System in Information Technology Fanni Rahmah Tsani; Umi Chotijah
Jurnal Riset Informatika Vol 5 No 1 (2022): Priode of December 2022
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v5i1.472

Abstract

Maintenance of information and communications technology scoped on the Lamongan Regency Government is the responsibility of the Lamongan Regency Communications and Information Department. The application of information technology is closely related to the problems that appear, such as Communication Network Interruption/Damage. In this case, the report is provided by the user via WhatsApp message, and no single point of contact is used for delivery, retard the refinement process and making it difficult for technicians to prioritize refinement. In this study, the authors built a decision-supporting to assist technicians in prioritizing refinement. The Multi-Objective Optimization Based Ratio Analysis (MOORA) method is appropriate for this study as it allows us to perform the ranking process based on different weighting attributes. The calculation process of the MOORA method is based on specified criteria and weightings. Criteria are the type of damage, risk of a complaint, duration of the claim, and type of service. In one day, the three regional apparatuses with the highest scores are selected, and recommendations for prioritized refinement are provided. In this study, we found that samples with high criterion weights and scores tended to be prioritized over other samples. The results MOORA calculated show the library service as the best alternative with a value of 0.396 on ten regional apparatus tested.
Support Vector Classification with Hyperparameters for Analysis of Public Sentiment on Data Security in Indonesia Siti Ernawati; Risa Wati; Nuzuliarini Nuris
Jurnal Riset Informatika Vol 5 No 1 (2022): Priode of December 2022
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v5i1.481

Abstract

The development of Information Technology makes increasing use of the internet. This raises the vulnerability of data security. Cyber attacks in Indonesia caused many tweets on social media Twitter. Some are positive, and some are negative. The problem of this study is to determine the public sentiment towards data security in Indonesia, while the purpose of this study is how the response or evaluation of the government of Indonesia to the many perceptions of people who lack confidence in data security in Indonesia. Data obtained from twitter with as much as 706 data was processed using python with a percentage of 10% test data and 90% training data. Weighting is done using TF-IDF, and then the Data is processed using the Support Vector Machine algorithm using the SVC (Support Vector Classification) library. Support Vector Classification with RBF kernel classifies Text well to obtain AUC value with good classification category. Utilizing one of the hyperparameter techniques, which is a grid search technique that can compare the accuracy of test results. The test results using SVC with RBF kernel obtained an accuracy value of 0.87, Precision of 0.82, recall of 0.94, and F1_Score of 0.87. This study is expected to be used by decision-makers related to public confidence in data security in Indonesia
Pregnancy Risk Level Classification Using The CRISP-DM Method Reka Dwi Syaputra; Achmad Solichin
Jurnal Riset Informatika Vol 5 No 1 (2022): Priode of December 2022
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v5i1.487

Abstract

Independent midwife practices have the task of reminding and maintaining the quality of standardized reproductive health services for pregnant women. Independent midwife practices have had patient visits since the covid-19 pandemic from 2020 to 2021, especially at the yetti puranama midwife, which consists of 320 pregnancy examinations, 130 delivery care, and 50 referrals. The covid-19 pandemic has impacted maternal mortality rates because there are still many restrictions on all services. Maternal health services include pregnant women who are routinely unable to go to the puskesmas or other healthcare facilities due to fear of contracting covid-19, which delays the examination of pregnancy gravida, abortion, temperature, pregnancy distance, haemoglobin, blood pressure, ideal weight, and decisions. So that the problem that occurs is an increase in the risk of pregnancy, resulting in death and increased maternal mortality. In solving this problem, the research takes a machine-learning approach. The research aims to build a classification of pregnancy risk levels that can predict early treatment in this study using the random forest method with cross-validation 2. This study obtained the results of an accuracy value of 98%, precision of 94%, and recalled 100% in the random forest method.
Texture Feature Extraction of Pathogen Microscopic Image Using Discrete Wavelet Transform Hasan Basri
Jurnal Riset Informatika Vol 5 No 1 (2022): Priode of December 2022
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v5i1.488

Abstract

This study used a case study of Jabon leaves, and the pathogen is one of the causes of disease that attack the leaves of jabon, one of the leaf spots and leaf blight. Discovery of leaf spot disease in different pathogens and leaf blight. The pathogen was obtained from the leaf spot of Curvularia sp. 1 and Pestalotia sp., while the pathogen came from Curvularia sp. 2 and Botrytis sp. Identify the pathogen as soon as possible to minimize its effects. Improper handling can lead to increased virulence and resistance to the pathogen. Improper handling also can cause a disease outbreak (disease epidemic) in a region. This study is the first step in identifying the pathogens responsible for Jabon leaf disease. In this study, the Application of Koch's Postulates method to achieve the purification of pathogens and retrieve the microscopic pathogen image as the data acquisition stage. Furthermore, use of the segmentation stage to separate the object pathogen from the background, and one of the methods used is Otsu Thresholding. The extraction process of pathogen microscopic image using Discrete Wavelet Transform (DWT), DWT extraction results can be obtained using energy and entropy information.
DEVELOPMENT OF HYPEBID MARKETPLACE INFORMATION SYSTEM WITH REAL-TIME ONLINE AUCTION FEATURE Avriananta, Jovan; Hermawan, Arief
Jurnal Riset Informatika Vol. 8 No. 1 (2025): Desember 2025
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v8i1.471

Abstract

Conventional auctions still face a number of challenges, such as limited access, unclear processes, and low time efficiency. Auction participants are generally required to be physically present, which limits the number of participants and reduces transparency and transaction speed. Based on this background, this study aims to develop HypeBid, a web- and mobile-based marketplace information system that supports online and real-time auction processes. This system is built using a client-server architecture with React Native, Express.js, PostgreSQL, and Supabase technologies. Development was carried out through stages of needs analysis, system design, and implementation of key features such as user registration, product verification, live bidding, integrated payment systems, and transaction reports. System testing was conducted using the black box testing method involving two groups of users, namely buyers and auction officers. The test results showed that all features functioned as expected, without any functional errors. Thus, HypeBid is considered to be a clearer, more flexible, and efficient alternative solution compared to conventional auction methods.
DEVELOPMENT OF WEBSITE-BASED LEARNING MEDIA ON MEDIA ELEMENTS AND TELECOMMUNICATION NETWORKS AT SMK NEGERI 1 PAINAN Prihamdani, Ferju; Mary, Thomson; Novita, Rini
Jurnal Riset Informatika Vol. 8 No. 2 (2026): Maret 2026
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v8i2.485

Abstract

This study aims to develop a website-based learning media for the Basic Computer and Telecommunication Network Engineering subject, particularly the Media and Telecommunication Networks topic for Grade X TJKT students at SMK Negeri 1 Painan. The research was motivated by limited practical facilities and the lack of interactive learning media, which resulted in low student learning outcomes. This study employed the SDLC iterative model consisting of requirements analysis, design, development, testing, and implementation. Data were collected through expert validation sheets and practicality questionnaires for teachers and students. The developed media integrates visual materials, instructional videos, and interactive quizzes to support independent learning. Validation results indicate that the media achieved a software quality evaluation score of 88.19%, while practicality scores reached 95.48% from teachers and 86.75% from students, categorized as highly practical. These findings demonstrate that the proposed web-based learning media is feasible and effective in supporting the teaching and learning process in vocational education, particularly in improving students’ understanding of abstract networking concepts
OBJECT DETECTION FOR LOW-LIGHT ENVIRONMENT USING MULTISCALE RETINEX Nugroho, Anthonius Adi
Jurnal Riset Informatika Vol. 8 No. 2 (2026): Maret 2026
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v8i2.493

Abstract

Object detection is a critical task in computer vision, yet its performance degrades significantly under low-light conditions due to loss of detail and diminished features. This study proposes an image enhancement framework to improve detection robustness in challenging lighting. The methodology integrates Multiscale Retinex (MSR) for image enhancement and SSD MobileNet V2 for object detection. MSR was configured with optimal parameters (scale1:10, scale2:60, scale3:180, σ:100, β:30) to enhance brightness while preserving crucial image details. The experimental results demonstrate that Retinex correction is highly effective in extreme low-light scenarios. In 0 lux conditions, where objects were completely undetectable without processing, the proposed method enabled detection with confidence levels between 62% and 96%, yielding an average accuracy increase of 50%. In 15 lux conditions, accuracy improved by 6.6%. However, the system degraded at intensities above 25 lux, suggesting that the enhancement is most beneficial in near-dark environments. In conclusion, Multiscale Retinex significantly enhances the capability of SSD MobileNet V2 for object detection in environments with illumination below 77 lux. This approach provides a viable solution for improving the reliability of surveillance and automated systems operating in unpredictable lighting.
Decision Support System for Supplier Selection on Time Concept with AHP and SAW Method Warih Dwi Cahyo; Wahyu Ari Wibowo; Suwarno Suwarno; Rizki ripai
Jurnal Riset Informatika Vol 5 No 1 (2022): Priode of December 2022
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v5i1.460

Abstract

Seeing the rapid development of global business causes companies to compete as the best to meet global market demands. In the current era of globalization, technological development is beneficial for human life. All human activities today can be done quickly and easily using a computer. Decision Support System is a computer-based system that assists decision-making in utilizing specific data and models to solve various unstructured problems. Decision makers in selecting the best supplier for Time Concept are still having difficulties, and this is because there are no appropriate criteria and weights. Making a decision support system is expected to help solve the problems in Time Concept. Moreover, it can provide benefits or convenience for Time Concept when selecting the best supplier. The author uses the method of Analytical Hierarchy Process (AHP) and Simple Additive Weighting (SAW). According to the system test results, the Consistency Ratio (CR) calculation value is 0.0752. The comparison assessment is considered CONSISTENT if the Consistency Ratio (CR) value is not greater than 0.1000. So that the comparison of the criteria does not need to be recalculated because it is CONSISTENT.
DEVELOPMENT OF AN ECONOMIC GROWTH DATA VISUALIZATION DASHBOARD FOR PALEMBANG CITY USING THE AGILE METHOD silvia, Mutiara Marsa; Nasir, Muhammad
Jurnal Riset Informatika Vol. 8 No. 2 (2026): Maret 2026
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v8i2.486

Abstract

The presentation of economic growth data for Palembang City on the official website of Badan Pusat Statistik (BPS) still faces challenges in terms of usability, navigation, and data visualization. The complex menu structure, poorly organized data presentation, and limited interactive features reduce efficiency in accessing and interpreting economic information. To date, there is no dedicated interactive dashboard that centrally integrates and visualizes Gross Regional Domestic Product (GRDP) data for Palembang City in a user-oriented manner, creating a gap in the provision of accessible regional economic analysis tools. This study aims to develop an Economic Growth Data Visualization Dashboard for Palembang City to present GRDP data in a clearer, more interactive, and user-friendly format. The system was developed using the Agile Development method, consisting of planning, design, development, testing, and evaluation stages. The dashboard was built using Next.js as the frontend framework and MySQL as the database management system. It presents GRDP data at current prices (ADHB), constant prices (ADHK), expenditure components, and business sector categories through interactive charts and dynamic tables. Black-Box testing confirmed that all system features functioned properly. Usability testing using the System Usability Scale (SUS) with 50 respondents resulted in a score of 84.8, categorized as Excellent. The system is feasible as a decision-support tool for regional economic data analysis.

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