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Jurnal Riset Informatika

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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 417 Documents

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Analysis of User Satisfaction of Acces by KAI Application Using User Experience Questtionnaire (UEQ) Method Lailatul Qomariyah; Prita Dellia; Vella Sifa Nurhidayati; Aristya Miftahun Nur Risky; Zidan Zam Zami
Jurnal Riset Informatika Vol. 7 No. 3 (2025): Juni 2025
Publisher : Kresnamedia Publisher

including in the transportation sector. Advances in information technology have encouraged companies to innovate in providing faster, more practical, and efficient services to meet user needs. One of them is PT Kereta Api Indonesia officially releasing the access by KAI application. However, in the access by KAI application there are indications of poor user experience which shows user dissatisfaction in using the access by KAI application. This study aims to determine the extent to which the Access by KAI application meets user expectations in terms of ease of use, effectiveness in helping users achieve their goals, and to determine the level of user experience felt during the use of the application. The method used in this study is the User Experience Questionnaire (UEQ) method to measure user experience with 6 assessment scales including attractiveness, perspicuity, efficiency, dependability, stimulation, and novelty. The results of the UEQ questionnaire showed that respondents had a positive impression of the Access by KAI application where the survey results showed a positive evaluation with a mean value> 0.8 and all aspects of the Access by KAI application were categorized in "Above Average" this means that in general it has quite good performance but still needs improvement.

ANALYSIS OF PUBLIC SENTIMENT TOWARDS 2024 PRESIDENTIAL CANDIDACY USING NAÏVE BAYES ALGORITHM Rianggi; Ruhyana, Nanang
Jurnal Riset Informatika Vol. 7 No. 1 (2024): December 2024
Publisher : Kresnamedia Publisher

This study analyzes public sentiment towards presidential nominations using text mining techniques and machine learning. The dataset consists of 670 tweets collected from social media. The analysis process includes a data pre-processing phase, encompassing text cleaning, case folding, tokenization, stopword removal, and stemming using the Sastrawi library for the Indonesian language. Sentiment labeling was was performed using NLTK's SentimentIntensityAnalyzer, categorizing tweets into positive, negative, or neutral sentiments. The analysis results reveal the sentiment distribution among the analyzed tweets. Data modeling was performed using the Naive Bayes algorithm, which achieved an accuracy of 97.78% on the Iris dataset as an implementation example. The confusion matrix and classification report demonstrate the model's excellent performance in distinguishing sentiment classes. This research provides insights into public opinion regarding presidential nominations and demonstrates the effectiveness of text mining techniques and machine learning in sentiment analysis. The method can be applied to understand public opinion trends in other political and social contexts

Shapley Additive Explanations Interpretation of the XGBoost Model in Predicting Air Quality in Jakarta Iffadah, Adhisa Shilfadianis; Trimono; Dwi Arman Prasetya
Jurnal Riset Informatika Vol. 7 No. 3 (2025): Juni 2025
Publisher : Kresnamedia Publisher

Air quality degradation has become an increasing global problem since 2008, including in Jakarta. By 2024, air pollution in Jakarta is estimated to cause 8,400 deaths and losses of around 34 billion rupiah. To address air pollution, air quality prediction is needed using historical data of Jakarta Air Quality Index from January 2021 to May 2024. The XGBoost ensemble model was chosen for its ability to handle complex data and prevent overfitting. And Shapley Additive Explanations (SHAP) to understand how the model makes decisions. Results showed the XGBoost model achieved MAPE 4.44%. Analysis with Shapley Additive Explanations (SHAP) identified PM2.5 was significantly affected by max and PM10 features, while O3, CO, SO2, and NO2 remained relevant. An increase in PM10 tends to increase PM2.5 concentrations, suggesting the need to control this parameter to improve air quality. These results are important to provide a better understanding of the dynamics of air quality as well as provide a reference for the government in formulating more effective policies or preventive measures in Jakarta.

KNOWLEDGE-BASED HIJAB PRODUCT SELECTION RECOMMENDATION SYSTEM AT CANDY SCARVES Nur Rohmani, Mayda; Hartanti, Dwi; Ayu Kusuma Asri, Anindhiasti
Jurnal Riset Informatika Vol. 7 No. 3 (2025): Juni 2025
Publisher : Kresnamedia Publisher

The primary objective of this study is to construct a knowledge-driven hijab product selection recommendation system for Candy Scarves. This system is designed to help customers find hijabs that match their criteria by utilizing customer characteristics and product attributes. The study uses a knowledge-based recommendation approach supported by case-based techniques. The construction of the system is orchestrated through the application of the Rapid Application Development (RAD) paradigm, encompassing a sequence of iterative stages—ranging from requirement formulation and architectural design to accelerated prototyping and eventual deployment—thus privileging adaptability and user-centered refinement over linear progression. Data modeling using sample data totaling 25 hijab products and 6 attributes. The system provides recommendations based on criteria for hijab models, materials, hijab colors, skin colors, motifs, and prices. The empirical findings reveal that the hijab item exhibiting the utmost degree of similarity is the Umama Hijab with voal material, mocha hijab color, brown skin color, and plain motifs with a result of 0.90303. The results of this analysis are able to provide personal recommendations effectively and have the potential to increase customer satisfaction and product sales.

IMPLEMENTATION OF DATA MINING ON MUSLIM WOMEN'S CLOTHING SALES USING THE FP-GROWTH METHOD Aldinata, Riko; Raissa Amanda Putri
Jurnal Riset Informatika Vol. 7 No. 3 (2025): Juni 2025
Publisher : Kresnamedia Publisher

The Muslim women's fashion industry in Indonesia is growing rapidly, leading to intense competition and requiring business owners to optimize their sales strategies and inventory management. This study aims to identify consumer purchasing patterns at TM Collection Store by applying the FP-Growth data mining method. The data used consists of 1,000 sales transactions from January to April 2024. Data collection was conducted through historical data observation, interviews, and literature review, followed by processing using the FP-Growth algorithm in Google Colab. The analysis results reveal strong associations between products, such as the combination of Paris Premium, shirt cuffs XL, and shirt cuffs L, which show high confidence values and significant lift. These patterns provide valuable insights for decision-making related to restocking and promotional strategies. The findings also help improve operational efficiency by more accurately predicting customer demand. Therefore, the implementation of the FP-Growth algorithm proves effective in processing transaction data to generate relevant information and support more targeted business decisions. This data-driven strategy offers an innovative solution to enhance competitiveness in the continuously growing Muslim women's fashion industry.

Social Network and Sentiment Analysis for Enhancing Social CRM in Indonesian Educational Technology Platforms Khairunnisa, Rifaa; Siregar*, Johannes Hamonangan
Jurnal Riset Informatika Vol. 7 No. 4 (2025): September 2025
Publisher : Kresnamedia Publisher

The rapid advancement of digital technology has significantly transformed the education sector, including in Indonesia. According to the 2024 report by Badan Pusat Statistik (BPS), e-learning is among the primary reasons Indonesians access the internet. This trend has positioned educational technology (EdTech) platforms such as Ruangguru, Pahamify, and Zenius as key players in the country’s e-learning ecosystem. Simultaneously, social media has become a space where users actively express their experiences regarding the services they use. This study aims to examine user interaction dynamics and public sentiment toward these three EdTech platforms through an integrated approach combining Social Network Analysis (SNA) and Lexicon-Based Sentiment Analysis. Data were collected from platform X and preprocessed for analysis. Network analysis used Gephi to evaluate structural properties and centrality measures, while sentiment analysis used a combination of the InSet lexicon and user-generated vocabulary. To further capture discussion themes, topic modeling using the BERTopic algorithm was also applied to categorize dominant topics from user conversations. The results show that each platform exhibits different social network characteristics. Zenius demonstrates efficient information flow, Ruangguru displays tightly connected user interactions, and Pahamify presents a more dispersed structure. Overall, the sentiment analysis showed that Ruangguru and Zenius had relatively higher proportions of positive sentiment, with 44.6% and 41.4%, respectively. These findings highlight how integrating SNA and sentiment analysis can form a strong foundation for developing Social CRM strategies to enhance the quality of digital education services in Indonesia.

Factor Analysis of E-Learning Acceptance in SMK Sore Tulungagung Using Technology Acceptance Model (TAM) Indah, Rhohmah; Maya Safitri, Eristya; Puspa Rinjeni, Tri
Jurnal Riset Informatika Vol. 7 No. 4 (2025): September 2025
Publisher : Kresnamedia Publisher

Although the use of E-Learning has been expanded as a modern learning solution, its implementation at SMK SORE Tulungagung still faces obstacles such as low student participation, difficulty in accessing E-Learning features, and delays in submitting assignments. This indicates that there are obstacles in the acceptance of learning technology by students. This study aims to analyze the factors that influence the acceptance of E-Learning using the Technology Acceptance Model (TAM) framework with the main variables: Perceived Usefulness (PU), Perceived Ease of Use (PEOU), Attitude Toward Using (ATU), Behavioral Intention to Use (BIU), and Actual Use (AU). The method used is quantitative with a predictive approach, data was obtained by distributing questionnaires to students majoring in Computer and Network Engineering, Data analysis was carried out using validity, reliability, and structural modeling (SEM-PLS) tests. The results showed that PU and PEOU significantly influenced students' attitudes and intentions in using E-Learning. In addition, BIU contributed to the use of the actual system and external factors such as complexity and voluntariness were also analyzed to determine the indirect effect on technology acceptance.

EXPERT SYSTEM DEVELOPMENT TO IDENTIFY EMPLOYEE PERSONALITY TYPES USING DEMPSTER SHAFER THEORY Julia Fajaryanti; Rogayah Rogayah
Jurnal Riset Informatika Vol 4 No 3 (2022): Period of June 2022
Publisher : Kresnamedia Publisher

Human resources are an important asset for the company to develop and realize the company's goals. One of the efforts to optimize the capacity of employees is to know their personality. Personality is the form possessed by an individual in behaving and all the characteristics that distinguish one individual from another. Knowing the personality of employees is important for the company and the employees themselves. Because by knowing a person's personality, the company can maximize the potential of employees and can place certain positions that suit the personality of the employee. This study aims to implement dempster-shafer theory on an inference engine in building an expert system to identify employee personality types. Dempster-shafer theory can perform probability calculations so that evidence can be carried out based on the level of confidence and logical reasoning. The system developed is able to identify the personality type of the employee through the nature or symptoms that exist in the employee. In addition, the system can display the results of the diagnosis with an explanation of the personality type, its nature in work and occupations or positions that are suitable for that personality type. Based on the results of the accuracy test obtained from the comparison of expert system diagnoses with the analysis of an expert, the accuracy value reaches 85%.

Active Learning Query by Committee Labeling Method to Increase Accuracy and Efficiency of Sentiment Analysis Classification Dipa Anasta Iskandar; R. Mohamad Atok
Jurnal Riset Informatika Vol. 7 No. 4 (2025): September 2025
Publisher : Kresnamedia Publisher

This study proposes the Query by Committee (QBC) labeling method to improve the accuracy of classification models—specifically XLM-RoBERTa—and to increase labeling efficiency compared to manual, supervised labeling, which generally requires more time and resources. The dataset consists of unannotated healthcare-industry application reviews scraped from Google Play. Six distinct labeling strategies were applied as input for fine-tuning XLM-RoBERTa models under identical hyperparameter settings. The six labeling approaches were evaluated namely Rating-based labeling, Lexicon-based labeling, QBC for Rating-Vader labeling, QBC for Rating-Pseudo labeling, QBC for Vader-Pseudo labeling, and QBC triplet for Rating-Pseudo-Vader labeling. Each labeled dataset was split using stratified random sampling, and class weights were set to “auto” during training to address label imbalance. All models were subsequently tested on the IndoNLU SmSA test dataset, with performance compared in terms of accuracy, precision, recall, and F1-score. Results indicate that the triplet QBC approach (combining Rating, VADER, and Pseudo labeling) outperformed all other methods, achieving an accuracy of 91.4%, a precision of 91.28%, a recall of 91.4%, and an F1-score of 91.21%. These findings demonstrate that the QBC labeling method can serve as an effective and efficient alternative to manual annotation for similar classification tasks

Integration of OCR Technology with ETL Processes for Automating Data Pipeline of Financial Disbursement Documents at BPS Sukabumi Regency Muhammad Raihan Izharul Haq; Gina Purnama Insany; Somantri
Jurnal Riset Informatika Vol. 7 No. 4 (2025): September 2025
Publisher : Kresnamedia Publisher

In the digital era, managing archival data poses challenges for many institutions, including Badan Pusat Statistik (BPS) of Sukabumi Regency, especially when dealing with unstructured PDF documents. This study develops a data pipeline by effectively integrating Optical Character Recognition (OCR) technology with Extract, Transform, Load (ETL) processes. Unstructured data from financial disbursement documents, such as SPM and SP2D, were automatically extracted with high accuracy, achieving an average of 98.52% for SPM using a combination of OCR and PDFPlumber, and 100% for SP2D extracted using PDFPlumber. Extraction results were stored in a data warehouse, then transformed using Apache Spark and loaded into data marts. ETL process was automated using Apache Airflow, which operated reliably according to dependencies. The processed data were presented through an interactive Looker Studio dashboard in real-time, supporting efficient archive management and more informed decision-making. This study not only provides a solution to existing archival management problems but also opens opportunities for further development in the application of big data technologies and business process automation in public sector.

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Home Page

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Editorial Team

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Contact Name
Tati Mardiana

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jurnal.jri@kresnamediapublisher.com

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Journal Mail Official
jurnal.jri@kresnamediapublisher.com

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Location
Kota banjar,

Jawa barat

INDONESIA