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Journal of Emerging Information Systems and Business Intelligence (JEISBI)
Published by Universitas Negeri Surabaya
ISSN : -     EISSN : 27743993     DOI : 10.26740/jeisbi
Core Subject : Science, Education,
Computer Science & IT Decision Sciences, Operations Research & Management Languange, Linguistic, Communication & Media Library & Information Science
Journal of Emerging Information Systems and Business Intelligence (JEISBI) aims to provide scholarly literature focused on studies and research in the fields of Information Systems (IS) and Business Intelligence (BI). This journal also includes public reviews on the development of theories, methods, and applications relevant to these topics. All published works are presented exclusively in English to reach a global audience of readers and researchers. The journal’s scope includes but is not limited to the following fields: Data Mining Generative Artificial Intelligence Big Data Analytics Business Intelligence Enterprise Architecture UI/UX Business Process Management Enterprise System System Development Decision Support System IS/IT Strategy and Planning IT Investment and Productivity IT Project Governance IS Business Value Audit SI/TI Cybersecurity and Risk Management IS/IT Operations and Service Management IT Ethics Organizational and Human Behavior Technology Digital Sociology
Arjuna Subject : Ilmu Komputer (semua kategori) - Sistem Informasi
Articles 15 Documents
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Search results for , issue "Vol. 6 No. 2 (2025): Vol. 06 Issue 02" : 15 Documents clear
Prediction and Analysis of Customer Churn at Telkomsel Using Machine Learning Approach Achmad Mauludi Asror; I Kadek Dwi Nuryana
Journal of Emerging Information Systems and Business Intelligence Vol. 6 No. 2 (2025): Vol. 06 Issue 02
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jeisbi.v6i2.65825

Abstract

Customer churn is one of the main problems in the telecommunications industry, including Telkomsel, the largest cellular operator in Indonesia. This study aims to build a classification model to predict customer churn and analyze the factors influencing churn using the CRISP-DM approach. Data was obtained through an online questionnaire from 100 respondents who are active students of Universitas Negeri Surabaya. The research process includes stages of data preparation (normalization, encoding, and removal of irrelevant attributes) and the application of classification algorithms such as Logistic Regression, Decision Tree, Random Forest, K-Nearest Neighbors, Support Vector Machine, and Naïve Bayes. Evaluation was carried out using metrics such as accuracy, precision, recall, and F1-Score. The results show that Random Forest is the best algorithm with an F1-Score of 87.50% on an 80:20 data ratio. Feature analysis indicates that the attribute of previous churn status has the greatest influence on churn prediction
Comparative Analysis Of Laravel, Lumen, Guzzle, Leaf, and Slim Framework Performance On Rest API Using One Way Anova Moch. Faisal; Nuryana, I Kadek Dwi
Journal of Emerging Information Systems and Business Intelligence Vol. 6 No. 2 (2025): Vol. 06 Issue 02
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jeisbi.v6i2.66146

Abstract

The rapid advancement of technology has increased the importance of selecting the right framework for RESTful API development. This study compares the performance of five popular PHP frameworks—Laravel, Lumen, Guzzle, Leaf, and Slim—in terms of response time, CPU usage, memory usage, throughput, and error rate. Using Apache JMeter as the testing tool, load testing was conducted across various endpoints with simulated virtual users (up to 75 users). The methodology involved designing a RESTful API with a PostgreSQL database, implementing it using the five frameworks, and performing load tests to measure the defined performance parameters. Statistical analysis using One-Way ANOVA was conducted to determine significant performance differences among the frameworks. The results indicate that each framework has distinct strengths and weaknesses under specific conditions. Frameworks like Lumen and Guzzle demonstrated superior performance in terms of response time and CPU usage, while Slim performed better with higher throughput under certain scenarios. These findings provide critical insights for developers and decision-makers in selecting the most efficient framework based on project requirements. Keyword: RESTful API, PHP frameworks, performance analysis, load testing, Apache JMeter, One-Way ANOVA.
A Comparative Analysis Opinion Mining Sea Games On Social Media Lathifatuz zuhroh; Nuryana, I Kadek Dwi
Journal of Emerging Information Systems and Business Intelligence Vol. 6 No. 2 (2025): Vol. 06 Issue 02
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jeisbi.v6i2.66875

Abstract

Twitter is a social media platform that is freely accessible to everyone. As a result, numerous social phenomena and issues quickly emerge and spread globally through users' tweets. The 2023 SEA Games in Cambodia was no exception, as it sparked public opinion, particularly among Indonesian citizens, due to several incidents during the event that were considered unethical or inappropriate. This study aims to analyze public sentiment regarding these events using the C5.0 and Naïve Bayes algorithms. The performance of both algorithms will be compared to determine which one yields better results. The dataset consists of 1,000 tweets collected between March 14 and April 14, 2023. The findings indicate that Naïve Bayes outperforms C5.0, achieving an accuracy of 70%, compared to 67% for C5.0 . Keyword: Twitter, Sentiment Analysis, Naïve Bayes, C5.0, Sea Games.
Online Store Web Software Engineering with Sales Forecasting Implementation : Online Store Web Software Engineering with Sales Forecasting Implementation Achmad Alvin Ardiansyah; Bonda Sisephaputra, M.Kom
Journal of Emerging Information Systems and Business Intelligence Vol. 6 No. 2 (2025): Vol. 06 Issue 02
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jeisbi.v6i2.67215

Abstract

This research focuses on the development of a web-based online store system with the implementation of a sales forecasting feature to support the business operations of UMKM Fay Brownies. The system was designed using the Extreme Programming (XP) software development method, which emphasizes an iterative, collaborative, and adaptive approach to changing user requirements. Several technology stacks were utilized in the development, including Next.js, Laravel, Django, and MySQL. The main features of the application include user authentication, shopping cart, ordering, payment, shipping cost calculation, automatic notifications via WhatsApp, and an admin dashboard for managing business aspects such as products and sales. The system is equipped with a sales forecasting module that employs seven methods: ARIMA, Single Exponential Smoothing, Simple Moving Average, Double Moving Average, Weighted Moving Average, Long Short-Term Memory (LSTM), and Auto Regressive models to predict product sales based on historical data. System evaluation showed that the application successfully meets user needs in conducting transactions easily and securely, while also providing accurate sales forecasts to support decision-making regarding raw material stock and business planning. The results of this study are also expected to serve as a reference for the development of e-commerce applications with forecasting capabilities that can be adopted by other small and medium-sized businesses.
Decision Support System For Determining The Best Employees To Get Incentives Using The Web-Based Multi Factor Evaluation Process (MFEP) Method Dimas Prananda; Warih Utami, Ardhini
Journal of Emerging Information Systems and Business Intelligence Vol. 6 No. 2 (2025): Vol. 06 Issue 02
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jeisbi.v6i2.69087

Abstract

One way for companies to enhance employee performance and motivation is by discovering the most effective methods of providing incentives. However, due to the multitude of factors that must be considered, this process is often complex. Therefore, the objective of this research is to create and develop a web-based Decision Support System that employs the Multi-Factor Evaluation Process (MFEP) method to assist in determining the employees who are most deserving of compensation. This research leverages the Rapid Application Development (RAD) methodology, which encompasses several phases: requirements planning, user design, construction, and cutover. During the construction phase, the system was tested using the Blackbox method to ensure that all functions operated correctly without examining the program code. The research findings indicate that the decision support system can recommend the employees who are most eligible for compensation in accordance with company standards. The MFEP method implemented in this system yields unbiased evaluation results by considering various assessment factors. The rapid system development (RAD) method facilitated a swift and structured development process. The results of the Blackbox testing demonstrate that the system operates effectively in line with the established requirements. With this system, it is anticipated that the process of identifying top-performing employees will become more efficient, objective, and transparent. Additionally, this system can enhance employee motivation to achieve the highest levels of performance.
TOPIC MODELING OF UNESA LAKE REVIEW ON GOOGLE MAPS USING LATENT DIRICHLET ALLOCATION (LDA) METHOD Kurrotul Uyun; I Kadek Dwi Nuryana
Journal of Emerging Information Systems and Business Intelligence Vol. 6 No. 2 (2025): Vol. 06 Issue 02
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jeisbi.v6i2.69653

Abstract

User reviews on digital platforms hold valuable information that can be used to improve service quality. This study aims to explore the topics that appear in visitor reviews of Lake UNESA based on rating categories with a topic modeling approach using the Latent Dirichlet Allocation (LDA) method. The analysis process follows the stages in the Knowledge Discovery in Databases (KDD) framework, starting from the selection of Google Maps review data, text preprocessing (cleaning, letter normalization, tokenization, word normalization, and stopword removal), and data transformation into bag-of-words representation through bigram-trigram formation and dictionary-corpus creation. Topic modeling is performed using LDA, and the results are evaluated and interpreted through pyLDAvis and wordcloud visualization. Model validation is carried out through Word Intrusion Task and Topic Intrusion Task testing, with accuracy levels of 0.91 and 0.88, respectively. The results show that LDA is able to identify topics optimally. Each rating category produces different topics that represent visitor perceptions of aspects that are not yet available, still k-aspects such as atmosphere, cleanliness, culinary, and facilities. These findings are expected to provide data-based insights to support the development and management of Lake UNESA more effectively.
Broiler Chicken Meat Production Process Modeling at UD. Hari Ayam Using the Business Process Model and Notation (BPMN) Method Elwino, Elwino Alif Ramadhan; Nuryana, I Kadek Dwi
Journal of Emerging Information Systems and Business Intelligence Vol. 6 No. 2 (2025): Vol. 06 Issue 02
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jeisbi.v6i2.69788

Abstract

Hari Ayam is a business that has been operating since 1988 and currently produces around 100–150 kilograms of chicken meat per day. The production process covers everything from processing to distribution to customers. However, the business faces several challenges, particularly the lack of clear and standardized documentation of its business processes. This issue makes it difficult to objectively assess performance or improve efficiency. In addition, the entire operation still relies heavily on the owner, resulting in a high workload and a lack of optimal task delegation. The absence of proper records related to transactions, production, income, and expenses further complicates the overall management of the business. This study aims to apply the Business Process Model and Notation (BPMN) method using the Bizagi application to map out the chicken meat production process at UD. Hari Ayam. The research focuses on the production and distribution stages and is limited to the design phase of the business process lifecycle. Through evaluation and simulation, the study is expected to provide a more efficient and structured view of the business processes. The results are intended to offer practical input for the business owner to improve workflow, enhance operational efficiency, and facilitate better oversight in support of the company’s future development.
Application of Market Basket Analysis to Optimize Marketing Strategies in MSMEs Diah Cookies Oksana Khoirunnida; Ghea Sekar Palupi
Journal of Emerging Information Systems and Business Intelligence Vol. 6 No. 2 (2025): Vol. 06 Issue 02
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jeisbi.v6i2.70068

Abstract

MSMEs Diah Cookies are micro businesses in the culinary sector that produce various types of pastries and other preparations. Even though it has daily sales transaction data, the use of this data in analyzing consumer behavior is still not optimal. This study aims to identify consumer purchasing patterns using Market Basket Analysis with Apriori algorithm. and designing a marketing strategy based on daily sales transaction data for the January-November 2024 period. The data is processed using RapidMiner Studio software. The results of the study showed that there was a pattern of buying products that were often purchased at the same time, such as the combination of Nastar Cheese and Sago Cheese associated with Kastangel Ori, Choco Rollcake with Cheese Rollcake, and Akuroti variant of Choco Chunk and Tiramisu associated with Oreo Cream Cheese. These patterns are used as the basis for the preparation of marketing strategies, such as product bundling, store layout arrangements, and product arrangement in storefronts (planograms). Based on the results of the study, the application of Market Basket Analysis has proven to be effective in providing relevant insights to support marketing strategy decision-making in Diah Cookies MSMEs.
Evaluating User Acceptance of KAI Access: A Comparison of TAM and UTAUT Susanto, Deafitria Putri; Nuryana, I Kadek Dwi
Journal of Emerging Information Systems and Business Intelligence Vol. 6 No. 2 (2025): Vol. 06 Issue 02
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jeisbi.v6i2.70116

Abstract

This study aims to analyze and compare two technology acceptance models Technology Acceptance Model (TAM) and Unified Theory of Acceptance and Use of Technology (UTAUT) in measuring user acceptance of the KAI Access mobile application in Surabaya. The research adopts a quantitative approach, using questionnaires distributed to 200 active users of the KAI Access app. Data were analyzed using Partial Least Square-Structural Equation Modeling (PLS-SEM) with SmartPLS software. Results show that all variables in the TAM model significantly influence behavioral intention, particularly perceived usefulness and perceived ease of use. Meanwhile, in the UTAUT model, only effort expectancy and facilitating conditions have a significant effect. The R-square and Q-square values indicate that TAM has stronger predictive capability than UTAUT in this context. These findings offer useful insights for improving the KAI Access application and can serve as a reference for future research on technology acceptance in public digital services.
Analysis Of User Satisfaction Towards The ShopeePay Application Using The EUCS (End User Computing Satisfaction) Method Rafeda Ramma, Rafif; Nuryana, I Kadek Dwi
Journal of Emerging Information Systems and Business Intelligence Vol. 6 No. 2 (2025): Vol. 06 Issue 02
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jeisbi.v6i2.70125

Abstract

This study examines user satisfaction with the ShopeePay application using the End User Computing Satisfaction (EUCS) method. Despite some negative feedback regarding the payment system’s limitations and complexity, the research employed a quantitative approach with 160 respondents selected through purposive sampling. Data analysis was conducted using SmartPLS with Partial Least Squares Structural Equation Modeling (PLS-SEM) techniques to assess user satisfaction. The findings reveal that overall, users are satisfied with the ShopeePay application, indicating a positive reception of the platform. This research contributes to understanding consumer attitudes towards digital payment applications and provides insights for improving user experience in the rapidly evolving fintech sector. The study’s methodology and results offer valuable information for both researchers and practitioners in the field of mobile payment systems and user satisfaction analysis.

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