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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 288 Documents
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Implementation of the Support Vector Machine (SVM) Algorithm in Predicting Transaction Cancellations at Shopee E-commerce: Implementasi Algoritma Support Vector Machine (SVM) Dalam Memprediksi Pembatalan Transaksi Pada E-commerce Shopee Maulidia, Ridhotul; Yustanti, Wiyli
Journal of Emerging Information Systems and Business Intelligence (JEISBI) Vol. 6 No. 1 (2025)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jeisbi.v6i1.64414

Abstract

In the digital era, shopping through e-commerce such as Shopee has become increasingly popular. However, transaction cancellation is still an obstacle that causes financial losses for sellers. This research aims to predict transaction cancellation on the Shopee platform using the Support Vector Machine (SVM) algorithm, which is expected to help sellers reduce the risk of loss. The data used comes from the transaction history of Shopee store nafystore.id and is processed using the CRISP-DM method, including business understanding, data preparation, modeling, and deployment. The data preparation process includes cleaning, encoding, normalization, and dimension reduction using Principal Component Analysis (PCA), as well as handling data imbalance with SMOTE. Model testing was conducted using K-Fold Cross-Validation at 3, 5, and 10 folds with different SVM kernels, where the linear kernel showed the best performance with 95.57% accuracy, 95.96% precision, 95.57% recall, and 95.58% F1-Score. The implementation of a web-based system is done using Streamlit to make it easier to use for sellers. The results of this research provide benefits for sellers in identifying cancellation factors, such as Total Payment and Estimated Shipping Fee Deductions. This research not only enriches the application of SVM algorithm in e-commerce analysis, but also provides a reference for other e-commerce platforms to improve transaction efficiency and customer satisfaction.
WEB-BASED EMPLOYEE RECRUITMENT DECISION SUPPORT SYSTEM USING THE FUZZY ANALYTICAL HIERARCHY PROCESS METHOD (CASE STUDY PT. AGROFARM NUSA RAYA) Setia Mahardika, Vicky Pratama; Sisephaputra, Bonda
Journal of Emerging Information Systems and Business Intelligence (JEISBI) Vol. 6 No. 1 (2025)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jeisbi.v6i1.64415

Abstract

Employees or employees are part the most important thing for the company. So therefore, When recruiting, it must be done carefully maximum in order to obtain quality employees, quality and according to needs. In the process recruitment of new employees, PT. Agrofarm Nusa Raya still using the manual method and not yet apply methods for decision making. A large number of prospective employees make HRD find it difficult to select prospective employees who match the criteria for acceptance in company. To overcome this problem So a Decision Support System is needed web-based by applying the Fuzzy method Analytical Hierarchy Process (Fuzzy AHP). Criteria used for the employee recruitment process namely interviews, psychological tests, Islamic knowledge, work experience, education and certificates. Results The end of the research shows that the application Employee recruitment decision support system can be developed using the Extreme method Programming (XP). In the calculation results applying the Fuzzy AHP method obtained results that the highest score is 1,660 with the name Akbar,while the lowest value is -1.810 with the name Priest. With the existence of a Decision Support System Those who implement Fuzzy AHP will make it easier HRD work in the decision making process related to the final results of employee recruitment.
Business Process Reengineering in the Training Service Business Process of CV. Maxindo Consulting to Improve Company Performance Efficiency Ruben Emanuel Widagdo; Suartana, I Made
Journal of Emerging Information Systems and Business Intelligence (JEISBI) Vol. 5 No. 4 (2024)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jeisbi.v5i4.64521

Abstract

In facing global competition, companies must ensure that employees can work effectively and efficiently, with both high-quality and high-quantity output. Employee training has become one of the crucial methods to meet these standards, considering the importance of competent human resources in supporting a company’s competitiveness. CV. Maxindo Consulting is a company that specializes in employee training services across various sectors. However, its current business processes, such as proposal requests and training evaluations, remain time-consuming, hindering work efficiency and potentially harming the company’s image. Therefore, Business Process Reengineering (BPR) is proposed as a solution. This BPR approach is expected to enhance service quality, operational efficiency, and the company’s positive image. The BPR method, as explained by Hammer and Champy (1994) and Davenport & Short (1990), represents a novel approach to business process improvements that can optimize company services and performance.
Comparison Of Web Load Speed Between Babel Transpiler and SWC On Website-Based Applications Rangga Prasetya, Rangga Prasetya; Sujatmiko, Bambang
Journal of Emerging Information Systems and Business Intelligence (JEISBI) Vol. 6 No. 1 (2025)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jeisbi.v6i1.64610

Abstract

Transpilers play an important role in software development by translating code from one programming language to another, allowing developers to take advantage of modern features and capabilities without having to change the entire project. For example, by using a transpiler like Babel, developers can write the latest JavaScript code that remains compatible with older browsers, similar to translating a book into multiple languages ​​to make it accessible to a wider audience. Babel is the most commonly used transpiler. On the other hand, there is SWC which is a new transpiler that is claimed to be faster than Babel. This study aims to determine the difference in the speed of the Babel and SWC transpilers. The data for this study were taken from several pages using Google Lighthouse. The data were analyzed using a parametric test, namely the paired sample t-test. The results of the study showed that SWC had a significant difference in speed compared to Babel in the First Contenfulpaint (FCP) and Speed ​​​​Index (SI) indicators. Babel is superior in Total Blocking Time (TBT). While in the Largest Contenfulpaint (LCP), Babel is significantly superior to SWC. This shows that SWC is faster than Babel transpiler in web load speed because in the speed indicators, namely FCP and SI, SWC is significantly superior.
IT Risk Management Planning on Avesina Application RSUD Waluyo Jati Kab. Probolinggo Using OCTAVE Allegro: IT Risk Management Planning on Avesina Application RSUD Waluyo Jati Kab. Probolinggo Using OCTAVE Allegro Jamil, Safaru Khoiron; Ghea Sekar Palupi
Journal of Emerging Information Systems and Business Intelligence (JEISBI) Vol. 6 No. 1 (2025)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jeisbi.v6i1.64629

Abstract

The application of information technology at RSUD Waluyo Jati Kraksan can pose a risk. The example of information technology risks that have occurred is the occurrence of additional working hours caused by system errors in the Avesina application related to room procurement. The purpose of this study is to determine the IT risks that arise from the implementation of the Avesina application, and provide the necessary mitigation in managing these risks. The method used in this research is OCTAVE Allegro. Before determining the risk, asset profiling and wadan of the asset are carried out. The risks arising from the implementation of the avesina application are data input errors, application hacking, applications can be easily accessed by unauthorized people, and account sharing. There are several stages used to determine the mitigation approach. Of the four risks, it was found that only two risks needed to be mitigated. The mitigation provided focuses on the asset containers that need to be controlled, namely the SIM RS Unit and management. The mitigation includes procuring a renewable security system, procuring a 2-factor authentication system, and monitoring compliance with information technology.
Sentiment Analysis of 2024 Election Fraud Using SVM and Naïve Bayes Algorithms Hilmi, Faalih Hibban; Indriyanti, Aries dwi
Journal of Emerging Information Systems and Business Intelligence (JEISBI) Vol. 5 No. 4 (2024)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jeisbi.v5i4.64703

Abstract

Elections are one of the main pillars of democracy, where the people's voice is the main determinant in government formation. Election fraud not only harms political competitors but also undermines public trust in democracy. The role of social media Twitter in widely disseminating information and disinformation adds to the challenge of maintaining election integrity. Sentiment analysis is the process of collecting and understanding individual opinions related to an event. Support Vector Machine (SVM) and Naïve Bayes algorithms are often used in this analysis due to their effectiveness and efficiency in text classification. This research aims to analyze public sentiment related to the 2024 presidential election fraud and compare the effectiveness of SVM and Naïve Bayes in sentiment classification. The study was conducted quantitatively, involving the stages of data collection, preprocessing, labeling, TF-IDF weighting, classification, and evaluation. The results of the sentiment analysis of public opinion on the 2024 presidential election fraud showed 42.5% negative sentiment, 38.6% neutral, and 18.9% positive. The dominance of negative sentiments reflects the public's concerns about election integrity. The high neutral sentiment indicates public doubt. To overcome this, transparency, strengthening supervisory institutions, electronic election technology, and strict law enforcement are needed. The SVM algorithm with RBF kernel produces 58% accuracy, better than Naïve Bayes with 51%.
Software Quality Evaluation Of MV5PAS Airport Authority Region III Using FURPS Model: Software Quality Evaluation Of MV5PAS Airport Authority Region III Using FURPS Model Suhartono, Darell Timotius; Indriyanti, Aries Dwi
Journal of Emerging Information Systems and Business Intelligence (JEISBI) Vol. 6 No. 1 (2025)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jeisbi.v6i1.64722

Abstract

The widespread use of software to enhance organizational efficiency makes software development essential. The Airport Authority Region III Office uses MV5PAS software for operational activities, aiming to improve public services. Ensuring software quality is crucial for optimal performance, which this study evaluates using the FURPS model. This model assesses software based on five characteristics: functionality, usability, reliability, performance, and supportability.The study employs a mixed-method evaluation approach. Functionality and usability are assessed via a questionnaire with 25 respondents, while reliability is tested using WAPT V.10.1. Performance is measured with GTMetrix web analysis, and supportability is tested across different OS and browsers.Results indicate that MV5PAS meets the FURPS model standards in four categories. Functionality scores 0.983 (0 ≤ X ≤ 1), reliability achieves R = 0.982, usability scores 80.2, and supportability is confirmed through successful multi-device testing. However, performance requires improvement, receiving a “D” on the GTMetrix Grade. This highlights the need for quality enhancements to optimize the system’s overall efficiency.
Analysis of User Satisfaction of the "Lalamove" Application Using the SERVQUAL and EUCS Method Dewi, Anisa Tri Puspa; Bisma, Rahadian
Journal of Emerging Information Systems and Business Intelligence (JEISBI) Vol. 5 No. 4 (2024)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jeisbi.v5i4.65340

Abstract

Lalamove is a logistics platform connecting customers with trained drivers and couriers, offering secure, rapid, and convenient delivery solutions, focusing on quality, safety, and customer satisfaction. This study analyzes user satisfaction with the Lalamove application using twovice Quality (SERVQUAL) and End User  Computing Satisfaction (EUCS). The SERVQUAL method assesses service quality across five dimensions: tangible ty, dimensions, responsiveness, assurance, and empathy. Meanwhile, EUCS evaluates information system user satisfaction based on five dimensions: content, accuracy, Format, ease of use, and timeliness. Employing a quantitative approach with a survey method, this research involved 309 respondents comprising Lalamove application users, including customers and drivers. The results indicate that reliability and responsiveness (SERVQUAL), Format, and insourcing support (EUCS) significantly influence customer satisfaction. Additionally, the overall EUCS variables positively impact customer satisfaction. These findings suggest that combining SERVQUAL and EUCS provides a holistic understanding of service quality and user satisfaction, encompassing aspects of operational and user experience. Primary recommendations include enhancing system performance, improving delivery timeliness, and optimizing the application's interface design and usability.
Analysis of Factors Influencing Acceptance of the Online Population Administration Information System in Mojokerto Regency Using Technology Acceptance Model (TAM 3) AYER, FIDIANTI RAMADANI SUHADI; Indriyanti, Aries Dwi
Journal of Emerging Information Systems and Business Intelligence (JEISBI) Vol. 5 No. 4 (2024)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jeisbi.v5i4.65853

Abstract

This research investigates factors influencing the acceptance of the POSKeTanMu online population administration system in Mojokerto Regency using the Technology Acceptance Model (TAM 3). POSKeTanMu (Pelayanan Online Sistem Kependudukan Tanpa Ketemu) is a self-service online population administration system for residents of Mojokerto Regency. A quantitative approach with Partial Least Squares Structural Equation Modeling (PLS-SEM) was applied, analyzing data from 159 respondents through questionnaires. Findings indicate that Behavioral Intention (BI) significantly influences user independence and responsibility. Key factors affecting technology acceptance include perceived usefulness, ease of use, social influence, and experience, while computer anxiety and system usability showed no significant impact. Additionally, the study explores the implementation of the Double Track program using a qualitative case study approach, guided by Thomas Lickona’s character-building theory. Data collection involved interviews, observations, and documentation, analyzed using Miles and Huberman’s framework. Results highlight ease of use, perceived benefits, and social perceptions as major drivers of technology adoption. Positive user experiences and social support play crucial roles in enhancing e-government adoption. This research contributes to the development of POSKeTanMu and provides strategic recommendations for the government to strengthen digital service implementation. Findings offer valuable insights for policy formulation to improve e-government services and promote broader technology adoption in society.
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 (JEISBI) 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

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