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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 11 Documents
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Search results for , issue "Vol. 6 No. 4 (2026): Vol. 06 Issue 04" : 11 Documents clear
Business Process Digitization in “Marbil Collection” Home Industry Using Business Process Model and Notation Kumalasari, Nadya; Nuryana, I Kadek Dwi
Journal of Emerging Information Systems and Business Intelligence (JEISBI) Vol. 6 No. 4 (2026): Vol. 06 Issue 04
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jeisbi.v6i4.71001

Abstract

Digital transformation has become an essential requirement for small and medium enterprises, including home industries engaged in handicrafts such as bag production. However, most home industry players still run business processes manually, potentially causing various operational problems such as production delays, distribution inaccuracies, and order recording errors. This research aims to analyze and re-model business processes at Marbil Collection, a home-based business that produces bags on a make-to-order basis. The approach used was Business Process Model and Notation (BPMN), a visual standard for systematically describing business workflows. This research identified four main processes in operations, namely ordering, procurement of raw materials and production, distribution and order completion, and employee payroll. These processes were mapped in the current business process model (as-is), and then redesigned into a proposed model (to-be) that supports automation and digitization. The results of the modeling showed significant gaps in the manual system used, especially in terms of service speed, transparency, and documentation. The proposed digital process design provides a structured solution to improve efficiency, accuracy, and customer experience. This research is expected to be the basis for developing a simple information system that suits the needs of the home industry, as well as making a practical contribution to similar businesses that want to start digitalization from an understanding of their own business processes.
Analysis Of Gui Testing And E-Recruitment Site Performance Using Katalon Studio And Jmeter With Two Way Anova Method: Analysis Of Gui Testing And E-Recruitment Site Performance Using Katalon Studio And Jmeter With Two Way Anova Method Mardhikasiandi, Daryl; Fatrianto Suyatno, Dwi
Journal of Emerging Information Systems and Business Intelligence (JEISBI) Vol. 6 No. 4 (2026): Vol. 06 Issue 04
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jeisbi.v6i4.71020

Abstract

A Graphical User Interface (GUI) is a method of interaction between users and software that displays a graphical interface easily understood by users when operating a system. It can measure the quality of a site. However, in addition to the GUI that can affect the quality of a website, performance can also affect the quality of a website. Performance is a process carried out to evaluate the performance of a website when the traffic load is high. This study aims to analyze the differences in GUI quality and performance on five e-recruitment platforms, namely Jobstreet, LinkedIn, Karir.com, Glints, and Kalibrr, using the Two-Way ANOVA method. GUI testing with Katalon Studio showed that JobStreet and Karir.com had high response times on the Login and Profile features due to difficulty in recognizing complex elements. Glints failed on the second and third tests in the Sign Up feature, while LinkedIn showed a high response time due to difficulty in recognizing attributes in the Search for Jobs feature, and Kalibrr appeared stable. Performance testing with JMeter, Jobstreet, Karir.com, and Kalibrr showed stable performance with low response time, stable throughput, and a 0% error rate. Glints experienced a 100% error rate because access was denied with a 403 code of “Forbidden”, while LinkedIn showed a spike in error rate as the number of threads increased. Two-way ANOVA analysis showed that in GUI testing, there were significant differences in response time and success rate based on application and feature. In performance testing, response time and error rate also showed significant differences, but throughput did not show significant differences by application and feature.
Risk Assessment on Wilujeng Hospital IT Process Using COBIT 2019 Framework Sari, Sindy Rosita; Nuryana, I Kadek Dwi
Journal of Emerging Information Systems and Business Intelligence (JEISBI) Vol. 6 No. 4 (2026): Vol. 06 Issue 04
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jeisbi.v6i4.71189

Abstract

The utilization of information technology within hospitals introduced risks to both operational efficiency and patient information security. Risks at Wilujeng Hospital include information systems tracking not being updated by staff, human error due to a lack of computer skills, no available personnel dedicated to the protection of computer information, and no clear task for all staff members on how to understand systems. The hospital did not perform a risk management capability assessment which is an important part of assessing risk management maturity. This study reviewed Wilujeng Hospital's risk management capabilities using the COBIT 2019 framework and provided suggestions for improving governance. Data collection and analysis were based on the COBIT 2019 principles, specifically on capabilities through design factor analysis. The study examined three relevant components of COBIT, including DSS05 (Managed Security Services), APO07 (Managed Human Resources), and APO12 (Managed Risk). The findings indicated that all three areas achieved competency level 1 with respective scores of 61.5%, 80.55%, and 58.33%. This indicates that the hospital's risk management capabilities are still evolving and that security and human resources management should be improved to enhance IT governance and data protection.
Web-based Profanity Detection Using a Combination of Lexicon and Support Vector Machine: Web-based Profanity Detection Using a Combination of Lexicon and Support Vector Machine Ainandita Riwipapusa; Yustanti, Wiyli
Journal of Emerging Information Systems and Business Intelligence (JEISBI) Vol. 6 No. 4 (2026): Vol. 06 Issue 04
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jeisbi.v6i4.71408

Abstract

Advances in information and communication technology, particularly the internet and social media, have made it easier for people to express their opinions openly, but have also increased the potential for the spread of profanity and hate speech. This study proposes a web-based profanity detection solution by combining lexicon-based methods and Support Vector Machine (SVM). The Knowledge Discovery in Database (KDD) process was implemented for data extraction and analysis, starting from Twitter data collection, preprocessing (cleaning, case folding, tokenizing, stemming), transformation using TF-IDF, to manual labeling. The SVM model was trained using a 3-fold cross-validation scheme, and evaluation was conducted using a classification report and confusion matrix. The results of the study showed a model accuracy of 93% on the test data with an average F1-score of 0.93, as well as optimal performance in detecting sentences categorized as profanity. The developed web application prototype successfully ran all profanity word detection and sensing features automatically, as proven by the black box testing results. The analysis test also ran smoothly, with a test using 10 sentences containing profanity words achieving 100% accuracy, and a test using 10 sentences without profanity words achieving 95% accuracy. This system is expected to contribute to creating a more positive digital space through adaptive and accurate profanity word detection.
Classification Agorithm Analysis For Predicting The Type Of Senior High School On Alumni Smp 2 Balong Ponorogo: Classification Agorithm Analysis For Predicting The Type Of Senior High School On Alumni Smp 2 Balong Ponorogo Kurnia Putri, Nabiilah Winda; Yustanti, Wiyli
Journal of Emerging Information Systems and Business Intelligence (JEISBI) Vol. 6 No. 4 (2026): Vol. 06 Issue 04
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jeisbi.v6i4.71676

Abstract

This study aims to analyze the performance of various classification algorithms in predicting the type of Senior High School (SLTA) that students choose based on academic scores and achievements. The study was conducted at SMPN 2 Balong Ponorogo using the SEMMA (Sample, Explore, Modify, Model, Assess) approach. Secondary data from 1,113 students were used and processed through the stages of data exploration, normalization, feature selection (using Pearson Correlation, Mutual Information, Random Forest, and Lasso Logistic Regression), and dimension reduction using Principal Component Analysis (PCA). Eight classification algorithms were tested, namely Logistic Regression, K-Nearest Neighbors, Support Vector Machine, Random Forest, XGBoost, LightGBM, CatBoost, and Naïve Bayes. Model evaluation is done using accuracy, precision, recall, F1-score, and confusion matrix metrics. The results show that the Random Forest and KNN models with the Hybrid Feature Selection approach provide the best performance, with the F1-score value reaching 84%. This research contributes to data-based decision making for student guidance in choosing the right further education pathway.
Clustering of Goat Buyers in West Java with K-Means Algorithm Faizatul Mukaromah; Nuryana, I Kadek Dwi
Journal of Emerging Information Systems and Business Intelligence (JEISBI) Vol. 6 No. 4 (2026): Vol. 06 Issue 04
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jeisbi.v6i4.72022

Abstract

The advancement of information technology has encouraged the utilization of data as a strategic resource across various fields, including the livestock sector. This study aims to implement the K-Means algorithm to segment goat buyers in West Java Province based on demographic characteristics such as age, marital status, geographic location, type of goat purchased, and transaction methods. This segmentation is expected to assist business actors in understanding purchasing patterns and designing more targeted marketing and distribution strategies. The study uses 1,250 transaction records and follows the stages of selection, preprocessing, transformation, data mining, and interpretation using the Knowledge Discovery in Databases (KDD) approach. Geographic distances between buyer locations and reference points were calculated using the Haversine formula. To determine the optimal number of clusters, the Elbow Method and Silhouette Score were used, with the best result obtained at a Silhouette score of 0.16 for 3 clusters. Each cluster was analyzed based on modal characteristics such as age, marital status, district, type of goat purchased, number of goats per transaction, purchase purpose, delivery method, payment method, as well as Recency, Frequency, and Monetary (RFM). The results indicate that the K-Means algorithm is effective in grouping goat buyers into relevant and meaningful segments. This information can be used by farmers and stakeholders to improve distribution efficiency, stock optimization, and data-driven marketing strategies. This study also emphasizes the importance of integrating technologies such as Python and Streamlit for interactive visualization and ID-based buyer tracking in advanced analytics.  
Implementation of Business Intelligence for Sales Analysis and Customer Segmentation at XYZ Store Azharani, Gerin; Nuryana, I Kadek Dwi
Journal of Emerging Information Systems and Business Intelligence (JEISBI) Vol. 6 No. 4 (2026): Vol. 06 Issue 04
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jeisbi.v6i4.72050

Abstract

XYZ Retail Store has a large amount of transaction data, but it has not been optimally utilized for strategic decision making. The research aims to implement a Business Intelligence. Business Intelligence helps analyze sales data and segment key business entities, namely customers, suppliers, and products. The research methodology includes designing a datawarehouse using Kimball's Nine Step method with MySql as the database platform. Extract, Transform, Load (ETL) process is performed to prepare the data before processing with Online Analytical Processing (OLAP) approach for multidimensional sales analysis, and Data Mining with K-Means clustering algorithm to perform segmentation. The results obtained from the entire analysis, visualized using the tools of tableau.
Healthcare Data Analysis Through Business Intelligence: A Case Study With Power BI Cahyaningrum, Avikatria; Nuryana, I Kadek Dwi
Journal of Emerging Information Systems and Business Intelligence (JEISBI) Vol. 6 No. 4 (2026): Vol. 06 Issue 04
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jeisbi.v6i4.72051

Abstract

This research aims to analyze hospital health data with the application of Power BI-based Business Intelligence (BI) to support a more precise and efficient decision-making process. The research data is taken from a public repository that provides a hospital management system database structure with complex inter-table relationships. The initial stages were carried out with the ETL (Extract, Transform, Load) process to integrate and clean the data before being entered into the data warehouse with the star and galaxy schema model. Next, analysis was conducted using Online Analytical Processing (OLAP) for medical service usage and other trends. In addition, the application of data mining using the Random Forest algorithm is also carried out for the classification of hospital busyness levels and prediction of patient re-visits based on historical data.
Business Intelligence Implementation For Hotel Room Reservation Data Analysis Nisa, Khoirotun; Nuryana, I Kadek Dwi
Journal of Emerging Information Systems and Business Intelligence (JEISBI) Vol. 6 No. 4 (2026): Vol. 06 Issue 04
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jeisbi.v6i4.72053

Abstract

The growth of the hotel industry in Indonesia is driven by increasing mobility for business and tourism purposes. However, this growth presents new challenges in hotel management, particularly in analyzing customer behavior, optimizing room availability, and making strategic decisions. Manual management of reservations and customer data is considered ineffective. This study implements Business Intelligence (BI) for analyzing hotel reservation data from 2022–2024 using OLAP and data mining techniques. BI enables the analysis of room popularity, active customer identification, payment method trends, and customer profiles. Additionally, this study applies clustering for room segmentation and forecasting methods to predict future income and reservation trends. Data is processed using ETL into a star schema-based data warehouse, visualized through Power BI dashboards. Results show that BI provides valuable insights into customer behavior, room occupancy trends, and financial performance, supporting management in improving operational efficiency and revenue.
Implementation of AI Number Generator and A Using GDLC in Android Games Ilham, Mochammad Ilham Study Wartana; Nuryana, I Kadek Dwi
Journal of Emerging Information Systems and Business Intelligence (JEISBI) Vol. 6 No. 4 (2026): Vol. 06 Issue 04
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jeisbi.v6i4.72061

Abstract

The rapid development of information technology has brought significant changes in various aspects of life, including cultural preservation through digital media. Traditional Indonesian games such as sack racing and hide-and-seek have experienced a decline in interest among the younger generation due to modernization. This research aims to develop Android-based offline games of sack racing and hide-and-seek with the implementation of artificial intelligence (AI) using the Random Number Generator (RNG) algorithm for sack racing games and the A* pathfinding model algorithm for hide-and-seek games. The development methodology used is Game Development Life Cycle (GDLC) with Unity as the main game engine. The implementation of RNG in the sack racing game serves to produce dynamic and unpredictable NPC behavior, such as variations in jump speed and movement patterns. While the A* algorithm pathfinding model in hide-and-seek game allows the searcher NPC to find the optimal path in searching for hiding players, creating a realistic and challenging gaming experience. This research uses functional, performance, and user experience testing to evaluate the effectiveness of the AI implementation.

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