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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 360 Documents
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Analysis of Business Process Improvement for Mutawwif and Tour Leader Training at Nikmatour Travel Using a Business Process Management Approach Irham Rusydiharjo; Zahwa Aisya Nabila; Aulia Rahman Ramadhan; Kemas Ibnu Sadid; Raulia Riski; Muhammad Brilliant Agung Wicaksono
Journal of Emerging Information Systems and Business Intelligence (JEISBI) Vol. 7 No. 3 (2026): Vol. 07 Issue 03
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jeisbi.v7i3.78331

Abstract

The Umrah travel industry in Indonesia continues to grow rapidly, driving the need for structured and efficient human resource training standards. However, the training process for Mutawwifs and Tour Leaders (TLs) at Nikmatour Travel is still conducted conventionally, with limited face-to-face sessions, unsystematic distribution of materials, and the absence of an automated evaluation mechanism, resulting in inefficiency and uneven competency among field staff. This study aims to analyze and redesign the Mutawwif and TL training business processes at Nikmatour Travel using a Business Process Management (BPM) lifecycle approach combined with Value-Added Analysis (VAA). Data was collected through observations and semi-structured interviews with management, instructors, and training participants at Nikmatour Travel. The as-is process was mapped using BPMN and analyzed with VAA. The analysis results showed that of the ten as-is activities, four were classified as Non-Value-Adding (NVA), five as Business Value-Adding (BVA), and one as Value-Adding (VA). The designed to-be process integrates an e-learning platform, automated notifications, digital evaluations, and real-time competency monitoring, and successfully eliminates all NVA activities.Quantitative estimates indicate a 44.3% reduction in total cycle time, from 18 hours to approximately 10 hours per training cycle, with an average BVA activity efficiency of 65%.
Service Quality Measurement Models in Electronic Government Services Dewi Rara Prameisty; Rahadian Bisma
Journal of Emerging Information Systems and Business Intelligence (JEISBI) Vol. 7 No. 3 (2026): Vol. 07 Issue 03
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jeisbi.v7i3.78518

Abstract

Advances in digital technology are driving governments to improve the quality of digital public services or as known as e-government services, necessitating appropriate models and methods to measure them. This study seeks to determine the measurement models for service quality and analytical methods used in e-government research through a Systematic Literature Review (SLR) approach. The results indicate that various models have been applied, including WebQual 4.0, E-SERVQUAL, E-GovQual, and hybrid models, with E-GovQual being the most superior and relevant model as it is tailored to assess the quality of digital public services. Additionally, various analytical methods are employed to support the service quality measurement process in alignment with the study’s objectives. These findings confirm that the selection of appropriate methods, particularly E-GovQual and suitable analytical methods, plays a crucial role in producing more accurate assessments of e-government service quality.
Web-Based Umrah Departure Quota Prediction Application UsingaMachineLearning Approach (Case Study: PT. Rahmatan BerkahWisata) Muhammad Rifqi Ardani; Ardhini Warih Utami
Journal of Emerging Information Systems and Business Intelligence (JEISBI) Vol. 7 No. 3 (2026): Vol. 07 Issue 03
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jeisbi.v7i3.78881

Abstract

PT. Rahmatan Berkah Wisata faces uncertainties regarding Umrah departure quotas due to capacity determination that is still conducted manually. This study aims to design a web-based quota prediction applicationutilizingMachine Learning with the Random Forest Regression algorithm, as well as to evaluate the accuracy and functionality of the system. System development employs the Rapid Application Development (RAD) method, encompassing the stages of requirement planning, user design, construction, and cutover. The research data consistsof historical pilgrim data from the 2022–2026 period, which includes the year, package name, package type, tripduration, number of pilgrims, and departure season.The results show that the Random Forest Regression model achieved a Mean Absolute Error (MAE) of 3.265, a Mean Squared Error (MSE) of 15.186, and an R-squared (R2) value of 0.707 (70.7%), demonstrating its capability to provide sufficiently accurate predictions. The model wassuccessfully implemented into a web-based application featuring Umrah data management, prediction and retraining scheduling, departure prediction, prediction reporting, and model retraining. Furthermore, functional testingusing Black Box Testing across three user roles (President Director, Operational Manager, and Pilgrim Staff) was successfully executed across all test scenarios. Based on the findings, this application can effectively serveasadecision support tool for determining Umrah departure quotas.
Cyber Resilience Readiness Assessment Using NIST Cybersecurity Framework 2.0 in Local Government Aziev Hanif Bahtiar; Rahadian Bisma
Journal of Emerging Information Systems and Business Intelligence (JEISBI) Vol. 7 No. 2 (2026): Vol. 07 Issue 02
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jeisbi.v7i2.80828

Abstract

ABSTRACT The acceleration of public service digitalization through the Electronic-Based Government System (Sistem Pemerintahan Berbasis Elektronik, SPBE) requires comprehensive information security strengthening within local government agencies. This study measures the cyber resilience readiness level of the Communication and Informatics Office (Diskominfo) of Mojokerto City using the NIST Cybersecurity Framework (CSF) 2.0. A descriptive qualitative method with a case study approach was applied through in-depth interviews, field observations, and document analysis to map the organization's existing conditions onto the six core functions of NIST CSF 2.0: Govern, Identify, Protect, Detect, Respond, and Recover, comprising 22 categories and 106 subcategories. The measurement shows that the average cyber resilience readiness score of Diskominfo Mojokerto City is 2.10 on a 4.00 scale. This score places the agency at the Risk Informed level (Tier 2), indicating that the organization possesses risk awareness but has not yet implemented it consistently or through automation across all functions. Specifically, the Govern function achieved the highest score (2.47), supported by a solid administrative and legal foundation, whereas the Recover function was identified as the most critical vulnerability with the lowest score (1.67) due to the absence of a formal disaster recovery plan. Strategic recommendations to reach the Repeatable target profile (Tier 3) include the formulation of a Disaster Recovery Plan, formalization of vendor supply-chain security policies, and the implementation of centralized automated security monitoring technologies such as SIEM and threat intelligence. These findings provide an objective baseline to support the agency's transition from a reactive posture toward a mature and standardized cyber resilience program. Keyword: NIST CSF 2.0, Cyber Resilience, SPBE, Local Government, Readiness Level, Information Security Governance.
Comparison of XGBoost and LightGBM for Product Demand Forecasting at KOPERASI XYZ Muhammad Andhika Fahrezzy; Aries Dwi Indriyanti
Journal of Emerging Information Systems and Business Intelligence (JEISBI) Vol. 7 No. 2 (2026): Vol. 07 Issue 02
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jeisbi.v7i2.80864

Abstract

ABSTRACT Accurate product demand forecasting is essential for small internal retail units such as cooperative stores (koperasi), where manual, intuition-based stock decisions frequently cause overstock and stockouts. This study develops and compares two gradient boosting algorithms, XGBoost and LightGBM, to forecast daily product demand at KOPERASI XYZ, an internal store located in Sunter, North Jakarta. Historical transaction records from 1 January 2024 to 22 December 2025 were aggregated into daily demand series for four product categories: mineral water, tea drinks, cigarettes, and milk. A total of 37 predictive features were engineered, comprising lag features, rolling and expanding statistics, and calendar features that encode 34 Indonesian national holidays. The data were divided using a chronological (time-based) split of 60:20:20, and both models were tuned using Grid Search cross-validation with a log-transformed target. Forecasting performance was evaluated using the Root Mean Squared Error (RMSE) and the Mean Absolute Percentage Error (MAPE). The results show that LightGBM outperforms XGBoost, achieving an overall RMSE of 24.53 and a MAPE of 9.72% (highly accurate), compared with an RMSE of 27.52 and a MAPE of 11.75% for XGBoost, corresponding to improvements of 10.86% in RMSE and 17.28% in MAPE. The best model was integrated into an interactive Streamlit dashboard that visualizes forecasts and recommends monthly stock levels to support data-driven inventory planning. Keywords:Demand Forecasting, XGBoost; LightGBM, Gradient Boosting, Time Series, Inventory Management
Combining Purchase Value and Product Preferences for Fashion Retail Customer Segmentation Darren Waluya Ardianto; I Kadek Dwi Nuryana
Journal of Emerging Information Systems and Business Intelligence (JEISBI) Vol. 7 No. 2 (2026): Vol. 07 Issue 02
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jeisbi.v7i2.80883

Abstract

The rapid growth of transactions in the fashion retail industry produces large volumes of customer data whose behavioral patterns remain difficult to exploit when segmentation is performed manually and descriptively. Uniform mass marketing is particularly ineffective for premium fashion, where preserving brand exclusivity demands a more personal approach. This study develops a customer segmentation model that does not stop at grouping customers by transaction value, but also reveals the shopping-style preferences embedded within each segment, and presents the results through an analytical dashboard for business stakeholders. Recency, Frequency, and Monetary (RFM) parameters were derived from transaction data and transformed using logarithmic scaling and a Robust Scaler to handle skewed distributions and outliers. The parameters were then grouped with K-Means clustering, whose optimal number of clusters was determined through the Elbow Method, Silhouette Score, and Davies-Bouldin Index. Within each resulting cluster, Latent Dirichlet Allocation (LDA) was applied independently to uncover hidden purchase topics, which were interpreted as fashion-style personas. The findings show that 1,152 customers were divided into four tiers, with K=4 as the optimal configuration, supported by a Silhouette Score of 0.5952 and a Davies-Bouldin Index of 0.5644. LDA identified distinct style personas across tiers, with topic coherence shaped by each segment's vocabulary diversity. The resulting Streamlit dashboard passed all 45 functional test scenarios, confirming that integrating value-based and preference-based analysis yields richer customer profiles for targeted marketing.
Time Series Regression to Analyzing and Forecasting Tourist Visitation in Batang Regency Fatma Abiyya Fawasi; Wiyli Yustanti
Journal of Emerging Information Systems and Business Intelligence (JEISBI) Vol. 7 No. 2 (2026): Vol. 07 Issue 02
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jeisbi.v7i2.80891

Abstract

This study aims to develop a forecasting model for the number of tourists at five tourist destinations in Batang Regency using historical tourist visit data and external factors, as well as to implement the best model into an application. The methods used are Random Forest Regressor, XGBoost Regressor, GRU, and LSTM, with stages based on Knowledge Discovery in Databases (KDD), feature selection, hyperparameter tuning, and evaluation using MAPE, RMSE, MAE, and R2. The results show that the best model was obtained from XGB-4, with an average MAPE of 4.96%, RMSE of 526.93, MAE of 428.07, and R2 of 0.972. The features that significantly affect the model include Exponantial Moving Average (EMA), trend, lagging, volatility, temperature max, and rain. The best model was implemented in an application called LARAS BATANG, which stands for Layanan Ramalan Wisata Batang. The application displays tourism forecasting results with several destination options and forecasting periods. In addition, the application can show a forecasting prototype that includes a summary, a time series line chart, a donut chart of visitor data proportions, and a feature importance bar chart.
Technology Readiness as a Higher-Order Driver of Perceived Ease and Usefulness of AI Tools Imanuella Yesha Metanoia; Ghea Sekar Palupi
Journal of Emerging Information Systems and Business Intelligence (JEISBI) Vol. 7 No. 2 (2026): Vol. 07 Issue 02
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jeisbi.v7i2.80897

Abstract

Whether creative professionals embrace artificial intelligence (AI) depends not only on the tools themselves but on the psychological disposition users bring to technology. This study investigates technology readiness as a higher-order driver of the perceived ease of use and perceived usefulness that underpin AI adoption among Indonesian digital creatives. Grounded in the Technology Readiness and Acceptance Model (TRAM), technology readiness is modeled as a second-order reflective construct comprising two enabler dimensions, optimism and innovativeness, and two inhibitor dimensions, discomfort and insecurity, and is linked to perceived ease of use and perceived usefulness. Data from 355 practitioners were analyzed using partial least squares structural equation modeling (PLS-SEM) with the repeated-indicators approach and 5,000 bootstrap resamples. The higher-order construct was valid and reliable, and all four dimensions loaded significantly, with the reverse-coded inhibitor dimensions, insecurity and discomfort, carrying the highest loadings, followed by optimism and innovativeness. Technology readiness significantly and positively influenced both perceived ease of use and perceived usefulness, and perceived ease of use strongly influenced perceived usefulness. Perceived ease of use also partially mediated the effect of technology readiness on perceived usefulness. The findings show that overcoming discomfort and insecurity is as central to readiness as optimism and innovativeness, and that a favorable disposition makes AI tools feel both easier to use and, through that, more useful. The study contributes a dimension-level understanding of technology readiness and offers guidance for cultivating AI readiness in the creative industry.
User Acceptance of SIPINTAR for Final Project Management Using Extended TAM Deirdre Papilaya; Dwi Fatrianto Suyatno
Journal of Emerging Information Systems and Business Intelligence (JEISBI) Vol. 7 No. 2 (2026): Vol. 07 Issue 02
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jeisbi.v7i2.80913

Abstract

Academic information systems play an important role in supporting effective and efficient higher-education management. Universitas Negeri Surabaya developed SIPINTAR (Academic Mobility and Final Project Management Information System) to replace SIMONTASI, which has been officially used since 2025 to manage the entire final-project process for students. Although SIPINTAR has strong potential to improve the efficiency of academic processes, no study has specifically examined user acceptance of the system. This study aims to analyze the influence of Extended Technology Acceptance Model (TAM) constructs, with the addition of subjective norm and job relevance as external variables, on user acceptance of SIPINTAR in the final-project module. A quantitative approach was employed using Partial Least Squares Structural Equation Modeling (PLS-SEM) processed with SmartPLS 4. Data were collected through a questionnaire distributed to 133 respondents consisting of students, lecturers, and study program coordinators who use SIPINTAR. The results show that six of the seven hypotheses were supported; subjective norm, perceived ease of use, and perceived usefulness were found to significantly shape attitude, intention, and actual system use, while the effect of job relevance on perceived usefulness was not significant. Overall, SIPINTAR has been well accepted by its users, driven mainly by ease of use, perceived usefulness, and social influence from the academic environment. These findings are expected to serve as an evaluation basis for Universitas Negeri Surabaya and system developers to improve the quality and effectiveness of SIPINTAR going forward.
Adoption of Gemini 2.5 Flash Image Nano Banana Visual Generative AI Technology for Photo Manipulation using TAM Ria Kusuma Rahayu; Ghea Sekar Palupi
Journal of Emerging Information Systems and Business Intelligence (JEISBI) Vol. 7 No. 2 (2026): Vol. 07 Issue 02
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jeisbi.v7i2.80991

Abstract

Advances in technology and improvements in computer processing power have opened up opportunities to implement various applications and algorithms that were once impossible to run on portable devices such as laptops and smartphones. One method that is gaining increasing recognition is artificial intelligence (AI), specifically Gemini 2.5 Flash Image (Nano Banana). This AI model is designed to offer advanced capabilities in creating realistic and detailed visual content. Data were collected from 125 respondents and analyzed using Structural Equation Modeling (SEM) with SmartPLS. The results of the study indicate that intrinsic motivation has a significant effect on perceived ease of use and perceived usefulness. Perceived Ease of Use has a significant effect on Perceived Usefulness. Social Influence and Perceived Usefulness have a significant effect on Behavioral Intention compared to Intrinsic Motivation and Perceived Ease of Use. Behavioral Intention influences Actual System Use in the real-world use of Gemini 2.5 Flash Image (Nano Banana). Keyword: Technology Acceptance Model (TAM), Gemini 2.5 Flash Image (Nano Banana), Artificial Intelligence

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