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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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Business Process Analysis and Modeling Using the Business Process Model Notation (Bpmn) Method (Case Study: Simo Jaya Printing) Aziz; Nuryana, I Kadek Dwi
Journal of Emerging Information Systems and Business Intelligence (JEISBI) Vol. 6 No. 3 (2025): Vol. 06 Issue 03
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jeisbi.v6i3.70359

Abstract

Percetakan Simo Jaya is one of the providers of offset printing services in Surabaya. Percetakan Simo Jaya has constraints in managing its existing business processes. A business process is a set of tools used to organize activities in order to improve understanding of how these activities are interconnected to achieve a specific goal. In business process modeling, a method called Business Process Model and Notation (BPMN) is used, which is a standard for modeling business processes that provides graphical notation to describe them. The business processes at Percetakan Simo Jaya include the ordering process, raw material purchasing process, printing process, and the goods collection and delivery process. Based on the identified issues within the business processes at Percetakan Simo Jaya, this study will examine and propose improvements. The proposed improvements are expected to serve as a consideration for Percetakan Simo Jaya in refining its business processes to enhance service quality and operational effectiveness.
DESIGN OF INVENTORY STOCK INFORMATION SYSTEM USING LARAVEL FRAMEWORK WITH FIFO METHOD Bagus, Bagus Laksono Yudo Atmojo; Nuryana, I Kadek Dwi
Journal of Emerging Information Systems and Business Intelligence (JEISBI) Vol. 6 No. 3 (2025): Vol. 06 Issue 03
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jeisbi.v6i3.70863

Abstract

Toko Anugerah Rianto is a business engaged in retail, namely cosmetics since 2016, located in Rungkut, Surabaya. Of course, in running his business, the business owner has his own challenges in running it. Based on the results of interviews conducted with business owners, the obstacle in running their business is controlling the availability of stock in the warehouse. This obstacle is caused by the absence of recording incoming and outgoing goods so that stock-outs and inaccurate availability often occur. This problem causes consumers to wait for stock to be available. The purpose of this research is to design an inventory information system that can overcome the problems of the business being run. This design uses the Research and Development (R&D) method which consists of 4D (define, design, develop, disseminate). The stock inventory management method applied in this study is the First In First Out (FIFO) method where the first item in is the first item out to avoid expired goods. The result of this research is a website-based inventory information system that is a solution to the problems of Anugerah Rianto Store to support accurate data collection of incoming and outgoing goods.
Information System with Face Recognition and Geolocation at MA Al Bukhary Satrianto, Augusta; Sisephaputra, Bonda
Journal of Emerging Information Systems and Business Intelligence (JEISBI) Vol. 6 No. 3 (2025): Vol. 06 Issue 03
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jeisbi.v6i3.70921

Abstract

Modern technology continues to advance rapidly, impacting various aspects of life, including attendance management systems. One notable innovation is the integration of face recognition and geolocation technology for attendance systems. This technology provides a more efficient and accurate solution for recording attendance compared to conventional methods. Face recognition allows for automatic and quick identity verification, while geolocation ensures that users are in the correct location when marking their attendance. This study aims to design and develop an attendance information system utilizing both face recognition and geolocation technologies. Face recognition is implemented using a TensorFlow model trained to accurately recognize faces. Geolocation data is obtained from the GPS devices on users' smartphones and is used to verify their presence at authorized locations. The system is implemented as an interconnected website and mobile application for storing attendance information. The results of the research and testing indicate that the developed face recognition system can accurately identify faces and distinguish between real and fake faces. Additionally, the geolocation system can effectively verify users' locations and detect the use of spoofed locations. The educators at MA Al Bukhary possess the necessary devices to use this information system, meeting the technology requirements tested. From the user requirements testing, the system received a score of 88,06, categorized as excellent (A).
Comparison You Only Look Once (Yolo) Algorithm On Physical Violence Video Detection Rahayu, Aulia Anisa Puji; Yustanti, Wiyli
Journal of Emerging Information Systems and Business Intelligence (JEISBI) Vol. 6 No. 3 (2025): Vol. 06 Issue 03
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jeisbi.v6i3.71185

Abstract

Physical violence is one of the crimes that often occurs in various environments and can have a serious impact on victims, both physically and mentally. One of the obstacles in handling it is the delay in detecting acts of violence. The solution to this problem is to implement the best algorithm between You Only Look Once (YOLO) version 8 and version 9 to detect physical violence through video automatically and quickly. The dataset used consists of two classes, namely violence and non-violence, which have gone through the process of extraction, data cleaning, and labeling using Roboflow. The model was trained using Google Collaboratory, and the training results were evaluated using mAP, precision, recall, and F1-score metrics. Based on the test results, YOLOv9 obtained the best performance with a precision of 0.8096, recall of 0.8665, F1-score of 0.8363, and mAP of 0.8117. The detection system is then implemented into a web-based application using the Flask framework, which allows users to Upload videos and detect acts of violence automatically. The test results show that the application runs according to its function and is able to detect physical violence well. This research is expected to be a supporting solution in video-based security surveillance systems.
Forecasting Book Inventory Needs at CV. Irmandiri Pustaka Using Holt-Winters Method Auliyaurroshidin, Maulana; Nuryana, I Kadek Dwi
Journal of Emerging Information Systems and Business Intelligence (JEISBI) Vol. 6 No. 3 (2025): Vol. 06 Issue 03
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jeisbi.v6i3.71248

Abstract

This study aims to forecast inventory needs at CV. Irmandiri Pustaka using the Holt-Winters Multiplicative method. The company distributes Student Worksheets (LKS), with seasonal demand that rises at the start of each semester. Accurate forecasting is crucial to manage inventory efficiently and avoid overstocking or shortages. Holt-Winters was chosen for its ability to capture both trend and seasonal patterns. The forecast uses sales data from January 2024 to June 2025. Forecast accuracy is evaluated using Mean Absolute Percentage Error (MAPE), Mean Absolute Deviation (MAD), and Mean Squared Error (MSE). Initial parameter settings produced high errors, prompting optimization through Excel’s Solver feature. The optimized parameters α = 0.01, β = 0.01, and γ = 0.04 reduced the MAPE to 25.19%. These results show that the Holt-Winters method can provide reliable inventory forecasts, especially after the second seasonal cycle, and serve as a helpful tool for inventory planning decisions.
Comparative Study of Time Series Forecasting on Iron Sales Using CNN, MLP, and LSTM Nabila Putri Listyanto; Yustanti, Wiyli
Journal of Emerging Information Systems and Business Intelligence (JEISBI) Vol. 6 No. 3 (2025): Vol. 06 Issue 03
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jeisbi.v6i3.71361

Abstract

Sales forecasting is essential for businesses to predict future demand and inform strategic and operational planning, especially in the building materials retail industry. Accurate sales prediction supports inventory management, cost control, and supply chain efficiency. This study compares the performance of 3 deep learning models, Convolutional Neural Network (CNN), Multilayer Perceptron (MLP), and Long Short-Term Memory (LSTM), in forecasting daily iron sales at PT Surya Aneka Bangunan from 2016 to 2020. The models were trained on 80% of the historical data and tested on 20%. Model performance was evaluated using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Coefficient of Determination R². The results show that the CNN model achieved the best performance with an MAE of 0.293, RMSE of 0.357, MAPE of 0.081, and R² of 0.9989, indicating high accuracy and stability. The MLP model produced higher errors, while the LSTM model had the lowest MAPE but greater error variability. These findings suggest that the CNN model is the most reliable for capturing temporal patterns in iron sales data. The study contributes to the development of adaptive sales forecasting systems and opens opportunities for applying similar methods in other retail sectors to support data driven decision making.
Detection of Dirty Bowel Disease Through Palm Image Analysis Using CNN-VGG16 Algorithm Kurniasari, Calycha; Yustanti, Wiyli
Journal of Emerging Information Systems and Business Intelligence (JEISBI) Vol. 6 No. 3 (2025): Vol. 06 Issue 03
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jeisbi.v6i3.71712

Abstract

Early detection of disease is very important in improving the quality of human health. The quality of life of patients suffering from gross bowel disease can be significantly affected, including daily activities, work, and interpersonal relationships. One promising innovative method in the healthcare field is disease detection through palm image analysis. The solution to this problem is done by implementing the Convolutional Neural Network (CNN) algorithm using the VGG16 architecture model which can be operated by uploading palm images to detect Dirty Bowel Disease, Other Diseases (Not Dirty Bowel), and Healthy Hands through a web-based application. Based on the test results, the test accuracy value is 0.4800, F1-Score for the dirty gut disease category is 0.62, F1-Score for Other Diseases (Not Dirty Intestines) is 0.54, F1-Score for the Healthy Hands category is 0.29, and the overall F1-Score is 0.50. The white box test results show that the system can run well in all test scenarios applied. While the black box testing results show that the application functions as expected. In addition, the prediction results using the image import feature are supported by a confidence score with an average value of 48.89% for all three categories.
Implementation of EfficientNet-B0 CNN Model for Web-Based Strawberry Plant Disease Detection choirullah, Sultan; Yustanti, Wiyli
Journal of Emerging Information Systems and Business Intelligence (JEISBI) Vol. 6 No. 3 (2025): Vol. 06 Issue 03
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jeisbi.v6i3.72957

Abstract

Strawberry production in Indonesia has high economic value but is often hindered by plant diseases that reduce yield quality and quantity. Manual disease identification requires time, cost, and expertise, making it inefficient for farmers. This study proposes a web-based strawberry disease detection system by applying a Convolutional Neural Network (CNN) model using the EfficientNet-B0 architecture. The dataset consists of leaf, fruit, and flower images of strawberries in both healthy and infected conditions. The research followed the CRISP-DM framework, including business understanding, data preparation, modeling, evaluation, and deployment. The model was trained using transfer learning and fine-tuning techniques, with evaluation conducted through a confusion matrix and K-Fold Cross Validation. Experimental results indicate that the EfficientNet-B0 model achieved an overall accuracy of approximately 95.2% and demonstrated stable performance in classifying various strawberry plant diseases. The model achieved perfect accuracy (100%) in several classes such as Healthy Leaf, Leaf Spot, and Healthy Flower, while maintaining high accuracy in other classes like Fruit (95.2%) and Anthracnose Fruit Rot (94.7%), confirming its effectiveness in capturing essential visual features for accurate disease classification. The deployment of the model into a website using the Streamlit framework enables users to upload strawberry images and obtain automatic, fast, and accurate disease detection results. This system is expected to provide a practical solution to help farmers improve productivity and minimize losses caused by plant diseases.

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