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All Journal Jurnal Edukasi dan Penelitian Informatika (JEPIN) JIEET (Journal of Information Engineering and Educational Technology) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal Abdi: Media Pengabdian Kepada Masyarakat Indonesian Journal of Electrical Engineering and Computer Science Journal of Informatics and Computer Science (JINACS) J-Icon : Jurnal Komputer dan Informatika Journal of Emerging Information Systems and Business Intelligence (JEISBI) Jurnal Ilmu Teknologi Informasi Indonesia (JITIFNA) Journal of Renewable Energy and Smart Device
Wiyli Yustanti
Universitas Negeri Surabaya
Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Social Sciences Other

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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 Ridhotul Maulidia; Wiyli Yustanti
Journal of Emerging Information Systems and Business Intelligence (JEISBI) Vol. 6 No. 1 (2025): Vol. 06 Issue 01
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.
Comparison You Only Look Once (Yolo) Algorithm On Physical Violence Video Detection Aulia Anisa Puji Rahayu; Wiyli Yustanti
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.
Comparative Study of Time Series Forecasting on Iron Sales Using CNN, MLP, and LSTM Nabila Putri Listyanto; Wiyli Yustanti
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 Calycha Kurniasari; Wiyli Yustanti
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 Sultan choirullah; Wiyli Yustanti
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.
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.
Co-Authors Advendi Kristiyandaru Agus Prihanto Ahmad Khoiru Shofa Ainandita Riwipapusa Aliyah Alifi Amalia Putri Andhika Edo Prasetyo Anita Qoiriah Aulia Anisa Puji Rahayu Ayunita Leliana Bambang Dibyo Wiyono Bertha Yonata Bunga meilita Calycha Kurniasari Dhiany Hana Amalia Dini Amalia Djoko Suwito Ervin Yohannes Farendi Giotivano Fatma Abiyya Fawasi Hapsari Peni Agustin Tjahyaningtijas Irhamah - Jaka Nugraha Martinus Ade Meidyan Miftakhul Alief Faturrahman Mohammad Hasan Machfoed Muhammad Dafi Bagas Muwirotul Hasanah Nabiilah Winda Kurnia Putri Nabila Putri Listyanto Naim Rahmawati Naim Rochmawati Naufal Aditya Rahman Novi Marlena Nur Iriawan Octania Sriwahyuni Pradini Puspitaningayu Rahadian Bisma Ricky Eka Ridhotul Maulidia Salsabilla Putri Saharani Savira Rahmania Putri Ariyanto Siti Mahmudah Putri Yanna Stevi Aprilianti Cahyani Sultan choirullah Susi Purwani Tama Priyadi WIDOWATI BUDIJASTUTI Windy Chikita Cornia Putri Yoyok Prastyo Yuni Yamasari