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All Journal Journal of Information System Exploration and Research Global Educational Research Review
Hakim, Ade Anggian
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Religion Computer Science & IT Decision Sciences, Operations Research & Management Education Engineering Social Sciences

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Journal : Journal of Information System Exploration and Research

 1 
The Optimization of Credit Scoring Model Using Stacking Ensemble Learning and Oversampling Techniques Rofik, Rofik; Aulia, Reza; Musaadah, Khalimah; Ardyani, Salma Shafira Fatya; Hakim, Ade Anggian
Journal of Information System Exploration and Research Vol. 2 No. 1 (2024): January 2024
Publisher : shmpublisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joiser.v2i1.203

Abstract

Credit risk assessment plays an important role in efficient and safe banking decision-making. Many studies have been conducted to analyze credit scoring with a focus on achieving high accuracy. However, predicting credit scoring decisions also requires model construction that handles class imbalance and proper model implementation. This research aims to increase the accuracy of credit assessment by balancing data using Synthetic Minority Oversampling (SMOTE) and applying ensemble stacking learning techniques. The proposed model utilizes a base learner consisting of Random Forest, SVM, Extra-Tree Classifier, and XGboost as a meta-learner. Then to handle unbalanced classes using SMOTE. The research process was carried out in several stages, namely Data Collection, Preprocessing, Oversampling, Modeling, and Evaluation. The model was tested using the German Credit dataset by applying cross-validation. The evaluation results show that the stacking ensemble learning model developed has optimal performance, with an accuracy of 83.21%, precision of 79.29%, recall of 91.78%, and f1-score of 85.08%. This research shows that optimizing the stacking ensemble learning model with data balancing using SMOTE in credit scoring can improve performance in credit scoring.
The Influence of Determining the K-Value on Improving the Diabetes Classification Model using the K-NN Algorithm Korina, Nanda Putri; Prasetiyo, Budi; Hakim, Ade Anggian; Septian, M Rivaldi Ali
Journal of Information System Exploration and Research Vol. 2 No. 2 (2024): July 2024
Publisher : shmpublisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joiser.v2i2.344

Abstract

Diabetes mellitus is still an important health problem globally, so it requires an efficient classification model to help determine a patient's diagnosis. This study aims to determine the K-value on the accuracy performance of the diabetes classification model using the K-Nearest Neighbors (K-NN) algorithm. This research utilizes a simulated dataset generated through interaction with ChatGPT, we investigate various K-values ​​in the K-NN model and assess its accuracy using a confusion matrix. Based on experiments, we found that the K-NN classification model with a K=6 obtained an optimal accuracy of 97.62%. Thus, our findings highlight the important role of selecting optimal K-values ​​in improving the performance of diabetes classification models.
Prototyping Disaster Preparedness Information System: A Case of Pandeglang District, Indonesia Juanara, Elmo; Hakim, Ade Anggian; Maeda, Yasunobu
Journal of Information System Exploration and Research Vol. 3 No. 1 (2025): January 2025
Publisher : shmpublisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joiser.v3i1.495

Abstract

In December 2018, a tsunami triggered by the eruption of Anak Krakatau Volcano (AKV) devastated the coastal area of Pandeglang, Indonesia, claiming hundreds of lives and leaving thousands missing. This tragedy underscores the critical importance of enhancing tsunami awareness through disaster preparedness and education. However, the lack of disaster preparedness in vulnerable areas, such as Pandeglang, remains a significant challenge. This is evident from the absence of early warning systems and evacuation initiatives at the time of the tsunami, highlighting the urgent need for improved disaster resilience in at-risk communities. This research aims to develop the disaster preparedness information system to equip society with sufficient knowledge and skill in case of the next disaster. The method this research uses is Soft Systems Methodology (SSM) to obtaining system requirements to the development of prototype. The prototype of a disaster preparedness information system was developed as a result. The system can be accessed using a smartphone or computer. This study introduces a novel approach by proposing a new prototype of disaster preparedness information specifically tailored for vulnerable areas in developing countries.
Mask Detection System with Computer Vision-Based on CNN and YOLO Method Using Nvidia Jetson Nano Hakim, Ade Anggian; Juanara, Elmo; Rispandi, Rispandi
Journal of Information System Exploration and Research Vol. 1 No. 2 (2023): July 2023
Publisher : shmpublisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joiser.v1i2.175

Abstract

Health is an essential aspect of life. The World Health Organization (WHO) has officially declared the Corona Virus (Covid-19) a global pandemic that has spread to Indonesia. For preventive measures against Covid-19, the Indonesian government is trying to deal with the Covid-19 pandemic with 3M health protocol aimed at community activities, such as Memakai Masker (wearing masks), Mencuci Tangan (washing hands), and Menjaga Jarak (maintaining distance). In this study, software and hardware design was carried out to detect mask users and immediately warn violators who do not use masks automatically and can function automatically offline by utilizing digital image processing using NVIDIA Jetson Nano using the YOLO (You Only Look Once) method. The CNN YOLOv4-tiny model is chosen to obtain measurement results for mask user detection accuracy because it has a relatively minor computational value and is faster. The best camera detection angle is obtained at a vulnerable angle of 45O-90O or in the range of 90O-135O with value confidence that the average is 99.94% and the best accuracy is at a lux value greater than 70, and a minimum camera height of 1 meter and a maximum of 3 meters. Under conditions of lux 96 (bright), the maximum distance for detecting a face object is 12 meters, and the ability of the system to output a warning sound has been successfully integrated with a relay to run the mp3 module separately from the system, so as not to interfere with the Jetson Nano computation process and the model is successfully run on the Jetson Nano with an average computation of 13 frames per second.
Co-Authors Ali Murfi Ardyani, Salma Shafira Fatya Aulia, Reza Budi Prasetiyo, Budi Elmo Juanara, Elmo Korina, Nanda Putri Maeda, Yasunobu Musaadah, Khalimah Pranata, Mohamad Waskita Adi Rispandi Rofik Rofik, Rofik Septian, M Rivaldi Ali Susanti, Sinta Ari