Journal of Information System Exploration and Research最新文献

Optimization of Credit Scoring Model Using Stacking Ensemble Learning and Oversampling Techniques 利用堆叠集合学习和超采样技术优化信用评分模型

Journal of Information System Exploration and Research Pub Date : 2023-12-29 DOI: 10.52465/joiser.v2i1.203

Rofik Rofik, Reza Aulia, Khalimah Musaadah, Salma Shafira Fatya Ardyani, Ade Anggian Hakim

{"title":"Optimization of Credit Scoring Model Using Stacking Ensemble Learning and Oversampling Techniques","authors":"Rofik Rofik, Reza Aulia, Khalimah Musaadah, Salma Shafira Fatya Ardyani, Ade Anggian Hakim","doi":"10.52465/joiser.v2i1.203","DOIUrl":"https://doi.org/10.52465/joiser.v2i1.203","url":null,"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.","PeriodicalId":321749,"journal":{"name":"Journal of Information System Exploration and Research","volume":"2 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139147509","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Effect of Digitalization on Business Performance in the MSME Industry Context 数字化对中小微企业经营业绩的影响

Journal of Information System Exploration and Research Pub Date : 2023-12-26 DOI: 10.52465/joiser.v2i1.199

Fadhil Umar, M. R. A. Septian, Dwika Ananda Agustina Pertiwi

引用次数: 0

Mask Detection System with Computer Vision-Based on CNN and YOLO Method Using Nvidia Jetson Nano Nvidia Jetson Nano基于CNN和YOLO方法的计算机视觉掩模检测系统

Journal of Information System Exploration and Research Pub Date : 2023-07-28 DOI: 10.52465/joiser.v1i2.175

Ade Anggian Hakim, Elmo Juanara, Rispandi Rispandi

{"title":"Mask Detection System with Computer Vision-Based on CNN and YOLO Method Using Nvidia Jetson Nano","authors":"Ade Anggian Hakim, Elmo Juanara, Rispandi Rispandi","doi":"10.52465/joiser.v1i2.175","DOIUrl":"https://doi.org/10.52465/joiser.v1i2.175","url":null,"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.","PeriodicalId":321749,"journal":{"name":"Journal of Information System Exploration and Research","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124616231","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Bankruptcy Prediction Using Genetic Algorithm-Support Vector Machine (GA-SVM) Feature Selection and Stacking 基于遗传算法-支持向量机(GA-SVM)特征选择与叠加的破产预测

Journal of Information System Exploration and Research Pub Date : 2023-07-19 DOI: 10.52465/joiser.v1i2.180

Wiena Faqih Abror, A. Alamsyah, M. Aziz

{"title":"Bankruptcy Prediction Using Genetic Algorithm-Support Vector Machine (GA-SVM) Feature Selection and Stacking","authors":"Wiena Faqih Abror, A. Alamsyah, M. Aziz","doi":"10.52465/joiser.v1i2.180","DOIUrl":"https://doi.org/10.52465/joiser.v1i2.180","url":null,"abstract":"Bankruptcy is an impact caused by a company's financial failure. Financial failure in the company must be avoided so as not to cause losses to the company. In the research that was carried out utilizing a data set from the Taiwan Economic Journal as many as 6,819 to be trained using machine learning algorithms using classification techniques. The goal obtained from the research conducted is to obtain a classification technique with the best accuracy results. The method used in this research is preprocessing using the synthetic minority over-sampling technique to handling unbalanced data sets. Then, the results of the balanced data set will be processed using a genetic algorithm-support vector machine feature selection algorithm to reduce the attributes of the data set. Data sets that have experienced reduced attributes will be trained using the stacking method with a single classifier base learner in the form of k-nearest neighbors, naïve bayes, decision trees with classification and regression tree models, gradient boosting decision trees, and light gradient boosting. The meta-learner used in the stacking method is extreme gradient boosting. The results of the accuracy obtained from the research conducted were 99.22%.","PeriodicalId":321749,"journal":{"name":"Journal of Information System Exploration and Research","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115106504","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Portfolio Selection Strategies in Bursa Malaysia Based on Quadratic Programming 基于二次规划的马来西亚交易所投资组合选择策略

Journal of Information System Exploration and Research Pub Date : 2023-07-16 DOI: 10.52465/joiser.v1i2.178

Liang Ling, Y. Dasril

{"title":"Portfolio Selection Strategies in Bursa Malaysia Based on Quadratic Programming","authors":"Liang Ling, Y. Dasril","doi":"10.52465/joiser.v1i2.178","DOIUrl":"https://doi.org/10.52465/joiser.v1i2.178","url":null,"abstract":"The study aims to select the efficient portfolio on stock listed in Bursa Malaysia by using the quadratic programming method. It can help the investors to gain expected returns from the diversification portfolio. However, there are some problems that should be considered such as the measurement of inputs for Mean-Variance Models (MVM), use of portfolio models through time and consistency with management objectives in the portfolio. These problems will affect the performance of selected portfolio and cause the loss problem. Therefore, this study implements a quadratic programming approach to select an efficient portfolio on stocks listed in Bursa Malaysia. The study will choose 15 potential companies which have the best performance in the Bursa Malaysia. Quadratic programming (QP) model can solve any type of mathematical optimisation problem in the study. Therefore, investors can optimise the investment portfolio returns by using QP methods. However, we can observe the efficient frontier which is a graph that representing a list of portfolios that optimising expected return for a different level of portfolio risk so can help the investors make a good decision. The findings of this study will give important inputs, especially to the investors to maximise their portfolio return at different level of risks.","PeriodicalId":321749,"journal":{"name":"Journal of Information System Exploration and Research","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116114789","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Naive Bayes and KNN for Airline Passenger Satisfaction Classification: Comparative Analysis 朴素贝叶斯与KNN在航空旅客满意度分类中的比较分析

Journal of Information System Exploration and Research Pub Date : 2023-07-14 DOI: 10.52465/joiser.v1i2.167

Annisa Nurdina, Audita Bella Intan Puspita

{"title":"Naive Bayes and KNN for Airline Passenger Satisfaction Classification: Comparative Analysis","authors":"Annisa Nurdina, Audita Bella Intan Puspita","doi":"10.52465/joiser.v1i2.167","DOIUrl":"https://doi.org/10.52465/joiser.v1i2.167","url":null,"abstract":"Air transportation is vital due to technological advancements and globalization. It is affordable and accessible worldwide, providing efficient services to reach destinations globally. This discussion focuses on full-service airlines that offer online-based services. Previous research indicates that available facilities and services influence passenger satisfaction. Previous research on customer satisfaction showed a correlation between satisfaction and services without accurate figures. In the present study, the customer satisfaction figure is measured using the Naive Bayes and K-Nearest Neighbour (K-NN) algorithm to obtain a tested level of accuracy. In this analysis, we will compare the effectiveness of Naive Bayes and K-NN algorithms in classifying airline passenger satisfaction. The results show that the accuracy of the Naive Bayes method of the two algorithms is higher than the K-NN method. The accuracy value of the Naive Bayes method is 84.48%, while the accuracy value of the K-NN method is 65.38%. From the test results, the precision value for Naive Bayes is 82.25%, and K-NN is 67.35%. Furthermore, the recall value for Naive Bayes is 82.43%, and K-NN is 74.33%.","PeriodicalId":321749,"journal":{"name":"Journal of Information System Exploration and Research","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129338726","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Decision Support System for Program Indonesia Pintar Recipients Using the Fuzzy Multi-Criteria Decision-Making Method 使用模糊多标准决策方法的印度尼西亚 Pintar 计划受助者决策支持系统

Journal of Information System Exploration and Research Pub Date : 2023-06-26 DOI: 10.52465/joiser.v1i2.157

A. Hamid, Muhammad Sandi Rais, Muhammad Idris Rois, Salamun Salamun, Yonhendri Yonhendri, Ahmad Zulfan, Lasmi Oyong

{"title":"Decision Support System for Program Indonesia Pintar Recipients Using the Fuzzy Multi-Criteria Decision-Making Method","authors":"A. Hamid, Muhammad Sandi Rais, Muhammad Idris Rois, Salamun Salamun, Yonhendri Yonhendri, Ahmad Zulfan, Lasmi Oyong","doi":"10.52465/joiser.v1i2.157","DOIUrl":"https://doi.org/10.52465/joiser.v1i2.157","url":null,"abstract":"Program Indonesia Pintar (PIP) is the development of Bantuan Siswa Miskin (BSM) program, which covers students from the learning stages of SD or MI, SMP or MTs, SMA or Sekolah Menengah Kejuruan (SMK), the PIP Program is a National Program that aims to eliminate barriers to poor students participating in studying by helping poor students get access to appropriate learning services, avoiding dropping out of school, attracting poor students to return to study, helping students fulfill their desires in upgrading activities. Through the Program Indonesia Pintar (PIP), school-age children from poor households or families can continue to study, do not drop out of school. No recipients are on the wrong target for assistance from the Smart Indonesia Program at SMP Negri 39 Pekanbaru City. The method used in the decision support system is Fuzzy Multi-Criteria Decision Making (FMCDM) which assesses alternative determinants so that they can be used in policy analysis in decision-making. The results of this decision support will help decide the best choice of several substitutes based on the selected criteria.","PeriodicalId":321749,"journal":{"name":"Journal of Information System Exploration and Research","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131052678","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Techniques of Applied Machine Learning Being Utilized for the Purpose of Selecting and Placing Human Resources within the Public Sector 应用机器学习技术被用于公共部门人力资源的选择和配置

Journal of Information System Exploration and Research Pub Date : 2022-12-09 DOI: 10.52465/joiser.v1i1.91

Panagiota Pampouktsi, Spyridon Avdimiotis, M. Maragoudakis, M. Avlonitis, Nikita Samantha, Praveen Hoogar, G. Ruhago, Wcyliffe Rono

{"title":"Techniques of Applied Machine Learning Being Utilized for the Purpose of Selecting and Placing Human Resources within the Public Sector","authors":"Panagiota Pampouktsi, Spyridon Avdimiotis, M. Maragoudakis, M. Avlonitis, Nikita Samantha, Praveen Hoogar, G. Ruhago, Wcyliffe Rono","doi":"10.52465/joiser.v1i1.91","DOIUrl":"https://doi.org/10.52465/joiser.v1i1.91","url":null,"abstract":"In strategic human resource management, one of the most critical issues to focus on is the correct selection and placement of people. Within the confines of this framework, the reason for the study that was conducted was to explore the machine learning approaches that proved to be the most effective in assisting with the recruitment of personnel and the assessment of their positions. To accomplish this goal, a in a series of tests involving workers in the public sector, categorization algorithms were used. The purpose of these tests was to determine which employees would be the ideal fit in which workstations and to determine how workers should be distributed. For supporting the decision support system, an algorithm model was created. Used in the process of recruiting and evaluating potential workers based on the results of the tests that were given. The most important results of this study support the idea that using the People's Evaluation for Recruitment and Promotion Algorithm Model (EERPAM) would make hiring and promoting people in a company fairer.","PeriodicalId":321749,"journal":{"name":"Journal of Information System Exploration and Research","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114744081","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Accuracy of Malaysia Public Response to Economic Factors During the Covid-19 Pandemic Using Vader and Random Forest 利用维德和随机森林分析马来西亚公众对2019冠状病毒病大流行期间经济因素反应的准确性

Journal of Information System Exploration and Research Pub Date : 2022-12-09 DOI: 10.52465/joiser.v1i1.104

J. Jumanto, M. A. Muslim, Y. Dasril, T. Mustaqim

引用次数: 6

Operational Supply Chain Risk Management on Apparel Industry Based on Supply Chain Operation Reference (SCOR) 基于供应链运营参考(SCOR)的服装行业运营供应链风险管理

Journal of Information System Exploration and Research Pub Date : 2022-12-09 DOI: 10.52465/joiser.v1i1.103

Dwika Ananda Agustina Pertiwi, Muhammad Yusuf, Devi Ajeng Efrilianda

引用次数: 2