Journal of Information System Exploration and Research
Journal of Information System Exploration and Research (JOISER) (e-ISSN: 2963-6361, p-ISSN: 2964-1160) is a journal that publishes and disseminates scientific research papers on information systems to a wide audience, particularly within the information system society. Articles devoted to discussing any and all aspects of the most recent and noteworthy advancements in the fields of Decision Science, Computer Science, and Computer Science Applications will be considered for publication. Submit your paper now through Online submission ONLY. The JOISER publication period is carried out every six months, namely in January and July. But, authors can submit their work to JOISER at any time throughout the year, as the submission process is continuous. The JOISER has been indexed by Google Scholar, Crossref, Copernicus, and BASE. The Journal of Information Systems Exploration and Research aim publishes articles concerning the design and implementation information system, data models, process models, algorithms, and software for information systems. Subject areas include data management, data mining, machine learning, information retrieval coordinated with structured data, internet and cloud data management, business process management, web semantics, visual and audio information systems, scientific computing, and data science. We welcome system papers that focus on decision science and machine learning, computer science application, pplication domains, Internet of Things, which present innovative, high-performance, and scalable solutions to data management problems for those domains.
Articles
25 Documents
<![CDATA[Techniques of Applied Machine Learning Being Utilized for the Purpose of Selecting and Placing Human Resources within the Public Sector ]]>
<![CDATA[Pampouktsi, Panagiota]]>;
<![CDATA[Avdimiotis, Spyridon]]>;
<![CDATA[Maragoudakis, Manolis]]>;
<![CDATA[Avlonitis, Markos]]>;
<![CDATA[Samantha, Nikita]]>;
<![CDATA[Hoogar, Praveen]]>;
<![CDATA[Ruhago, George Mugambage]]>;
<![CDATA[Rono, Wcyliffe]]>
<![CDATA[ Journal of Information System Exploration and Research Vol. 1 No. 1 (2023): January 2023 ]]>
Publisher : <![CDATA[shmpublisher ]]>
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DOI: 10.52465/joiser.v1i1.91
<![CDATA[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.]]>
<![CDATA[Utilization of Business Intelligence in Sales Information Systems ]]>
<![CDATA[Nurdin, Alya Aulia]]>;
<![CDATA[Salmi, Gading Nur]]>;
<![CDATA[Sentosa, Kevin]]>;
<![CDATA[Wijayanti, Annisa Rachma]]>;
<![CDATA[Prasetya, Ananda]]>
<![CDATA[ Journal of Information System Exploration and Research Vol. 1 No. 1 (2023): January 2023 ]]>
Publisher : <![CDATA[shmpublisher ]]>
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DOI: 10.52465/joiser.v1i1.101
<![CDATA[Business intelligence is one of the concepts that can facilitate the process of processing data of a company which will later become the basis for the decision-making process of the sales process. Distributor company needs an information system that can help the company in managing and analyzing data and can make sales and profit predictions in the future. This study aims to create an information system that can visualize data analysis and the results of forecasting sales data by avocado fruit distributor companies. In this study, we will apply the concept of Business Intelligence using Power BI Desktop which is equipped with sales prediction analysis on the sales information system. The data processing process in this study uses the process of integrating Excel tools with Power BI Desktop. The dataset of sales in this study was obtained from the Kaggle site and the software development in this study using the SDLC (system development life cycle) waterfall development method. In this study, we found that the development of business intelligence in the sales information system provides convenience that can be felt by distributors, namely in terms of revenue and time. In this case, forecasting is done with the forecast feature in Power BI Desktop with a confidence interval of 95%.]]>
<![CDATA[The Effect of Modern Strategy Implementation on Smart Infrastructure on Increasing Employee Performance at University in Indonesia ]]>
<![CDATA[Noor Dianti, Erika]]>;
<![CDATA[Khoirunnisa, Oktaria Gina]]>;
<![CDATA[Hidayah, Sayyidah Rohmatul]]>
<![CDATA[ Journal of Information System Exploration and Research Vol. 1 No. 1 (2023): January 2023 ]]>
Publisher : <![CDATA[shmpublisher ]]>
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DOI: 10.52465/joiser.v1i1.102
<![CDATA[The design of strategies to increase the potential benefits of an organization is very important for renewal by implementing modern strategies. Smart infrastructure is a digital system that functions to improve performance, welfare, and increase cost efficiency and resource consumption. Previous research shows a significant increase in smart infrastructure which is influenced by the ability of the community. This study aims to analyze the success of implementing a renewal strategy for Smart Infrastructure for employees at university which we can assess from the performance of the university employees. Primary data was collected through questionnaires with a sample of 40 respondents which was then processed quantitatively by ANOVA test and LSD test using the Statistical Package for the Social Sciences (SPSS). The results showed that the percentage rate accepted was 78%, so that the implementation of a smart infrastructure system could increase employee productivity in university.]]>
<![CDATA[Operational Supply Chain Risk Management on Apparel Industry Based on Supply Chain Operation Reference (SCOR) ]]>
<![CDATA[Pertiwi, Dwika Ananda Agustina]]>;
<![CDATA[Yusuf, Muhammad]]>;
<![CDATA[Efrilianda, Devi Ajeng]]>
<![CDATA[ Journal of Information System Exploration and Research Vol. 1 No. 1 (2023): January 2023 ]]>
Publisher : <![CDATA[shmpublisher ]]>
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DOI: 10.52465/joiser.v1i1.103
<![CDATA[The occurrence of uncertainty requires proper handling to avoid the adverse effects called risk. Risk tends to arise in the supply chain process called supply chain risk. The purpose of this research is to identify the possible level of risk that occurs and has the potential to disrupt supply chain activities, determine priority risk sources based on Supply Chain Operation References (SCOR). The object of this research is the apparel industry, which is a company engaged in fashion and apparel production. This study uses a qualitative and quantitative approach, the value of the instrument is assessed based on the results of the Aggregate Risk Potential (ARP) calculation in the House of Risk method phase 1. The results showed that there were 39 correlations between risk events and risk agents, with 22 correlations with a high scale and 1 correlation with a low scale, and 15 correlations on a medium scale.]]>
<![CDATA[Accuracy of Malaysia Public Response to Economic Factors During the Covid-19 Pandemic Using Vader and Random Forest ]]>
<![CDATA[Jumanto, Jumanto]]>;
<![CDATA[Muslim, Much Aziz]]>;
<![CDATA[Dasril, Yosza]]>;
<![CDATA[Mustaqim, Tanzilal]]>
<![CDATA[ Journal of Information System Exploration and Research Vol. 1 No. 1 (2023): January 2023 ]]>
Publisher : <![CDATA[shmpublisher ]]>
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DOI: 10.52465/joiser.v1i1.104
<![CDATA[This study conducted a sentiment analysis of the impact of the Covid-19 pandemic in the economic sector on people's lives through social media Twitter. The analysis was carried out on 23,777 tweet data collected from 13 states in Malaysia from 1 December 2019 to 17 June 2020. The research process went through 3 stages, namely pre-processing, labeling, and modeling. The pre-processing stage is collecting and cleaning data. Labeling in this study uses Vader sentiment polarity detection to provide an assessment of the sentiment of tweet data which is used as training data. The modeling stage means to test the sentiment data using the random forest algorithm plus the extraction count vectorizer and TF-IDF features as well as the N-gram selection feature. The test results show that the polarity of public sentiment in Malaysia is predominantly positive, which is 11,323 positive, 4105 neutral, and 8349 negative based on Vader labeling. The accuracy rate from the random forest modeling results was obtained 93.5 percent with TF-IDF and 1 gram.]]>
<![CDATA[Decision Support System for Program Indonesia Pintar Recipients Using the Fuzzy Multi-Criteria Decision-Making Method ]]>
<![CDATA[Hamid, Abdul]]>;
<![CDATA[Rais, Muhammad Sandi]]>;
<![CDATA[Rois, Muhammad Idris]]>;
<![CDATA[Salamun, Salamun]]>;
<![CDATA[Yonhendri, Yonhendri]]>;
<![CDATA[Zulfan, Ahmad]]>;
<![CDATA[Oyong, Lasmi]]>
<![CDATA[ Journal of Information System Exploration and Research Vol. 1 No. 2 (2023): July 2023 ]]>
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DOI: 10.52465/joiser.v1i2.157
<![CDATA[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.]]>
<![CDATA[Naive Bayes and KNN for Airline Passenger Satisfaction Classification: Comparative Analysis ]]>
<![CDATA[Nurdina, Annisa]]>;
<![CDATA[Puspita, Audita Bella Intan]]>
<![CDATA[ Journal of Information System Exploration and Research Vol. 1 No. 2 (2023): July 2023 ]]>
Publisher : <![CDATA[shmpublisher ]]>
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DOI: 10.52465/joiser.v1i2.167
<![CDATA[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%.]]>
<![CDATA[Mask Detection System with Computer Vision-Based on CNN and YOLO Method Using Nvidia Jetson Nano ]]>
<![CDATA[Hakim, Ade Anggian]]>;
<![CDATA[Juanara, Elmo]]>;
<![CDATA[Rispandi, Rispandi]]>
<![CDATA[ Journal of Information System Exploration and Research Vol. 1 No. 2 (2023): July 2023 ]]>
Publisher : <![CDATA[shmpublisher ]]>
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DOI: 10.52465/joiser.v1i2.175
<![CDATA[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.]]>
<![CDATA[Portfolio Selection Strategies in Bursa Malaysia Based on Quadratic Programming ]]>
<![CDATA[Ling, Liang Pei]]>;
<![CDATA[Dasril, Yosza]]>
<![CDATA[ Journal of Information System Exploration and Research Vol. 1 No. 2 (2023): July 2023 ]]>
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DOI: 10.52465/joiser.v1i2.178
<![CDATA[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.]]>
<![CDATA[Bankruptcy Prediction Using Genetic Algorithm-Support Vector Machine (GA-SVM) Feature Selection and Stacking ]]>
<![CDATA[Abror, Wiena Faqih]]>;
<![CDATA[Alamsyah, Alamsyah]]>;
<![CDATA[Aziz, Muhammad]]>
<![CDATA[ Journal of Information System Exploration and Research Vol. 1 No. 2 (2023): July 2023 ]]>
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DOI: 10.52465/joiser.v1i2.180
<![CDATA[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%.]]>
