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.
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<![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.]]>
