International Journal of Artificial Intelligence Research最新文献

{"title":"An Analysis of Employee Views and The Effectiveness of Implementing Flexible Work Arrangements In Improving Work-Life Balance on Employees of Life Insurance Companies","authors":"U. Rusilowati","doi":"10.29099/ijair.v6i1.300","DOIUrl":"https://doi.org/10.29099/ijair.v6i1.300","url":null,"abstract":"The purpose of this research is to find out and analyze the views of employees, the factors that become obstacles, strategies that can be taken to overcome them, and the application of flexible work arrangements in improving work-life balance. The research method used in this research is descriptive qualitative. The data collection technique was done by triangulation (observation, interview, and documentation). The results of this study indicate that in general the experience of employees with flexible working hours is included in the positive category, respondents who undergo flexible work arrangements mostly experience additional working hours which can reach 10 to 12 hours","PeriodicalId":334856,"journal":{"name":"International Journal of Artificial Intelligence Research","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129807335","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}

{"title":"Causal Relations of Factors Representing the Elderly Independence in Doing Activities of Daily Livings Using S3C-Latent Algorithm","authors":"Nurhaeka Tou, R. Rahmadi, C. Effendy","doi":"10.29099/IJAIR.V5I1.206","DOIUrl":"https://doi.org/10.29099/IJAIR.V5I1.206","url":null,"abstract":"The growth of the elderly population in Indonesia from year to year has always increased, followed by the problem of decreasing physical strength and psychological health of the elderly. These problems can affect the increase in dependence and decrease the independence of the elderly in ADL. In previous studies, various factors affect independence in ADLs such as cognitive, psychological, economic, nutrition, and health. However, In general, these studies only focus on predictive analysis or correlation of variables, and no research has attempted to identify the casual relationship of the elderly independence factors. Therefore, this study aimed to determine the mechanism of the causal relationship of the factors that influence the independence of the elderly in ADLs using a casual method called the Stable Specification Search for Cross-Sectional Data With Latent Variables (S3C-Latent). In this research we found strong causal and associative relationships between factors.The causal relationship of elderly independence in ADLs was influenced by cognitive, psychological, nutritional and health factors and gender with α values respectively (0.61; 0.61;1.00, 0.65;0.70). Cognitive factors associated with psychological, economic, nutrition, and health with a value of α (0.77; 1.00; 1.00; 0.64). Furthermore, psychological factors associated with economy, nutrition, and health with a value of α (0.77; 0.95; 0.63). Bisides, economic factors are associated with nutrition and health with α values of ( 0.86; 0.75) and nutrition with health with α values of 0.64. The last association was found between nutritional factors and gender with a value of α 0.76. This research is expected to increase the independence of the elderly in carrying out daily activities.","PeriodicalId":334856,"journal":{"name":"International Journal of Artificial Intelligence Research","volume":"300 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114498134","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}

{"title":"Uplift modeling VS conventional predictive model: A reliable machine learning model to solve employee turnover","authors":"D. Wijaya, Jumri Habbeyb Ds, Samuelta Barus, Beriman Pasaribu, Loredana Ioana Sirbu, A. Dharma","doi":"10.29099/IJAIR.V4I2.169","DOIUrl":"https://doi.org/10.29099/IJAIR.V4I2.169","url":null,"abstract":"Employee turnover is the loss of talent in the workforce that can be costly for a company. Uplift modeling is one of the prescriptive methods in machine learning models that not only predict an outcome but also prescribe a solution. Recent studies are focusing on the conventional predictive models to predict employee turnover rather than uplift modeling. In this research, we analyze whether the uplifting model has better performance than the conventional predictive model in solving employee turnover. Performance comparison between the two methods was carried out by experimentation using two synthetic datasets and one real dataset. The results show that despite the conventional predictive model yields an average prediction accuracy of 84%; it only yields a success rate of 50% to target the right employee with a retention program on the three datasets. By contrast, the uplift model only yields an average accuracy of 67% but yields a consistent success rate of 100% in targeting the right employee with a retention program.","PeriodicalId":334856,"journal":{"name":"International Journal of Artificial Intelligence Research","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133599774","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}

{"title":"Covid-19: Implementation e-voting Blockchain Concept","authors":"M. Kamil, A. Bist, U. Rahardja, N. Santoso, M. Iqbal","doi":"10.29099/ijair.v5i1.173","DOIUrl":"https://doi.org/10.29099/ijair.v5i1.173","url":null,"abstract":"The current situation of the Covid-19 pandemic is currently increasing public concern about the community. The government has especially recommended Stay at Home and the implementation of PSBB in various regions. One of the concerns is when the election of regional leaders to the general chairman. Even though there is already a safeguard regulation, this is not considered safe in the current Covid-19 pandemic. The solution in this research is the use of a blockchain-based E-voting system to help tackle election unrest during Covid-19. Where e-voting with blockchain technology can be carried out anywhere through the device without the need to be present in the voting booth, reducing data fraud, accurate and decentralized voting results that can be accessed by the public in real-time. The use of cryptographic protocols is applied for data transfer between system components as well as valid system security. This research method uses SUS trial analysis in a significant system of the Covid-19 pandemic situation. The implication that the SUS Score analysis shows 90 shows an acceptable E-voting system, meaning that the community can accept it because it brings positive and significant impacts such as effectiveness and efficiency.","PeriodicalId":334856,"journal":{"name":"International Journal of Artificial Intelligence Research","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114322063","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}

{"title":"Determination of Overall Equipment Efectiveness Superflex Machine Using Fuzzy Approach","authors":"Sesar Husen Santosa, Suhendi Irawan, Ilham Ardani","doi":"10.29099/ijair.v4i2.142","DOIUrl":"https://doi.org/10.29099/ijair.v4i2.142","url":null,"abstract":"This study aimed to present a Fuzzy logic approach in determining the value of OEE Superflex machine for producing nuggets. The effectiveness value of Superflex machine in producing nugget raw materials was determined by calculating the Availability, Performance and Quality Yield values. Fuzzy approach in determining the value of OEE can be used because this approach is able to describe the value of the effectiveness of thr machines based on the condition of the company's actual capacities. The Fuzzy OEE approach uses the Trapezoidal Membership Association because the maximum value of the membership degree has more than one value in each parameter. The Fuzzy OEE value shows that Superflex machine had an OEE value with bad parameters so that the company has to improve its machine performance","PeriodicalId":334856,"journal":{"name":"International Journal of Artificial Intelligence Research","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114389986","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}

{"title":"Analysis of Expert System for Early Diagnosis of Disorders During Pregnancy Using the Forward Chaining Method","authors":"Basiroh Basiroh, S. Kareem, H. Nurdiyanto","doi":"10.29099/ijair.v5i1.203","DOIUrl":"https://doi.org/10.29099/ijair.v5i1.203","url":null,"abstract":"Nowadays technological developments are increasingly having a positive influence on the development of human life, including in the health sector. One of them is an expert system that can transfer an expert's knowledge into a computer application to simplify and speed up the diagnosis of a disorder or disease in humans. The purpose of this final project is to design an application to diagnose diseases that occur during pregnancy which is caused by the existence of these pregnancies to simplify and speed up the diagnosis of diseases experienced by pregnant women. This study uses the forward chaining method. By involving experts in this expert system analysis according to current needs. Users are given easy access to information on several types of pregnancy disorders and their symptoms, as well as consultation through several questions that the user must answer to find out the results of the diagnosis. While experts are facilitated in system management, both the process of adding, updating and, deleting data.","PeriodicalId":334856,"journal":{"name":"International Journal of Artificial Intelligence Research","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133892956","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}

{"title":"Fuzzy C-Means Clustering Algorithm For Grouping Health Care Centers On Diarrhea Disease","authors":"Ahmad Chusyairi, Pelsri Ramadar Noor Saputra","doi":"10.29099/ijair.v5i1.191","DOIUrl":"https://doi.org/10.29099/ijair.v5i1.191","url":null,"abstract":"In Indonesia, public health services at the city or district level are carried out by regional public hospitals or “puskesmas” (health care centers), especially in Banyuwangi regency, East Java, Indonesia that has 45 health care centers spread throughout the villages. This research focused on the deaths of babies caused by diarrhea diseases, which are the second leading cause of death among children younger than 5 years globally. All of the health care centers need to be divided into 3 groups to find out which health care centers have the least, most moderate, and many diarrhea sufferers. Fuzzy C-Means algorithm is used to overcome this problem. The result from this research shown that 2 health care centers have the smallest member of diarrhea sufferers, 14 health care centers have a medium member of diarrhea sufferers, and the rest have a large number of diarrhea sufferers. From the result of this study, it can be a reference for the health department center in dealing with diarrheal diseases, accordingly, the infant mortality rate due to diarrheal diseases can be lowered to health care centers that have high diarrhea sufferers.","PeriodicalId":334856,"journal":{"name":"International Journal of Artificial Intelligence Research","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131493095","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}

{"title":"Extractive Text Summarization of Student Essay Assignment Using Sentence Weight Features and Fuzzy C-Means","authors":"I. Made, Suwija Putra, Yonatan Adiwinata, Desy Purnami, Singgih Putri, Ni Putu Sutramiani","doi":"10.29099/ijair.v5i1.187","DOIUrl":"https://doi.org/10.29099/ijair.v5i1.187","url":null,"abstract":"One of the main tasks of a lecturer is to give students an academic assessment in the learning process. The assessment process begins with reading or checking the answers of student assignments that contain a combination of very long sentences such as essay or report assignments. This certainly takes a lot of time to get the primary information contained therein. It is necessary to summarize the answers so that the lecturer does not need to read the whole document but is still able to take the essence of the response to the task. This study proposes the application of summarizing text documents of student essay assignments automatically using the Fuzzy C-Means method with the sentence weighting feature. The sentence weighting feature is used by selecting the sentence with the highest weight in one cluster, helping the system to get the primary information from a document quickly. The results of this study indicate that the system succeeds in summarizing text with an average evaluation of the values of precision, recall, accuracy, and F-measure of 0.52, 0.54, 0.70, and 0.52, respectively.One of the main tasks of a lecturer is to give students an academic assessment in the learning process. The assessment process begins with reading or checking the answers of student assignments that contain a combination of very long sentences such as essay or report assignments. This certainly takes a lot of time to get the primary information contained therein. It is necessary to summarize the answers so that the lecturer does not need to read the whole document but is still able to take the essence of the response to the task. This study proposes the application of summarizing text documents of student essay assignments automatically using the Fuzzy C-Means method with the sentence weighting feature. The sentence weighting feature is used by selecting the sentence with the highest weight in one cluster, helping the system to get the primary information from a document quickly. The results of this study indicate that the system succeeds in summarizing text with an average evaluation of the values of precision, recall, accuracy, and F-measure of 0.52, 0.54, 0.70, and 0.52, respectively.","PeriodicalId":334856,"journal":{"name":"International Journal of Artificial Intelligence Research","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128495464","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}

{"title":"Predicting the Spread of the Corona Virus (COVID-19) in Indonesia: Approach Visual Data Analysis and Prophet Forecasting","authors":"A. M. Husein, Jefri Poltak Hutabarat, Jeckson Edition Sitorus, Tonazisokhi Giawa, M. Harahap","doi":"10.29099/ijair.v5i1.192","DOIUrl":"https://doi.org/10.29099/ijair.v5i1.192","url":null,"abstract":"The development trend of the coronavirus pandemic (COVID-19) in various countries has become a global threat, including in Southeast Asia, such as Indonesia, the Philippines, Brunei, Malaysia, and Singapore. In this paper, we propose an Exploratory Data Analysis (EDA) model approach and a time series forecasting model using the Prophet method to predict the number of confirmed cases and cases of death in Indonesia in the next thirty days. We apply the EDA model to visualize and provide an understanding of this pandemic outbreak in various countries, especially in Indonesia. We present the trends in the spread of epidemics from the countries of China from which the virus originates, then mark the top ten countries and their development and also present the trends in Asian countries. We present an analytical framework comparing the predicted results with the actual data evaluated using the MAPE and MAE models, where the prophet algorithm produces good performance based on the evaluation results, the relative error rate of our estimate (MAPE) is around 6.52%, and the model average false 52.7% (MAE) for confirmed cases, while case mortality was 1.3% for the MAPE and MAE models around 236.6%. The results of the analysis can be used as a reference for the Indonesian government in making decisions to prevent its spread in order to avoid an increase in the number of deaths","PeriodicalId":334856,"journal":{"name":"International Journal of Artificial Intelligence Research","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126795590","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}

{"title":"Comparison Analysis of K-Nearest Neighbor and Naïve Bayes in Determining Talent of Adolescence","authors":"Y. Jusman, Widdya Rahmalina, Juni Zarman","doi":"10.29099/ijair.v4i1.118","DOIUrl":"https://doi.org/10.29099/ijair.v4i1.118","url":null,"abstract":"Adolescence always searches for the identity to shape the personality character. This paper aims to use the artificial intelligent analysis to determine the talent of the adolescence. This study uses a sample of children aged 10-18 years with testing data consisting of 100 respondents. The algorithm used for analysis is the K-Nearest Neigbor and Naive Bayes algorithm. The analysis results are performance of accuracy results of both algorithms of classification. In knowing the accurate algorithm in determining children's interests and talents, it can be seen from the accuracy of the data with the confusion matrix using the RapidMiner software for training data, testing data, and combined training and testing data. This study concludes that the K-Nearest Neighbor algorithm is better than Naive Bayes in terms of classification accuracy.","PeriodicalId":334856,"journal":{"name":"International Journal of Artificial Intelligence Research","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123036178","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}