Pub Date : 2022-01-01DOI: 10.5267/j.dsl.2021.10.001
T. Purwandari, S. Zahroh, Y. Hidayat, Sukonob Sukonob, M. Mamat, Jumadil Saputra
COVID-19 has spread to more than a hundred countries worldwide since the first case reported in late 2019 in Wuhan, China. As one of the countries affected by the spread of COVID-19 cases, the local government of Malaysia has issued several policies to reduce the spread of this outbreak. One of the measures taken by the Malaysian government, namely the Movement Control Order, has been carried out since March 18, 2020. In order to provide precise information to the government so that it can take the appropriate measures, many researchers have attempted to predict and create the model for these cases to identify the number of cases each day and the peak of this pandemic. Therefore, hospitals and health workers can anticipate a surge in COVID-19 patients. In this research, confirmed, recovered, and death cases prediction was performed using the neural network as one of the machine learning methods with high accuracy. The neural network model used is the Multi-Layer Perceptron, Neural Network Auto-Regressive, and Extreme Learning Machine. The three models calculated the average percentage error (APE) values for 7 days and obtained APE values for most cases less than 10%; only 1 case in the last day of one method had an APE value of approximately 11%. Furthermore, based on the best model, then the forecast is made for the next 7 days. In conclusion, this study identified that the MLP model is the best model for 7-step ahead forecasting for confirmed, recovered, and death cases in Malaysia. However, according to the result of testing data, the ELM performs better than the MLP model.
{"title":"Forecasting model of COVID-19 pandemic in Malaysia: An application of time series approach using neural network","authors":"T. Purwandari, S. Zahroh, Y. Hidayat, Sukonob Sukonob, M. Mamat, Jumadil Saputra","doi":"10.5267/j.dsl.2021.10.001","DOIUrl":"https://doi.org/10.5267/j.dsl.2021.10.001","url":null,"abstract":"COVID-19 has spread to more than a hundred countries worldwide since the first case reported in late 2019 in Wuhan, China. As one of the countries affected by the spread of COVID-19 cases, the local government of Malaysia has issued several policies to reduce the spread of this outbreak. One of the measures taken by the Malaysian government, namely the Movement Control Order, has been carried out since March 18, 2020. In order to provide precise information to the government so that it can take the appropriate measures, many researchers have attempted to predict and create the model for these cases to identify the number of cases each day and the peak of this pandemic. Therefore, hospitals and health workers can anticipate a surge in COVID-19 patients. In this research, confirmed, recovered, and death cases prediction was performed using the neural network as one of the machine learning methods with high accuracy. The neural network model used is the Multi-Layer Perceptron, Neural Network Auto-Regressive, and Extreme Learning Machine. The three models calculated the average percentage error (APE) values for 7 days and obtained APE values for most cases less than 10%; only 1 case in the last day of one method had an APE value of approximately 11%. Furthermore, based on the best model, then the forecast is made for the next 7 days. In conclusion, this study identified that the MLP model is the best model for 7-step ahead forecasting for confirmed, recovered, and death cases in Malaysia. However, according to the result of testing data, the ELM performs better than the MLP model.","PeriodicalId":38141,"journal":{"name":"Decision Science Letters","volume":"2 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74726267","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}
Pub Date : 2022-01-01DOI: 10.5267/j.dsl.2022.5.003
Yuanshyi Peter Chiu, S. Chiu, Tiffany Chiu, Hui-Chi Wang
Production managers face the growing trend of rapid-response orders and inevitable production defects and failures; they must carefully measure these factors’ effects to minimize operating expenditures and operational disruption. Inspired by assisting producers decide the optimal runtime policy under these real situations, this work investigates the collective impact of rework, expedited-rate, external source, and machine failures on such a specific fabrication system. A partial outsourcing and expedited manufacturing rate are considered in the studied system to reduce the batch fabricating time. Additionally, defects rework and repair failure machines are implemented to retain the quality and avoid production disruption. Our research scheme consists of (1) developing a model for the mentioned manufacturing characteristics; and (2) analytical and optimization techniques for deciding the best batch runtime decision by minimizing the system’s overall expenses. Lastly, we provide numerical examples to demonstrate the model’s applicability and disclose important, in-depth characteristics that facilitate managerial decision-making.
{"title":"The collective effect of rework, expedited-rate, external source, and machine failures on manufacturing runtime planning","authors":"Yuanshyi Peter Chiu, S. Chiu, Tiffany Chiu, Hui-Chi Wang","doi":"10.5267/j.dsl.2022.5.003","DOIUrl":"https://doi.org/10.5267/j.dsl.2022.5.003","url":null,"abstract":"Production managers face the growing trend of rapid-response orders and inevitable production defects and failures; they must carefully measure these factors’ effects to minimize operating expenditures and operational disruption. Inspired by assisting producers decide the optimal runtime policy under these real situations, this work investigates the collective impact of rework, expedited-rate, external source, and machine failures on such a specific fabrication system. A partial outsourcing and expedited manufacturing rate are considered in the studied system to reduce the batch fabricating time. Additionally, defects rework and repair failure machines are implemented to retain the quality and avoid production disruption. Our research scheme consists of (1) developing a model for the mentioned manufacturing characteristics; and (2) analytical and optimization techniques for deciding the best batch runtime decision by minimizing the system’s overall expenses. Lastly, we provide numerical examples to demonstrate the model’s applicability and disclose important, in-depth characteristics that facilitate managerial decision-making.","PeriodicalId":38141,"journal":{"name":"Decision Science Letters","volume":"4 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88748939","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}
Pub Date : 2022-01-01DOI: 10.5267/j.dsl.2022.3.001
Suree Khemthong, Puripat Charnkit
The aim of this study is to identify the influence of food attributes, perceived value, consumer trust and nutritional health behavior that contribute to consumer repurchase intention of foods with health claims. Descriptive Statistics was used to analyze the demographic profile of the 313 sample of respondents. Path analysis was conducted for analyzing the causal and effect relationship between variables expressed by means of a path coefficient. The results showed that there were only indirect effects between food attributes and Intention to repurchase. However, perceived value was shown to have both positive direct and indirect effects on intention to repurchase, which were significantly mediated by nutritional health behavior and consumer trust. Research results suggest food managers develop the value of food products and monitor the customer trust and the changes of market on the food with health claims for the competition in a current environment.
{"title":"A path analysis study of repurchase intention of food with health claim under the effect of food attributes","authors":"Suree Khemthong, Puripat Charnkit","doi":"10.5267/j.dsl.2022.3.001","DOIUrl":"https://doi.org/10.5267/j.dsl.2022.3.001","url":null,"abstract":"The aim of this study is to identify the influence of food attributes, perceived value, consumer trust and nutritional health behavior that contribute to consumer repurchase intention of foods with health claims. Descriptive Statistics was used to analyze the demographic profile of the 313 sample of respondents. Path analysis was conducted for analyzing the causal and effect relationship between variables expressed by means of a path coefficient. The results showed that there were only indirect effects between food attributes and Intention to repurchase. However, perceived value was shown to have both positive direct and indirect effects on intention to repurchase, which were significantly mediated by nutritional health behavior and consumer trust. Research results suggest food managers develop the value of food products and monitor the customer trust and the changes of market on the food with health claims for the competition in a current environment.","PeriodicalId":38141,"journal":{"name":"Decision Science Letters","volume":"11 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83959217","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}
Pub Date : 2022-01-01DOI: 10.5267/j.dsl.2022.2.003
Liubov Lelyk, V. Olikhovskyi, N. Mahas, M. Olikhovska
With the complication of the business environment of commercial economic activity, competition intensifies, which threatens the bankruptcy of enterprises, the prevention of which requires quality monitoring and timely identification of crises using methods of comprehensive assessment and analysis of economic security. This research is aimed at conducting component-by-component and, as a result, integrated assessment of the state of economic security of the enterprise. Methodological research tools include analysis of the main components, causation and vector regression modeling. A resource-functional security model is developed (which consists of partial indicators and components of economic security of business) and a resource-functional approach to calculations is also applied. Using the data of the expert survey, the values of indicators of structural components of economic security of the enterprise are determined. Using the resource-functional approach, the integrated values of sub-indices and the integral values of the general level of economic security of the enterprise are calculated. According to the results of the assessment, it is established that the integrated level of economic security of the enterprise is 7.04 (sufficient level of security). However, the components of economic security identified critically low values, namely - the financial component (0.452), the information component (0.554), the institutional and legal component (0.647). The results of the study are of practical value for the development of technological schemes - algorithms for strengthening the financial, informational and institutional and legal security of the enterprise, making sound (using economic and mathematical tools) management decisions to ensure the trajectory of sustainable economic development.
{"title":"An integrated analysis of enterprise economy security","authors":"Liubov Lelyk, V. Olikhovskyi, N. Mahas, M. Olikhovska","doi":"10.5267/j.dsl.2022.2.003","DOIUrl":"https://doi.org/10.5267/j.dsl.2022.2.003","url":null,"abstract":"With the complication of the business environment of commercial economic activity, competition intensifies, which threatens the bankruptcy of enterprises, the prevention of which requires quality monitoring and timely identification of crises using methods of comprehensive assessment and analysis of economic security. This research is aimed at conducting component-by-component and, as a result, integrated assessment of the state of economic security of the enterprise. Methodological research tools include analysis of the main components, causation and vector regression modeling. A resource-functional security model is developed (which consists of partial indicators and components of economic security of business) and a resource-functional approach to calculations is also applied. Using the data of the expert survey, the values of indicators of structural components of economic security of the enterprise are determined. Using the resource-functional approach, the integrated values of sub-indices and the integral values of the general level of economic security of the enterprise are calculated. According to the results of the assessment, it is established that the integrated level of economic security of the enterprise is 7.04 (sufficient level of security). However, the components of economic security identified critically low values, namely - the financial component (0.452), the information component (0.554), the institutional and legal component (0.647). The results of the study are of practical value for the development of technological schemes - algorithms for strengthening the financial, informational and institutional and legal security of the enterprise, making sound (using economic and mathematical tools) management decisions to ensure the trajectory of sustainable economic development.","PeriodicalId":38141,"journal":{"name":"Decision Science Letters","volume":"76 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85650218","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}
Pub Date : 2022-01-01DOI: 10.5267/j.dsl.2022.2.005
A. Obidat, M. Almahameed, Mohammad Alalwan
Although the critical success factors might be different between the pre and post-adoption stages of mobile wallets, there have been few studies conducted to examine those factors for the post-adoption stage when compared to the number of studies conducted to examine those factors for the pre-adoption stage. Yet, the post-adoption stage of mobile wallets is crucial to the success and sustainability of the mobile wallets’ ecosystem. Thus, this study developed and examined a model by integrating relevant factors into the Technology Acceptance Model 2 (TAM2). Data were collected from 578 mobile wallet users in Jordan using an electronic questionnaire. A structural equation modelling approach was utilized to analyze the data. The results revealed that perceived usefulness and perceived ease of use have statistically significant positive direct effects on the intention to continuous use of mobile wallets, while subjective norm does not. In addition to that, results indicated that trust, security, and ubiquity have statistically significant positive direct effects on perceived usefulness and perceived ease of use, and, in turn, on the intention to continuous use of mobile wallets. Moreover, this study found that perceived ease of use and subjective norms have statistically significant positive direct effects on perceived usefulness, and, in turn, on the intention of continuous use of mobile wallets. While risk does not have a significant effect on perceived usefulness, it has been found to have a statistically significant negative direct effect on perceived ease of use, and, in turn, on the intention to continuous use of mobile wallets. The findings of this study should help stakeholders to develop more effective consumer retention tactics and formulate appropriate marketing decisions.
{"title":"An empirical examination of factors affecting the post-adoption stage of mobile wallets by consumers: A perspective from a developing country","authors":"A. Obidat, M. Almahameed, Mohammad Alalwan","doi":"10.5267/j.dsl.2022.2.005","DOIUrl":"https://doi.org/10.5267/j.dsl.2022.2.005","url":null,"abstract":"Although the critical success factors might be different between the pre and post-adoption stages of mobile wallets, there have been few studies conducted to examine those factors for the post-adoption stage when compared to the number of studies conducted to examine those factors for the pre-adoption stage. Yet, the post-adoption stage of mobile wallets is crucial to the success and sustainability of the mobile wallets’ ecosystem. Thus, this study developed and examined a model by integrating relevant factors into the Technology Acceptance Model 2 (TAM2). Data were collected from 578 mobile wallet users in Jordan using an electronic questionnaire. A structural equation modelling approach was utilized to analyze the data. The results revealed that perceived usefulness and perceived ease of use have statistically significant positive direct effects on the intention to continuous use of mobile wallets, while subjective norm does not. In addition to that, results indicated that trust, security, and ubiquity have statistically significant positive direct effects on perceived usefulness and perceived ease of use, and, in turn, on the intention to continuous use of mobile wallets. Moreover, this study found that perceived ease of use and subjective norms have statistically significant positive direct effects on perceived usefulness, and, in turn, on the intention of continuous use of mobile wallets. While risk does not have a significant effect on perceived usefulness, it has been found to have a statistically significant negative direct effect on perceived ease of use, and, in turn, on the intention to continuous use of mobile wallets. The findings of this study should help stakeholders to develop more effective consumer retention tactics and formulate appropriate marketing decisions.","PeriodicalId":38141,"journal":{"name":"Decision Science Letters","volume":"19 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89673917","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}
Pub Date : 2022-01-01DOI: 10.5267/j.dsl.2022.4.001
D. Yuliawan, D. Hakim, B. Juanda, A. Fauzi
Vulnerability is one of the prominent features of rural areas due to their distinctive characteristics, such as remoteness, geographical conditions, and socio-economic dependence on primary sectors. Addressing the vulnerability of rural areas in terms of the rural development paradigm is both urgent and relevant. This study aims to address this issue using the current state-of-the-art machine learning method, using the socio-ecological framework and integrated vulnerability index of villages in Lampung Province in Indonesia. The study attempts to predict and classify villages' vulnerability to be applied for better planning and rural development. Based on random forest classification and decision tree algorithm, the results show that the village governance system represented by rural water management and the level of education of village leaders are suitable prediction variables related to the low vulnerability index. This study can draw lessons learned to improve rural development in developing countries.
{"title":"Classification and prediction of rural socio-economic vulnerability (IRSV) integrated with social-ecological system (SES)","authors":"D. Yuliawan, D. Hakim, B. Juanda, A. Fauzi","doi":"10.5267/j.dsl.2022.4.001","DOIUrl":"https://doi.org/10.5267/j.dsl.2022.4.001","url":null,"abstract":"Vulnerability is one of the prominent features of rural areas due to their distinctive characteristics, such as remoteness, geographical conditions, and socio-economic dependence on primary sectors. Addressing the vulnerability of rural areas in terms of the rural development paradigm is both urgent and relevant. This study aims to address this issue using the current state-of-the-art machine learning method, using the socio-ecological framework and integrated vulnerability index of villages in Lampung Province in Indonesia. The study attempts to predict and classify villages' vulnerability to be applied for better planning and rural development. Based on random forest classification and decision tree algorithm, the results show that the village governance system represented by rural water management and the level of education of village leaders are suitable prediction variables related to the low vulnerability index. This study can draw lessons learned to improve rural development in developing countries.","PeriodicalId":38141,"journal":{"name":"Decision Science Letters","volume":"1 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82837431","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}
Pub Date : 2022-01-01DOI: 10.5267/j.dsl.2022.6.003
S. Purusotham, T. J. Kumar, T. Vimala, K.J. Ghanshyam
This paper considers a practical truncated traveling salesman problem (TTSP), in which the salesman is only required to cover a subset of out of given cities (rather than covering all the given cities as in conventional travelling salesman problem (TSP)) with minimal traversal distance. Thus, every feasible solution tour contains exactly cities including the starting city. However, extensive research on TSP has been received and various efficient solution techniques including exact, heuristic, and metaheuristic algorithms are devoted, a very limited attention has been given to TTSP models because of its solution structure. The TTSP model comprises two types of problems including city selection i.e. as a salesman's trip need not include all the cities, the challenge is to identify which combination of cities are to be visited and which sequence of cities will constitute minimal traversal distance. A hybrid genetic algorithm (GA) comprising sophisticated mutation operators is developed to tackle this problem efficiently. Comparative computational findings suggest that the proposed GA has capability to outperform existing approaches in terms of TTSP results. In addition, the proposed GA report improved results and will serve as a basis for forthcoming TTSP studies.
{"title":"An efficient hybrid genetic algorithm for solving truncated travelling salesman problem","authors":"S. Purusotham, T. J. Kumar, T. Vimala, K.J. Ghanshyam","doi":"10.5267/j.dsl.2022.6.003","DOIUrl":"https://doi.org/10.5267/j.dsl.2022.6.003","url":null,"abstract":"This paper considers a practical truncated traveling salesman problem (TTSP), in which the salesman is only required to cover a subset of out of given cities (rather than covering all the given cities as in conventional travelling salesman problem (TSP)) with minimal traversal distance. Thus, every feasible solution tour contains exactly cities including the starting city. However, extensive research on TSP has been received and various efficient solution techniques including exact, heuristic, and metaheuristic algorithms are devoted, a very limited attention has been given to TTSP models because of its solution structure. The TTSP model comprises two types of problems including city selection i.e. as a salesman's trip need not include all the cities, the challenge is to identify which combination of cities are to be visited and which sequence of cities will constitute minimal traversal distance. A hybrid genetic algorithm (GA) comprising sophisticated mutation operators is developed to tackle this problem efficiently. Comparative computational findings suggest that the proposed GA has capability to outperform existing approaches in terms of TTSP results. In addition, the proposed GA report improved results and will serve as a basis for forthcoming TTSP studies.","PeriodicalId":38141,"journal":{"name":"Decision Science Letters","volume":"PP 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84168997","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}
Pub Date : 2022-01-01DOI: 10.5267/j.dsl.2021.12.001
Ashutosh Kumar Singh, K. Kumar, K. G. Sundari, R. Ranjan, B. Surekha
In the current paper, the authors are intended to manufacture the aluminum based metal matrix composite (MMC) employing the stir casting process. Further, the fabricated composite sample is investigated for machining characteristics during the die sink electrical discharge machining process (EDM). EDM is most commonly employed to satisfy the special needs of industry such as developing deep holes and complex contours from high strength materials such as composites, alloys, smart materials, and functionally graded materials. In the current study A356 and 4%, tungsten carbide (WC) powder are considered as matrix and strengthening materials respectively to fabricate the MMCs. During the machining activity, the input factors like discharge current (Ip), Voltage (Vg), Pulse On-Time (Ton), and flushing pressure (P) are optimized for achieving optimum surface roughness (SR), Tool Wear Rate (TWR) and Material Removal Rate (MRR). To estimate the ideal set of process factors grey regression analysis (GRA) is used. From the results, it was observed that the GRA is found to perform better than the RSM.
{"title":"Experimental investigations and multi criteria optimization during machining of A356/WC MMCs using EDM","authors":"Ashutosh Kumar Singh, K. Kumar, K. G. Sundari, R. Ranjan, B. Surekha","doi":"10.5267/j.dsl.2021.12.001","DOIUrl":"https://doi.org/10.5267/j.dsl.2021.12.001","url":null,"abstract":"In the current paper, the authors are intended to manufacture the aluminum based metal matrix composite (MMC) employing the stir casting process. Further, the fabricated composite sample is investigated for machining characteristics during the die sink electrical discharge machining process (EDM). EDM is most commonly employed to satisfy the special needs of industry such as developing deep holes and complex contours from high strength materials such as composites, alloys, smart materials, and functionally graded materials. In the current study A356 and 4%, tungsten carbide (WC) powder are considered as matrix and strengthening materials respectively to fabricate the MMCs. During the machining activity, the input factors like discharge current (Ip), Voltage (Vg), Pulse On-Time (Ton), and flushing pressure (P) are optimized for achieving optimum surface roughness (SR), Tool Wear Rate (TWR) and Material Removal Rate (MRR). To estimate the ideal set of process factors grey regression analysis (GRA) is used. From the results, it was observed that the GRA is found to perform better than the RSM.","PeriodicalId":38141,"journal":{"name":"Decision Science Letters","volume":"42 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77972374","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}
Pub Date : 2022-01-01DOI: 10.5267/j.dsl.2022.5.004
Ricky Bryan Quiñones Fabian, Ruben Aldair Andamayo Alcantara, Abel Jesus Inga Lopez, Jaime Antonio Huaytalla Pariona, J. A. D. Quispe
In developing countries, child labor has become a significant problem with adverse effects in the present and future for society and individuals. There are many causes that obligate children to abandon school and start working. Economic, social, familiar, and personal problems can expel children from school, inhibiting them from living appropriately. Polls like the ENAHO in Peru tried to recollect relevant data as much as possible to explain this problem. With many variables, it is necessary to have a methodology to build an algorithm with enough explanatory power to explain the situation. Therefore, this research elaborated an algorithm through Lasso to proportionate a statistical explanation of child labor. Due to the type of data, the regression was logistic.
{"title":"An algorithm to estimate the risk of child labor","authors":"Ricky Bryan Quiñones Fabian, Ruben Aldair Andamayo Alcantara, Abel Jesus Inga Lopez, Jaime Antonio Huaytalla Pariona, J. A. D. Quispe","doi":"10.5267/j.dsl.2022.5.004","DOIUrl":"https://doi.org/10.5267/j.dsl.2022.5.004","url":null,"abstract":"In developing countries, child labor has become a significant problem with adverse effects in the present and future for society and individuals. There are many causes that obligate children to abandon school and start working. Economic, social, familiar, and personal problems can expel children from school, inhibiting them from living appropriately. Polls like the ENAHO in Peru tried to recollect relevant data as much as possible to explain this problem. With many variables, it is necessary to have a methodology to build an algorithm with enough explanatory power to explain the situation. Therefore, this research elaborated an algorithm through Lasso to proportionate a statistical explanation of child labor. Due to the type of data, the regression was logistic.","PeriodicalId":38141,"journal":{"name":"Decision Science Letters","volume":"os-39 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87425444","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}
Pub Date : 2022-01-01DOI: 10.5267/j.dsl.2022.7.002
Ahmad Fawaid Ridwan, H. Napitupulu, S. Sukono
Stock is one of the investment assets that has its charm for investors. It is very liquid and has a high rate of return, but it has a high risk. The strategy commonly used to minimize investment risk is to diversify through portfolio formation. A good allocation of funds must be determined in forming an optimal portfolio. In addition, the method of stock selection needs to be considered so the stocks are well diversified and the portfolio developed has good performance. This study aims to compare stock selection between K-Means and Average Linkage clustering approaches in forming an investment portfolio. Clustering analysis is used to group IDX80 stocks based on their attributes. In forming a portfolio with the Mean-VaR model, the stock selection decision criteria used are by selecting stocks with the highest positive returns from each cluster. As a result, the two clustering techniques show the superiority of the Silhouette score for a certain number of clusters, but there are still more advantages in Average Linkage. The portfolio approached by Average Linkage resulted in a better performance than the portfolio approached by K-Means. Therefore, Average Linkage clustering can be used as a better recommendation in decision-making to select stocks so as to produce optimal portfolio performance.
{"title":"Decision-making in formation of mean-VaR optimal portfolio by selecting stocks using K-means and average linkage clustering","authors":"Ahmad Fawaid Ridwan, H. Napitupulu, S. Sukono","doi":"10.5267/j.dsl.2022.7.002","DOIUrl":"https://doi.org/10.5267/j.dsl.2022.7.002","url":null,"abstract":"Stock is one of the investment assets that has its charm for investors. It is very liquid and has a high rate of return, but it has a high risk. The strategy commonly used to minimize investment risk is to diversify through portfolio formation. A good allocation of funds must be determined in forming an optimal portfolio. In addition, the method of stock selection needs to be considered so the stocks are well diversified and the portfolio developed has good performance. This study aims to compare stock selection between K-Means and Average Linkage clustering approaches in forming an investment portfolio. Clustering analysis is used to group IDX80 stocks based on their attributes. In forming a portfolio with the Mean-VaR model, the stock selection decision criteria used are by selecting stocks with the highest positive returns from each cluster. As a result, the two clustering techniques show the superiority of the Silhouette score for a certain number of clusters, but there are still more advantages in Average Linkage. The portfolio approached by Average Linkage resulted in a better performance than the portfolio approached by K-Means. Therefore, Average Linkage clustering can be used as a better recommendation in decision-making to select stocks so as to produce optimal portfolio performance.","PeriodicalId":38141,"journal":{"name":"Decision Science Letters","volume":"26 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83798035","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}
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