Pub Date : 2023-09-30DOI: 10.30598/barekengvol17iss3pp1641-1652
James Uriel Livingstone Mangobi
Dengue hemorrhagic fever (DHF) is an acute febrile disease caused by the dengue virus, which is transmitted by various species of Aedes mosquitoes. The SEIR model is a mathematical model for studying the spread of dengue fever. In this model, it is assumed that some mosquito eggs have been infected because infected mosquitoes can transmit the virus to their eggs. The main vector of this disease is the Aedes albopictus mosquito. Analysis was carried out to assess the stability of the equilibrium point, and numerical simulations were carried out to see changes in population numbers due to changes in parameter values. A disease-free equilibrium (DFE) point, which is stable given the basic reproductive number . An endemic point whose stability is guaranteed if the value . The numerical simulations show that an increasing mosquito mortality rate decreases the number of exposed, susceptible humans. Furthermore, an increase in the average bite of an infected mosquito will increase the number of exposed, susceptible humans. For the mosquito population, increasing mosquitoes’ mortality rate will decrease the number of exposed, susceptible mosquitoes. Finally, an increase in the average bite of an infected mosquito will increase the number of exposed, susceptible mosquitoes.
{"title":"SEIR MODEL SIMULATION WITH PART OF INFECTED MOSQUITO EGGS","authors":"James Uriel Livingstone Mangobi","doi":"10.30598/barekengvol17iss3pp1641-1652","DOIUrl":"https://doi.org/10.30598/barekengvol17iss3pp1641-1652","url":null,"abstract":"Dengue hemorrhagic fever (DHF) is an acute febrile disease caused by the dengue virus, which is transmitted by various species of Aedes mosquitoes. The SEIR model is a mathematical model for studying the spread of dengue fever. In this model, it is assumed that some mosquito eggs have been infected because infected mosquitoes can transmit the virus to their eggs. The main vector of this disease is the Aedes albopictus mosquito. Analysis was carried out to assess the stability of the equilibrium point, and numerical simulations were carried out to see changes in population numbers due to changes in parameter values. A disease-free equilibrium (DFE) point, which is stable given the basic reproductive number . An endemic point whose stability is guaranteed if the value . The numerical simulations show that an increasing mosquito mortality rate decreases the number of exposed, susceptible humans. Furthermore, an increase in the average bite of an infected mosquito will increase the number of exposed, susceptible humans. For the mosquito population, increasing mosquitoes’ mortality rate will decrease the number of exposed, susceptible mosquitoes. Finally, an increase in the average bite of an infected mosquito will increase the number of exposed, susceptible mosquitoes.","PeriodicalId":475966,"journal":{"name":"Barekeng","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136342133","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 : 2023-09-30DOI: 10.30598/barekengvol17iss3pp1749-1760
Irvanal Haq, Muhammad Nur Aidi, Anang Kurnia, Efriwati Efriwati
An understanding of factors that affect the recovery time from a disease is important for the community, medical staff, and also the government. This research analyzed factors that affect the recovery time of Covid-19 sufferers in West Sumatra. In addition, the consumption of a herbal made from Sungkai leaves, which is believed by some people in West Sumatra to accelerate the healing from Covid-19, was also included in the analysis. The recovery time here was categorized into two classes (binary): 1 for within 2 weeks, and 0 for more than 2 weeks. The methods used were logistic regression and geographically weighted logistic regression (GWLR). GWLR provides estimates of parameters for each location. The data used in this study is Covid-19 data of 2021 taken from the Regional Research and Development Agency (Litbangda) of West Sumatra with a total of 764 observations collected from 19 regencies/cities in West Sumatra. The results showed that there was no difference between the logistic regression model and the GWLR models based on the values of AIC and the ratio of deviance and degrees of freedom (df). The addition of spatial factors through GWLR models did not provide additional information regarding the recovery of Covid-19 sufferers within 2 weeks or more than 2 weeks. The logistic regression model gives the result that, at significance level α = 10%, residence, vaccination status, and symptoms significantly affect the recovery time within 2 weeks or more for Covid-19 sufferers, while other variables, namely sex, age, Sungkai leaves consumption status, and ginger consumption status have no significant effects.
{"title":"A COMPARISON OF LOGISTIC REGRESSION AND GEOGRAPHICALLY WEIGHTED LOGISTIC REGRESSION (GWLR) ON COVID-19 DATA IN WEST SUMATRA","authors":"Irvanal Haq, Muhammad Nur Aidi, Anang Kurnia, Efriwati Efriwati","doi":"10.30598/barekengvol17iss3pp1749-1760","DOIUrl":"https://doi.org/10.30598/barekengvol17iss3pp1749-1760","url":null,"abstract":"An understanding of factors that affect the recovery time from a disease is important for the community, medical staff, and also the government. This research analyzed factors that affect the recovery time of Covid-19 sufferers in West Sumatra. In addition, the consumption of a herbal made from Sungkai leaves, which is believed by some people in West Sumatra to accelerate the healing from Covid-19, was also included in the analysis. The recovery time here was categorized into two classes (binary): 1 for within 2 weeks, and 0 for more than 2 weeks. The methods used were logistic regression and geographically weighted logistic regression (GWLR). GWLR provides estimates of parameters for each location. The data used in this study is Covid-19 data of 2021 taken from the Regional Research and Development Agency (Litbangda) of West Sumatra with a total of 764 observations collected from 19 regencies/cities in West Sumatra. The results showed that there was no difference between the logistic regression model and the GWLR models based on the values of AIC and the ratio of deviance and degrees of freedom (df). The addition of spatial factors through GWLR models did not provide additional information regarding the recovery of Covid-19 sufferers within 2 weeks or more than 2 weeks. The logistic regression model gives the result that, at significance level α = 10%, residence, vaccination status, and symptoms significantly affect the recovery time within 2 weeks or more for Covid-19 sufferers, while other variables, namely sex, age, Sungkai leaves consumption status, and ginger consumption status have no significant effects.","PeriodicalId":475966,"journal":{"name":"Barekeng","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136342300","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 : 2023-09-30DOI: 10.30598/barekengvol17iss3pp1725-1738
Devi Azarina Manzilir Rohmah, Ani Budi Astuti, Achmad Efendi
Binary logistic regression is utilized in research to understand the relationship between multiple independent variables and a binary response variable. In logistic regression modelling, parameter estimation is regarded as a vital stage. The performance of this estimation is often affected by the sample size and data characteristics, and to deal with this problem, the Bayesian method can be employed as an estimation. This research aims to use Regression Logistic with Bayesian estimation to figure out the determinant of recent in-migrants status in Special Region of Yogyakarta 2021, where Yogyakarta’s recent in-migrants in 2021 took the first position in Indonesia, whereas this city has the lowest regional minimum wage in Indonesia. The Bayesian method was used in this study to obtain a better estimate than previous studies using maximum likelihood estimation, because Bayesian is unbiased for unbalanced cases which are often found in logistic regression. This research results show that particular variables such as resident age, resident marital status, resident main activities, resident latest education, and resident homeownership have significant effect on resident migrating to Special Region of Yogyakarta, Indonesia
{"title":"A STATISTICAL ANALYTICS OF MIGRATION USING BINARY BAYESIAN LOGISTIC REGRESSION","authors":"Devi Azarina Manzilir Rohmah, Ani Budi Astuti, Achmad Efendi","doi":"10.30598/barekengvol17iss3pp1725-1738","DOIUrl":"https://doi.org/10.30598/barekengvol17iss3pp1725-1738","url":null,"abstract":"Binary logistic regression is utilized in research to understand the relationship between multiple independent variables and a binary response variable. In logistic regression modelling, parameter estimation is regarded as a vital stage. The performance of this estimation is often affected by the sample size and data characteristics, and to deal with this problem, the Bayesian method can be employed as an estimation. This research aims to use Regression Logistic with Bayesian estimation to figure out the determinant of recent in-migrants status in Special Region of Yogyakarta 2021, where Yogyakarta’s recent in-migrants in 2021 took the first position in Indonesia, whereas this city has the lowest regional minimum wage in Indonesia. The Bayesian method was used in this study to obtain a better estimate than previous studies using maximum likelihood estimation, because Bayesian is unbiased for unbalanced cases which are often found in logistic regression. This research results show that particular variables such as resident age, resident marital status, resident main activities, resident latest education, and resident homeownership have significant effect on resident migrating to Special Region of Yogyakarta, Indonesia","PeriodicalId":475966,"journal":{"name":"Barekeng","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136336652","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 : 2023-09-30DOI: 10.30598/barekengvol17iss3pp1257-1264
Anita Saragih, Dian Cahyawati Sukanda, Ning Eliyati
Based on Covid-19 case data as of July 2022, South Sumatra Province has the 15th highest rank out of 34 provinces in Indonesia, with confirmed cases totalling 82,407. This showed that the spread of Covid-19 in South Sumatra was still high. This study aimed to determine the cluster of regions in South Sumatra based on Covid-19 case data. Clustering regions used agglomerative hierarchical method. The process began with standardizing the data, calculating the similarity distance between objects, determining the optimal number of clusters using the Silhouette method, and the last was clustering analysis. This study found that the optimal number of clusters consisted of two clusters. The clustering process starts with objects 2 and objects 4 because these two objects have the closest similarity distance. In conclusion, objects with the closest similarity distance (in one cluster) have the same data movement (fluctuation).
{"title":"CLUSTERIZATION OF REGION IN SOUTH SUMATERA BASED ON COVID-19 CASE DATA","authors":"Anita Saragih, Dian Cahyawati Sukanda, Ning Eliyati","doi":"10.30598/barekengvol17iss3pp1257-1264","DOIUrl":"https://doi.org/10.30598/barekengvol17iss3pp1257-1264","url":null,"abstract":"Based on Covid-19 case data as of July 2022, South Sumatra Province has the 15th highest rank out of 34 provinces in Indonesia, with confirmed cases totalling 82,407. This showed that the spread of Covid-19 in South Sumatra was still high. This study aimed to determine the cluster of regions in South Sumatra based on Covid-19 case data. Clustering regions used agglomerative hierarchical method. The process began with standardizing the data, calculating the similarity distance between objects, determining the optimal number of clusters using the Silhouette method, and the last was clustering analysis. This study found that the optimal number of clusters consisted of two clusters. The clustering process starts with objects 2 and objects 4 because these two objects have the closest similarity distance. In conclusion, objects with the closest similarity distance (in one cluster) have the same data movement (fluctuation).","PeriodicalId":475966,"journal":{"name":"Barekeng","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136336657","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}
Breast cancer is the most common cancer in women and the leading cause of cancer-related death in Indonesia. Analysis of survival data is important for improving the treatment and care of breast cancer patients. This study aims to estimate the parameters, find the survival function, and hazard function of breast cancer patients using a parametric method with an exponential distribution. Previous studies have shown that the Maximum Likelihood Estimation (MLE) method is suitable for estimating the survival function from exponential survival data by censoring. In this study, the exponential distribution was found to be the best for data on breast cancer patients from Surabaya Ontology Hospital. The estimated parameters are θ = 33.9157, and the survival function is calculated using The estimated hazard function for patient death or failure is 0.0295. The results of this study can contribute to the development of better treatment and care strategies for breast cancer patients. However, further research is needed because this study only used monthly time units.
{"title":"SURVIVAL FUNCTION AND HAZARD FUNCTION ANALYSIS OF EXPONENTIAL DISTRIBUTION IN TYPE I CENSORED SURVIVAL DATA: A CASE STUDY OF BREAST CANCER PATIENTS","authors":"Ardi Kurniawan, Anggara Teguh Previan, Zidni Ilmatun Nurrohmah","doi":"10.30598/barekengvol17iss3pp1795-1802","DOIUrl":"https://doi.org/10.30598/barekengvol17iss3pp1795-1802","url":null,"abstract":"Breast cancer is the most common cancer in women and the leading cause of cancer-related death in Indonesia. Analysis of survival data is important for improving the treatment and care of breast cancer patients. This study aims to estimate the parameters, find the survival function, and hazard function of breast cancer patients using a parametric method with an exponential distribution. Previous studies have shown that the Maximum Likelihood Estimation (MLE) method is suitable for estimating the survival function from exponential survival data by censoring. In this study, the exponential distribution was found to be the best for data on breast cancer patients from Surabaya Ontology Hospital. The estimated parameters are θ = 33.9157, and the survival function is calculated using The estimated hazard function for patient death or failure is 0.0295. The results of this study can contribute to the development of better treatment and care strategies for breast cancer patients. However, further research is needed because this study only used monthly time units.","PeriodicalId":475966,"journal":{"name":"Barekeng","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136336806","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}
Scientific publication is a measure of the performance of a university. Universities that are owned and operated by the government and whose establishment is carried out by the President of Republic Indonesia are state universities (PTN). One of the efforts that can be made to determine the quantity and quality of state university scientific publications is to conduct PTN clustering based on the productivity of scientific publications. This clustering aims to see the position of state universities in Indonesia into 3 categories, namely “high”, “medium”, and “low”. One of the clustering methods that can be used is cluster analysis. The cluster analysis used in this study is k-means and k-medoids with Silhoutte's validity. Based on the results of the analysis, it was found that the Silhouette k-means value (0.8018) was higher than the Silhouette k-medoids value (0.7281). Therefore, in this case, it can be concluded that the k-means method is better than the k-medoids. The results of cluster analysis using K-Means are 1) PTN with high productivity of scientific publications, namely ITB, ITS, UGM, and UI. The four PTNs are PTN as Legal Entity (PTN-BH) located in Java, 2) PTN with medium scientific publication productivity consists of 16 PTN which were dominated by PTN-BH and PTN as Public Service Board (PTN-BLU) with the largest location in Java, and 3) PTN with low scientific publication productivity consisted of 102 PTN which were dominated by PTN as general state financial management (PTN-Satker) with most locations outside Java.
{"title":"CLUSTERING OF STATE UNIVERSITIES IN INDONESIA BASED ON PRODUCTIVITY OF SCIENTIFIC PUBLICATIONS USING K-MEANS AND K-MEDOIDS","authors":"Ermawati Ermawati, Idhia Sriliana, Riry Sriningsih","doi":"10.30598/barekengvol17iss3pp1617-1630","DOIUrl":"https://doi.org/10.30598/barekengvol17iss3pp1617-1630","url":null,"abstract":"Scientific publication is a measure of the performance of a university. Universities that are owned and operated by the government and whose establishment is carried out by the President of Republic Indonesia are state universities (PTN). One of the efforts that can be made to determine the quantity and quality of state university scientific publications is to conduct PTN clustering based on the productivity of scientific publications. This clustering aims to see the position of state universities in Indonesia into 3 categories, namely “high”, “medium”, and “low”. One of the clustering methods that can be used is cluster analysis. The cluster analysis used in this study is k-means and k-medoids with Silhoutte's validity. Based on the results of the analysis, it was found that the Silhouette k-means value (0.8018) was higher than the Silhouette k-medoids value (0.7281). Therefore, in this case, it can be concluded that the k-means method is better than the k-medoids. The results of cluster analysis using K-Means are 1) PTN with high productivity of scientific publications, namely ITB, ITS, UGM, and UI. The four PTNs are PTN as Legal Entity (PTN-BH) located in Java, 2) PTN with medium scientific publication productivity consists of 16 PTN which were dominated by PTN-BH and PTN as Public Service Board (PTN-BLU) with the largest location in Java, and 3) PTN with low scientific publication productivity consisted of 102 PTN which were dominated by PTN as general state financial management (PTN-Satker) with most locations outside Java.","PeriodicalId":475966,"journal":{"name":"Barekeng","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136337245","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}
Stock investment is an investment opportunity. This stock investment carries relatively high risk and therefore requires additional analysis to minimize losses and maximize profits. Expected Monetary Value (EMV) is a simple modeling method for estimating the value of an investment that will provide the greatest future return. The expected monetary value (EMV) method involves multiplying the total value of each scenario by the probability of that scenario occurring. However this method has weaknesses in terms of how many cases occur what is the value of each case and what is the probability of each case occurring. Binomial State Price is a method commonly used to calculate stock options and real options but includes the step of modeling the value of an investment in many situations and opportunities that arise in the future. In this paper, our objective is to develop the EMV method with the binomial state pricing model to determine the investment that offers the most favorable payoff. In short, we can develop the expected monetary value (EMV) method and the binomial state pricing model. It was found that this model always recommends stocks which have high dividens.
{"title":"DEVELOPMENT OF EXPECTED MONETARY VALUE USING BINOMIAL STATE PRICE IN DETERMINING STOCK INVESTMENT DECISIONS","authors":"Giovanny Theotista, Margareta Febe, Yvone Marshelly","doi":"10.30598/barekengvol17iss3pp1703-1712","DOIUrl":"https://doi.org/10.30598/barekengvol17iss3pp1703-1712","url":null,"abstract":"Stock investment is an investment opportunity. This stock investment carries relatively high risk and therefore requires additional analysis to minimize losses and maximize profits. Expected Monetary Value (EMV) is a simple modeling method for estimating the value of an investment that will provide the greatest future return. The expected monetary value (EMV) method involves multiplying the total value of each scenario by the probability of that scenario occurring. However this method has weaknesses in terms of how many cases occur what is the value of each case and what is the probability of each case occurring. Binomial State Price is a method commonly used to calculate stock options and real options but includes the step of modeling the value of an investment in many situations and opportunities that arise in the future. In this paper, our objective is to develop the EMV method with the binomial state pricing model to determine the investment that offers the most favorable payoff. In short, we can develop the expected monetary value (EMV) method and the binomial state pricing model. It was found that this model always recommends stocks which have high dividens.","PeriodicalId":475966,"journal":{"name":"Barekeng","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136336328","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 : 2023-09-30DOI: 10.30598/barekengvol17iss3pp1533-1542
M. Imron, Hani Khaulasari, Diva Ayu SNM, Jauharotul Inayah, Eka Eliyana S
Magelang City has experienced a significant decline in the rice production sector, triggering the need for forecasting research as the next crucial step. This research aims to forecast rice production in Magelang city. By applying Double Exponential Smoothing and ARIMA methods, the most suitable forecasting model is identified. Data on rice production was obtained from the Badan Pusat Statistik (BPS) of Magelang city. The results revealed that the ARIMA (0,1,1) model with MSE of 479,259 was the best choice. This model is expressed as . Using this model, rice production was forecast from July to December 2023, the forecasting results showed that rice paddy production is expected to fluctuate in the coming months. For July 2023, production is projected to be around 65,1762 units, followed by 51,4779 units in August, 58,2432 units in September, and so on.
{"title":"COMPARISON OF FORECASTING RICE PRODUCTION IN MAGELANG CITY USING DOUBLE EXPONENTIAL SMOOTHING AND AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA)","authors":"M. Imron, Hani Khaulasari, Diva Ayu SNM, Jauharotul Inayah, Eka Eliyana S","doi":"10.30598/barekengvol17iss3pp1533-1542","DOIUrl":"https://doi.org/10.30598/barekengvol17iss3pp1533-1542","url":null,"abstract":"Magelang City has experienced a significant decline in the rice production sector, triggering the need for forecasting research as the next crucial step. This research aims to forecast rice production in Magelang city. By applying Double Exponential Smoothing and ARIMA methods, the most suitable forecasting model is identified. Data on rice production was obtained from the Badan Pusat Statistik (BPS) of Magelang city. The results revealed that the ARIMA (0,1,1) model with MSE of 479,259 was the best choice. This model is expressed as . Using this model, rice production was forecast from July to December 2023, the forecasting results showed that rice paddy production is expected to fluctuate in the coming months. For July 2023, production is projected to be around 65,1762 units, followed by 51,4779 units in August, 58,2432 units in September, and so on.","PeriodicalId":475966,"journal":{"name":"Barekeng","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136336951","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}
Institut Teknologi Kalimantan (ITK) is one of the state universities in Indonesia which has 5 majors, one of them is the Department of Mathematics and Information Technology (JMTI). JMTI has six study programs, and only three study programs have graduates, namely Mathematics, Information Systems, and Informatics. Every year the number of new students continues to grow, but this is not proportional to the number of graduates, because some students study for more than 8 semesters. Because of this, the quality of study programs being poor. In this research, a model was built that could classify student study timeliness, using the naïve Bayes algorithm. The data used is data from JMTI student graduates from the 2013 to 2019 batch. The 2013 to 2018 batch data will be training data and validation data, while the 2019 batch data will be testing data. This research compare accuracy and F1-score naïve Bayes algorithm without correlation and with correlation. The best model obtained from training data is a model with variables that have gone through a correlation test, namely 70:30, 80:20, and 90:10. The attributes selected after the correlation test, namely, IP Tahap Bersama, GPA, Final GPA, Length of Study (Semester), dan Graduation GPA (Category), yield results for accuracy and an F1-score of 1.
加里曼丹理工学院(ITK)是印度尼西亚的一所国立大学，有5个专业，其中一个是数学和信息技术系(JMTI)。JMTI有6个专业，只有3个专业有毕业生，分别是数学、信息系统和信息学。每年新生的数量都在持续增长，但这与毕业生的数量不成比例，因为有些学生学习超过8个学期。正因为如此，学习项目的质量很差。在本研究中，我们使用naïve贝叶斯算法建立了一个对学生学习时效性进行分类的模型。所用数据为2013年至2019年JMTI学生毕业生的数据。2013 - 2018批数据为训练数据和验证数据，2019批数据为测试数据。本研究比较准确率与F1-score naïve无相关与有相关贝叶斯算法。从训练数据中得到的最佳模型是变量经过相关性检验的模型，即70:30、80:20和90:10。经过相关检验后选择的属性，即IP Tahap Bersama, GPA, Final GPA, Length of Study (Semester)， dan Graduation GPA (Category)，得出准确性结果，f1得分为1。
{"title":"STUDY TIME CLASSIFICATION OF MATHEMATICS AND INFORMATION TECHNOLOGY DEPARTMENT OF KALIMANTAN INSTITUTE OF TECHNOLOGY USING NAÏVE BAYES ALGORITHM","authors":"Fatrysia Wikarya Sucipto, Ramadhan Paninggalih, Indira Anggriani","doi":"10.30598/barekengvol17iss3pp1419-1428","DOIUrl":"https://doi.org/10.30598/barekengvol17iss3pp1419-1428","url":null,"abstract":"Institut Teknologi Kalimantan (ITK) is one of the state universities in Indonesia which has 5 majors, one of them is the Department of Mathematics and Information Technology (JMTI). JMTI has six study programs, and only three study programs have graduates, namely Mathematics, Information Systems, and Informatics. Every year the number of new students continues to grow, but this is not proportional to the number of graduates, because some students study for more than 8 semesters. Because of this, the quality of study programs being poor. In this research, a model was built that could classify student study timeliness, using the naïve Bayes algorithm. The data used is data from JMTI student graduates from the 2013 to 2019 batch. The 2013 to 2018 batch data will be training data and validation data, while the 2019 batch data will be testing data. This research compare accuracy and F1-score naïve Bayes algorithm without correlation and with correlation. The best model obtained from training data is a model with variables that have gone through a correlation test, namely 70:30, 80:20, and 90:10. The attributes selected after the correlation test, namely, IP Tahap Bersama, GPA, Final GPA, Length of Study (Semester), dan Graduation GPA (Category), yield results for accuracy and an F1-score of 1.","PeriodicalId":475966,"journal":{"name":"Barekeng","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136341432","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}
Indonesia is a tropical country that is vulnerable to the impacts of climate change. Climate change causes an effect on the level of comfort (heat stress) that can affect the level of human immunity, one of the indices to calculate the level of human comfort (heat stress) is the Thermal Humidity Index (THI). Climate change scenarios modeled in Earth System Models (ESMs). ESM has a coarse resolution and is subject to considerable bias. This research is using secondary data. The data source used in this study comes from the Coupled Model Intercomparison Project (CMIP5). This research will focus on projected heat stress which is calculated based on THI with the temperature and humidity variables. Therefore, in this research to reduce the bias correction method used Statistical Downscaling (SD) and nonparametric regression. The results of the bias correction using the Statistical Downscaling (SD) method and Nonparametric Regression Fourier-Polynomial Local Series in this study the R-square value for Relative Humidity yields 95% and for Temperature yields 94%. The projection of climate change based on the value of the Temperature Humidity Index (THI) in Indonesia in the category of 50% of the population of Indonesians feeling comfortable conditions occurred in 2006-2059. Then the population of citizens in Indonesia felt uncomfortable conditions occurred in 2060 to 2100 with a THI value of 27.0730°C - 27.7800°C.
{"title":"STATISTICAL DOWNSCALING USING REGRESSION NONPARAMETRIC OF FOURIER SERIES-POLYNOMIAL LOCAL OF CLIMATE CHANGE","authors":"Tiani Wahyu Utami, Fatkhurokhman Fauzi, Eko Yuliyanto","doi":"10.30598/barekengvol17iss3pp1411-1418","DOIUrl":"https://doi.org/10.30598/barekengvol17iss3pp1411-1418","url":null,"abstract":"Indonesia is a tropical country that is vulnerable to the impacts of climate change. Climate change causes an effect on the level of comfort (heat stress) that can affect the level of human immunity, one of the indices to calculate the level of human comfort (heat stress) is the Thermal Humidity Index (THI). Climate change scenarios modeled in Earth System Models (ESMs). ESM has a coarse resolution and is subject to considerable bias. This research is using secondary data. The data source used in this study comes from the Coupled Model Intercomparison Project (CMIP5). This research will focus on projected heat stress which is calculated based on THI with the temperature and humidity variables. Therefore, in this research to reduce the bias correction method used Statistical Downscaling (SD) and nonparametric regression. The results of the bias correction using the Statistical Downscaling (SD) method and Nonparametric Regression Fourier-Polynomial Local Series in this study the R-square value for Relative Humidity yields 95% and for Temperature yields 94%. The projection of climate change based on the value of the Temperature Humidity Index (THI) in Indonesia in the category of 50% of the population of Indonesians feeling comfortable conditions occurred in 2006-2059. Then the population of citizens in Indonesia felt uncomfortable conditions occurred in 2060 to 2100 with a THI value of 27.0730°C - 27.7800°C.","PeriodicalId":475966,"journal":{"name":"Barekeng","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136341436","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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