Grouping of Districts Based on Poverty Factors in Papua Province Uses The K-Medoids Algorithm

Poverty is an essential issue for every country, including Indonesia. Poverty can be caused by the scarcity of basic necessities or the difficulty of accessing education and employment. In 2019 Papua Province became the province with the highest poverty percentage at 27.53%. Seeing this, the district groupings formed in describing poverty conditions in Papua Province are based on similar characteristics using the variables Percentage of Poor Population, Gross Regional Domestic Product, Open Unemployment Rate, Life Expectancy, Literacy Rate, and Population Working in the Agricultural Sector using K-medoids clustering algorithm. The results of this study indicate that the optimal number of clusters to describe poverty conditions in Papua Province is 4 clusters with a variance of 0.012, where the first cluster consists of 10 districts, the second cluster consists of 5 districts, the third cluster consists of 12 districts, and the fourth cluster consists of 2 districts.

Determination of the Shortest Route on the Distribution System using Ant Colony Optimization (ACO) Algorithm (Case Study: Alfamidi Palu Branch – PT. Midi Utama Indonesia)

The distribution system of goods is one of the most important parts for every company. The company certainly has many route options to visit, and this is expected to be conducted efficiently in terms of time. In the distribution of goods by Alfamidi company in Palu City which has 51 outlets include into the category of Traveling Salesman Problem (TSP) because of many route options that can be visited. The problem can be solved by employing the Ant Colony Optimization (ACO) method which is one of the algorithms Ant Colony System (ACS). The ACS acquires principles based on the behavior of ant colonies and applies three characteristics to determine the shortest route namely status transition rules, local pheromone renewal and global pheromones. The result showed that the shortest route of the distribution of goods based on the calculation of selected iterations was ant 1 with the shortest total distance obtained 86.98 km.

Comparison of Simple and Segmented Linear Regression Models on the Effect of Sea Depth toward the Sea Temperature

The linear regression model is employed when it is identified a linear relationship between the dependent and independent variables. In some cases, the relationship between the two variables does not generate a linear line, that is, there is a change point at a certain point. Therefore, themaximum likelihood estimator for the linear regression does not produce an accurate model. The objective of this study is to presents the performance of simple linear and segmented linear regression models in which there are breakpoints in the data. The modeling is performed onthe data of depth and sea temperature. The model results display that the segmented linear regression is better in modeling data which contain changing points than the classical one.Received September 1, 2021Revised November 2, 2021Accepted November 11, 2021

Application of the Spatial Autoregressive (SAR) Method in Analyzing Poverty in Indonesia and the Self Organizing Map (SOM) Method in Grouping Provinces Based on Factors Affecting Poverty

The economy is a benchmark to determine the extent of the development of a country. Indonesia, which is now a developing country, is ranked 5th as the poorest country in Southeast Asia. Of course, the government must pay attention because until now, poverty has become one of Indonesia's main problems. Ending poverty everywhere and in all its forms is goal 01 of the Sustainable Development Goals (SDGs) program. One of the efforts that can be done is by planning as part of the implementation of the target, namely eliminating poverty and appropriate social protection for all levels of society so that the SDGs are achieved. Therefore, it is important to do a spatial analysis by making a model of poverty estimation in Indonesia and grouping to identify areas in Indonesia that have the highest poverty mission. The clustering method used in this grouping is Self Organizing Map (SOM). In this study, Spatial Autoregressive (SAR) analysis was used to create a predictive model. This is because poverty is very likely to have a spatial influence or be influenced by location to other areas in the vicinity. The results of the SAR model that can be formed are . Furthermore, the region with the highest mission is grouped using the Self Organizing Map (SOM) clustering based on variables that significantly affect the amount of poverty in Indonesia. From the results of the analysis obtained four clusters, each of which has its characteristics to classify 34 provinces in Indonesia. The clusters formed include cluster 1 consisting of 17 provinces, cluster 2 consisting of 9 provinces, cluster 3 consisting of 1 province, and cluster 4 consisting of 7 provinces.

Forecasting COVID-19 Cases in Indonesia Using Hybrid Double Exponential Smoothing

The COVID-19 epidemic has spread throughout countries around the world. In Indonesia, this case was detected in early March 2020, and until now, there is still an increase in positive cases of COVID-19. The purpose of this paper is to predict COVID-19 cases in Indonesia using a time series approach. The method used is H-WEMA method because this method can capture trend data patterns following the conditions of COVID-19 cases in Indonesia. Based on the analysis results, H-WEMA can predict COVID-19 cases very well. The forecasted results of the COVID-19 cases in Indonesia still have an upward trend, so it needs the cooperation of all elements of community to reduce the spread of COVID-19. Received September 8, 2021Revised October 15, 2021Accepted November 3, 2021

Implementation of Markov Chain in Detecting Opportunities for Natural Disasters in Klaten (Case Study: Number of Floods, Landslides, and Hurricanes 2019-2020)

Java Island is one of the areas that is very fertile and densely populated, but on the other hand, Java Island is also one of the areas that is most frequently hit by natural disasters, one of which is Klaten Regency. Natural disaster itself is an event that threatens and disrupts human life caused by nature. Some of the natural disasters that often occur simultaneously in Klaten Regency are floods, landslides, and hurricanes. These three disasters usually occur during the rainy season. This of course makes the government need to take action by seeing the large chance of a disaster occurring in order to optimize disaster management. Then research will be carried out that aims to determine the chances of natural disasters occurring in the next few years. Forecasting will be carried out using the Markov chain method, with this method the probability value of the future period can be estimated using the current period probability value based on the characteristics of the past period. So that the value of the steady state chance of floods and landslides in period 36 (December 2023) and hurricanes in period 15 (March 2022) with the chances of a disaster are 34.21%, 15.38%, and 73.53%, respectively.Received August 31, 2021Revised October 27, 2021Accepted November 11, 2021

Generalized Linear Mixed Model and Lasso Regularization for Statistical Downscaling

Rainfall is one of the climatic elements in the tropics which is very influential in agriculture, especially in determining the growing season. Thus, proper rainfall modeling is needed to help determine the best time to start cultivating the soil. Rainfall modeling can be done using the Statistical Downscaling (SDS) method. SDS is a statistical model in the field of climatology to analyze the relationship between large-scale and small-scale climate data. This study uses response variables as a small-scale climate data in the form of rainfall and explanatory variables as a large-scale climate data of the General Circulation Model (GCM) output in the form of precipitation. However, the application of SDS modeling is known to cause several problems, including correlated and not stationary response variables, multi-dimensional explanatory variables, multicollinearity, and spatial correlation between grids. Modeling with some of these problems will cause violations of the assumptions of independence and multicollinearity. This research aims to model the rainfall in Indramayu Regency, West Java Province using a combined regression model between the Generalized linear mixed model (GLMM) and Least Absolute Selection and Shrinkage Operator (LASSO) regulation (L1). GLMM was used to deal with the problem of independence and Lasso Regulation (L1) was used to deal with multicollinearity problems or the number of explanatory variables that is greater than the response variable. Several models were formed to find the best model for modeling rainfall. This research used the GLMM-Lasso model with Normal spread compared to the GLMM model with Gamma response (Gamma-GLMM). The results showed that the RMSE and R-square GLMM-Lasso models were smaller than the Gamma-GLMM models. Thus, it can be concluded that GLMM-Lasso model can be used to model statistical downscaling and solve the previously mentioned constraints. Received February 10, 2021Revised March 29, 2021Accepted March 29, 2021

Solving Fuzzy Transportation Problem Using ASM Method and Zero Suffix Method

The transportation problem is a special case for linear programming. Sometimes, the amount of demand and supply in transportation problems can change from time to time, and thus it is justified to classify the transportation problem as a fuzzy problem. This article seeks to solve the Fuzzy transportation problem by converting the fuzzy number into crisp number by ranking the fuzzy number. There are many applicable methods to solve linear transportation problems. This article discusses the method to solve transportation problems without requiring an initial feasible solution using the ASM method and the Zero Suffix method. The best solution for Fuzzy transportation problems with triangular sets using the ASM method was IDR 21,356,787.50, while the optimal solution using the Zero Suffix method was IDR 21,501,225.00. Received February 5, 2021Revised April 16, 2021Accepted April 22, 2021

Aplication of ARIMA Model for Forecasting Additional Positive Cases of Covid-19 in Jember Regency

The autoregressive integrated moving average (ARIMA) model is a popular method for forecasting univariate time series dataset. This method consists of four major stages, namely: identification, parameter assessment, diagnostic examination, and forecasting using the ARIMA model (p, d, q). ARIMA model can be applied in various fields, one of which is medical field. Currently, there had been a daily increase in the number of patients infected with Corona virus. Jember is one of the regencies in East Java with a high number of confirmed patients. On February 5, 2021, it was recorded that 5,872 patients were confirmed positive for Corona, 5,241 patients had been declared cured, and 352 patients were declared dead. Given the high number of confirmed cases of Covid-19 in Jember, the authors would like to conduct a prediction research on the increasing number of confirmed cases of Covid-19 in Jember Regency for the upcoming period using the ARIMA model (p,d,q). The research was conducted in the Jember Regency, East Java. The data were collected from March 28, 2020 to January 30, 2021. The study showed that the ARIMA model (1,2,3) was the best model for predicting the additional positive cases of Covid-19 per week in Jember, with the sum squared resid of 7.9496. The data forecast for the additional positive cases of Covid-19 for the next 6 periods is: 224,56 patients, 247,84 patients, 273,53 patients, 301,89 patients, 333,18 patients, and 367,72 patients. Received February 10, 2021Revised April 8, 2021Accepted April 22, 2021

Mardia’s Skewness and Kurtosis for Assessing Normality Assumption in Multivariate Regression

In Multivariate regression, we need to assess normality assumption simultaneously, not univariately. Univariate normal distribution does not guarantee the occurrence of multivariate normal distribution [1]. So we need to extend the assessment of univariate normal distribution into multivariate methods. One extended method is skewness and kurtosis as proposed by Mardia [2]. In this paper, we introduce the method, present the procedure of this method, and show how to examine normality assumption in multivariate regression study case using this method and expose the use of statistics software to help us in numerical calculation. Received February 20, 2021Revised March 8, 2021Accepted March 10, 2021