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Kurniawan, Muhammad Hasan Sidiq

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Author-ID : 1409062

Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Mathematics

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Survival Analysis Based on Average Response Time of Maritime Search and Rescue (SAR) Incidents in 2019 Using Kaplan-Meier Method and Log-Rank Test Kurniawan, Muhammad Hasan Sidiq; Mahara, Duhania Oktasya
Enthusiastic : International Journal of Applied Statistics and Data Science Volume 1 Issue 1, April 2021
Publisher : Universitas Islam Indonesia

Indonesia is the largest archipelagic country in the world (based on area and population), which makes it as one of countries with the most significant maritime activities. Therefore, there has been a high rate of maritime accidents in Indonesia. The National Search and Rescue Agency (BASARNAS) as a non-ministerial government agency with the primary task of Search and Rescue (SAR) operation deals with several types of accidents, including maritime accidents. Response time as the time to receive news about the accidents until the SAR unit comes to the rescue is very crucial in this matter. Average response time is stipulated based on BASARNAS’s regulations to estimate information about the survival probability of the victims. This research concerns with the survival analysis using Kaplan-Meier Method and Log-Rank Test. The researchers categorized maritime accidents into three categories: ‘Low’, ‘Medium’, and ‘High’. This classification aims to find out whether the survival function of each category has the same or different function and to investigate whether there are differences from the given responses or not. The survival analysis with Kaplan-Meier method revealed that the three categories had different survival functions. The survival analysis was followed by a Log-Rank Test. The final result shows that there is no difference in the responses given by the three categories when maritime accidents occur. Received February 10, 2021Revised March 29, 2021Accepted March 29, 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 Islamy, Ulimazzada; Novianti, Afdelia; Hidayat, Freditasari Purwa; Kurniawan, Muhammad Hasan Sidiq
Enthusiastic : International Journal of Applied Statistics and Data Science Volume 1 Issue 2, October 2021
Publisher : Universitas Islam Indonesia

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.

The Implementation of the Generalized Space-Time Autoregressive (GSTAR) Model for Inflation Prediction Hestuningtias, Feby; Kurniawan, Muhammad Hasan Sidiq
Enthusiastic : International Journal of Applied Statistics and Data Science Volume 3 Issue 2, October 2023
Publisher : Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/enthusiastic.vol3.iss2.art5

The macroeconomic indicator used to measure a country’s economic balance is inflation. The increase in the price of goods and services causes an increase in inflation, which impacts the decrease in the value of money so that people’s purchasing power for goods and services will decrease and result in slow economic growth. One way to determine future inflation is by forecasting. The Generalized Space-Time Autoregressive (GSTAR) model is a time series model involving time and location. This study aims to predict future inflation using the GSTAR model, which uses differencing without uniform location weights, inverse distance, and normalized cross-correlation. The results showed that the models obtained were the GSTAR (2,1) and GSTAR (5,1)I(1) models. The best model to predict inflation is the GSTAR (5,1)I(1) model with the normalized cross-correlation weight, which had Root Mean Square Error (RMSE) value of 0.5743, which was smaller than the GSTAR (2,1) model.

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