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Hasih Pratiwi
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Study Program of Statistics, Universitas Sebelas Maret, Surakarta 57126, Indonesia
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INDONESIA
Indonesian Journal of Applied Statistics
Published by Universitas Sebelas Maret
ISSN : -     EISSN : 2621086X     DOI : https://doi.org/10.13057/ijas
Core Subject : Economy, Science, Agriculture, Social,
Agriculture, Biological Sciences & Forestry Computer Science & IT Earth & Planetary Sciences Economics, Econometrics & Finance Environmental Science
Indonesian Journal of Applied Statistics (IJAS) is a journal published by Study Program of Statistics, Universitas Sebelas Maret, Surakarta, Indonesia. This journal is published twice every year, in May and November. The editors receive scientific papers on the results of research, scientific studies, and problem solving research using statistical method. Received papers will be reviewed to assess the substance of the material feasibility and technical writing.
Arjuna Subject : Matematika - Statistik dan Probabilitas
Articles 123 Documents
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Implementasi Clustering K-Medoids dalam Pengelompokan Kabupaten di Provinsi Aceh Berdasarkan Faktor yang Mempengaruhi Kemiskinan Freditasari Purwa Hidayat; Royhan Pina Putra; M Dendi Alfitrah; Edy Widodo
Indonesian Journal of Applied Statistics Vol 5, No 2 (2022)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v5i2.55080

Abstract

The economy is one of the parameters to see how the development of a country. Ending poverty anywhere and in any form is goal 01 of the Sustainable Development Goals (SDGs) program. Until now, poverty has become one of the main problems in Indonesia, so poverty must be a concern of the government. Based on data from the Central Statistics Agency (BPS) shows that as of September 2020 the percentage of poor people in Aceh Province is still the highest on the island of Sumatra, which is 15.43%. The purpose of this study is to classify districts based on factors that affect poverty in Aceh Province. The method used in this study is the K-Medoids Cluster Analysis algorithm. The optimal number of clusters is 2 clusters with cluster 1 consisting of 11 districts and cluster 2 consisting of 12 districts. Cluster 1 has a higher percentage of poor population and poverty depth index than cluster 2, while cluster 2 has higher Gini Ratio, AHH, and RLS values than cluster 1.Keywords : Clusters, Economy, Poverty, SDGs
Upaya Penegakan Emansipasi Wanita melalui Optimalisasi Pembangunan Gender dengan Metode Regresi Panel Inu Alifiyah Phalufi; Raden Nabila Alya Hartarie; Ellena Novitriani; Edy Widodo
Indonesian Journal of Applied Statistics Vol 5, No 2 (2022)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v5i2.58034

Abstract

The role of women nowadays is no different from the men, only to a reasonable extent. The role of women's emancipation itself has been upheld in Indonesia, as those who will be the spearheads in family education for their children that must have broad skills and insights. The Human Development Index (HDI) is mostly becoming an important index as a measurement of the success level in quality of human life (community) building efforts. By conducting an analysis using the panel regression method in the Regency / City of West Sulawesi (as a province in Indonesia that has the 4th lowest HDI score) to find out how much women's participation can affect the level of quality of life in Indonesia and as an evaluation of which components must be improved by government for the next period in the welfare of its people. This analysis concludes that the Mamuju regency is known as the region that contributes the largest weight to the increase in GDI while the Pasangkayu regency contributes the lowest weight to the increase in GDI so that the government should make the development of supporting facilities for community welfare more equitable.Keywords : GDI, Woman Emancipation, Panel Regression
Analisis Risiko Kredit Perbankan Menggunakan Algoritma K-Nearest Neighbor dan Nearest Weighted K-Nearest Neighbor Dian Tri Wilujeng; Mohamat Fatekurohman; I Made Tirta
Indonesian Journal of Applied Statistics Vol 5, No 2 (2022)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v5i2.58426

Abstract

Bank is a business entity that collects public funds in the form of savings and also distributes them to the public in the form of credit or other forms.  Credit risk analysis can be done in various ways such as marketing analysis and big data using machine learning.  One example of a machine learning algorithm is K-Nearest Neighbor (KNN) and the development of the K-Nearest Neighbor algorithm is Neighbor Weighted KNearest Neighbor (NWKNN).  The K-Nearest Neighbor (KNN) algorithm is one of the machine learning methods that can be used to facilitate the classification of complex data.  The purpose of this study is to determine the results of the application of the algorithm and the comparison of the use of the KNN and NWKNN algorithms in banking credit.  The results obtained are that NWKNN is able to predict credit risk better, especially in classifying potential customers with potential losses compared to KNN. Keywords: Machine learning, KNN, NWKNN
Keterkaitan Indeks Harga Konsumen (IHK) Kelompok Bahan Makanan dengan Kelompok Makanan Jadi, Minuman, Rokok, dan Tembakau di Indonesia Tahun 2014-2019 (Pendekatan Vector Error Correction Model) Lira Azima; Erni Tri Astuti
Indonesian Journal of Applied Statistics Vol 5, No 2 (2022)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v5i2.54988

Abstract

Pricing a commodity depends on the price of other commodities. As the largest contributor to inflation, the pattern of price movements in CPI of prepared food, beverages, cigarette, and tobacco group is inseparable from CPI of foodstuff group as the raw material for that group. This condition indicates that in analyzing the pattern of price movements of a commodity, it cannot be separated from the influence of other commodities. The aims of the study is to examine the linkages between CPI of foodstuff group and CPI of prepared food, beverages, cigarette, and tobacco group, also its response and contribution when there is shock during January 2014 until December 2019 in Indonesia using Vector Error Correction Model (VECM). The results suggest that in long-term CPI of prepared food, beverages, cigarette, and tobacco group has positive effect on CPI of foodstuff group. Impulse Response Function (IRF) shows that shocks to CPI of foodstuff group is positively responded by CPI of prepared food, beverages, cigarette, and tobacco group, and vice versa. In addition, Forecast Error Variance Decomposition (FEVD) show that the variation of CPI of prepared food, beverages, cigarette, and tobacco group are dominated by contribution of CPI of foodstuff group.Keywords : consumer price index, VECM, impulse response function, forecast error variance decomposition
Pengelompokan Dan Perbandingan Pembangunan Sosial Provinsi Di Indonesia Anne Indiarti Banjar Nahor
Indonesian Journal of Applied Statistics Vol 5, No 2 (2022)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v5i2.61224

Abstract

Social development still not become priority in policy formulation in Indonesia. The reality of development without social aspects will not be able to be enjoyed evenly by the community. This study examines social development in Indonesia by grouping social development issues and comparing social development achievements in 2016-2017 to find out which areas should be a priority. Eleven social development indicators was used to present social development in Indonesia. Biplot analysis as an initial indication of regional grouping based on social development indicators, and cluster analysis to facilitate interpretation of grouping results. The percentage of diversity data that can be worked on by biplot analysis are 65 percent for 2016 and 61,3 percent for 2017. The results of biplot analysis produce character variables from each province based on the quadrant. It can be seen that in quadrant II the members of the Province of Bangka Belitung Islands, East Java, Central Java, North Sulawesi, West Nusa Tenggara, Central Sulawesi are characterized by high scores on health dimension variables, literacy rates and the percentage of households with adequate access. Based on the cluster analysis produce the group of provinces according to three levels of social development namely low, medium, high. Papua Province is the only province that does not change and still exists at a low level of social development.Keywords: social development, biplot analysis, cluster analysis
Pemodelan Tingkat Kerawanan Pangan Rumah Tangga di Indonesia Tahun 2021 dengan Pendekatan Regresi Logistik Ordinal Tasya Aguilera; Yogo Aryo Jatmiko
Indonesian Journal of Applied Statistics Vol 5, No 2 (2022)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v5i2.65141

Abstract

Until 2021, Indonesia has succeeded in reducing the prevalence rate of the population with moderate or severe food insecurity. But on the other hand, Indonesia's Global Food Security Index (GFSI) score which has declined in the last three years shows that Indonesia's food security is getting weaker in various aspects. The condition of food security that begins to weaken can trigger food insecurity. Food insecurity that can have an impact on health, nutrition and health system problems is a national health problem that needs attention. Therefore, this study aims to examine the level of household food insecurity and the variables that influence it. This study uses The National Socioeconomic Survey (Susenas) March 2021 data which was analyzed using partial proportional odds model (PPOM) ordinal logistics regression method. In general, the results show that variables area of residence, gender, age, education, business field, number of household members, residence ownership status, and per capita expenditure affect the level of household food insecurity in Indonesia in 2021.Keywords: food insecurity; ordinal logistic regression; PPOM
Pengelompokan Kabupaten/Kota di Provinsi Jawa Barat Berdasarkan Dampak Kerusakan Bencana Banjir Menggunakan K-Medoids Dela Gustiara; Anisa Dwi Mulyaningsih; Rahmi Anadra; Edy Widodo
Indonesian Journal of Applied Statistics Vol 5, No 2 (2022)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v5i2.65668

Abstract

The territory of Indonesia is located in geographical, geological, hydrological, and demographic conditions that allow Indonesia to be prone to disasters. The most common natural disaster in Indonesia is flooding. If accumulated, there have been 682 flood events in the country since the beginning of 2022. In Indonesia, especially West Java Province, flooding is the most common disaster, especially during the rainy season. So a study will be conducted that aims to determine the grouping of districts / cities in West Java Province based on the occurrence of flood disasters. The data used in this study were obtained from the publication of the National Disaster Management Agency. In this study, there are 4 variables of the impact of flood disasters, namely total deaths, total submerged houses, total damaged houses and total injured. The clustering method used in this research is K-Medoids. K-Medoids is one of the clustering methods that uses the partition clustering method in grouping a set of n objects into a number of k clusters. From the results of the K-Medoids analysis, three clusters were obtained. The first cluster consists of 3 districts/cities with high impact of flood disasters, the second cluster consists of 23 districts/cities with moderate impact of flood disasters, and the third cluster consists of 1 district/city with low impact of flood disasters. Based on the results of the analysis, efforts can be made by the government to focus more on designing steps that must be taken in preventing or overcoming the impact of flood disasters.Keywords: Cluster; K-Medoids; Floods West Java
Analisis Spasial Angka Kematian Balita di Pulau Papua Menggunakan Mixed Geographically Weighted Regression Muhammad Fathu Rahman; Hamada Syafia; Sya'adatul Maf Ula; Nur Azizah Amini; Arief Priambudi, Tiodora Hadumaon Siagia
Indonesian Journal of Applied Statistics Vol 6, No 1 (2023)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v6i1.75064

Abstract

One of the goals of the Sustainable Development Goals is to end under five mortality which can be prevented by at least 25 per 1000 live births by 2030. Based on Badan Pusat Statistik (BPS) data, in 2020 the Under Five Mortality Rate (U5MR) in Papua Province is 49.04, while in West Papua Province of 47.23. This figure makes the island of Papua the island with the highest U5MR compared to other islands in Indonesia. The problem of U5MR has different influencing factors for each region, so it is important to include spatial effects in the analysis. The Mixed GWR model can be used to overcome spatial linkages between regions, accommodate variations in the form of spatial heterogeneity, and handle variations in parameters that are global and local in nature. Therefore, this study aims to analyze the variables that affect U5MR in Papua Island using Mixed GWR. This study uses secondary data sourced from BPS. The unit of analysis for this research is the districts/cities in Papua Island. The dependent variable in this study is U5MR, while the independent variables include the percentage of women aged at first pregnancy less than 21 years, Gross Regional Domestic Product per capita, the percentage of households with the main type of fuel in the form of solid fuel, the average length of schooling, and the percentage of households with access to source of proper drinking water. The results showed that the percentage of women aged at first pregnancy less than 21 years, the percentage of households with the main type of fuel in the form of solid fuel, the average length of schooling, and the percentage of households with access to source of proper drinking water had a significant effect on U5MR in several districts/cities on the island of Papua. Therefore, it is hoped that district/city governments on the island of Papua in developing programs/policies to reduce U5MR can adjust to the conditions of each region.Keywords: Under Five Mortality Rate; Papua island; Mixed GWR
Regresi Data Panel dalam Analisis Faktor-Faktor yang Mempengaruhi Tingkat Pengangguran Terbuka di Provinsi Daerah Istimewa Yogyakarta Tahun 2017-2021 Agung Pamuji; Ezra Putranda Setiawan; Amir Mishbahul Munir
Indonesian Journal of Applied Statistics Vol 6, No 2 (2023)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v6i2.80010

Abstract

Open unemployment is one of the most important problems in a country's economy. High unemployment rates can cause serious economic impacts, such as reduced productivity, poverty, and social instability. This problem is one that currently being faced by Indonesia, including in the Province of Special Region of Yogyakarta. This study aims to analyze the factors that influence the open unemployment rate in the Special Region of Yogyakarta Province in the 2017-2021 period. The factors tested include the Human Development Index (HDI), Regional Gross Domestic Product (Regional GDP), and District/City Minimum Wage. This study uses the panel data regression method using annual data from each district/city in the Special Region of Yogyakarta Province during the 2017-2021 period. The results showed that the Human Development Index (HDI) and District/City Minimum Wage variables positively and significantly affected the open unemployment rate in Special Region of Yogyakarta Province during the study period. Meanwhile, the GRDP variable has an insignificant effect on the open unemployment rate in the Special Region of Yogyakarta Province.Keywords: Data Panel; TPT; IPM; UMK; PDRB
Analisis Pertumbuhan Ekonomi Provinsi Jawa Timur Beserta Faktor-Faktornya Menggunakan Model Robust Geographically Weighted Regression-Pendugaan M Halimah Nur Mushaharah; Rosita Kusumawati; Bayutama Isnaini
Indonesian Journal of Applied Statistics Vol 7, No 2 (2024)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v7i2.90381

Abstract

Economic growth is a successfull benchmark of nation-building and plays a role in a nation’ welfare. Economic growth in districts/cities measured by gross regional domestic product at constant prices (GRDP constant prices). East Java is the second province after DKI Jakarta as the biggest contributor to the Indonesian economy in 2022 with  economic growth rate values calculated by GRDP constant prices. However, East Java’s economic growth has gaps. As 23 of 38 districts/cities in East Java have economic growth values lower than national economic growth. Glaring gaps could be identified as an outlier and it could happen by many factors. This study aims to model economic growth and its factors with a robust analysis model against an outlier called robust geographically weighted regression-M estimation (RGWR-M). This study used secondary data from BPS Provinsi Jawa Timur and KEMENKEU’s SIKD data portal namely HDI, public health center, population density, economic activity, capital expenditure, and GRDP constant prices in East Java Province in 2022. The result showed that RGWR-M is the best model when an outlier is detected compared to OLS and GWR in analyzing factors that are suspected to affect economic growth in East Java Province in 2022 with  MAD’s values. RGWR-M model produces six mapping groups of significantly influential variables and one group of uninfluential variables. The groups formed to have different parameter estimation results in each district/cities.Keywords: GRDP constant prices; outlier; RGWR-M.

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