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Hasih Pratiwi
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ijas@mipa.uns.ac.id
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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 11 Documents
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Search results for , issue "Vol 6, No 1 (2023)" : 11 Documents clear
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
Aplikasi Metode Market Basket Analysis dengan Algoritma Apriori untuk Mengetahui Pola Belanja Konsumen pada Online Shop Amerta Fashion Iut Tri Utami; Rahmila Dapa
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.v6i1.79963

Abstract

In this era of globalization, society has been facilitated in various ways due to advances in technology, one of which is the use of the internet. One of the goals of the continuous development of technology is to meet human needs. According to a global survey conducted by Nielsen Online in 2009, more than 85% of the world's population has used the internet for buying and selling transactions. The increase in purchases made online, especially during this pandemic period, shows that people have begun to be technology literate. The large number and rapid growth of data often overwhelm business actors in processing data so that they are left unorganized, even though the data obtained can produce useful information to support decisions or assist in determining marketing strategies. This difficulty also occur at Online Shop Amerta Fashion. This study uses one of the data mining methods, namely Market Basket Analysis with a priori algorithm which goes through the stages of data collection, formation of association rules, and concluding. Market basket analysis can determine consumer buying patterns and also find out which items are selling well. The results obtained is that consumers of Online Shop Amerta Fashion didn’t only buy 1 item in a transaction. Boyfriend Jeans Highwaist and Wrap Top Summer are 2 types of items that are popularly purchased by consumers so that they become the most influential items in the overall purchasing transactions from Online Shop Amerta Fashion from January 4, 2021 to June 18, 2021.Keywords: data mining; association rules; market basket analysis; Apriori; Online Shop Amerta Fashion
Analisis Data Time Series Menggunakan Model Kernel: Pemodelan Data Harga Saham MDKA Suparti Suparti; Rukun Santoso
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.v6i1.79385

Abstract

Classic time series data analysis techniques, such as autoregressive, model stationary data in which the values of prior observations influence the current observations through a process known as linear regression. There are several requirements for error assumptions in autoregressive, including independence, normal distribution with a zero mean and constant variance. It is frequently discovered that these assumptions are challenging to verify when modelling real data. Kernel time series regression is an alternative model that does not require error assumptions. Non-stationary time series data can be effectively modelled using the kernel time series method. Time series data that isn't yet stationary is made stationary first, then the data is modified by forming the current stationary time series data as the response variable and the previous period data as the predictor variable. Next, regression kernel modelling is carried out while applying kernel weight function and determining the optimal bandwidth. For development of science, the optimal bandwidth can be achieved by minimizing the MSE, CV, GCV, or UBR values. It is possible to use R2 or MAPE as the kernel time series regression model's goodness metric. A strong model is generated while modelling MDKA stock price data using kernel regression utilizing the Gaussian kernel function and optimal bandwidth selection using GCV since R2 is 0.9828372 more than 0.67 and MAPE is 1.985681% under 10%.Keywords: 3 time series; kernel regression; GCV; MDKA stock price.
Penentuan Rate Asuransi Kendaraan Bermotor Menggunakan Kredibilitas Bayesian Rahmila Dapa; Iut Tri Utami
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.v6i1.79813

Abstract

This paper uses Credibility to determine new rate based on data of historical claim in a motor vehicle insurance in Bandung, Indonesia. Rate is formed based on past loss through experience rating. Credibility is one of the examples of experience rating that considers group historical claims. One of the credibility methods is Bayesian credibility that considers rate as a random variable. Bayesian credibility is used based on claim frequency and claim severity from a group of policy holders in order to create new rates. In this paper, claim frequency followed the Poisson distribution while claim severity followed the Lognormal distribution. Result of analysis showed that rate values based on claim frequency and severity are higher than the rate values that were used back in 2010.Keywords: bayesian credibility; rate; claim frequency; claim severity
Penerapan Metode Fuzzy Time Series (FTS) Cheng dan Markov-Chain untuk Peramalan Indonesia Crude Oil Price (ICP) Deby Fakhriyana; Indira Ihnu Brilliant
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.v6i1.79907

Abstract

In Indonesia, crude oil plays a significant role in the country’s economy as it serves as a source of income and meets the country's energy needs. Therefore, fluctuations in crude oil prices have a significant impact on the economic activities of the society. Forecasting the price of Indonesian crude oil is thus crucial. The international price of crude oil in Indonesia is known as the Indonesian Crude Oil Price (ICP). One commonly used statistical method for forecasting is the ARIMA method. However, the ARIMA method has certain assumptions that need to be fulfil, and many real-world data cannot meet these assumptions. Hence, forecasting using the Fuzzy Time Series (FTS) method, which does not rely on assumptions, is employed. Some popular FTS methods include the Cheng FTS method and the Markov Chain FTS method. This study implements the Cheng FTS and Markov Chain FTS methods on the ICP data from May 2018 to June 2023 to determine the most appropriate method for forecasting. The analysis results using the Cheng FTS method on the testing data yield a Mean Absolute Percentage Error (MAPE) value of 4,083%, while the Markov Chain FTS method has MAPE value of 4,585%. The Cheng FTS method selected as the appropriate model for forecasting the ICP data since it has a smaller MAPE value. Using the Cheng FTS method, the predicted ICP value for July 2023 is US$72,907 per barrel.Keywords: ICP; FTS Cheng; FTS Markov Chain; MAPE
Front Matter Vol 6 No 1 (2023) Hasih Pratiwi
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.v6i1.83587

Abstract

Analisis Perbandingan Metode Hierarchical dan Non-Hierarchical dalam Pembentukan Cluster Provinsi di Indonesia Berdasarkan Indikator Women Empowerment Pikata Aselnino; Arie Wahyu Wijayanto
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.v6i1.68876

Abstract

The focus on improving the quality of women’s live to lessen discrimination and gender inequality is set in the fifth’s goals of SDGs. In Indonesia, the RPJMN 2020-2024 contains measure to improve the contribution of women to equitable development. The Central Bureau of Statistics has developed several indicators related to gender, including Gender Development Index (GDI) an Gender Empowerment Index (GEI), which contain women’s improvement on education and health as well as their participation in economic and political fields. The Ministry of Women’s Empowerment and Child Protection did a quadrant analysis to split Indonesia’s 34 provinces into four categories based solely on GDI and GEI using the national average as a constraint. This study compares the Hierarchical, K-Means, and Fuzzy C-Means method to form number of clusters in Indonesia based on the gender development and empowerment in 2021 in order to complement the quadrant analysis. To choose the number of optimum cluster, Elbow method and Calinski-Harabasz Index were used and the best k value is five. From the validation with Silhoutte Index, K-Means was chosen as the best clustering model.Keywords: clustering; fuzzy; k-means; hierarchical; women empowerment
Back Matter Vol 6 No 1 (2023) Hasih Pratiwi
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.v6i1.83588

Abstract

Machine Learning Predictive Modeling of Agricultural Sustainability Indicators Raden Roro Shafira Meisy Sudarsono; Harimukti Wandebori
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.v6i1.64245

Abstract

Modern-day researchers are provided with data abundance that has its drawback: increased analysis complexity. Approaching this issue through traditional data analysis techniques provides only partial solutions to the complex situation. This research offers analytical and predictive models based on machine‐learning algorithms (linear regression, random forest, and generalized additive model) that can be used to assess and improve the Common Agricultural Policy (CAP) impact over agricultural sustainability in European Union (EU) countries, providing the identification of proper instruments that can be adopted by EU policymakers and CAP Council in financial management of the policy. The chosen methodology elaborates custom‐developed models based on a dataset containing 22 relevant indicators, considering three main dimensions contributing to the EU sustainable agriculture development goals in the CAP context: social, environment, and economic. The results showed that sustainable agriculture parameters influenced by the relevant indicators could be modeled with both linear and non-linear regression approaches by utilization of real-time data using machine learning. The predictive analytic models provide satisfactory performance and could be adopted by researchers and practitioners as policy impact monitoring and controlling tools, not only the EU but also for other countries that have or plan to adopt similar agricultural policies.Keywords: Agricultural policies, common agricultural policy, machine learning, rural development, sustainable agriculture
Model Simulation of Continuous Time Markov Chain Susceptible Infected Recovered-Bacterial Population for Cholera Disease Aulia Maulani Syifa Nur Hidayati; Respatiwulan Respatiwulan; Sri Subanti
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.v6i1.71801

Abstract

Epidemic is an outbreak of an infectious disease rapidly in a population at a certain place and time. Epidemic models are used to explains the spread pattern of disease. The continuous time Markov chain susceptible infected recovered-bacterial population in the aquatic reservoir (CTMC SIR-B) model is a stochastic model, which considers the effect of bacterial population. The human population are classified into 3 groups. There are susceptible, infected, and recovered groups. Then, there are bacterial population which can infectious the cholera disease to human. CTMC SIR-B model considers treatment and water sanitation parameters. The spread of cholera disease can be modeled as CTMC SIR-B. Cholera is an acute intestinal infectious disease caused by the bacterium Vibrio cholerae. Cholera can be transmitted through the human digestive system. The symptoms of cholera disease are diarrhea, vomiting, and dehydration. The dehydration if not handled properly, may cause death. The aims of this research are to build and simulate the CTMC SIR-B model for cholera disease. The result of the model simulation shows that there is no significant difference between various values of treatment and water sanitation parameters. The pattern of the cholera disease spread describes that the transmission of cholera can occur from human to human even though there is no population of bacteria in the aquatic reservoir.Keywords: cholera; ctmc sir-b; epidemic model; stochastic. 

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