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INDONESIA
Indonesian Journal of Statistics and Its Applications
Published by Institut Pertanian Bogor
ISSN : 25990802     EISSN : 25990802     DOI : -
Core Subject : Science, Education,
Computer Science & IT Mathematics Other
Indonesian Journal of Statistics and Its Applications (eISSN:2599-0802) (formerly named Forum Statistika dan Komputasi), established since 2017, publishes scientific papers in the area of statistical science and the applications. The published papers should be research papers with, but not limited to, the following topics: experimental design and analysis, survey methods and analysis, operation research, data mining, statistical modeling, computational statistics, time series and econometrics, and statistics education. All papers were reviewed by peer reviewers consisting of experts and academicians across universities and agencies
Arjuna Subject : Matematika - Statistik dan Probabilitas
Articles 192 Documents
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Proposing Additional Indicators for Indonesia Youth Development Index with Smaller Level Analysis: A Case Study in South Kalimantan Province Suryo Adhi Rakhmawan
Indonesian Journal of Statistics and Applications Vol 5 No 2 (2021)
Publisher : Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v5i2p220-227

Abstract

South Kalimantan is a province in Indonesia with many youths and has the lowest score in Indonesia Youth Development Index (YDI) 2017. However, the lowest score is the gender and discrimination dimension which incomplete to be analyzed because there are some indicators that are not included in the dimension. To solve the problems, it is necessary to build a measurement that can monitor a smaller level. Through this research, the author provides a measurement for describing the level of youth development in classifications for South Kalimantan in 2018. This index is built with the analysis factor method. It consists of five dimensions used in Indonesian YDI 2017 with some additional indicators. The result of this research shows that the index is a valid measure due to its significant correlation with Indonesia YDI 2017. The other result is the youth living in urban areas tend to have a higher index than youth who live in rural areas. While the youth who are male, also tend to have a higher development index than the female population. The suggestion for the South Kalimantan government is to improve the youth, the development priority for every classification can be started from the classification and dimension of youth index with the lowest achievement.
Nested Mixed Models with Repeated Measurements for Analyzing Gross Profit of Public Companies in West Java: Model Campuran Tersarang dengan Pengamatan Berulang untuk Analisis Data Laba Bruto Perusahaan Terbuka di Jawa Barat Witri, Alina; Notodiputro, Khairil Anwar; Anisa, Rahma
Indonesian Journal of Statistics and Applications Vol 6 No 2 (2022)
Publisher : Statistics and Data Science Program Study, IPB University, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v6i2p296-308

Abstract

The company's gross profit plays an important role in boosting the Gross Regional Domestic Product (PDRB) which will affect the revenue of local governments, known as Pendapatan Asli Daerah. Local governments often need information how gross profits of companies are different within each sector. It is not easy to investigate this matter especially if these companies are observed repeatedly and subsectors are nested within the sector. In this study, three factors were involved, i.e., sectors, subsectors which are nested in a particular sector, and time. It is assumed that the sectors and time of observation are fixed, whereas the subsectors are random. The response variable is the average gross profit per subsector of public companies in West Java. The objective of this study is to identify the variation of the subsectors, the effects of sectors as well as time on the average of the gross profit. Since the study involves fixed and random factors and the gross profit rate was observed more than one time, then a nested mixed model with repeated measurement is used. The results showed that there was no sector effect on the average gross profit, there is a variation in the average gross profit per subsector that is nested within the sector, and the time of observation did not influence the average gross profit.
Implementation of Ensemble Self-Organizing Maps for Missing Values Imputation Titin Siswantining; Kathan Gerry Vivaldi; Devvi Sarwinda; Saskya Mary Soemartojo; Ika Mattasari; Herley Al-Ash
Indonesian Journal of Statistics and Applications Vol 6 No 1 (2022)
Publisher : Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v6i1p1-12

Abstract

The purpose of this study is to implement the ensemble self-organizing maps (E-SOM) method to impute missing values at the preprocessing data stage, which is an important stage when making predictions or classifications. The Ensemble Self-Organizing Maps (E-SOM) is the development of the SOM imputation method, in which the E-SOM method is implemented by applying an ensemble framework using several SOMs to improve generalization capabilities. In this study, the E-SOM imputation method is implemented in South African heart disease data using random forest as a classification model. The results of the model evaluation showed that for accuracy in testing data, the Random Forest model formed from E-SOM imputed data yields better accuracy values than the Random Forest model formed from SOM-imputed data for variations of 36, 49, 64, and 81 neurons, while for variation of 25 neurons both models produce the same accuracy value. From the variation of the number of ensembles applied, the E-SOM imputation method with a combination of 81 neurons and 15 ensemble numbers produced a Random Forest model with the most optimal value of accuracy.
A New Perspective to Measuring Interdependence among Stock, Oil and Currency Markets: A Canonical Correlation Analysis Idowu Oluwasayo Ayodeji
Indonesian Journal of Statistics and Applications Vol 6 No 1 (2022)
Publisher : Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v6i1p23-40

Abstract

With a view to explaining various seemingly-contrasting results often reported in financial linkages literature, the study investigates the possibility of the existence of more than one unique relationship among stock, oil and currency markets. It also quantified the impact of selected macroeconomic variables on these relationships. Three prominent markets of stock, oil and exchange rates were examined from the United States, United Kingdom and Nigeria. The model adopted was the canonical correlation specification. Canonical solution identified two significant unique association patterns each among US, UK and Nigerian markets, indicating that their linkages vary with time. We also observed that the effect of macroeconomic variables on the link among financial markets vary by country and data frequency. Overall, inflation rates played the most significant role in the linkages among financial markets. The study concluded that the previous results on interdependence among financial markets are not conflicting but rather complimentary as they evidenced the multiple patterns of association among markets.
Small Area Estimation Using Empirical Bayes Poisson Gamma on Adolescent Fertility Rate in Indonesia: Small Area Estimation Menggunakan Empirical Bayes Poisson Gamma pada Angka Fertilitas Remaja di Indonesia Septianingsih, Putri; Wulansari, Ika Yuni
Indonesian Journal of Statistics and Applications Vol 7 No 2 (2023)
Publisher : Statistics and Data Science Program Study, IPB University, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v7i2p114-129

Abstract

High population growth is one of the main population problems facing Indonesia. One of the government's efforts to overcome this is by preventing adolescent fertility. The Adolescence Fertility Rate (AFR) produced by the IDHS is designed until provincial level, whereas the availability of AFR at the district/city level is needed as an indicator of regional development measurement. The purpose of this research is to produce an estimation of AFR at the district/city level in Indonesia and find out which auxiliary variables significantly influence it and evaluate the performance of the model in estimating AFR. The analytical method used is descriptive analysis to explain the characteristics of adolescent fertility and auxiliary variables and also direct estimation and the indirect estimation method using Small Area Estimation Empirical Bayes Poisson Gamma. The results showed that the number of villages, school facilities, health facilities, health workers, telephone lines and operators significantly affected the fertility of adolescents and the results of the SAE EB Poisson Gamma estimation were better than the direct estimation method. Suggestions proposed are the government need to increase attention to districts/cities that have AFR that is higher than the average AFR or National AFR and increase the number of school facilities and the number of health workers.
Comparison of Negative Binomial Regression Model and Geographically Weighted Poisson Regression on Infant Mortality Rate in South Sulawesi Province Siswanto, Siswanto; Saputra R, Edy; Sunusi, Nurtiti; Ilyas, Nirwan
Indonesian Journal of Statistics and Applications Vol 6 No 2 (2022)
Publisher : Statistics and Data Science Program Study, IPB University, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v6i2p170-179

Abstract

The number of infant mortality cases is an important indicator to assess the quality of a country's public health. A number of studies argue that the case of infant mortality has a close relation to the living area condition and the social status of the parents. Indirectly, the quality of life of babies in a country will impact the nation's quality of life in general. Therefore, many efforts are required to reduce the infant mortality in Indonesia. One of the steps that could be done to overcome this issue is to analyze the causative factors. The statistical method that has been developed for data analysis taking into account current spatial factors is the Geographically Weighted Poisson Regression (GWPR) with a weighted Bisquare kernel function. Based on the partial estimation with the GWPR model, there are seven groups based on significant variables that affect the number of infant deaths in South Sulawesi Province. Of the seven groups formed, the first group is the Selayar Islands where all variables have a significant effect. This needs to be a concern for the South Sulawesi provincial government to improve facilities and infrastructure in the Selayar Islands, of course the location which is very far from the city center can affect access to drug reception, medical personnel and so on. Based on the results of the analysis of the factors that affect the number of infant deaths in South Sulawesi Province using a negative binomial regression approach and GWPR with a bisquare kernel weighting, it can be concluded that the GWPR model used is the best for analyzing the number of infant deaths in South Sulawesi Province because it has an AIC value. The smallest is 167.668.
A Dynamic Factor Model for Nowcasting Household Consumption Amon Ra, Az Zahra; Notodiputro, Khairil Anwar; Silvianti, Pika
Indonesian Journal of Statistics and Applications Vol 6 No 2 (2022)
Publisher : Statistics and Data Science Program Study, IPB University, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v6i2p202-212

Abstract

A Dynamic Factor Model (DFM) is one of the time series models that can be used to forecast within a very short period in the future known as nowcasting. This model can be used to accommodate the frequency difference that exists between monthly explanatory variables and a response variable which is measured quarterly. This model has been commonly used in economics especially to forecast household consumption for the purpose of constructing economic policies. The economic condition of a country can be reflected in the country's Gross Domestic Product (GDP). Consumption is an important component of GDP because of its large proportion of GDP. One of the household economic activities to meet the various needs of goods and services is referred to as household consumption. This paper discusses the DFM to forecast household consumption based on the varimax and quartimax rotations. The results show that both rotational methods can be used for transmitting household consumption with the same precision.
Nested Linear Mixed Models with Repeated Measurement for Analyzing Telecommunication Products: Model Linier Campuran Tersarang dengan Pengukuran Berulang untuk Menganalisis Produk Telekomunikasi Rahmawati, Fardilla; Notodiputro, Khairil Anwar; Rahman, La Ode Abdul
Indonesian Journal of Statistics and Applications Vol 7 No 1 (2023)
Publisher : Statistics and Data Science Program Study, IPB University, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v7i1p1-14

Abstract

Nested linear mixed model is a model that combines fixed factors and random factors. Observations made over time with the same object being observed are called repeated measurements. This research was conducted to determine the determinant factors of internet data quota sales which are influenced by SA (Sales Area), MC (Mutual Check), PC (Product Category), and time factors using a nested linear mixed model with repeated measurement. SA, PC, and time factors as fixed factors while the MC factor nested in SA as a random factor. The results showed that the interaction effect between three fixed factors, namely between SA, PC, and time have a significant effect on the sales volume of internet data quota. Moreover, variation in the sales volume between MC factors was significant. The interaction between MC and PC, and the interaction between MC and time were significant on the sales volume of internet data quota.
A Study on Accuracy of Paddy Harvest Area Estimation on Area Sampling Frame Method: Kajian Ketepatan Pendugaan Luas Panen Padi pada Metode Pengambilan Kerangka Sampel Area Mulianto Raharjo; Anang Kurnia; Hari Wijayanto
Indonesian Journal of Statistics and Applications Vol 6 No 1 (2022)
Publisher : Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v6i1p41-49

Abstract

There was unsynchronized national rice data until 2017, which indicating that influenced by the differences in calculation methods between government agencies. The Indonesian Central Bureau of Statistics (BPS Statistics), the most responsible agency for national rice data, collected rice plant areas data using the paddy statistical assessment method (SP-Padi). Subjective elements from various parties potentially influenced the result of this assessment method. The development of a new method to overcome this matter has been started by the government since 1993. In 2018 the method, which is named the Area Sample Frame (ASF) method, was officially used by the government under the coordination of BPS. The ASF method divides the area into grids: blocks, segments, and sub-segments. This new method has several issues related to the methodology used in determining the sampling method. This study was conducted to evaluate the accuracy of paddy harvest area estimation on the ASF method through a sampling simulation process of the ASF method with various scenarios. With 20 simulated scenario combinations, it was found that the difference percentage average between the harvested area of the population and the harvested area of the sample to the sub-district area was 0.00062%, and the mean square error (MSE) was 0.0041%. So it can be concluded that the ASF methodology is an unbiased method and is good enough to accommodate various strata diversity in any region.
Study of Clustering Time Series Forecasting Model for Provincial Grouping in Indonesia Based on Rice Price: Kajian Model Peramalan Clustering Time Series untuk Penggerombolan Provinsi Indonesia berdasarkan Harga Beras Muhammad Ulinnuha; Farit M Afendi; I Made Sumertajaya
Indonesian Journal of Statistics and Applications Vol 6 No 1 (2022)
Publisher : Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v6i1p50-62

Abstract

Most indonesians consume rice as the main staple. The high low price of rice has an impact on farmers and communities, especially those who cannot afford it. Rice price forecasting is one of the important information to be considered for future rice prices. The data used is secondary data sourced from bps publication, Rural Consumer Price Statistics: Food Group, from January 2008 to December 2019 for 32 provinces in Indonesia. Time series  modeling and forecasting is usually done on a single variable using ARIMA. however, modeling becomes inefficient if there are many variables, so clustering time series analysis is performed using correlation distance with the clustering method of average linkage hierarchy. Cluster level ARIMA modeling with 4 clusters provides high efficiency because only by doing 4 times modeling results in accuracy values not much different from individual level modeling. the results obtained by individual-level ARIMA Modeling resulted in an average MAPE of 3.36%, while cluster-level ARIMA modeling with 4 clusters resulted in an average MAPE value of 4.27%, with a second MAPE difference of -0.91%. Formally conducted z test, the results obtained there is no difference between individual-level MAPE and cluster-level MAPE. This means that cluster-level modeling is relatively good and representative.

Page 12 of 20 | Total Record : 192

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