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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): diterbitkan berkala 2 (dua) kali dalam setahun yang memuat tulisan ilmiah yang berhubungan dengan bidang statistika dan aplikasinya. Artikel yang dimuat berupa hasil penelitian bidang statistika dan aplikasinya dengan topik (tapi tidak terbatas): rancangan dan analisis percobaan, metodologi survey dan analisis, riset operasi, data mining, pemodelan statistika, komputasi statistika, time series dan ekonometrika, serta pendidikan statistika.
Arjuna Subject : -
Articles 4 Documents
 1 
Search results for , issue "Vol 4 No 4 (2020)" : 4 Documents clear
KAJIAN PENGARUH PENAMBAHAN INFORMASI GEROMBOL TERHADAP PREDIKSI AREA NIRCONTOH PADA DATA BINOMIAL Beny Trianjaya; Anang Kurnia; Agus M Soleh
Indonesian Journal of Statistics and Applications Vol 4 No 4 (2020)
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.v4i4.333

Abstract

Employment data is one of the important indicators related to the development progress of a country. Labor conditions in the territory of Indonesia can only be compared between times through the Survei Angkatan Kerja Nasional (Sakernas) data. Data generated from Sakernas and published by BPS is the number of employed and unemployed. The obstacle in estimating the semester unemployment rate at the regency/municipality level is the lack of a number of examples. One of the indirect estimates currently developing is small area estimation (SAE). This study developed the generalized linear mixed model (GLMM) by adding cluster information and examines the development of modifications with several model scenarios. The purpose of this study was to develop a prediction model for basic GLMM on a small area approach by adding cluster information as a fixed effect or random effect. The simulation results show that Model-2, a model that adds a fixed effect k-cluster and also adds a mean from the estimated effect of random areas in the sample area, is the best model with the smallest relative bias (RB) and Relative root mean squares error (RRMSE). This model is better than the basic GLMM model (Model-0) and Model-1 (a model which only adds a mean from the estimated random effect area in the sample area). Model-2 is applied to estimate the proportion of unemployed sub-district level in Southeast Sulawesi Province. Estimating the proportion of unemployed with calibration Model-2 produced an estimated aggregation of the unemployment proportion of Southeast Sulawesi Province at 0.0272. These results are similar to BPS (0.0272). Thus, the results of the estimated proportion of unemployment at the sub-district level with a calibration Model-2 can be said to be feasible to use.
PENGEMBANGAN MODEL PERAMALAN SPACE TIME: Studi Kasus: Data Produksi Padi di Sulawesi Selatan Evita Choiriyah; Utami Dyah Syafitri; I Made Sumertajaya
Indonesian Journal of Statistics and Applications Vol 4 No 4 (2020)
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.v4i4.584

Abstract

Based on Statistics Indonesia (BPS) South Sulawesi is one of the national rice granary province. There are three regions, Bone, Wajo, and Gowa that contribute to the high production of rice in South Sulawesi. However, rice production in Indonesia especially South Sulawesi often declined sharply due to climate disturbances, such as drought or flood. Therefore, Indonesia's government should provide a forecast related to rice production accurately to ensure the availability of food stocks as an integral part of national food security. Moreover, rainfall as climate factors should be included to produce an appropriate forecast model that can be expected to generate the estimation of the rice production data accurately. This research focused on comparing the forecasting model of rice production data by SARIMAX and GSTARIMAX model and used rainfall as explanatory variables. The SARIMAX model is a multivariate time series forecasting model that can accommodate the seasonal components. In contrast, the GSTARIMAX model, which is equipped with an inverse distance spatial weight matrix, is a space-time forecasting model that involves interconnection between locations. The GSTARIMAX model built for rice production forecasting in Bone, Wajo, and Gowa is GSTARIMAX (2,1,0)(0,1,1)12. Rainfall as an explanatory variable was significant at each location. The comparison of rice production forecasting models for the next six periods in four locations showed that the GSTARIMAX model provided more stable forecasting results than the SARIMAX model, viewed from the average MAPE value of the GSTARIMAX mode in each location.
DAMPAK KEBIJAKAN PEMERINTAH TERHADAP PEMBANGUNAN EKONOMI D. I. YOGYAKARTA 2001-2017: Simulasi dengan Analisis Sistem Persamaan Simultan Anissa Dika Larasati; Vera Lisna
Indonesian Journal of Statistics and Applications Vol 4 No 4 (2020)
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.v4i4.654

Abstract

Economic development includes increasing economic growth and alleviating poverty. D.I Yogyakarta is a province with the lowest economic growth and per capita income compared to other provinces in Java. Besides, it has the highest poverty rate. With this condition, it is feared that economic development and economic contribution in D.I Yogyakarta which are relatively low are difficult to increase. This study aims to analyze the simultaneous relationship between indicators of economic development in the province of D.I Yogyakarta, explores the variables that influence it, and perform policy simulations to improve economic development. The indicators used to describe economic growth in this study are Regional Gross Domestic Product (regional GDP), household consumption, and community savings in banks. While the indicators that are used to reflect the poverty level are the percentage of poor people. The estimation method used is simultaneous Two-Stage Least Squares (2SLS) equation system which consisted of three structural equation and one identity equation using the historical data from the year 2001-2017. The results of the simulation show a 6% increase in government expenditure can improve economic growth to 5.41% and reduce the percentage of poor people by 0.41% points.
KAJIAN VARIANCE MEAN RATIO PADA SIMULASI SEBARAN DATA BINOMIAL NEGATIF Choirun Nisa; Muhammad Nur Aidi; I Made Sumertajaya
Indonesian Journal of Statistics and Applications Vol 4 No 4 (2020)
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.v4i4.689

Abstract

The negative binomial distribution is one of the data collection counts that focuses on success and failure events. This study conducted a study of the distribution of negative binomial data to determine the characterization of the distribution based on the value of Variance Mean Ratio (VMR). Simulation data are generated based on negative binomial distribution with a combination of p and n parameters. The results of the VMR study on negative binomial distribution simulation data show that the VMR value will be smaller when the p-value is large and the VMR value is more stable as the sample size increases. Simulation data of negative binomial distribution when p≥0.5 begins to change data distribution to the distribution of Poisson and binomial. The calculation VMR value can be used as a reference for detecting patterns of data count distribution.

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