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All Journal FORUM STATISTIKA DAN KOMPUTASI Media Statistika Statistika Jurnal Media Infotama Journal of the Indonesian Mathematical Society Sinkron : Jurnal dan Penelitian Teknik Informatika Informatika Pertanian Jurnal Penelitian Pertanian Tanaman Pangan BAREKENG: Jurnal Ilmu Matematika dan Terapan SINTECH (Science and Information Technology) Journal Indonesian Journal of Statistics and Its Applications Jambura Journal of Mathematics International Journal of Remote Sensing and Earth Sciences (IJReSES) Eksakta : Berkala Ilmiah Bidang MIPA Jurnal Aplikasi Statistika & Komputasi Statistik FIBONACCI: Jurnal Pendidikan Matematika dan Matematika Xplore: Journal of Statistics STATISTIKA
Anang Kurnia
Department Of Statistics, IPB University, Indonesia
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Journal : Indonesian Journal of Statistics and Its Applications

 1 
PENERAPAN ANALISIS REGRESI SPLINE UNTUK MENDUGA HARGA CABAI DI JAKARTA Hestiani Wulandari; Anang Kurnia; Bambang Sumantri; Dian Kusumaningrum; Budi Waryanto
Indonesian Journal of Statistics and Applications Vol 1 No 1 (2017)
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.v1i1.47

Abstract

The chili is an important commodity in Indonesia, which has a fairly large price fluctuations. Fluctuations in prices often raises the risk of loss even have contributed to inflation. Chili price data is time series data that is not independent between observations (autocorrelation) and do not spread to normal. In addition, chili price data does not have the diversity of homogeneous data. One method that can be used to predict the pattern of the data is spline regression. The data used in this study is data the average weekly price of chili in Jakarta from January, 2010 to October, 2015. The best spline model is a second order spline models with three knots. The model has a value of Mean Absolute Percentage Error (MAPE) of 9.57% and determination coefficient of 86.41%. The model obtained in this research is already well in predicting the pattern of the chili price, but it was only able to predict well for a period of one month. Prediction chili prices in Jakarta for November are in the range of Rp 35.565. Keywords: chili price, regression, spline.
BINOMIAL REGRESSION IN SMALL AREA ESTIMATION METHOD FOR ESTIMATE PROPORTION OF CULTURAL INDICATOR Yudistira Yudistira; Anang Kurnia; Agus Mohamad Soleh
Indonesian Journal of Statistics and Applications Vol 2 No 2 (2018)
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.v2i2.63

Abstract

In sampling survey, it was necessary to have sufficient sample size in order to get accurate direct estimator about parameter, but there are many difficulties to fulfill them in practice. Small Area Estimation (SAE) is one of alternative methods to estimate parameter when sample size is not adequate. This method has been widely applied in such variation of model and many fields of research. Our research mainly focused on study how SAE method with binomial regression model is applied to obtained estimate proportion of cultural indicator, especially to estimate proportion of people who appreciate heritages and museums in each regency/city level in West Java Province. Data analysis approach used in our research with resurrected data and variables in order to be compared with previous research. The result later showed that binomial regression model could be used to estimate proportion of cultural indicator in Regency/City in Indonesia with better result than direct estimation method.
ANALISIS AMMI DENGAN RESPON GABUNGAN PADA UJI STABILITAS TANAMAN PADI GOGO DI KABUPATEN PACITAN Abdullah Ilman Fahmi; Rahma Anisa; Anang Kurnia; Indonesian Journal of Statistics and Its Applications IJSA
Indonesian Journal of Statistics and Applications Vol 3 No 1 (2019)
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.v3i1.173

Abstract

Gogo rice is one of the results of various rice cultivation development by planting in a dry land. Gogo rice is expected to give yield a better production of paddy in dry rice fields. The varieties Inpago 7, Inpago 8, Inpago 8 IPB, Inpago 9, Inpago 10, Situ Gintung, Situ Patenggang, Situ Bagendit, Gajah Mungkur, Slengreng TG, Slegreng GK, Srijaya, Towuti, Merah Wangi, dan Inpari 24 were used in this study. This study aims to identify the Gogo rice varieties that are stable and superior in six Pacitan Garden Experimental Plant locations based on a combined response using the AMMI method. The AMMI analysis combines an additive variety analysis as the main effects of treatment with multiple principle component analysis by bilinier modeling for interaction effect. This study used two combined responses, which described the plant productivity and the resistancy. The result of this study explained that some varieties, Inpago 8, Inpago 10, and Situ Patenggang, were stable varieties in all planting location based on the combined responses. According to productivity stability and plant resistancy superior gogo rice variety is Inpago 8 and Inpago 10.
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.
ANALISIS INFLASI MENGGUNAKAN DATA GOOGLE TRENDS DENGAN MODEL ARIMAX DI DKI JAKARTA Newton Newton; Anang Kurnia; I Made Sumertajaya
Indonesian Journal of Statistics and Applications Vol 4 No 3 (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.v4i3.694

Abstract

Inflation is an important economic indicator in showing the economic symptoms of a region's price level. DKI Jakarta is the capital of Indonesia chosen as the center of the economic barometer because it can provide the greatest contribution and influence on the Indonesian economy. The ARIMAX model was used for forecasting by adding independent variables in the Google trends data. Google trends data were explored based on seven expenditure groups published by IHK. The purpose of this study was to determine the effect of forecast Google trends using BPS inflation data in DKI Jakarta. The result of the exploration of Google Trends data was forecasted to get the best forecast model results. The result of data analysis indicates that the forecast results approached the original BPS data with the best forecast model is ARIMAX (2.0.3) all variables X. Google Trends data can be used as forecasting but cannot be used as a reference policy decision.
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.
Co-Authors . Hanniva . Marzuki . Sutriyati Abdullah Ilman Fahmi Agus Buono Agus M Soleh Agus Mohamad Soleh Agus Mohamad Soleh Ahmad Ansori Mattjik Aji Hamim Wigena Anik Djuraidah Anshari Luthfi Maulana Achiar Anwar Fitrianto Ardiansyah, Muhlis Arie Anggreyani Arief Gusnanto ASEP SAEFUDDIN Astri Fatimah Azka Ubaidillah Bagus Sartono Bambang Sumantri Beny Trianjaya Budi Waryanto Christiana Anggraeni Putri Cici Suhaeni Citra Jaya Dede Dirgahayu Deiby T Salaki Dewi Juliah Ratnaningsih Dian Handayani Dian Kusumaningrum Dian Kusumaningrum Dian Kusumaningrum, Dwi Agustin Nuriani Sirodj Dwi Agustin Nuriani Sirodj Efriwati Efriwati Farit Mochamad Afendi Farit Mohamad Afendi Fitri Dewi Shyntia Fitri Khairani Gerry Alfa Dito Hari Wijayanto Hari Wijayanto Hari Wijayanto Hestiani Wulandari Hidayat, Muhammad Husnun Nashir I Made Sumertajaya I Made Sumertajaya I Wayan Mangku Ikhlasul Amalia Rahmi Ina Widayanty Indah Herlawati Indahwati Indahwati Indahwati Indahwati Indahwati Indahwati Indonesian Journal of Statistics and Its Applications IJSA Irvanal Haq Iwan Kurniawan Jodi jhouranda Siregar K A Notodiputro Kusman Sadik Kusman Sadik Maulida Fajrining Tyas Muhammad Nur Aidi Muhammad Nur Aidi Muhammad Nur Aidi Mulianto Raharjo Newton Newton Nurul Hidayati Pingkan Awalia Purba, Widyo Pura Rahardiantoro, Septian Rahma Anisa Rahma Anisa Retsi Firda Maulina Ristiyanti Ristiyanti Rysda Rysda Ryska Putri Madyasari Sari Agustini Hafman Septian Rahardiantoro Setia Pramana Setyowati, Indah Rini Siti Muchlisoh Thooriq Ghaith Topan . Ruspayandi Triscowati, Dwi Wahyu Utami Dyah Syafitri Viarti Eminita Widoretno Widoretno Yani Nurhadryani Yenni Kurniawati Yudi Fathul Amin Yudistira Yudistira