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Anang Kurnia
Department Of Statistics, IPB University, Indonesia
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Energy Engineering Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Physics Transportation Other

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Journal : Statistika

 1 
PENDEKATAN MARGINAL PADA ANALISIS DATA SURVIVAL “BERKORELASI” Dian Handayani; Anang Kurnia
STATISTIKA: Forum Teori dan Aplikasi Statistika Vol 3, No 1 (2003)
Publisher : Program Studi Statistika Unisba

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/jstat.v3i1.523

Abstract

Didalam konteks data survival yang berkorelasi, yaitu pada saat objek terkelompok (misal karena perlakuan, ikatankeluarga atau karena pengamatan berulang), maka peubah respon didalam kelompok pada dasarnya akan berkorelasi, sehinggakita akan menganggap dan mengasumsikan bahwa data tersebut berkorelasi.Shoukri dan Pause (1998) telah menunjukan bahwa metode penduga maksimum likehood (MLE) memberikan hasil yang tidakkonsisten. Sedangkan Liang dan Zeger (1986) serta Zeger dan Liang (1986) telah mengembangkan metode GEE untukmengkoreksi kasus data berjorelasi. Telah banyak penulis yang memberikan evaluasi terhadap GEE dan memberikan kesimpulanbahwa GEE adalah salahsatu pendekatan yang robust dalam menduga ragam untuk data terkelompok. Selain itu alternatif lainyang bisa digunakan adalah GJE yang dikembangkan oleh Therneau (1993).Dalam makalah ini akan dicoba pendekatan GEE dalam analisis survival untuk kasus data terkelompok yang dikenal sebagaipendekatan marginal.Pendekatan GEE dikembangkan serupa dan berlandaskan pada model Cox Proportional Hazards.Pendekatan margianl membeikan hasil pendugaan ragam yang cukup baik sehingga cukup efektif mengoreksi pengaruh dataterkelompok. Namun demikian masih terdapat kelemahan yang sangat mengganggu yaitu makna dari pengelompokan data,dimana tidak semua kelompok mempunyai makna yang berarti.
Penerapan Algoritma Tree Augmented Naive Bayesian pada Penentuan Peubah Penting Pingkan Awalia; Aji Hamim Wigena; Anang Kurnia
STATISTIKA: Forum Teori dan Aplikasi Statistika Vol 11, No 2 (2011)
Publisher : Program Studi Statistika Unisba

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/jstat.v11i2.1053

Abstract

In the era of free market competition today, improving product quality is very important. Consumerpreferences through product level of analysis is one method that many manufacturers conducted toevaluate the product. Multivariable regression is a statistical method used to determine the importantvariables. The weakness of this method is the strict assumption. This problem will be completed bythe method of bayesian networks. There are several algorithms to build the BN. This study uses TANand NB because of its simplicity. This study shows that the most accurate method at the chosen levelof classification accuracy is the TAN by 83%. The importance variable is the aspect liking of strengthof after taste.
ANALYZING THE CONSUMER’S RICE PRICE USING MULTIPLE LINEAR REGRESSION AND X-12 ARIMA Dian Kusumaningrum,; Asep Saefuddin; Anang Kurnia
STATISTIKA: Forum Teori dan Aplikasi Statistika Vol 4, No 2 (2004)
Publisher : Program Studi Statistika Unisba

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/jstat.v4i2.876

Abstract

Rice is one of the main foods in Indonesia. A change of rice price will cause a major effect in the lives of consumers. Onthe other hand, there are so many factors that influence the rice price. Thus finding key factors which are significant to therice price, as well as forecasting the consumer’s rice price are needed in order to maintain the stabilization of rice price.The second objective is to find key factors which influence the rice price by using multiple linear regression models. Theparameters were estimated by ordinary least square methods. There are 6 variables that are significant at α=5%, which arethe consumer’s rice price at the previous period, rice production at the current and previous period, farmer’s GKP price,realization of domestic stock, and total rice import. The rice price will increase if the GKP price and realization of domesticstock increase whereas total rice import and the consumer’s rice price at the previous period have negative influencestowards the rice price. In this model rice production at the current and previous period have positive signs, contradictory tothe microeconomic theory where when the rice production increases, there will be an excess supply and the price will drop.That condition will occur only if the commodity is a free commodity and the rice is at the sufficiency level but inIndonesia, rice is affected by the government’s policy and the rice productivity is left behind by the demand. Forecastingthe consumer’s rice price for the next five years was the last objective of this research. ARIMA Box–Jenkins Method, X-12ARIMA, Winter’s Method, and Trend Analysis were compared to find the best statistical model to forecast the consumer’srice price. X-12 ARIMA turns out to be the best method because it has the smallest MAPE, MAD, and MSD value. Thisresult is a satisfactory because according to Findley et al. (1998) X-12 ARIMA has the capability to adjust seasonal andtrading day factors which usually causes fluctuations in an economic time series data. Besides that, the X-12 ARIMAmethod also enhances the lack of other forecasting techniques used in this research to add regression effects. TheregARIMA makes it possible to add the user defined parameters, in this case the length of month parameter. The length ofmonth parameter rescales the monthly observation by a weight corresponding to the month relative length with respect tothe average length. The seasonal adjusted data from the original time series data is aimed to simplify the data withoutloosing important information.
Co-Authors . Marzuki . Sutriyati Abdullah Ilman Fahmi Agus Buono Agus M Soleh Agus Mohamad Soleh Agus Mohamad Soleh Aji Hamim Wigena Anik Djuraidah Ardiansyah, Muhlis Arie Anggreyani Arief Gusnanto ASEP SAEFUDDIN Azka Ubaidillah Bagus Sartono Bambang Sumantri Beny Trianjaya Budi Waryanto Christiana Anggraeni Putri Cici Suhaeni Citra Jaya Dede Dirgahayu Deiby T Salaki Dian Handayani Dian Kusumaningrum Dian Kusumaningrum Dian Kusumaningrum, Farit Mochamad Afendi Farit Mohamad Afendi Fitri Khairani Hari Wijayanto Hari Wijayanto Hestiani Wulandari Hidayat, Muhammad I Made Sumertajaya I Wayan Mangku Ikhlasul Amalia Rahmi Ina Widayanty Indah Herlawati Indahwati Indahwati Indahwati Indahwati Indonesian Journal of Statistics and Its Applications IJSA Iwan Kurniawan Jodi jhouranda Siregar K A Notodiputro Kusman Sadik Maulida Fajrining Tyas Muhammad Nur Aidi Mulianto Raharjo Newton Newton Nurul Hidayati Pingkan Awalia Purba, Widyo Pura Rahardiantoro, Septian Rahma Anisa Retsi Firda Maulina Ristiyanti Ristiyanti Rysda Rysda Sari Agustini Hafman Setia Pramana Setyowati, Indah Rini Siti Muchlisoh Topan . Ruspayandi Triscowati, Dwi Wahyu Utami Dyah Syafitri Viarti Eminita Yenni Kurniawati Yudistira Yudistira