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All Journal Media Statistika Jurnal Ilmu Dasar Jurnal Sains dan Seni ITS CAUCHY: Jurnal Matematika Murni dan Aplikasi Jurnal Teknik Industri: Jurnal Keilmuan dan Aplikasi Teknik Industri BAREKENG: Jurnal Ilmu Matematika dan Terapan Inferensi
Muhammad Sjahid Akbar
Departemen Statistika Institut Teknologi Sepuluh Nopember Surabaya
Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Energy Engineering Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Physics Social Sciences Transportation

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Journal : Media Statistika

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BAGGING REGRESI LOGISTIK ORDINAL PADA STATUS GIZI BALITA Akbar, Muhammad Sjahid; Mukarromah, Adatul; Paramita, Lalita
MEDIA STATISTIKA Vol 3, No 2 (2010): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (340.151 KB) | DOI: 10.14710/medstat.3.2.103-116

Abstract

World Health Organization-National Centre for Health Statistic (WHO-NCHS) is standart nutritional status used in Indonesia, it based on Kartu Menuju Sehat (KMS). These Indices can be expressed in terms of Z-score based Weight-for-Age. This Indices need comparison considering the fact which cause nutritional status not only Weight-for-Age. The aim from this research to obtain bagging ordinal logistics regression for WHO-NCHS nutritional status and new nutritional status. A new nutritional status expressed in terms of cluster, while classification function expressed from logit model of ordinal logistics regression. The result for new nutritional status bagging obtained at 60 bootstrap replicated that is 76.345%, this model can decrease misclassification until 22.046%. While bagging for WHO-NCHS nutritional status can increase accurate classification from single data set 75.863% at 150 bootstrap replicated.   Keywords: Child nutritional status, Bagging, Ordinal logistics regression.
APPLICATION OF THE DYNAMIC FACTOR MODEL ON NOWCASTING SECTORAL ECONOMIC GROWTH WITH HIGH-FREQUENCY DATA Supriyatna, Putu Krishnanda; Prastyo, Dedy Dwi; Akbar, Muhammad Sjahid
MEDIA STATISTIKA Vol 17, No 2 (2024): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.17.2.128-139

Abstract

Economic growth is crucial for planning, yet delayed data releases challenge timely decision-making. Nowcasting offers near-real-time insights using high-frequency indicators (released monthly, weekly, or even daily) to predict low-frequency variables (quarterly or yearly). This study uses high-frequency indicators (monthly), such as stock price changes, air quality, transportation data, financial conditions, and Google Trends, to nowcast quarterly GDP through the Dynamic Factor Model (DFM). The data used span from January 2010 until March 2023, which is split into two: January 2010 until March 2022 for training data and the rest as testing data. Compared to the benchmark Autoregressive Moving Average with Exogenous Variables (ARMAX) model, DFM demonstrates superior accuracy with lower symmetric Mean Absolute Percentage Error (sMAPE). In addition, to evaluate the model performance in nowcasting the GDP across the sector using DFM, the additional metrics, i.e., Root Mean Square Error (RMSE), Mean Absolute Deviation (MAD), and Adjusted R-squared, concluded that in the industrial and transportation sectors results in sufficient nowcasting of GDP, Meanwhile, In the financial sector, the results of the nowcasting GDP give poor estimation results that need improvement.
Co-Authors Adatul Mukarromah Bambang Widjanorko Otok Bambang Widjanorko Otok Bella Puspa Dewani Damayanti, Salma Dedy Dwi Prastyo Destri Susilaningrum Dina Yuanita Fadhilah, Rahmi Fredi Suryadi Fredi Suryadi Lalita Paramita Latifah, Turfah Nita Cahyani Puthy, Khem Riskianto Riskianto Safira, Dinda Ayu Sri Harini Suhartono Suhartono Supriyatna, Putu Krishnanda Wibawati Wibawati Yani Trimardiani