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All Journal Statistika CAUCHY: Jurnal Matematika Murni dan Aplikasi BAREKENG: Jurnal Ilmu Matematika dan Terapan Jurnal Ilmiah Pendidikan dan Pembelajaran Jambura Journal of Mathematics Jurnal E-Komtek Tematik : Jurnal Teknologi Informasi Komunikasi Indonesian Journal of Contemporary Multidisciplinary Research Euclid Asian Journal of Applied Education PRIMER: Jurnal Ilmiah Multidisiplin
Gumgum Darmawan
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Religion Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

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Sampling Design for Car Survey Using Stratified Random Sampling Widodo, Valeno Glenedias; Suparman, Yusep; Darmawan, Gumgum
Indonesian Journal of Contemporary Multidisciplinary Research Vol. 2 No. 5 (2023): September 2023
Publisher : PT FORMOSA CENDEKIA GLOBAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55927/modern.v2i5.5872

Abstract

Perumda Pasar Juara wants to know the number of cars that enter the Kosambi Market parking area for one month. However, limited access to available data means that Perumda has to count vehicles manually. Therefore, an appropriate sampling design is needed to estimate the number of cars. The data used is the result of observations based on preliminary sampling on October 14-20, 2022 at 08:00-17:00 WIB. The variables of the data used are car arrival time, day dummy, and hour dummy. The method used is dummy regression analysis and stratified sampling design. The regression analysis results show that there are four strata where each stratum has a three-parameter Weibull distribution. Based on the results, the minimum sample size required with a 5% error rate is 116 hours per day and is allocated according to the strata
Modelling Primary Energy by Long Memory Time Series Darmawan, Gumgum; Budhi Handoko
Indonesian Journal of Contemporary Multidisciplinary Research Vol. 2 No. 6 (2023): November 2023
Publisher : PT FORMOSA CENDEKIA GLOBAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55927/modern.v2i6.6970

Abstract

This research employs long memory modeling techniques to analyze and forecast global energy data spanning from 1965 to 2022. Focusing on the ARFIMA (Autoregressive Fractionally Integrated Moving Average) model, the study demonstrates its efficacy in predicting energy consumption trends. The evaluation of forecasting results for the subsequent four years reveals a remarkable Mean Absolute Percentage Error (MAPE) below 5%. This outcome underscores the effectiveness of incorporating long memory components in energy modeling, offering a robust approach for accurate and reliable predictions. The findings contribute to the advancement of energy forecasting methodologies, providing valuable insights for policymakers, energy analysts, and researchers in the pursuit of sustainable and informed energy planning
Co-Authors Akbar, Muhammad Faizal Alfarisi, Widi Wildani Bertho Tantular Budhi Handoko Budi Nurani Ruchjana Dedi Rosadi Eko Yulian eko yulian, eko Faidah, Defi Yusti Hadi, Juandi Hendrawati, Triyani Hirlan Khaeri Karin, Nabila Khaeri, Hirlan Neneng Sunengsih Novianti Indah Putri Puteri, Dian Islamiaty Resa Septiani Pontoh Ruchjana, Budi N Sastradipraja, C K Sitepu, Aldi Anugerah Sitohang, Yosep Oktavianus Sri Sutjiningtyas Sudartianto, Sudartianto Suparman, Yusep Utami, Yosi Febria Widodo, Valeno Glenedias Zen Munawar