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Journal : Indonesian Journal of Contemporary Multidisciplinary Research

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