Peramalan Jumlah Penumpang Pesawat Di Bandara Sultan Iskandar Muda Dengan Metode SARIMA (Seasonal Autoregressive Integrated Moving Average)

Durrah, Fara Inka; Yulia, Yulia; Parhusip, Tessa Prihartina; Rusyana, Asep

doi:10.24815/jda.v1i1.11847

Cited by 12 publications

(12 citation statements)

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“…Peramalan merupakan perhitungan objektif menggunakan kumpulan data masa lalu, untuk memperkirakan sesuatu dimasa yang akan datang [2]. Pada umumnya metode peramalan yang dilakukan berdasarkan pada histori permintaan, penawaran ataupun persediaan aktual berdasarkan periode waktu tertentu yang telah diketahui pola data tersebut kemudian dibandingkan dari hasil peramalan guna menjadi acuan dalam kegiatan produksi.…”

Section: B Peramalanunclassified

Forecasting Needs Of Mountain Types Of DDD Bike Using The Seasonal Autoregressive Integrated Moving Average Model Approach

Muhjidin

Sukmono

2021

PELS

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One of the bicycle manufacturers in Indonesia, namely PT. DDD is a manufacture engaged in the production of various types of bicycles with a make to stock production system. Market demand that fluctuates every year results in a lack of readiness to meet market needs. So a re-planning is needed in order to meet all market demands. The Box Jenkins statistical method, the Seasonal Autoregressive Integrated Moving Average model, is one of the appropriate approaches to solve problems at PT. DDD. The advantages of the SARIMA model can be used to forecast seasonal or non-seasonal time series simultaneously. The best SARIMA model approach to forecasting demand for mountain bikes at PT. DDD is SARIMA (0,0,0)(0,1,1)12 with the equation Zt=Zt-12+ΘQat-12+at with the smallest MAPE value of 32.35%. So that the model is said to be feasible to predict mountain bikes and the model can predict up to 12 periods in 2021.

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Section: B Peramalanunclassified

Forecasting Needs Of Mountain Types Of DDD Bike Using The Seasonal Autoregressive Integrated Moving Average Model Approach

Muhjidin

Sukmono

2021

PELS

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“…Penelitian yang terkait peramalan jumlah penumpang juga pernah dilakukan oleh [4], [6], [8], dan [12]. Sedangkan, penelitian terkait dengan metode ARIMA pernah dilakukan oleh [11].…”

Section: Pendahuluanunclassified

Peramalan Jumlah Penumpang Pesawat Terbang Di Pintu Kedatangan Bandar Udara Internasional Pattimura Ambon Dengan Menggunakan Metode Arima Box-Jenkins

et al. 2019

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The Autoregressive Integrated Moving Average (ARIMA) model is often used to forecast time series data. In the era of globalization, rapidly progressing times, one of them in the field of transportation. The aircraft is one of the transportation that the residents can use to support their activities, both in business and tourism. The objective of the research is to know the forecasting of the number of passengers of airplanes at the arrival gate of Pattimura Ambon International Airport using ARIMA Box-Jenkins method. The best model selection is ARIMA (0, 1, 3) because it has significant parameter value and MSE value is smaller.

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“…ARIMA by Box Jenkins Methodology for Estimation and Forecasting Models in Higher Education [5]. Forecasting the Number of Aircraft Passengers at Sultan Iskandar Muda Airport Using the SARIMA Method (Seasonal Autoregressive Integrated Moving Average) [6]. Predictive modeling of pelagic fish catch in malaysia using seasonal ARIMA models [7].…”

Section: Introductionmentioning

confidence: 99%

Forecasting Arrival of Foreign Tourists Using Seasonal Arima Box-Jenkins

Ilmayasinta

2021

BAREKENG: J. Il. Mat. & Ter.

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Indonesia's economy is influenced by many factors, including the tourism sector. Through this tourism sector, it is possible for many foreign tourists to visit Indonesia. There are so many foreign tourists who come to Indonesia, forecasting is needed to find out the estimates of foreign tourists in the following months based on existing data. The method that used is the Seasonal Autoregressive Integrated Moving Average (SARIMA) method. The foreign tourist’s coming to Indonesia through Soekarno Hatta Airport were taken from the center agency on statistics (BPS) Indonesia. Data on the number of foreign tourists who come to Indonesia through Soekarno Hatta Airport is data with a seasonal pattern. The data used is secondary data obtained from Soekarno Hatta Airport for the period January 2010 to June 2015. In this case it is used to predict the value of the data for the next 6 months using the best model is the . Forecasting results show the number of each month increases from the previous year. In July it showed the highest yield of 342536, which was 297878 in the previous year. Forecasting results show the number of each month increases from the previous year. In July, the highest yield was 342536, which was 297878 in the previous year.

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Peramalan Jumlah Penumpang Pesawat Di Bandara Sultan Iskandar Muda Dengan Metode SARIMA (Seasonal Autoregressive Integrated Moving Average)

Cited by 12 publications

References 0 publications

Forecasting Needs Of Mountain Types Of DDD Bike Using The Seasonal Autoregressive Integrated Moving Average Model Approach

Forecasting Needs Of Mountain Types Of DDD Bike Using The Seasonal Autoregressive Integrated Moving Average Model Approach

Peramalan Jumlah Penumpang Pesawat Terbang Di Pintu Kedatangan Bandar Udara Internasional Pattimura Ambon Dengan Menggunakan Metode Arima Box-Jenkins

Forecasting Arrival of Foreign Tourists Using Seasonal Arima Box-Jenkins

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