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

2018 ◽  
Vol 1 (1) ◽  
pp. 1-11
Author(s):  
Fara Inka Durrah ◽  
Yulia Yulia ◽  
Tessa Prihartina Parhusip ◽  
Asep Rusyana
Keyword(s):  
Moving Average ◽  
Secondary Data ◽  
Final Conclusion ◽  
Seasonal Patterns ◽  
Autoregressive Integrated Moving Average ◽  
International Airport

ABSTRAK. Penelitian ini bertujuan untuk mengetahui model peramalan dimana jumlah keberangkatan penumpang pada PT. Angkasa Pura II (Perseron). Kantor cabang bandar udara Internasional Iskandar Muda dengan menggunakan Metode Seasonal Autoregresive  Integrated Moving Average (SARIMA). Data jumlah keberangkatan di bandar udara Internasional Iskandar muda merupakan data dengan pola musiman. Data yang digunakan merupakan data sekunder yang diperoleh dari Bandara Sultan Iskandar Muda periode Bulan Januari 2010 hingga Desember 2016. Model terbaik yang diperoleh yaitu ARIMA (0,1,1)(0,0,1)12. Sedangkan berdasarkan data peramalan yang diperoleh dapat diketahui bahwa diprediksi jumlah penumpang pesawat tetinggi pada tahun 2017 akan terjadi pada Bulan Desember, dan jumlah penumpang pesawat terendah diprediksikan akan terdapat pada Bulan Maret 2017. Kesimpulan akhir yang diperoleh yaitu jumlah penumpang pada Tahun 2017 akan mengalami peningkatan dibandingkan dengan Tahun sebelumnya.ABSTRACT. This study aims to determine the model of forecasting where the number of passengers at PT. Angkasa Pura II (Perseron). International branch of Iskandar Muda International Airport using Seasonal Autoregresive Integrated Moving Average (SARIMA) method. Data on the number of departures at Iskandar International Airport are young data with seasonal patterns. The data used are secondary data obtained from Sultan Iskandar Muda Airport during January 2010 to December 2016. The best model is ARIMA (0,1,1) (0,0,1)12. While based on forecasting data obtained can be seen that predicted the number of passengers in 2017 will occur in December, and the lowest number of passengers is predicted to be in March 2017. Final conclusion obtained that the number of passengers in the Year 2017 will increase compared with the previous Year.

PREDIKSI HARGA SAHAM PT. BRI, Tbk. MENGGUNAKAN METODE ARIMA (Autoregressive Integrated Moving Average)

2014 ◽  
Vol 14 (2) ◽  
pp. 60
Author(s):  
Greis S Lilipaly ◽  
Djoni Hatidja ◽  
John S Kekenusa
Keyword(s):  
Stock Price ◽  
Moving Average ◽  
Secondary Data ◽  
Research Use ◽  
Autoregressive Integrated Moving Average ◽  
Minimum Data ◽  
Research Show

PREDIKSI HARGA SAHAM PT. BRI, Tbk. MENGGUNAKAN METODE ARIMA (Autoregressive Integrated Moving Average) Greis S. Lilipaly1) , Djoni Hatidja1) , John S. Kekenusa1) ABSTRAK Metode ARIMA adalah salah satu metode yang dapat digunakan dalam memprediksi perubahan harga saham. Tujuan dari penelitian ini adalah untuk membuat model ARIMA dan memprediksi harga saham PT. BRI, Tbk. bulan November 2014. Penelitian menggunakan data harga saham  harian  maksimum dan minimum PT. BRI, Tbk. Data yang digunakan yaitu data sekunder yang diambil dari website perusahaan PT. BRI, Tbk. sejak 3 Januari 2011 sampai 20 Oktober 2014 untuk memprediksi harga saham bulan November 2014. Dari hasil penelitian menunjukkan bahwa data tahun 2011 sampai Oktober 2014 bisa digunakan untuk memprediksi harga saham bulan November 2014. Hasilnya model ARIMA untuk harga saham maksimum adalah ARIMA (2,1,3) dan harga saham minimum adalah model (2,1,3) yang dapat digunakan untuk memprediksi data bulan November 2014 dengan validasi prediksi yang diambil pada bulan Oktober 2014 untuk selanjutnya dilakukan prediksi bulan November 2014. Kata Kunci: Metode ARIMA, PT. BRI, Tbk., Saham THE PREDICTION STOCK PRICE OF PT. BRI, Tbk. USE ARIMA METHOD (Autoregressive Integrated Moving Average) ABSTRACT ARIMA method is one of the method that used to prediction the change of stock price. The purpose of this research is to make model of ARIMA and predict stock price of PT. BRI, Tbk. in November 2014. The research use maximum and minimum data of stock price daily of PT. BRI, Tbk. Data are used is secondary data that taking from website of PT. BRI, Tbk. since January 3rd 2011 until October 20th 2014 to predict stock price in November 2014. From this research show that data from 2011 until October 2014 can be used to predict the stock price in November 2014. The result of ARIMA’s model for the maximum stock price is ARIMA (2,1,3) and the minimum stock price is (2,1,3) can use to predict the data on November 2014 with predict validation that take on October 2014 and with that predict November 2014. Keywords: ARIMA method, PT. BRI, Tbk., Stock

scholarly journals Rainfall Forecast of Merauke Using Autoregressive Integrated Moving Average Model

2018 ◽  
Vol 73 ◽  
pp. 12010 ◽  
Author(s):  
Yenni P. Pasaribu ◽  
Hariani Fitrianti ◽  
Dessy Rizki Suryani
Keyword(s):  
Global Climate ◽  
Human Life ◽  
Moving Average ◽  
Arima Model ◽  
Secondary Data ◽  
Crop Failure ◽  
Maximum Rainfall ◽  
Autoregressive Integrated Moving Average ◽  
Agriculture Sector ◽  
Rainfall Forecast

Climate is an important element for human life, one of them is to agriculture sector. Global climate change leads to increased frequency and extreme climatic intensity such as storms, floods, and droughts. Rainfall is climate factor that causes the failure of harvest in Merauke. Therefore, rainfall forecast information is very useful in anticipating the occurrence of extreme events that can lead to crop failure. The purpose of this research is to model rainfall using autoregressive integrated moving average (ARIMA) model. The ARIMA model can be used to predict future events using a set of past data, including predicting rainfall. This research was conducted by collecting secondary data from Agency of Meteorology, Climatology, and Geophysics (BMKG) from 2005 until 2017, then the data was analyzed using R.3.4.2. software. The analysis result showed that ARIMA model (2.0,2) as the right model to predict rainfall in Merauke. The result of forecasting based on ARIMA model (2.0,2) for one period ahead is 179 mm of average rainfall, 46 mm of minimum rainfall, and 295 mm of maximum rainfall. Thus it can be concluded that the intensity of rainfall in Merauke has decreased and there was a seasonal shift from the previous period.

scholarly journals PERAMALAN JUMLAH PENUMPANG PESAWAT TERBANG DI PINTU KEDATANGAN BANDAR UDARA INTERNASIONAL PATTIMURA AMBON DENGAN MENGGUNAKAN METODE ARIMA BOX-JENKINS

2019 ◽  
Vol 13 (3) ◽  
pp. 135-144
Author(s):  
Sasmita Hayoto ◽  
Yopi Andry Lesnussa ◽  
Henry W. M. Patty ◽  
Ronald John Djami
Keyword(s):  
Time Series ◽  
Model Selection ◽  
Time Series Data ◽  
Moving Average ◽  
Arima Model ◽  
Series Data ◽  
Autoregressive Integrated Moving Average ◽  
Significant Parameter ◽  
International Airport ◽  
Forecast Time

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.

scholarly journals FORECASTING ARRIVAL OF FOREIGN TOURISTS USING SEASONAL ARIMA BOX-JENKINS

2021 ◽  
Vol 15 (2) ◽  
pp. 223-230
Author(s):  
Nur Ilmayasinta
Keyword(s):  
Moving Average ◽  
Seasonal Pattern ◽  
Secondary Data ◽  
Autoregressive Integrated Moving Average ◽  
Tourism Sector ◽  
Existing Data

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.

scholarly journals Trend Analysis in Sugarcane Growth in Mumias Sugar Belt, Western Kenya; for the Period 1985-2015

2021 ◽  
Vol 3 (1) ◽  
pp. 31-40
Author(s):  
Protas Khaemba ◽  
PHILOMENA MUIRURI ◽  
THOMAS KIBUTU
Keyword(s):  
Trend Analysis ◽  
Management Practices ◽  
Moving Average ◽  
Secondary Data ◽  
Predictive Ability ◽  
Forecast Period ◽  
Western Kenya ◽  
Autoregressive Integrated Moving Average ◽  
Sugarcane Varieties ◽  
Good Agreement

The study was carried out to examine trends in the output and acreage in the Mumias Sugar belt from the period 1985-2015. We used secondary data collected from Mumais Sugar Company records for the period 1985-2015 for the study. The trend analysis of sugarcane production in the Mumias Sugar Belt is important, where sugarcane is the major cash crop and absorbs a majority of the agrarian population in the region. The study used the expert modeler, an autoregressive integrated moving average (ARIMA), to predict the output. The forecast period was 2016 through March 2021 and employed two scenarios: I) forecast with +2 harvesting age predictor modification and ii) forecast with +10 hectares predictor modification. The predicted value showed good agreement with the observed values from the series plot, indicating that the model has a good predictive ability. The application of the model revealed that the results in the prediction tables show that, in each of the six forecasted quarters, increasing the harvesting age by two months is expected to generate about 4.52 more tons of yields per hectare than increasing area harvested by 10 hectares that would decrease the yield by 0.01 tons per hectare. The study recommends research and development on sugarcane varieties that mature early, making sugarcane-based Agri- enterprises and sustainable. In addition, Mumias Sugar Company should seek profitable techniques to increase the recovery per cent, and farmers seek good management practices to increase the efficiency of the sugarcane farms in the sugar belt.

scholarly journals ANALISA PERAMALAN WINTER’S EXPONENTIAL SMOOTHING BROWN DIBANDINGKAN DENGAN AUTOREGRESSIVE INTEGRATED MOVING AVERAGE PADA DATA JUMLAH KEJADIAN HIPERTENSI

2019 ◽  
Vol 4 (1) ◽  
pp. 1-10
Author(s):  
Wisoedhanie Widi .A ◽  
Nanik Dwi A.
Keyword(s):  
Community Health Centers ◽  
Moving Average ◽  
Secondary Data ◽  
Exponential Smoothing ◽  
Smoothing Method ◽  
Preventive Action ◽  
Autoregressive Integrated Moving Average ◽  
Planning And Control ◽  
Exponential Smoothing Method ◽  
And Control

In an attempt to see and examine the situation and conditions that occur in the future to do forecasting (forecasting). Hypertension is a major disease in the ten Clinics Together and almost every month new hypertension cases occur, so the incidence of hypertension is becoming the trend and forecasting needs to be done. The purpose of this research is to do forecasting on the data the number of incident hypertension in Clinics With the city of Malang with Exponential Smoothing method using winter's Brown compared to Autoregressive Integrated Moving Arima. This type of research is the study of non reactive (non reactive research) which is a type of secondary data for research.Unit samples in this research are patients who come for the medication and patients in Clinics With hypertension Malang. in 2013 to 2016. Research data using Minitab software. The results of this study showed that both methods of forecasting results shows that tend to decrease in the year 2018 with the lowest incidence in December that as many as 58 incidents on Exponential Smoothing method of winter's and some 80 events on the method of Autoregressive Integrated Moving Average. The existence of a trend of decrease in the incidence of hypertension can be supported by the growing health services at community health centers With has been doing various efforts in preventive action, promotif and collaborative in the handling of problems Hypertension.Through these research results, it is advisable to draw up a health center party planning and control and eradication programs work for transmission of diseases of hypertension (P2P) with reference to the results of the forecasting incidence of hypertension in the year 2018.

scholarly journals Peramalan Harga Bitcoin Menggunakan Metode ARIMA (Autoregressive Integrated Moving Average)

2018 ◽  
Vol 1 (1) ◽  
pp. 21-31
Author(s):  
Nany Salwa ◽  
Nidya Tatsara ◽  
Ridha Amalia ◽  
Aja Fatimah Zohra
Keyword(s):  
Time Series Data ◽  
Moving Average ◽  
Arima Model ◽  
Secondary Data ◽  
Series Data ◽  
Forecasting Model ◽  
Virtual Currency ◽  
Autoregressive Integrated Moving Average ◽  
The Mean ◽  
Level 2

ABSTRAK. Bitcoin merupakan mata uang virtual yang saat ini banyak diminati sebagai alternatif investasi. Metode ARIMA adalah salah satu metode yang digunakan untuk peramalan data deret waktu. Tujuan dari penelitian ini adalah untuk membuat model dan meramalkan harga bitcoin.  Data yang digunakan adalah data sekunder yaitu berupa data harga bitcoin selama 60 periode mulai dari tanggal 10 Januari 2018 sampai dengan 10 Maret 2018 untuk memprediksikan harga bitcoinselama 30 periode kedepan mulai tanggal 11 Maret 2018 sampai dengan 09 April 2018. Dari hasil penelitian menunjukkan bahwa data harga bitcoin selama 60 periode tidak memenuhi asumsi stasioneritas terhadap rata-rata untuk itu dilakukan proses differencing tingkat 2 agar data menjadi stasioner. Model ARIMA yang dihasilkan adalah ARIMA(0,2,1) yaitu  Zt = μ - 0,9647Zt-1 + at dan model tersebut cocok digunakan untuk peramalan data harga bitcoin. Hasil peramalan dengan menggunakan model ARIMA(0,2,1) menunjukkan bahwa harga bitcoin untuk 30 periode kedepannya mengalami penurunan secara perlahan dan hasil peramalan mendekati data sebenarnya. ABSTRACT. Bitcoin is a virtual currency that is currently much interested as an alternative investment. ARIMA method is one of the methods used for forecasting time series data. The purpose of this research is to create a model and predicted the price of the bitcoin.  The data used are secondary data that is in the form of price bitcoin during 60 periods starting from January 10, 2018 up to 10 March 2018 to predict price bitcoin for 30 the next periods began March 11 and ended on 9 April 2018 2018. Based on the results of the study showed that the price of bitcoin during 60 periods did not fullfiled the assumptions of stasioneritas towards the mean. Therefore using the differencing level 2 process, so the data becomes stationary. The result of ARIMA model is ARIMA(0, 2, 1) Zt = μ - 0,9647Zt-1 + at and the model fits the data used for forecasting price bitcoin. The results of the forecasting model using ARIMA (0, 2, 1) shows that the price of the bitcoin for 30 periods has decreased gradually and forecasting results close to the actual data.

scholarly journals EPIDEMIOLOGY FORECASTING ANALYSIS OF DENGUE HAEMORRAGHIC FEVER WITH SEASONAL AUTOREGRESSIVE INTEGRATED MOVING AVERAGE IN TROPICAL AREA

2019 ◽  
Vol 5 (2) ◽  
pp. 38-47 ◽  
Author(s):  
Siswanto Siswanto ◽  
Risva Risva ◽  
Nana Marliana
Keyword(s):  
Infectious Diseases ◽  
Health Center ◽  
Dengue Hemorrhagic Fever ◽  
Moving Average ◽  
Secondary Data ◽  
Hemorrhagic Fever ◽  
Autoregressive Integrated Moving Average ◽  
Descriptive Research ◽  
Tropical Area ◽  
Forecasting Analysis

Background: Health problems that often occur in tropical countries are infectious diseases, one of which often causes outbreaks was Dengue Hemorrhagic Fever (DHF). This disease often causes problems especially in endemic areas and even outbreaks that occur with death from sufferers.Objectives: To forecasting of the Dengue Hemorrhagic Fever in the working area of the Puskesmas Temindung. Methods: This was analytical descriptive research with forecasting design using secondary data and primary from informant who understand the problem. Forecasting using SARIMA method (Seasonal Autoregressive Integrated Moving Average).Results: The results showed that the total of DHF cases in Temindung Health Center could be predicted by the SARIMA (1,1,1) (1,0,0) model with means square error (MSE) of 0.001394688 forecasting results obtained from October 2018 to September 2019 cases, which tend to fluctuate but illustrates an increase in cases of DHF compared to the previous year's data. Conclusion: Forecast of the DHF is for the next 12 months starting from October 2018 as many as 7 cases, in November 4 cases, in December 4 cases; then starting in January 2019 as many as 3 cases, February 2 cases, March 3 cases, April 3 cases, May 3 cases, June 4 cases, July 3 cases, August 3 cases and September 3 cases with a total number of 42. Forecasting results show dengue cases tend to fluctuate every month but have increased cases from the previous year. 

scholarly journals Forecasting and Effectiveness Analysis of Domestic Airplane Passengers in Yogyakarta Adisutjipto Airport with Autoregressive Integrated Moving Average with Exogeneous (ARIMAX) Model

2020 ◽  
Vol 3 ◽  
pp. 365-369
Author(s):  
Sarah Khairunnisa ◽  
Nusyrotus Sa’dah ◽  
Isnani ◽  
Rohmah Artika ◽  
Prihantini
Keyword(s):  
Moving Average ◽  
Long Distance ◽  
Autoregressive Integrated Moving Average ◽  
The Public ◽  
International Airport ◽  
Effectiveness Analysis ◽  
Better Than

Airplane is one of the public transportations options that many people choose when traveling long distance. Nowadays, it is notes that the number of passengers domestic flight has increased from the previous months. This increase, especially occurs on the holidays, such as year-end holidays, Eid, and others. The increase of airplane passengers is inversely proportional to the number of available airplane. Forecasting the number of airplane passangers is necessary to prepare additional facilities when there is increasing passengers. This research focused on forecasting domestic airplane passengers at Adisucipto Airport, Yogyakarta using ARIMAX method to forecast the number of domestic airplane passengers and the effectiveness of domestic passengers at the international airport. The purpose of this research is to determine the best ARIMAX model and forecast airplane passengers in Adisucipto airport. The results will show the effectiveness of ARIMAX model with the effect of calendar variance on domestic airplane passenger forecasting at international airport. Based on the result of AIC and RMSE values, it shows that the ARIMAX(1,0,1) model with calendar variation is better than ARIMA(1,0,1) in predicting the number of airplane passengers at Yogyakarta Adisutjipto airport.

scholarly journals Pemodelan dan prediksi jumlah penumpang pelabuhan bakauheni selama periode tsunami Selat Sunda menggunakan autoregressive integrated moving average

2020 ◽  
Vol 4 (1) ◽  
pp. 32
Author(s):  
Dani Al Mahkya ◽  
Dian Anggraini ◽  
Andi Fitriawati ◽  
Radot MH Siahaan
Keyword(s):  
Mathematical Modeling ◽  
Time Series ◽  
Visual Information ◽  
Time Series Data ◽  
Moving Average ◽  
Series Data ◽  
Percentage Error ◽  
Seasonal Patterns ◽  
Autoregressive Integrated Moving Average ◽  
Other Information

Bakauheni Port is a ferry port located in Bakauheni District, South Lampung. This port is one of the major ports located on Sumatera island connecting Sumatera and Java and is located in the Sunda Strait. The tsunami that occurred in the Sunda Strait on December 22, 2018 indirectly affected the sea crossing node, especially the Bakauheni-Merak route. This can lead to changes in time series data patterns. The phenomenon is expected to be captured through a mathematical modeling that can be used as a decision making in the future. The purpose of this study was to model and predict the number of Bakauheni port passengers during the Sunda Strait Tsunami period using the Autoregressive Integrated Moving Average (ARIMA). The ARIMA approach uses past information as a basis for modeling. Based on visual information on the number of Bakauheni Port passengers, there was an increase in December in general. Other information is that there are seasonal patterns that occur with a span of 7 days. This was indicated by the pattern of repeated increases in the number of passengers every Sunday. After the tsunami, the number of passengers decreased for 2 days. In the 3 days after the Tsunami or during the Christmas holiday on December 25, 2018, the number of passengers has increased again. Based on the analysis and discussion that has been done, the best time series model obtained is ARIMA([5],1,2)(2,1,0)7 with a Mean Absolute Percentage Error (MAPE) of 9.55%.

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