Peramalan Jumlah Penumpang Pesawat Di Bandara Internasional Ahmad Yani Dengan Metode Holt Winter’s Exponential Smoothing Dan Metode Exponential Emoothing Event Based

Sofiana, Sofiana; Suparti, Suparti; Hakim, Arief Rachman; Triutami, Iut

doi:10.14710/j.gauss.v9i4.29448

Cited by 8 publications

(11 citation statements)

References 3 publications

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“…Beberapa penelitian tentang time series forecasting memilih ARIMA (Autoregressive Integrated Moving Average) sebagai metode forecasting yang dikombinasikan atau dibandingkan keakuratannya dengan metode-metode lain seperti yang dilakukan oleh [4], [5], [6], dan [7]. Selain menggunakan ARIMA, exponential smoothing juga sering digunakan untuk peramalan seperti yang dilakukan oleh [8], [9] dan [10]. Namun, penelitian tentang peramalan atau prediksi masih menjadi bidang penelitian aktif karena belum ada metode yang dapat diterapkan secara universal pada semua kondisi peramalan dengan tingkat akurasi terbaik.…”

Section: Pendahuluanunclassified

Rancang Bangun Aplikasi Peramalan Jumlah Penumpang Menggunakan Long Short-Term Memory (LSTM)

Davi,

Winarko

2023

infotekmesin

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Public services such as public transportation are closely related to user satisfaction. Busway DKI Jakarta is one of the first public transportation services in Southeast and South Asia. In order to maintain passenger satisfaction, the management of Busway continues to improve services such as adding buses and opening a new line. Opening a new lane or adding buses must necessarily be adjusted also with the increasing number of passengers. So to know the number of passengers in the future, it is necessary to forecast the number of passengers through existing historical data. The historical data used is time series data from January 2015 to January 2016. The method used in forecasting is Long Short-Term Memory (LSTM), one of the machine learning methods. The method is measured in accuracy using Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE). As a comparison of LSTM accuracy in forecasting, we also use the Exponential Smoothing method. Based on the results of forecasting, the most and least dominant method of producing RMSE and MAPE is the LSTM method. Only in corridor 3 LSTM can not provide RMSE and MAPE values below baseline values. While corridor 5 LSTM can give better results after the data transformation process by using exponential smoothing. However, overall the LSTM method provides the best accuracy based on the lowest average RMSE and MAPE values, namely RMSE of 2640.53 and MAPE of 9.14%.

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Section: Pendahuluanunclassified

Rancang Bangun Aplikasi Peramalan Jumlah Penumpang Menggunakan Long Short-Term Memory (LSTM)

Davi,

Winarko

2023

infotekmesin

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“…Perbandingan dengan sebelumnya pada penelitian Sofiana, Exponential Smoothing digunakan untuk peramalan penambahan jumlah penumpang pesawat. Peramalan ini ditujukan juga untuk perbandingan dengan menggunakan Holt Winter's dengan Event Base penelitian ini menghasilkan bahwa peningkatan terjadi sejak 2013 sampai 2019 dengan pengujian algoritma Exponential Smoothing [16]. Penelitian lain dari Aris Purwanto mengenai sistem peramalan produksi jagung dimana mengguankan exponential smoothing untuk menguji pola trend dan pengujian pemulusan dengan parameter 0,4 dengan menghasilkan eror pada MAPE lebih kecil yaitu 9,38% [17].…”

Section: Hasil Dan Pembahasanunclassified

Penerapan Metode Single Exponential Smoothing Untuk Analisa Peramalan Penjualan

Santoso¹,

Rumetna

Isnaningtyas³

2021

mib

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Pharmacies are part of the distribution category of drug sales and purchases. The demand for drugs becomes a focal point in the distribution of customers to drugs. Employees act to prepare and record drug purchase orders for the following month. The activity of sorting the available and unavailability of drug supply data often takes a long time. Exponential smoothing method is a method of forecasting moving averages by providing easy-to-analyze weighting. Time series sales data with Exponential Smoothing forecasting method is expected to be able to handle optimal inventory for controlling drug stock inventory. The use of data for the last 6 months as reference data for recording the past for forecasting experiments for the next 3 months. Experiments with different Alpha tests for accuracy in real life. The results of the experiment with alpha 0.3 are close to the real value in forecasting the next 3 months.

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“…Persaingan antar maskapai semakin terlihat untuk menarik minat masyarakat agar menggunakan jasa mereka, yang dalam hal ini akan berpengaruh pada tingkat hitungan Seat Load Factor (SLF). Bagi maskapai, perhitungan ini juga akan berguna sebagai bahan pertimbangan untuk pengajuan extra flight sehingga ketika permintaan tiket meningkat dapat memaksimalkan keuntungan di momen tersebut (Sofiana et al, 2020).…”

Section: Pendahuluanunclassified

Kajian Dugaan Praktik Kartel PT Garuda Indonesia, Tbk Mempengaruhi Pergerakan Harga Saham di BEI

Widiarma¹,

Syahrenny

2021

JIM

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The purpose of this study was to determine the effect of the alleged cartel practices carried out by the airline PT Garuda Indonesia, Tbk, namely by the increasingly high price of airline tickets and how their impact on the stock price movement itself. This practice is not permitted in Indonesia and almost all countries in the world, but this becomes very dilemmatic in the current aviation business if the ticket price is not raised the company will bear substantial losses but on the other hand consumers are very disadvantaged and can sacrifice the country's economy if countries that depend heavily on air transportation such as Indonesia. Considering that PT Garuda Indonesia, Tbk is a state-owned company of the Republic of Indonesia which has been listed on the Indonesia Stock Exchange under the GIAA stock code, whether news of alleged cartel practices by GIAA could affect the public investors in the capital market in making a decision to make a selling or buying transaction on GIAA shares.Keywords: cartel practices, garuda indonesia, capital markets

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Peramalan Jumlah Penumpang Pesawat Di Bandara Internasional Ahmad Yani Dengan Metode Holt Winter’s Exponential Smoothing Dan Metode Exponential Emoothing Event Based

Cited by 8 publications

References 3 publications

Rancang Bangun Aplikasi Peramalan Jumlah Penumpang Menggunakan Long Short-Term Memory (LSTM)

Rancang Bangun Aplikasi Peramalan Jumlah Penumpang Menggunakan Long Short-Term Memory (LSTM)

Penerapan Metode Single Exponential Smoothing Untuk Analisa Peramalan Penjualan

Kajian Dugaan Praktik Kartel PT Garuda Indonesia, Tbk Mempengaruhi Pergerakan Harga Saham di BEI

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