Implementasi Metode Exponential Smoothing  Untuk Peramalan Kedatangan Wisatawan Mancanegara Pada Pulau Bali

Sulthan, Nafis; Eltivia, Nurafni; Riwajanti, Nur Indah

doi:10.29062/mahardika.v18i2.145

Cited by 5 publications

(4 citation statements)

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“…Metode kedua yang digunakan merupakan Exponential Smoothing metode peramalan rata-rata bergerak yang mana data diberi bobot sesuai dengan fungsi expotential [12]. Teknik ini dapat menghilangkan perubahan yang tidak disengaja dalam deret waktu, memperlihatkan metode dengan tren data terbaru, dan bekerja secara akurat dalam memahami prediksi jangka pendek.…”

Section: Metode Exponential Smoothingunclassified

Prediksi Jumlah Penerimaan Mahasiswa Baru Dengan Metode Linear Regression dan Exponential Smoothing

Luthfi Mawardi,

Umar Alfaruq,

Gandung Triyono

2024

ijcs

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Penerimaan mahasiswa baru adalah elemen kunci dalam manajemen perguruan tinggi yang memerlukan perencanaan yang cermat serta kebijakan dan strategi untuk mengambil keputusan kegiatan belajar mengajar. Untuk merencanakan prediksi jumlah penerimaan mahasiswa baru yang akurat adalah penting dalam pengambilan keputusan terkait alokasi sumber daya, perencanaan fasilitas, dan program akademik. Oleh karena itu, tujuan dari penelitian ini adalah untuk memprediksi jumlah mahasiswa baru di Fakultas Teknologi Industri dan Informatika UHAMKA menggunakan metode Linier Regression dan Exponential Smoothing. Metode ini digunakan untuk menangani fluktuasi dalam data historis yang mungkin disebabkan oleh perubahan demografi, persaingan antar institusi, dan faktor ekonomi lainnya. Penelitian Ini menggunakan metode data mining untuk melakukan prediksi jumlah penerimaan mahaiswa baru pada Program Studi Teknik Informatika Fakultas Teknologi Industri dan Informatika UHAMKA. Sumber data penelitian ini adalah jumlah calon mahasiswa baru di 15 Tahun ke belakang yaitu data 2008 sampai 2023. dengan menggunakan 2 metode yaitu linear regression dan exponential smoothing. Prediksi jumlah penerimaan mahasiswa baru menunjukkan nilai prediksi yang sangat baik yaitu antara 0-10%. Nilai MAPE pada prediksi adalah 0,99% dengan Regresi Linear dan 5,07% ketika menggunakan Exponential Smoothing. Dalam pengujian bahwa Linear Regression menghasilkan nilai MAPE lebih kecil daripada metode Exponential Smoothing, yang berarti metode hasil prediksinya lebih mendekati dengan jumlah penerimaan mahasiswa baru yang sebenarnya.

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Section: Metode Exponential Smoothingunclassified

Prediksi Jumlah Penerimaan Mahasiswa Baru Dengan Metode Linear Regression dan Exponential Smoothing

Luthfi Mawardi,

Umar Alfaruq,

Gandung Triyono

2024

ijcs

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“…Metode exponential smoothing merupakan metode peramalan rata-rata bergerak yang mana data diberi bobot sesuai dengan fungsi exponential [13]. Metode ini memfokuskan pada suatu objek pengamatan sebelumnya.…”

Section: Metode Exponential Smoothingunclassified

Perbandingan Metode Linear Regression dan Exponential Smoothing Dalam Peramalan Penerimaan Mahasiswa Baru

Apriliza

Darmansah²,

Oktavyani³

et al. 2022

Jur. Ris. Kom.

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In improving the quality of higher education, the Telkom Purwokerto Institute of Technology needs to plan and make decisions about what strategies and policies can support teaching and learning activities or other activities within the institution. To plan these strategies and policies, the institution needs reference data such as data on predictions of the number of students that will be accepted in the next five years. With this predictive data, the institution can also consider various things that must be improved and plan a better marketing strategy so that new student admissions in the following year can increase. This study aims to predict the number of students who will be accepted at the Telkom Purwokerto Institute of Technology in the next five years. The overall comparison of the linear regression and exponential smoothing methods is carried out to test the accuracy of the forecasting results. The forecasting accuracy used is MAE, MSE, and MAPE. Forecasting accuracy testing was carried out using the linear regression method with the MAE, MSE, and MAPE values of 115.28 each; 15238.46; and 0.1216054793, and the exponential smoothing method with = 0.1 has the value of the results of the MAE, MSE, and MAPE forecasting accuracy tests of 327.2938; 137036,2639; and 0.2875524468. The best method for forecasting new student admissions at the Telkom Purwokerto Institute of Technology. namely the linear regression method because it has the smallest MAE, MSE, and MAPE forecasting accuracy values. The results of forecasting new student admissions at the Telkom Purwokerto Institute of Technology for the next 5 years, from 2022 to 2026, respectively, are 1369.7; 1479.4; 1589.1;1698,8; and 1808.5.

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“…Previous research on forecasting the composite stock price index has been carried out using the ARIMA time series modeling method [5]. The difference between this study and previous research is that in this study the calculations were carried out by maximizing the existing facilities in Microsoft Excel using the exponential smoothing method [6,7].…”

Section: Introductionmentioning

confidence: 98%

The Impact of Covid-19 Pandemic on the Forecasting Indonesian Composite Stock Price Index

Aziza¹,

Eltivia²,

Riwajanti³

2021

Proceedings of 2nd Annual Management, Business and Economic Conference (AMBEC 2020)

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This study aims to obtain the Composite Stock Price Index's closing price forecast from January to October 2021. This research method uses the single exponential smoothing method. The sample used in this study is the composite stock price index at the closing price for all sectors and all companies on the Indonesia Stock Exchange on a week scale from October 2015 to October 2020. The forecasting results show that the Composite Stock Price Index will have a value expected to return to normal. The period before the Covid-19 pandemic. This study provides forecasting results based only on the combined stock price index data for five periods with Microsoft Excel calculations using predetermined formulas. Forecasting results show that in 2021 the stock price index will reach normal conditions like it was before the Covid-19 pandemic. Investors can use forecasting information on this composite stock index to determine the action of selling or buying shares because the composite price index reflects the performance conditions of all companies listed on the Indonesia Stock Exchange and shows the country's economic needs. Suggestions for further research are to be able to predict the combined price index of each sector.

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Implementasi Metode Exponential Smoothing Untuk Peramalan Kedatangan Wisatawan Mancanegara Pada Pulau Bali

Cited by 5 publications

References 0 publications

Prediksi Jumlah Penerimaan Mahasiswa Baru Dengan Metode Linear Regression dan Exponential Smoothing

Prediksi Jumlah Penerimaan Mahasiswa Baru Dengan Metode Linear Regression dan Exponential Smoothing

Perbandingan Metode Linear Regression dan Exponential Smoothing Dalam Peramalan Penerimaan Mahasiswa Baru

The Impact of Covid-19 Pandemic on the Forecasting Indonesian Composite Stock Price Index

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