scholarly journals PERBANDINGAN HASIL PERAMALAN JUMLAH WISATAWAN MANCANEGARA DENGAN METODE BOX-JENKINS DAN EXPONENTIAL SMOOTHING

2021 ◽  
Vol 2 (1) ◽  
pp. 1-13
Author(s):  
EMMA NOVITA SARI ◽  
BAMBANG SUSANTO ◽  
ADI SETIAWAN
Keyword(s):  
Mean Square Error ◽  
Original Data ◽  
Exponential Smoothing ◽  
Smoothing Method ◽  
Mean Square ◽  
Smoothing Methods ◽  
The Future ◽  
Exponential Smoothing Method

Forecasting the number of tourist visits is needed by tourism businesses to provide an overview of the number of tourists in the future so that problems that might occur can be overcome properly. This study aims to compare the results of forecasting the number of foreign tourists using the Box-Jenkins and Exponential Smoothing methods. There are two data used, namely data on the number of foreign visitors visiting Indonesia from January 2008 to December 2017 (Data I) and Bali according to the entrance of Ngurah Rai Airport from January 2009 to March 2020 (Data II). The best forecast results are obtained by comparing the Root of Mean Square Error (RMSE) values. The comparison of forecasting results in Data I shows that the Holt-Winters Exponential Smoothing method is more appropriate to predict the number of foreign tourists visiting Indonesia because it has a smaller RMSE value. While, the results of forecasting periods 2 and 3 in Data II show results that are far different from the original data. After tracking, it turns out this is caused by an unexpected factor, the Covid-19 pandemic which caused the number of tourists to drop significantly during this period.

scholarly journals PREDIKSI JUMLAH PENERIMAAN MAHASISWA BARU DENGAN METODE SINGLE EXPONENTIAL SMOOTHING (STUDI KASUS: AMIK ROYAL KISARAN)

JURTEKSI ◽  
2019 ◽  
Vol 5 (2) ◽  
pp. 125-132
Author(s):  
Wiwin Handoko
Keyword(s):  
Mean Square Error ◽  
Exponential Smoothing ◽  
Smoothing Method ◽  
Mean Square ◽  
Computer Engineering ◽  
Study Program ◽  
History Of ◽  
Data History ◽  
Exponential Smoothing Method ◽  
Single Exponential

Abstract: A problem requires a solution to solve it. One of them is by using Prediction (Forcasting). Prediction is used to assess the prediction of conditions in the future. at AMIK Royal Kisaran, when it comes to making lecture schedules often hampered because there is no estimated number of students. The data used in this study is the data history of the last 15 Academic Years, from 2003/2004 to 2017/2018. Then the data is processed with the Single Exponential Smoothing Method. Alpha value 0 <α <1. Single Exponential Smoothing makes a comparison with the alpha value until alpha is found which has the minimum error. To find the value of the error, the MSE (Mean Square Error) method is used. The results of the testing of this method are in the academic year 2018/2019 prediction of the number of students for the Informatics Management Study Program as many as 89 people and for Students for the Computer Engineering Study Program as many as 30 people. The Single Exponential Smoothing method can predict the number of students in the next period. Keywords: Prediction; Number of Students; Single Exponential Smoothing; Alpha Value; MSE Abstrak: Suatu masalah memerlukan sebuah solusi untuk menyelesaikannya. Salah satunya dengan menggunakan Prediksi (Forcasting). Prediksi digunakan untuk menilai prakiraan keadaan dimasa. di AMIK Royal Kisaran, ketika akan membuat jadwal kuliah sering terhambat karena tidak adanya perkiraan jumlah mahasiswa. Data yang digunakan pada penelitian ini adalah histori data 15 Tahun Akademik terakhir, mulai 2003/2004 sampai dengan 2017/2018. Kemudian data diolah dengan Metode Single Exponential Smoothing. Nilai alpha 0<α<1. Single Exponential Smoothing melakukan perbandingan dengan nilai alpha tersebut sampai ditemukan alpha yang memiliki error paling minimum. Untuk mencari nilai Error digunakan Metode MSE (Mean Square Error). Hasil dari pengujian terhadap metode ini adalah pada Tahun akademik 2018/2019 prediksi jumlah Mahasiswa untuk Program Studi Manajemen Informatika sebanyak 89 orang  dan untuk Mahasiswa untuk Program Studi Teknik Komputer sebanyak 30 orang. Metode Single Exponential Smoothing dapat membantu prediksi jumlah mahasiwa pada satu periode kedepan Kata kunci: Prediksi; Jumlah Mahasiswa; Single Exponential Smoothing; Nilai Alpha; MSE 

scholarly journals Peramalan Perencanaan Produksi Batu Tela Dengan Metode Exponential Smoothing (Studi Kasus Pada CV. Sinar Sowi Kabupaten Manokwari)

2019 ◽  
Vol 2 (2) ◽  
pp. 54-59
Author(s):  
Suwoko ◽  
Dirarini Sudarwadi ◽  
Nurwidianto
Keyword(s):  
Mean Square Error ◽  
Exponential Smoothing ◽  
Smoothing Method ◽  
Mean Absolute Deviation ◽  
Mean Square ◽  
Absolute Deviation ◽  
Forecasting Error ◽  
Alpha Models ◽  
The Mean ◽  
Exponential Smoothing Method

This study aims to find out how much forecasting the production of concrete brick at CV. Sinar Sowi. The data analysis method used is the Exponential Smoothing method by using forecasting error measurements namely Mean Square Error (MSE) and Mean Absolute Deviation (MAD). From the data that has been analyzed, the writer can conclude that the use of alpha model 0.1 Exponential Smoothing method, the value of the Exponential Smoothing method, the value of Mean Square Error is 11,114,950 and the value of Mean Absolute Deviation is 962. The use of alpha 0.5 model Exponential Smoothing method, the value of Mean Square Error is 1,114,776 and the value of Mean Absolute Deviation is 305. While the use of the alpha 0.9 model is Exponential Smoothing, the Mean Square Error value is -9.374 and the Mean Absolute Deviation value is -28. Of the three existing alpha models, namely 0.1; 0.5 and 0.9, then what will be used in forecasting is alpha 0.9 because the error value is the lowest, namely the Mean Square Error of -9,374 and Mean Absolute Deviation is -28. From the calculation of concrete brick forecasting at CV. Sinar Sowi in Manokwari Regency, the forecasting results were 39,698 units.

scholarly journals Sistem Informasi Peramalan Persediaan Barang Menggunakan Metode SES Dan DES

2019 ◽  
Vol 4 (1) ◽  
pp. 1-6
Author(s):  
Ratih Yulia Hayuningtyas
Keyword(s):  
Medical Equipment ◽  
Exponential Smoothing ◽  
Smoothing Method ◽  
Equal Weight ◽  
Mean Square ◽  
Double Exponential ◽  
Forecasting System ◽  
Double Exponential Smoothing ◽  
Exponential Smoothing Method ◽  
Single Exponential

Abstract: Sales is an activity in selling products that provide information about inventory. Arga Medical is a shop engaged in the sale of medical equipment, many of sales transactions in the Arga Medical will affect the inventory. Problems in the Arga Medical is predicting many of product that must available for the next month. Therefore this research makes inventory information forecasting system using Single Exponential Smoothing and Double Exponential Smoothing method. This inventory forecasting information system will result a inventory forecasting for next month. Single Exponential Smoothing Method gives equal weight to each data while Double Exponential Smoothing method is smoothing twice. The Data used in this research is the sales data during 2016. Both of these methods resulted inventory forecasting in the next month is Januari 2017 of 52 with Single Exponential Smoothing and 60 with Double Exponential Smoothing. Each method has a Mean Square Error value the smallest error value is the best method for forecasting inventory. Keywords: Forecasting, Inventory, Single Exponential Smoothing, Double Exponential Smoothing.

scholarly journals Prediction Of Drug Sales Using Methods Forecasting Double Exponential Smoothing (Case Study : Hospital Pharmacy of Condong Catur)

Telematika ◽  
2021 ◽  
Vol 18 (1) ◽  
pp. 106
Author(s):  
Annesa Maya Sabarina ◽  
Heru Cahya Rustamaji ◽  
Hidayatulah Himawan
Keyword(s):  
Exponential Smoothing ◽  
Smoothing Method ◽  
Test Results ◽  
Drug Sale ◽  
Smoothing Methods ◽  
Double Exponential ◽  
Double Exponential Smoothing ◽  
Exponential Smoothing Method ◽  
Study Hospital

Purpose: Knowing the best alpha value from the data for each type of drug with various alpha parameters in the Double Exponential Smoothing Method and knowing the prediction results on each type of drug data at the Condong Catur Hospital pharmacy.Design/methodology/approach: Applying the Double Exponential Smoothing method with alpha parameters 0.1; 0.2; 0.3; 0.4; 0.5; 0.6; 0.7; 0.8; 0.9Findings/result: The test results on a system built using test data show that the double exponential smoothing method provides accuracy below 20% by producing a different Alpha (α) for each type of drug because the trend patterns in each drug sale are different at the Pharmacy at the Condong Catur Hospital. .Originality/value/state of the art: Based on previous research, this study has similar characteristics such as themes, parameters and methods used. Previous researchers used smoothing methods such as Double Exponential Smoothing in predicting stock / sales of goods 

FORECASTING THE DEVELOPMENT OF SOCIALLY SIGNIFICANT DISEASES IN THE REGION BASED ON EXPONENTIAL SMOOTHING OF TIME SERIES

2020 ◽  
pp. 114-118
Author(s):  
Евгений Николаевич Коровин ◽  
Алина Николаевна Ненашева ◽  
Маргарита Анатольевна Сергеева
Keyword(s):  
Time Series ◽  
Mental Disorders ◽  
Action Plan ◽  
Exponential Smoothing ◽  
Smoothing Method ◽  
Malignant Neoplasms ◽  
General Increase ◽  
The Future ◽  
Future Situation ◽  
Exponential Smoothing Method

В данной статье рассматривается один из подходов прогнозирования и анализа развития социально значимых заболеваний в Тамбовской области с использованием метода экспоненциального сглаживания. Анализ социально значимых заболеваний является одной из важных задач, стоящих перед медициной. С помощью прогнозирования можно разработать план мероприятий по уменьшению уровня развития социально значимых заболеваний. Целью статьи является составления прогноза на 2020-2022 года на основе статистики заболеваемости в период с 2008 по 2019 год для изучения будущей ситуации по развитиюзаболеваний в Тамбовской области. В работе был использован метод экспоненциального сглаживания, так как данный метод является одним из распространённых в прогнозировании, дающий достаточно точный общий прогноз. Результаты прогнозирования были визуализированы в виде графиков и проанализированы. По итогу анализа было выявлено будущее понижение уровня заболеваемости туберкулезом и злокачественными новообразованиями, повышение уровня заболеваемости психическими расстройствами, а также выявлено общее повышение заболеваемости социально значимыми заболеваниями в Тамбовской области. Данное прогнозирование позволит медицинским организациям регионаразработать дальнейшие мероприятия по снижению заболеваемости с учетом динамики роста или уменьшения уровня развития социально значимых заболеваний This article discusses one of the approaches to predicting and analyzing the development of socially significant diseases in the Tambov region using the exponential smoothing method. The analysis of socially significant diseases is one of the important tasks facing medicine. With the help of forecasting, you can develop an action plan to reduce the level of development of socially significant diseases. The purpose of the article is to make a forecast for 2020-2022 based on morbidity statistics in the period from 2008 to 2019 to study the future situation in the development of diseases in the Tambov region. The method of exponential smoothing was used in the work, since this method is one of the most widespread in forecasting, which gives an accurate general forecast. The results of the forecast were visualized in the form of graphs and analyzed. The analysis revealed a future decrease in the incidence of tuberculosis and malignant neoplasms, an increase in the incidence of mental disorders, as well as a general increase in the incidence of socially significant diseases in the Tambov region. This forecasting will allow medical organizations in the region to develop further measures to reduce morbidity, taking into account the dynamics of growth or decrease in the level of development of socially significant diseases

PERAMALAN TINGKAT PENGANGGURAN DI INDONESIA MENGGUNAKAN METODE TIME SERIES DENGAN MODEL ARIMA DAN HOLT-WINTERS

2021 ◽  
Vol 26 (1) ◽  
pp. 13-28
Author(s):  
Agus Sulaiman ◽  
Asep Juarna
Keyword(s):  
Time Series ◽  
Mean Square Error ◽  
Moving Average ◽  
Exponential Smoothing ◽  
Mean Square ◽  
Autoregressive Integrated Moving Average ◽  
Model Time Series

Beberapa penyebab terjadinya pengangguran di Indonesia ialah, tingkat urbanisasi, tingkat industrialisasi, proporsi angkatan kerja SLTA dan upah minimum provinsi. Faktor-faktor tersebut turut serta mempengaruhi persentase data terkait tingkat pengangguran menjadi sedikit fluktuatif. Berdasarkan pergerakan persentase data tersebut, diperlukan sebuah prediksi untuk mengetahui persentase tingkat pengangguran di masa depan dengan menggunakan konsep peramalan. Pada penelitian ini, peneliti melakukan analisis peramalan time series menggunakan metode Box-Jenkins dengan model Autoregressive Integrated Moving Average (ARIMA) dan metode Exponential Smoothing dengan model Holt-Winters. Pada penelitian ini, peramalan dilakukan dengan menggunakan dataset tingkat pengangguran dari tahun 2005 hingga 2019 per 6 bulan antara Februari hingga Agustus. Peneliti akan melihat evaluasi Range Mean Square Error (RMSE) dan Mean Square Error (MSE) terkecil dari setiap model time series. Berdasarkan hasil penelitian, ARIMA(0,1,12) menjadi model yang terbaik untuk metode Box-Jenkins sedangkan Holt-Winters dengan alpha(mean) = 0.3 dan beta(trend) = 0.4 menjadi yang terbaik pada metode Exponential Smoothing. Pemilihan model terbaik dilanjutkan dengan perbandingan nilai akurasi RMSE dan MSE. Pada model ARIMA(0,1,12) nilai RMSE = 1.01 dan MSE = 1.0201, sedangkan model Holt-Winters menghasilkan nilai RMSE = 0.45 dan MSE = 0.2025. Berdasarkan data tersebut terpilih model Holt-Winters sebagai model terbaik untuk peramalan data tingkat pengangguran di Indonesia.

scholarly journals USULAN PERBAIKAN PERAMALAN PRODUKSI BAN PT. XYZ MELALUI PENDEKATAN METODE EXPONENTIAL SMOOTHING

2020 ◽  
Vol 5 (1) ◽  
pp. 13
Author(s):  
Diah Septiyana ◽  
Agung Bahtiar
Keyword(s):  
Mean Square Error ◽  
Exponential Smoothing ◽  
Mean Square

Ban merupakan salah satu bagian terpenting untuk industri otomotif, dimana perkembangan industry otomotif saat ini semakin meningkat sehingga kebutuhan atau pangsa pasar ban ikut meningkat. PT XYZ merupakan salah satu perusahaan di Tangerang yang bergerak dalam bidang manufaktur. Untuk penghematan biaya pengendalian dan proses produksi namun aktualnya sering kali penjualan tidak sesuai dengan apa yang diprediksi. Oleh sebab itu perlu dibuat peramalan penjualan yang lebih baik dan efisien agar penghematan biaya pengendalian dan proses produksi bisa tercapai. Penelitian ini bertujuan untuk menentukan jumlah peramalan penjualan produk ban tahun kedepan melalui exponential smoothing dan mengetahui nilai kesalahan dengan menggunakan MSE dan MAPE. Hasil penelitian menunjukkan hasil peramalan yang cukup mendekati antara aktual dengan peramalan meskipun terdapat peningkatan permintaan pada bulan Maret, Mei, Juni dan Agustus. Selisih yang cukup signifikan peningkatannya adalah pada bulan Agustus dengan nilai selisih 3 6% lebih tinggi dari peramalan. Nilai MAPE yang dihasilkan dari peramalan produksi ban rata-rata 11.19% dan Mean Square Error (MSE) terkecil sebesar 26,181,910 dengan rata-rata selama setahun sebesar 23,484,964,646.Kata Kunci: Peramalan, Ban Radial, Exponential Smoothing, MAPE, MSE

Sign in / Sign up

Export Citation Format

Share Document