Garuda - Garba Rujukan Digital

Alfa Value Scalability on Single and Double Exponential Smoothing Comparatives Yuli Astuti; Irma Rofni Wulandari; Muhammad Noor Arridho; Erni Seniwati; Dina Maulina
IJISTECH (International Journal of Information System and Technology) Vol 5, No 4 (2021): December
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v5i4.165

To find out sales forecasts in the future, it is not only based on estimates but must be calculated carefully based on the experience of previous sales transactions. This observation can be made based on sales data a few months ago to be used as actual data to get predictive value in the future period. Prediction or forecasting is done with two methods Single Exponential Smoothing (SES) and Double Exponential Smoothing (DES), from these two methods, will be sought the most suitable alpha value to get the percentage error value. There are two error values : Mean Absolute Deviation (MAD) and Mean Absolute Percentage Error (MAPE). By using sales data from February to December 2019, the predicted value of 430 orders was obtained in the SES method and resulted in a sales prediction of 402 orders in the DES method with the smallest error accuracy value of 26.88% in the SES method and an accuracy value of 22.71%. in the DES method with the acquisition of scalability of the right alpha value for both, namely 0.3 and the beta value of 0.3 in the DES method

Comparison of Algorithms on Machine Learning For Spam Email Classification Hery Iswanto; Erni Seniwati; Yuli Astuti; Dina Maulina
IJISTECH (International Journal of Information System and Technology) Vol 5, No 4 (2021): December
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v5i4.164

The rapid development of email use and the convenience provided make email as the most frequently used means of communication. Along with its development, many parties are abusing the use of email as a means of advertising promotion, phishing and sending other unimportant emails. This information is called spam email. One of the efforts in overcoming the problem of spam emails is by filtering techniques based on the content of the email. In the first study related to the classification of spam emails, the Naïve Bayes method is the most commonly used method. Therefore, in this study researchers will add Random Forest and K-Nearest Neighbor (KNN) methods to make comparisons in order to find which methods have better accuracy in classifying spam emails. Based on the results of the trial, the application of Naïve bayes classification algorithm in the classification of spam emails resulted in accuracy of 83.5%, Random Forest 83.5% and KNN 82.75%

Alfa Value Scalability on Single and Double Exponential Smoothing Comparatives Yuli Astuti; Irma Rofni Wulandari; Muhammad Noor Arridho; Erni Seniwati; Dina Maulina
IJISTECH (International Journal of Information System and Technology) Vol 5, No 4 (2021): December
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v5i4.165

To find out sales forecasts in the future, it is not only based on estimates but must be calculated carefully based on the experience of previous sales transactions. This observation can be made based on sales data a few months ago to be used as actual data to get predictive value in the future period. Prediction or forecasting is done with two methods Single Exponential Smoothing (SES) and Double Exponential Smoothing (DES), from these two methods, will be sought the most suitable alpha value to get the percentage error value. There are two error values : Mean Absolute Deviation (MAD) and Mean Absolute Percentage Error (MAPE). By using sales data from February to December 2019, the predicted value of 430 orders was obtained in the SES method and resulted in a sales prediction of 402 orders in the DES method with the smallest error accuracy value of 26.88% in the SES method and an accuracy value of 22.71%. in the DES method with the acquisition of scalability of the right alpha value for both, namely 0.3 and the beta value of 0.3 in the DES method

Comparison of Algorithms on Machine Learning For Spam Email Classification Hery Iswanto; Erni Seniwati; Yuli Astuti; Dina Maulina
IJISTECH (International Journal of Information System and Technology) Vol 5, No 4 (2021): December
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

The rapid development of email use and the convenience provided make email as the most frequently used means of communication. Along with its development, many parties are abusing the use of email as a means of advertising promotion, phishing and sending other unimportant emails. This information is called spam email. One of the efforts in overcoming the problem of spam emails is by filtering techniques based on the content of the email. In the first study related to the classification of spam emails, the Naïve Bayes method is the most commonly used method. Therefore, in this study researchers will add Random Forest and K-Nearest Neighbor (KNN) methods to make comparisons in order to find which methods have better accuracy in classifying spam emails. Based on the results of the trial, the application of Naïve bayes classification algorithm in the classification of spam emails resulted in accuracy of 83.5%, Random Forest 83.5% and KNN 82.75%

Title

Found 4 Documents
Search

Abstract

Abstract

Abstract

Abstract

Title Search

Found 4 Documents Search

Abstract

Abstract

Abstract

Abstract

Title

Found 4 Documents
Search