Article Per Year (5 Year)
p-Index From 2020 - 2025
1.434
P-Index
This Author published in this journals
All Journal Indo-MathEdu Intellectuals Journal Innovative: Journal Of Social Science Research
Darmawan , Gumgum
Unknown Affiliation
Humanities Computer Science & IT Law, Crime, Criminology & Criminal Justice Mathematics Public Health Other

Published : 9 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 4 Documents
Search
Journal : Indo-MathEdu Intellectuals Journal

 1 
Penerapan Metode Grey-Markov(1,1) Untuk Peramalan Penerimaan di Kantor Pengawasan dan Pelayanan Bea Cukai Tipe Madya Pabean Cikarang Mulya, Callista Audrey; Darmawan , Gumgum; Yusti Faidah, Defi; Ahdika, Atina
Indo-MathEdu Intellectuals Journal Vol. 4 No. 3 (2023): Indo-MathEdu Intellectuals Journal
Publisher : Lembaga Intelektual Muda (LIM) Maluku

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54373/imeij.v4i3.431

Abstract

The Customs Supervision and Service Office is given a revenue target that must be achieved annually. However, revenue at the Customs Supervision and Service Office tends to fluctuate because it is strongly influenced by various external factors that are difficult to predict. Projections need to be done to see if the given revenue target can be achieved. This study aims to conduct forecasting so that it can be estimated how much revenue will be at the end of the year (December 2023). Research is conducted using the Grey(1,1) and Grey-Markov(1,1) models. The analysis results show that the Grey-Markov(1,1) model provides better forecasting accuracy compared to the Grey(1,1) model with a MAPE value of 5.390541% and a Posterior Error Ratio of 0.190644. These results show that the Grey Markov(1,1) model is more accurate than the Markov(1,1) mode, and that this method (Grey Markov(1,1)) is very good for forecasting with little data.
Extreme Gradent Boosting Method Forecasting Rainfall in Lembang District, West Java Province Putri, Salma Azzahra; Darmawan , Gumgum; Arisanti, Restu; Clarissa Clorinda, Chrysentia
Indo-MathEdu Intellectuals Journal Vol. 4 No. 3 (2023): Indo-MathEdu Intellectuals Journal
Publisher : Lembaga Intelektual Muda (LIM) Maluku

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54373/imeij.v4i3.452

Abstract

Lembang is a notable regional tourism destination that bears considerable significance within the urban area of Bandung. Lembang is widely recognized for its flourishing agricultural sector, which supports a significant community of farmers engaged in the cultivation of fruits, vegetables, and ornamental plants, in addition to its intrinsic scenic beauty. Therefore, the acquisition of precipitation data is of considerable significance for individuals live in the area to maintain their economic endeavors. This study employs daily historical data from the period of 2018 to 2021, wherein approximately 70% of the data is categorized as sparse. This discourse aims to examine the utilization of the Extreme Gradient Boosting (XGboost) technique for predicting rainfall in the Lembang region, specifically emphasizing its effectiveness in handling limited data. The findings indicate that the model, when trained and tested using a 7:3 data split ratio, achieved a mean absolute error (MAE) of 1.834 for training and 4.473 for testing. Additionally, the root mean square error (RMSE) was calculated to be 3.319 for training and 7.637 for testing. The optimal hyperparameters consist of a learning rate of 0.005, a max_depth value of 10, and the utilization of 300 decision trees as n_estimators. The model effectively captures the pattern of sparse time series data and non-rainy days data, as evidenced by its low error metrics. However, it slightly underestimates the rainfall rate on the days with intense precipitation
Penerapan Metode Grey-Markov(1,1) Untuk Peramalan Penerimaan di Kantor Pengawasan dan Pelayanan Bea Cukai Tipe Madya Pabean Cikarang Mulya, Callista Audrey; Darmawan , Gumgum; Yusti Faidah, Defi; Ahdika, Atina
Indo-MathEdu Intellectuals Journal Vol. 4 No. 3 (2023): Indo-MathEdu Intellectuals Journal
Publisher : Lembaga Intelektual Muda (LIM) Maluku

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54373/imeij.v4i3.431

Abstract

The Customs Supervision and Service Office is given a revenue target that must be achieved annually. However, revenue at the Customs Supervision and Service Office tends to fluctuate because it is strongly influenced by various external factors that are difficult to predict. Projections need to be done to see if the given revenue target can be achieved. This study aims to conduct forecasting so that it can be estimated how much revenue will be at the end of the year (December 2023). Research is conducted using the Grey(1,1) and Grey-Markov(1,1) models. The analysis results show that the Grey-Markov(1,1) model provides better forecasting accuracy compared to the Grey(1,1) model with a MAPE value of 5.390541% and a Posterior Error Ratio of 0.190644. These results show that the Grey Markov(1,1) model is more accurate than the Markov(1,1) mode, and that this method (Grey Markov(1,1)) is very good for forecasting with little data.
Extreme Gradent Boosting Method Forecasting Rainfall in Lembang District, West Java Province Putri, Salma Azzahra; Darmawan , Gumgum; Arisanti, Restu; Clarissa Clorinda, Chrysentia
Indo-MathEdu Intellectuals Journal Vol. 4 No. 3 (2023): Indo-MathEdu Intellectuals Journal
Publisher : Lembaga Intelektual Muda (LIM) Maluku

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54373/imeij.v4i3.452

Abstract

Lembang is a notable regional tourism destination that bears considerable significance within the urban area of Bandung. Lembang is widely recognized for its flourishing agricultural sector, which supports a significant community of farmers engaged in the cultivation of fruits, vegetables, and ornamental plants, in addition to its intrinsic scenic beauty. Therefore, the acquisition of precipitation data is of considerable significance for individuals live in the area to maintain their economic endeavors. This study employs daily historical data from the period of 2018 to 2021, wherein approximately 70% of the data is categorized as sparse. This discourse aims to examine the utilization of the Extreme Gradient Boosting (XGboost) technique for predicting rainfall in the Lembang region, specifically emphasizing its effectiveness in handling limited data. The findings indicate that the model, when trained and tested using a 7:3 data split ratio, achieved a mean absolute error (MAE) of 1.834 for training and 4.473 for testing. Additionally, the root mean square error (RMSE) was calculated to be 3.319 for training and 7.637 for testing. The optimal hyperparameters consist of a learning rate of 0.005, a max_depth value of 10, and the utilization of 300 decision trees as n_estimators. The model effectively captures the pattern of sparse time series data and non-rainy days data, as evidenced by its low error metrics. However, it slightly underestimates the rainfall rate on the days with intense precipitation
Co-Authors Alamanda Putri, Fariza Alifia, Wanda Aliya Auliyazhafira, Shabira Amanah Dwiadi, Qurnia Arisanti, Restu Aurilia Pratiwi, Dhanti Budianti, Laila Clarissa Clorinda, Chrysentia Ery Sadewo, Ery Fitriani Azuri, Dila Haura, Zhafira Indriani , Ayu Ismatilah, Nuzila Janatin, Janatin Kusuma Putri, Aisha Mulya, Callista Audrey Najwa, Sandrina Nurhapilah, Hani Putri Syallya, Najma Rafifah Putri, Salma Azzahra Rahman Al Madan, Aulia Rhafi Ahdian, Muhammad Samaria Nauli, Theresia Sangrila, Ayu Wildani Alfarisi, Widi Yasyfi Avicenna, Muhammad Yusti Faidah, Defi