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All Journal Media Statistika Statistika CAUCHY: Jurnal Matematika Murni dan Aplikasi Jurnal Matematika: MANTIK BAREKENG: Jurnal Ilmu Matematika dan Terapan Jurnal Ilmiah Pendidikan dan Pembelajaran Jambura Journal of Mathematics Jurnal Ilmiah Matematika dan Pendidikan Matematika (JMP) Jurnal E-Komtek Jurnal Statistika dan Matematika (Statmat) Tematik : Jurnal Teknologi Informasi Komunikasi REKA ELKOMIKA: Jurnal Pengabdian kepada Masyarakat Indo-MathEdu Intellectuals Journal Formosa Journal of Science and Technology (FJST) Indonesian Journal of Contemporary Multidisciplinary Research Innovative: Journal Of Social Science Research Indonesian Journal of Advanced Research (IJAR) Euclid Asian Journal of Applied Education BEGIBUNG: Jurnal Penelitian Multidisiplin MARAS : Jurnal Penelitian Multidisplin Parameter: Jurnal Matematika, Statistika dan Terapannya Future Academia : The Journal of Multidisciplinary Research on Scientific and Advanced PRIMER: Jurnal Ilmiah Multidisiplin
Gumgum Darmawan
Departemen Statistika, Fakultas MIPA, Universitas Padjadjaran
Religion Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation Other

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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 Achmad Bachrudin Akbar, Muhammad Faizal Alamanda Putri, Fariza Aldi Anugerah Sitepu Alfarisi, Widi Wildani Alifia, Wanda Aliya Auliyazhafira, Shabira Amanah Dwiadi, Qurnia Angga Pratama Anindya Apriliyanti Pravitasari Apriliana, Linda Aribah, Rana Asrirawan Aurilia Pratiwi, Dhanti Azka Larissa Rahayu Bertho Tantular Budhi Handoko Budi Nurani Ruchjana Budianti, Laila Clarissa Clorinda, Chrysentia Dedi Rosadi Defi Yusti Faidah Deltha Airuzsh Lubis Dina Prariesa Eko Yulian eko yulian, eko Ery Sadewo, Ery Fajar Indrayatna Farhan Bagus Prakoso Ferdian Agustiana Fitriani Azuri, Dila Hadi, Juandi Haura, Zhafira Hirlan Khaeri I Gede Nyoman Mindra Jaya Indriani , Ayu Intan Nurma Yulita Ismatilah, Nuzila Janatin, Janatin Karin, Nabila Khaeri, Hirlan Kiki Amelia, Kiki Kusuma Putri, Aisha Muhamad Budiman Johra Muhammad Faizal Akbar Mulya Nurmansyah Ardisasmita Mulya, Callista Audrey Najwa, Sandrina Neneng Sunengsih Neneng Sunengsih Novianti Indah Putri Nurhapilah, Hani Nurul Gusriani Pian Widianingsih Puteri, Dian Islamiaty Putri Syallya, Najma Rafifah Putri, Salma Azzahra Rafidah, Raihanah Rahman Al Madan, Aulia Resa Septiani Pontoh Restu Arisanti Rhafi Ahdian, Muhammad Rina Sri Kalsum Siregar Rini Luciani Rahayu Rizal Amegia Saputra Ruchjana, Budi N Ruslan Ruslan Samaria Nauli, Theresia Sangrila, Ayu Sastradipraja, C K Setialaksana, Wirawan - Sitepu, Aldi Anugerah Sitohang, Yosep Oktavianus Sri Sutjiningtyas Sri Winarni Sri Yuliana Sudartianto, Sudartianto Talakua, Andrew Hosea Tri Wulanda Fitri Triyani Hendrawati Utami, Yosi Febria Widiantoro, Carissa Egytia Widodo, Valeno Glenedias Wildani Alfarisi, Widi Yasyfi Avicenna, Muhammad Yeny Krista Franty Yogo Aryo Jatmiko Yosep Oktavianus Sitohang Yunizar, Mahdayani Putri Yusep Suparman Yuyun Hidayat Zen Munawar Zulhanif Zulhanif