Article Per Year (5 Year)
p-Index From 2019 - 2024
1.066
P-Index
This Author published in this journals
All Journal Techno.Com: Jurnal Teknologi Informasi Al-Jabar : Jurnal Pendidikan Matematika Desimal: Jurnal Matematika Jambura Geoscience Review InPrime: Indonesian Journal Of Pure And Applied Mathematics Milang Journal of Mathematics and Its Applications
Mohamad Khoirun Najib
IPB University
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Education Engineering Mathematics Social Sciences

Published : 6 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 6 Documents
Search

 1 
Statistical bias correction on the climate model for el nino index prediction Sri Nurdiati; Ardhasena Sopaheluwakan; Yoga Abdi Pratama; Mohamad Khoirun Najib
Al-Jabar: Jurnal Pendidikan Matematika Vol 12, No 2 (2021): Al-Jabar: Jurnal Pendidikan Matematika
Publisher : Universitas Islam Raden Intan Lampung, INDONESIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (812.395 KB) | DOI: 10.24042/ajpm.v12i2.8884

Abstract

El Nino can harm many sectors in Indonesia by reducing precipitation levels in some areas. The occurrence of El Nino can be estimated by observing the sea surface temperature in Nino 3.4 region. Therefore, an accurate model on sea surface temperature prediction in Nino 3.4 region is needed to optimize the estimation of the occurrence of El Nino, such as ECMWF. However, the prediction model released by ECMWF still consists of some systematic errors or biases. This research aims to correct these biases using statistical bias correction techniques and evaluate the prediction model before and after correction. The statistical bias correction uses linear scaling, variance scaling, and distribution mapping techniques. The results show that statistical bias correction can reduce the prediction model bias. Also, the distribution mapping and variance scaling are more accurate than the linear scaling technique. Distribution mapping has better RMSE in December-March, and variance scaling has better RMSE in April-June also in October and November. However, in July-September, prediction from ECMWF has better RMSE. The application of statistical bias correction techniques has the highest refinement in January-March at the first lead time and in April at the fifth until the seventh lead time. 
Heterogeneous Correlation Map Between Estimated ENSO And IOD From ERA5 And Hotspot In Indonesia Sri Nurdiati; Fahren Bukhari; Muhammad Tito Julianto; Mohamad Khoirun Najib; Nuzhatun Nazria
Jambura Geoscience Review Vol 3, No 2 (2021): Jambura Geoscience Review (JGEOSREV)
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34312/jgeosrev.v3i2.10443

Abstract

El Nino-Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) can reduce the amount of rainfall in Indonesia. The previous study found that ENSO and IOD derived from the OISST dataset have an association with hotspots in Indonesia, especially in southern Sumatra dan Kalimantan. But the correlation results are still too small, and the correlation strength between regions has not been analyzed. Therefore, this study quantifies the association of the estimated ENSO and IOD derived from the ERA5 dataset on hotspots in Indonesia based on a Heterogeneous Correlation Map (HCM) and analyzes the correlation strength between regions in Indonesia. We use a singular value decomposition method to quantify this HCM. Besides OISST, ERA5 is an estimation data often used for weather forecast analysis. Therefore, this study quantifies the association of the estimated ENSO and IOD derived from the ERA5 dataset on hotspots in Indonesia based on a Heterogeneous Correlation Map (HCM) and analyzes the correlation strength between regions in Indonesia. Based on variance explained and correlation strength, the hotspot in Indonesia is more sensitive to ENSO and IOD derived from ERA5 than OISST. Consequently, the ERA5 data more useful to statistical analysis that requiring a substantial correlation.
Copula in Wildfire Analysis: A Systematic Literature Review Mohamad Khoirun Najib; Sri Nurdiati; Ardhasena Sopaheluwakan
InPrime: Indonesian Journal of Pure and Applied Mathematics Vol 3, No 2 (2021)
Publisher : Department of Mathematics, Faculty of Sciences and Technology, UIN Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/inprime.v3i2.22131

Abstract

AbstractCopula model is a method that can be implemented in various study fields, including analyzing wildfires. The copula distribution function gives a simple way to define joint distribution between two or more random variables. This study aims to review the application of copula in the analysis of wildfires using a Systematic Literature Review (SLR) and provide insight into research opportunities related to the application in Indonesia. The results show there are very few articles using the copula model in the analysis of wildfires. However, the increasing number of article citations each year shows the importance of such article research and has contributed to wildfire analysis development. In that article, 50% of studies applied the copula model to direct wildfire analysis (using fire data) in Canada, Portugal, and the US. Meanwhile, the other 50% use the copula model for indirect wildfire analysis (not using fire data) in Canada and the European region. The outcome of the presented review will provide the latest research positions and future research opportunities on the application of copula in the analysis of wildfires in Indonesia.Keywords: copula; wildfire; systematic literature review. AbstrakModel copula merupakan metode yang dapat diimplementasikan pada berbagai bidang penelitian, salah satunya pada analisis kebakaran hutan. Fungsi sebaran copula memberikan cara yang mudah untuk mendefinisikan sebaran peluang bersama antara dua peubah acak atau lebih. Tujuan penelitian ini mengulas penerapan model copula tersebut pada analisis kebakaran hutan dalam studi literatur menggunakan Systematic Literature Review (SLR) serta memberikan peluang riset ke depan terkait implementasinya pada analisis kebakaran hutan di Indonesia. Hasil penelitian menunjukkan bahwa model copula pada analisis kebakaran hutan masih sangat sedikit. Namun, peningkatan jumlah sitasi artikel tiap tahun menunjukkan pentingnya penelitian tersebut dan memiliki kontribusi pada perkembangan analisis kebakaran hutan. Pada artikel tersebut, sebanyak 50% penelitian menerapkan model copula pada analisis kebakaran secara langsung (menggunakan data kebakaran) di Kanada, Portugal, dan Amerika. Sementara, sebanyak 50% lainnya menerapkan model copula pada analisis kebakaran secara tak langsung (tidak menggunakan data kebakaran), yaitu di Kanada dan kawasan Eropa. Hasil tinjauan memberikan posisi riset terkini serta usulan riset ke depan mengenai penerapan model copula untuk analisis kebakaran hutan dan lahan di Indonesia.Kata kunci: copula; kebakaran hutan; studi literatur sistematik. 
El nino index prediction model using quantile mapping approach on sea surface temperature data Sri Nurdiati; Elis Khatizah; Mohamad Khoirun Najib; Linda Leni Fatmawati
Desimal: Jurnal Matematika Vol 4, No 1 (2021): Desimal: Jurnal Matematika
Publisher : Universitas Islam Negeri Raden Intan Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1223.607 KB) | DOI: 10.24042/djm.v4i1.7595

Abstract

El Nino is a global climate phenomenon caused by the warming of sea surface temperatures in the eastern Pacific Ocean. El Nino has a powerful effect on the intensity of rainfall in several areas in Indonesia. El Nino impacts can be minimized by predicting the El Nino index from the sea surface temperature in the Nino 3.4 area. Therefore, many researchers have tried to predict sea surface temperature, and many prediction data are available, one of which is ECMWF. But, in reality, the ECMWF data still contains systematic errors or bias towards the observations. Consequently, El Nino predictions using ECMWF data are less accurate. For that reason, this study aims to correct the ECMWF data in the Nino 3.4 area using statistical bias correction with a quantile mapping approach. This method uses ECMWF data from 1983-2012 as training data and 2013-2018 as testing data. For this case, the results showed that 60% of El Nino's predictions on the testing data had improved the mean value. Also, all of El Nino's predictions on the testing data have improved the standard deviation value. Moreover, data testing's expected error can be corrected for all months in the 1st to 4th lead times. But, in the 5th to 7th lead times, only November-June can be corrected.
Perbandingan AlexNet dan VGG untuk Pengenalan Ekspresi Wajah pada Dataset Kelas Komputasi Lanjut Sri Nurdiati; Mohamad Khoirun Najib; Fahren Bukhari; Muhammad Reza Ardhana; Salsabilla Rahmah; Trianty Putri Blante
Techno.Com Vol 21, No 3 (2022): Agustus 2022
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/tc.v21i3.6373

Abstract

Pengenalan emosi memainkan peran penting dalam komunikasi yang dapat dikenali dari ekspresi wajah. Terdapat banyak metode yang dapat digunakan untuk mengenali ekspresi wajah secara automatis, seperti convolutional neural network (CNN). Penelitian ini bertujuan untuk mengimplementasikan dan membandingkan model CNN dengan arsitektur AlexNet dan VGG untuk pengenalan ekspresi wajah menggunakan bahasa pemrograman Julia. Model CNN akan digunakan untuk mengklasifikasikan tiga ekspresi yang berbeda dari tujuh orang pengekspresi. Data diproses dengan beberapa teknik augmentasi data untuk mengatasi masalah keterbatasan data. Hasil penelitian menunjukkan bahwa ketiga arsitektur dapat mengklasifikasikan ekspresi wajah dengan sangat baik, yang ditunjukkan oleh nilai rata-rata akurasi pada data training dan testing yang lebih dari 94%. Untuk memenuhi nilai cross-entropy sebesar 0.1, arsitektur VGG-11 memerlukan jumlah epoch yang paling sedikit dibandingkan arsitektur lainnya, sedangkan arsitektur AlexNet memerlukan waktu komputasi yang paling sedikit. Waktu komputasi pada proses pelatihan sebanding dengan jumlah parameter yang terkandung pada model CNN. Akan tetapi, jumlah epoch yang sedikit belum tentu membutuhkan waktu komputasi yang sedikit. Selain itu, nilai recall, presisi, dan F1-score untuk masalah klasifikasi multi-class menunjukkan hasil yang baik, yaitu lebih dari 94%.
IMPLEMENTASI PENYELESAIAN PERSAMAAN BURGERS DENGAN METODE BEDA HINGGA DALAM BAHASA PEMROGRAMAN JULIA Fahren Bukhari; Sri Nurdiati; Mochamad Tito Julianto; Mohamad Khoirun Najib; Ruben Harry Valentdio
MILANG Journal of Mathematics and Its Applications Vol. 19 No. 1 (2023): MILANG Journal of Mathematics and Its Applications
Publisher : Dept. of Mathematics, IPB University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/milang.19.1.1-9

Abstract

Burgers equation is a partial differential equation used to modelling several events related to fluids. Burgers equation was firstly introduced by Harry Bateman in 1915 and later studied by Johannes Martinus Burgers in 1948. This study discusses solving Burgers equations with finite difference method. In this study, several parameters have been known for the Burgers equation and several cases of partitions are used in finite difference method. The result shows that the more partitions used, the numerical result obtained will be closer to the exact values. In this study, calculations are numerically carried out with the help of Julia programming language.
Co-Authors Ardhasena Sopaheluwakan Ardhasena Sopaheluwakan Elis Khatizah Fahren Bukhari Linda Leni Fatmawati Mochamad Tito Julianto Muhammad Reza Ardhana Muhammad Tito Julianto Nuzhatun Nazria Ruben Harry Valentdio Salsabilla Rahmah Sri Nurdiati Trianty Putri Blante Yoga Abdi Pratama