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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Techno.Com: Jurnal Teknologi Informasi CAUCHY: Jurnal Matematika Murni dan Aplikasi Jurnal Sains Matematika dan Statistika Al-Jabar : Jurnal Pendidikan Matematika Jurnal Matematika: MANTIK Desimal: Jurnal Matematika BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) Zeta - Math Journal Teorema: Teori dan Riset Matematika Jambura Journal of Mathematics Jambura Geoscience Review Jurnal Matematika UNAND Sains, Aplikasi, Komputasi dan Teknologi Informasi InPrime: Indonesian Journal Of Pure And Applied Mathematics Jambura Journal of Biomathematics (JJBM) Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Milang Journal of Mathematics and Its Applications Jurnal Sintak Jurnal Matematika Integratif Al-Aqlu : Jurnal Matematika, Teknik dan Sains Indonesian Journal of Mathematics and Applications Jurnal Pendidikan Progresif Indonesian Journal of Mathematics and Natural Sciences MILANG Journal of Mathematics and Its Applications
Najib, Mohamad Khoirun
Department Of Mathematics, Faculty Of Science And Mathematics, IPB University, Jl. Meranti, Kampus IPB Dramaga, Bogor 16680
Aerospace Engineering Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

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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.
Life Expectancy Prediction Using Decision Tree, Random Forest, Gradient Boosting, and XGBoost Regressions Chairunisa, Ghevira; Najib, Mohamad Khoirun; Nurdiati, Sri; Imni, Salsabila F.; Sanjaya, Wardah; Andriani, Rizka D.; Henriyansah; Putri, Renda S. P.; Ekaputri, Dhea
JURNAL SINTAK Vol. 2 No. 2 (2024): Vol. 2 No. 2 (2024): MARET 2024
Publisher : LPPM-ITEBA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62375/jsintak.v2i2.249

Abstract

Angka harapan hidup menggambarkan rata-rata lamanya waktu seseorang hidup sejak lahir di dunia. Angka harapan hidup menjadi salah satu aspek dalam menentukan indeks pembangunan manusia. Semakin tinggi Angka harapan hidup maka akan semakin tinggi nilai IPM. Tujuan penelitian ini adalah memprediksi angka harapan hidup melalui model yang paling akurat dengan menggunakan model decision tree regression, random forest regression, gradient boosting regression, dan XGBoost regression, serta analisis variabel penjelas yang paling mempengaruhi angka harapan hidup. Data yang digunakan dalam penelitian ini adalah dataset Global Country Information Dataset 2023. Data diperoleh dari situs Kaggle. Berdasarkan analisis diperoleh bahwa model random forest regression menunjukkan kinerja yang lebih unggul dalam memprediksi hasil, yang ditunjukkan dengan nilai RMSE yang lebih rendah dan nilai R² yang lebih tinggi. Kematian bayi dan rasio kematian ibu secara konsisten diidentifikasi sebagai prediktor yang signifikan di semua model, sedangkan populasi merupakan prediktor yang kurang memprengaruhi angka harapan hidup.
Deteksi Penyakit Jantung Menggunakan Metode Klasifikasi Decision Tree dan Regresi Logistik Bukhari, Fahren; Nurdiati, Sri -; Najib, Mohamad Khoirun; Amalia, Rizki Nurul
Sains, Aplikasi, Komputasi dan Teknologi Informasi Vol 5, No 1 (2023): Sains, Aplikasi, Komputasi dan Teknologi Informasi
Publisher : Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/jsakti.v5i1.10780

Abstract

Penyakit jantung merupakan salah satu penyakit paling umum dan kritis yang membahayakan kehidupan manusia. Selain diagnosis klinis, pembelajaran mesin dan pendekatan berbasis pembelajaran mendalam sangat penting dalam diagnosis penyakit jantung, seperti decision tree dan regresi logistik. Penelitian ini bertujuan membandingkan kedua metode klasifikasi tersebut untuk mendeteksi adanya penyakit jantung berdasarkan beberapa indikator. Data yang digunakan adalah data penyakit jantung yang dikeluarkan oleh University of California, Irvine (UCI) Machine Learning Repository.  Berdasarkan hasil yang diperoleh, model decision tree yang terbentuk menempatkan variabel thal (tipe detak jantung pasien) sebagai simpul akar, dikarenakan nilai entropy yang paling tinggi. Model decision tree memiliki akurasi terhadap data uji sebesar 75%. Sementara itu, model regresi logistik menempatkan variabel sex, cp_3, slope_1, ca, dan thal_2 sebagai variabel-variabel yang berpengaruh nyata. Model regresi logistik memiliki akurasi terhadap data uji sebesar 87%. Dari akurasi dari kedua model tersebut, regresi logistik lebih akurat untuk mendeteksi adanya penyakit jantung dibandingkan model decision tree.
Sensitivity and feature importance of climate factors for predicting fire hotspots using machine learning methods Hasafah Nugrahani, Endar; Nurdiati, Sri; Bukhari, Fahren; Khoirun Najib, Mohamad; Muliawan Sebastian, Denny; Nur Fallahi, Putri Afia
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp2212-2225

Abstract

Every year, Indonesia experiences a national crisis due to forest fires because the resulting impacts and losses are enormous. Hotspots as indicators of forest fires capable of quickly monitoring large areas are often predicted using various machine learning methods. However, there is still few research that analyzes the sensitivity and feature importance of each predictor that forms a machine learning prediction model. This study evaluates and compares machine learning methods to predict hotspots in Kalimantan based on local and global climate factors in 2001-2020. Using the most accurate machine learning model, each climate factor used as a predictor is analyzed for its sensitivity and feature importance. Four methods used include random forest, gradient boosting, Bayesian regression, and artificial neural networks. Meanwhile, measures of sensitivity and feature importance used are variance, density, and distributionbased sensitivity indices, as well as permutation and Shapley feature importance. Evaluation of the machine learning model concluded that the Bayesian linear regression model outperformed other models with an RMSE of 750 hotspots and an explained variance score of 68.96% on testing data. Meanwhile, tree-based models show signs of overfitting, including gradient boosting and random forest. Based on the results of sensitivity analysis and feature importance of the Bayesian linear regression model, the number of dry days is the most important feature in predicting fire hotspots in Kalimantan.
Copula in Wildfire Analysis: A Systematic Literature Review Najib, Mohamad Khoirun; Nurdiati, Sri; Sopaheluwakan, Ardhasena
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. 
PREDIKSI INDEKS PERFORMA SISWA BERDASARKAN WAKTU BELAJAR, NILAI SEBELUMNYA, KEGIATAN EKSTRAKURIKULER, WAKTU TIDUR, DAN BANYAKNYA SOAL YANG DIKERJAKAN DENGAN REGRESI LINEAR KUADRAT-TERKECIL: Prediction of Student Performance Index Based on Hours Studied, Previous Scores, Extracurricular Activities, Sleep Hours, and Sample Question Papers Practiced with Least-Squares Linear Regression Handoyo, Sapto Mukti; Najib, Mohamad Khoirun
Al-Aqlu: Jurnal Matematika, Teknik dan Sains Vol. 3 No. 1 (2025): Januari 2025
Publisher : Yayasan Al-Amin Qalbu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59896/aqlu.v3i1.121

Abstract

The students’ performance index is a measure used to represent the overall performance of students. One model that can be used to predict the students’ performance index is a machine learning-based regression model. Therefore, this study aims to apply a machine learning-based least-squares linear regression model to predict the performance index using these factors and interpret the model. The regression model utilized is available in the Julia programming package called MLJ. This model is evaluated based on several criteria, including R-squared, RMSE, and MAE. The results show that the previous scores have the most significant influence on the students’ performance index. Furthermore, the R-squared value for the test data is 0.988, the RMSE for the training data is 0.106, the RMSE for the test data is 0.108, the MAE for the training data is 0.84, and the MAE for the test data is 0.86. Based on the evaluation results, the model has good predictive performance with low average error, does not experience overfitting, and has good generalization ability.
Performance Comparison of Gradient-based Optimizer for Classification of Movie Genres Najib, Mohamad Khoirun; Irawan, Ade; Salsabilla, Fitra Nuvus; Nurdiati, Sri
Indonesian Journal of Mathematics and Applications Vol. 3 No. 1 (2025): Indonesian Journal of Mathematics and Applications (IJMA)
Publisher : Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.ijma.2025.003.01.1

Abstract

In this digital era, artificial intelligence has become very popular due to its very wide scope of application. Various models and methods in artificial intelligence are developed with their respective purposes. However, each model and method certainly requires a reliable optimizer in the training process. Many optimizers have been developed and are increasingly reliable lately. In this article, we classify the synopsis texts of several movies into nine different genre classes, leveraging Natural Language Processing (NLP) with Long Short Term Memory (LSTM) and Embedding to build models. Models are trained using several optimizers, including stochastic gradient descent (SGD), AdaGrad, AdaDelta, RMSProp, Adam, AdaMax, Nadam, and AdamW. Meanwhile, various metrics are used to evaluate the model, such as accuracy, recall, precision, and F1-score. The results show that the model structure with embedding, lstm, double dense layer, and dropout 0.5 returns satisfactory accuracy. Optimizers based on Adaptive moments provide better results when compared to classical methods, such as stochastic gradient descent. AdamW outperforms other optimizers as indicated by its accuracy on validation data of 89.48%. Slightly behind it are several other optimizers such as Adam, RMSProp, and Nadam. Moreover, the genres that have the highest metric values are the drama and thriller genres, based on the recall, precision and F1-score values. Meanwhile, the horror, adventure and romance genres have low recall, precision and F1-score values. Moreover, by applying Random Over Sampling (ROS) to balance the genre dataset, the model’s testing accuracy improved to 98.1%, enhancing performance across all genres, including underrepresented ones. Additional testing showed the model’s ability to generalize well to unseen data, confirming its robustness and adaptability.
Systematic Literature Review on the Application of Mathematics, Statistics, and Computer Science in Wildfire Analysis Najib, Mohamad Khoirun; Nurdiati, Sri
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Volume 13 Issue 1 April 2025
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/euler.v13i1.31000

Abstract

Wildfires pose a significant threat to ecosystems, human settlements, and air quality, making accurate prediction and analysis crucial for disaster mitigation. Traditional statistical methods often struggle with the vast and complex nature of wildfire data, necessitating advanced mathematical, statistical, and computational approaches. This study presents a systematic literature review of wildfire analysis techniques, focusing on trends from 2000 to 2025. By analyzing 6,498 articles using the PRISMA framework, we identify the most widely applied methods, such as correlation, regression, classification, clustering, and artificial neural networks, while highlighting underutilized yet promising techniques such as copula, fuzzy inference, image recognition, quantile mapping, and empirical orthogonal function (EOF). The findings reveal an increasing shift toward interdisciplinary, data-driven approaches, with a significant increase in high-impact publications over the last decade. We emphasize the need for further exploration of advanced methodologies to enhance wildfire prediction models and improve decision-making in fire-prone regions. This review bridges computational innovations with environmental challenges, this study provides a roadmap for future research in wildfire analysis and management.
PERBANDINGAN KINERJA METODE K-NEAREST NEIGHBOR (KNN), RANDOM FOREST, DAN DECISION TREE DALAM MEMPREDIKSI DIABETES: Comparing the Accuracy of K-Nearest Neighbour (KNN), Random Forest, and Decision Tree Methods in Predicting Diabetes Yulianty, Sherly; Najib, Mohamad Khoirun
Al-Aqlu: Jurnal Matematika, Teknik dan Sains Vol. 3 No. 2 (2025): Juli 2025
Publisher : Yayasan Al-Amin Qalbu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59896/aqlu.v3i2.299

Abstract

Diabetes is a disease with a growing number of sufferers and is the cause of death of 1.5 million people in the world in 2019. A treatment for diabetes is needed, one of which is by predicting diabetics. The K-Nearest Neighbour (KNN), Random Forest, and Decision Tree methods are some methods that can be used to predict diabetes classification. This research aims to compare the performance of KNN, Random Forest, and Decision Tree methods based on accuracy and computation time. The data used in this study are Pregnancies, Glucose, Insulin, Body Mass Index (BMI), and Age as independent variables and Outcome as a dependent variable. The results of research on data that has not been normalised with Min-Max show that the KNN method has a faster computation time than the other two methods, while based on the accuracy value the Decision Tree method has a higher value than the other two methods. Furthermore, the Min-Max normalised data shows a decrease in the accuracy value of the Decision Tree and Random Forest methods, while the accuracy value of the KNN method has increased. Therefore, the Min-Max normalisation treatment is better used for the KNN method.
Music Artist Recommendation System Based on Listening History Using SVD and MICE Imputation Approaches Martal, David Vijanarco; Najib, Mohamad Khoirun
Zeta - Math Journal Vol 10 No 1 (2025): May
Publisher : Universitas Islam Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31102/zeta.2025.10.1.70-80

Abstract

In the digital era, music streaming platforms face challenges in providing relevant music recommendations to users. This research aims to develop a music artist recommendation system based on the user's listening history using the SVD and MICE methods. In this research, MICE was applied together with ALS predictive model. SVD is used to identify latent patterns between users and artists, while MICE address the problem of missing data in listening history. The data used comes from the online music platform Last.fm. Analysis was carried out with Julia 1.8.5 software. The results show that the model with MICE provides more accurate and consistent recommendations compared to SVD, especially in the context of missing data. Accuracy using the MICE model provides results of up to 96%, while the SVD model provides an accuracy of 90,22%. This approach can increase the relevance of recommendations, helping users find artists according to their preferences. These findings support the application of MICE in music recommendation systems, with the potential to improve user experience on music streaming platforms.
Co-Authors Abisha, Nicholas Ade Irawan Ade Irawan Alifah, Nayla Nur Alifah, Rifdah Nur Amalia, Rizki Nurul Andriani, Rizka D. Annisa Permata Sari, Annisa Permata Antika, Ester Ardhana, Muhammad Reza Ardhasena Sopaheluwakan Ardhasena Sopaheluwakan Ardiyani, Evi Aziz, Muhammad Farhan Blante, Trianty Putri Chairunisa, Ghevira Ekaputri, Dhea Elis Khatizah Endar Hasafah Nugrahani Ester Antika Fahren Bukhari Fahren Bukhari Fahren Bukhari Faiqul Fikri Fatmawati, Linda Leni Fauzan, Muhammad Daryl Ginting, Dini Tri Putri Br Handoyo, Sapto Mukti Hasafah Nugrahani, Endar Henriyansah Herlambang, Karen Hilmi, Kautsar I Wayan Mangku Imni, Salsabila F. Junus, Kasiyah Kasiyah Junus Kautsar Hilmi Khatizah, Elis Khoerunnisa, Nazwa Linda Leni Fatmawati Martal, David Vijanarco Maulia, Syammira Dhifa Mochamad Tito Julianto Muhammad Adam Tripranoto Muhammad Reza Ardhana Muhammad Tito Julianto Muhammad Zidane Bayu Muliawan Sebastian, Denny Nadiyah, Fadilah Karamun Nisaa Nandika Safiqri NGAKAN KOMANG KUTHA ARDHANA Noval Nur Fallahi, Putri Afia Nuzhatun Nazria Pratama, Yoga Abdi Putri, Renda S. P. Rafhida, Syukri Arif Redytadevi, Tita Putri REFI REVINA Retno Budiarti Rohimahastuti, Fadillah Ruben Harry Valentdio Salsabila, Fitra Nuvus Salsabilla Rahmah Salsabilla, Fitra Nuvus Sanjaya, Wardah Setyawati, Suci Nur Sopaheluwakan, Ardhasena Sri Nurdiati Sriwahyuni, Lilis Sukmana, Ihwan SYAHID AHMAD MUKRIM Sya’adah, Syifa Noer Trianty Putri Blante Triwulandari, Raden Roro Carissa Valentdio, Ruben Harry Yoga Abdi Pratama Yulianty, Sherly