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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
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IMPUTATION OF MISSING DAILY RAINFALL DATA USING CONVOLUTIONAL NEURAL NETWORKS (CNN) WITH SPATIAL INTERPOLATION Sriwahyuni, Lilis; Nurdiati, Sri; Nugrahani, Endar Hasafah; Sukmana, Ihwan; Najib, Mohamad Khoirun
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 4 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss4pp2921-2936

Abstract

Accurate rainfall estimation is crucial in climate analysis and water resource planning. Observational data from weather stations play a vital role in climatological analysis as they represent actual conditions at specific locations. However, many observation stations in Indonesia need more complete data, hindering analysis and data-driven decision-making. To address this issue, this study aims to impute missing rainfall data for BMKG stations in East Java using the Convolutional Neural Network (CNN) method. Satellite data used in this study include ERA5 without interpolation and ERA5 with interpolation. The study employs a spatial interpolation approach. Data were split into training and testing datasets with various ratios: 95:5%, 90:10%, 80:20%, 70:30%, and 50:50%. The results show that the CNN method with spatially interpolated satellite data yields better results, with a Mean Absolute Error (MAE) of 7.50 on the training data and 7.05 on the testing data, indicating better generalization capability than the method without interpolation. The combination of CNN and ERA5 with interpolation was chosen for imputing missing rainfall data at BMKG stations in East Java due to its lower MAE.
PREDIKSI MASA STUDI MAHASISWA MATEMATIKA IPB BERDASARKAN INDEKS PRESTASI KUMULATIF MENGGUNAKAN JARINGAN SYARAF TIRUAN Nurdiati, Sri; Bukhari, Fahren; Najib, Mohamad Khoirun; Hilmi, Kautsar
MILANG Journal of Mathematics and Its Applications Vol. 18 No. 1 (2022): MILANG Journal of Mathematics and Its Applications
Publisher : School of Data Science, Mathematics and Informatics, IPB University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/milang.18.1.1-13

Abstract

Akreditasi sebuah program studi sangat dipengaruhi oleh masa studi dan Indeks Prestasi Kumulatif (IPK) lulusannya. Beberapa penelitian menunjukkan adanya keterkaitan antara kelulusan dengan IPK mahasiswa. Namun, model prediksi lama masa studi berdasarkan IPK masih sedikit. Oleh karena itu, penelitian ini bertujuan untuk memprediksi masa studi mahasiswa berdasarkan IPK menggunakan model jaringan syaraf tiruan (JST) berbasis backpropagation. Beberapa fungsi pelatihan diterapkan, meliputi gradient descent, Nesterov accelerated gradient descent, Adaptive moment estimation (Adam), dan Nesterov Adam (Nadam). Data yang digunakan dalam penelitian ini adalah data masa studi dan IPK semester 1-6 mahasiswa S1 Matematika IPB. Hasil penelitian menunjukkan bahwa model JST terbaik dihasilkan oleh jaringan dengan jumlah input node 6 yang dinormalisasi dengan batch normalization (BatchNorm), hidden node 10 dan output node 1. Parameter jaringan terbaik diperoleh dari percobaan menggunakan fungsi pelatihan gradient descent dan laju pembelajaran 0.5 dengan MAE sebesar 1.887 pada data testing. Fungsi pelatihan gradient descent memperlihatkan adanya penurunan nilai MAE ketika nilai laju pembelajaran meningkat. Sementara itu, pada fungsi pelatihan lainnya, terdapat tren bahwa semakin kecil nilai laju pembelajaran maka semakin kecil pula nilai MAE yang dihasilkan. Berdasarkan model JST terpilih, nilai IPK yang paling berpengaruh pada masa studi mahasiswa matematika IPB adalah nilai IPK pada semester 3, yaitu masa mahasiswa matematika IPB pertama kali menerima mata kuliah mayor dari Departemen Matematika secara keseluruhan. Kepentingan dari fitur ini sangat tinggi, mencapai 75.62%. Model JST terpilih menghasilkan MAPE sebesar 3.8% dan RMSPE sebesar 4.9% pada data testing.
IMPLEMENTASI PENYELESAIAN PERSAMAAN BURGERS DENGAN METODE BEDA HINGGA DALAM BAHASA PEMROGRAMAN JULIA Bukhari, Fahren; Nurdiati, Sri; Julianto, Mochamad Tito; Najib, Mohamad Khoirun; Valentdio, Ruben Harry
MILANG Journal of Mathematics and Its Applications Vol. 19 No. 1 (2023): MILANG Journal of Mathematics and Its Applications
Publisher : School of Data Science, Mathematics and Informatics, 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.
Perbandingan Metode Tree Based Classification untuk Masalah Klasifikasi Data Body Mass Index Alifah, Rifdah Nur; Najib, Mohamad Khoirun; Nurdiati, Sri; Sari, Annisa Permata; Herlambang, Karen; Noval; Ginting, Dini Tri Putri Br; Sya’adah, Syifa Noer
Indonesian Journal of Mathematics and Natural Sciences Vol. 47 No. 1 (2024): Volume 47 Nomor 1 Tahun 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/m2k97436

Abstract

Body mass index (BMI) atau indeks massa tubuh merupakan salah satu indikator yang dapat mengawasi dan menjelaskan status gizi seseorang. Penelitian ini bertujuan untuk mengklasifikasikan BMI berdasarkan gender, tinggi badan, dan berat badan dengan menggunakan metode Tree Based Classification yang terdiri atas model Decision Tree Classifier, Random Forest Classifier, Gradient Boosting Classifier, dan XGBoost menggunakan bahasa pemrograman python. Model Tree Based classification tersebut akan mengklasifikasikan BMI kedalam 6 kelas indeks. Hasil penelitian menunjukkan model klasifikasi XGBoost memiliki akurasi terbaik setelah dilakukan tuning hyperparameter dengan nilai akurasi data test 83.7%. Performa model terbaik sebelum tuning hyperparameter dihasilkan model Random Forest dengan nilai F1-score (macro) untuk data test sebesar 88%. Sementara itu, performa model terbaik setelah tuning hyperparameter dihasilkan model XGBoost dengan nilai F1-score (macro) untuk data test dan data train masing-masing sebesar 79% dan 85%. Berdasarkan model XGBoost, variabel prediktor yang paling berkontribusi terhadap BMI adalah berat badan dengan nilai permutation importance 68.1%.
Probabilistic Prediction Model Using Bayesian Inference in Climate Field: A Systematic Literature Ardiyani, Evi; Nurdiati, Sri; Sopaheluwakan, Ardhasena; Najib, Mohamad Khoirun; Rohimahastuti, Fadillah
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 7, No 3 (2023): July
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v7i3.13651

Abstract

Wildfires occur repeatedly every year and have a negative impact on natural ecosystems. Anticipation of wildfires is very necessary, therefore a prediction model is needed that can produce predictions with a good level of accuracy. One approach to develop probabilistic prediction models is Bayesian inference. The purpose of this research is to review the methods that can be used in developing probabilistic prediction models using the Bayesian approach. The methodology used is Systematic Literature Review (SLR) which can be used to provide a comprehensive review of Bayesian inference research in developing probabilistic prediction models. The research strategy used was the Boolean Technique applied to database sources including Scopus, IEEE Xplore, and ArXiv. The articles used have novelty and ease of explanation of Bayesian methods, especially predictions in the field of climate so that articles are selected based on inclusion and exclusion criteria. The results show that probabilistic models can provide more accurate results than deterministic models. The Bayesian Model Averaging (BMA) method is a widely used method because it is easy to implement and develop so that the prediction results can be more accurate. The development of probabilistic prediction models with a Bayesian approach has great potential to grow as seen from the development of the number of research publications over the past 5 years. The research position of probabilistic prediction models with Bayesian approaches in the field of climate is dominated by the research community in China with the main problems related to hydrology.TRANSLATE with x EnglishArabicHebrewPolishBulgarianHindiPortugueseCatalanHmong DawRomanianChinese SimplifiedHungarianRussianChinese TraditionalIndonesianSlovakCzechItalianSlovenianDanishJapaneseSpanishDutchKlingonSwedishEnglishKoreanThaiEstonianLatvianTurkishFinnishLithuanianUkrainianFrenchMalayUrduGermanMalteseVietnameseGreekNorwegianWelshHaitian CreolePersian //  TRANSLATE with COPY THE URL BELOW Back EMBED THE SNIPPET BELOW IN YOUR SITE Enable collaborative features and customize widget: Bing Webmaster PortalBack//
Prediksi Angka Harapan Hidup Menggunakan Regresi Linear Berganda, Lasso, Ridge, Elastic Net, dan Kuantil Lasso Fauzan, Muhammad Daryl; Najib, Mohamad Khoirun; Nurdiati, Sri; Khoerunnisa, Nazwa; Maulia, Syammira Dhifa; Triwulandari, Raden Roro Carissa; Aziz, Muhammad Farhan
Jurnal Sains Matematika dan Statistika Vol 10, No 2 (2024): JSMS Juli 2024
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/jsms.v10i2.27916

Abstract

Angka harapan hidup mejadi salah satu indikator penting dalam mengevaluasi kesejahteraan dan kualitas hidup suatu populasi atau negara. Metode yang biasa digunakan untuk memprediksi adalah regresi linear berganda. Terdapat banyak perkembangan model regresi linear berganda, seperti regresi lasso, ridge, elastic net, kuantil, serta kuantil lasso. Untuk melihat kontribusi setiap variabel independen pada model, digunakan metode Mean Absolute Shapley Values (MASV). Oleh karena itu, tujuan dari penelitian ini adalah membandingkan model regresi linear berganda, lasso, ridge, elastic net, kuantil, serta kuantil lasso dalam memprediksi nilai angka harapan hidup. Penelitian diawali dengan melakukan eksplorasi data. Selanjutnya, model-model regresi tersebut dilatih. Pelatihan model tersebut juga dilakukan berulang kali dengan mengacak data pada pembagian data latih dan data uji. Terakhir, kontribusi setiap variabel independen diukur. Performa model regresi linear berganda pada iterasi pertama cukup baik dengan nilai r-square lebih besar dari 85% baik pada data latih dan data uji. Namun, Performa model lasso, ridge, elastic net, kuantil, dan kuantil lasso tidak jauh berbeda dengan performa model regresi linear berganda. Ketika dilakukan pengacakan data latih dan data uji.  Model regresi kuantil lasso memiliki performa yang lebih konsisten dalam memprediksi nilai angka harapan hidup dibandingkan model lainnya. Pada setiap model regresi, tingkat kelahiran dan tingkat kematian bayi merupakan variabel yang memiliki kontribusi terbesar dalam memprediksi nilai angka harapan hidup, sedangkan persentase orang yang mengikuti sekolah formal dan persentase populasi yang tinggal di perkotaan bukan variabel independen yang cukup baik untuk memprediksi angka harapan hidup. Kata Kunci:  angka harapan hidup, model regresi, data latih, data uji.
Student Readiness Scores a Rasch Model’s for Facing E-Learning Using Decision Tree and Ensemble Methods Antika, Ester; Nurdiati, Sri; Junus, Kasiyah; Najib, Mohamad Khoirun
Jurnal Pendidikan Progresif Vol 14, No 1 (2024): Jurnal Pendidikan Progresif
Publisher : FKIP Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Abstract: Prediction of Rasch Model’s Student Readiness Scores for Facing E-Learning Using Decision Tree and Ensemble Methods. Objective: This research aims to predict student readiness score in facing e-learning using Rasch models and machine learning. Methods: This research is a quantitative research using a non test instrument ini the form of a questionnaire using a Likert scale. The sample used were IPB University students. Analysis techniques use Rasch model, decision tree, and ensemble. Finding: Item reliability value is 0,93, person reliability value is 0,97, and cronbachalpha is 0,99. The standard deviation value is 2,34 and the average logit of respondents is 1,9. 34% of students have high readiness with a person measure value >2,34. 4% of students have moderate readiness with a score of 1,9 < person measure < 2,34. 62% of students have low readiness with a person measure value < 1,9. The accuracy of the decision tree model reached 75,97%. Conclusion: Based on person measure from the Rasch model, it can be concluded that the majority of respondents (62%) have low ability to carry out e-learning. Male students and those who have experience in dealing with e-learning have a higher percentage of having high ability in dealing with e-learning at the university level. Moreover, machine learning models are able to predict students' abilities in dealing with e-learning based on the measure score from the Rasch model. Furthermore, ensemble models are able to increase the accuracy of decision tree models. We found that the ensemble model with the LogitBoost (adaptive logistic regression) method provides best model in term of its accuracy (82.17%) and execution time. Keywords: decision tree, e-learning, ensemble, machine learning, rasch model.DOI: http://dx.doi.org/10.23960/jpp.v14.i1.202437
Komentar untuk artikel Savitri et al.: Implementasi algoritma genetika dalam mengestimasi kepadatan populasi jackrabbit dan coyote Najib, Mohamad Khoirun; Nurdiati, Sri
Jambura Journal of Biomathematics (JJBM) Volume 3, Issue 2: December 2022
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34312/jjbm.v3i2.16857

Abstract

This article is a commentary on research conducted by Savitri et al which was published in Jambura Journal of Biomathematics volume 3 number 1 in 2022. It was found that there was an error in the MAPE calculation for the approximation of population density of coyote. The MAPE obtained for coyotes was 66.05% so there was a significant difference from what had been given before. With these results, there is an opportunity to estimate parameters with better accuracy.
Bias Correction of Lake Toba Rainfall Data Using Quantile Delta Mapping Rafhida, Syukri Arif; Nurdiati, Sri; Budiarti, Retno; Najib, Mohamad Khoirun
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 9, No 2 (2024): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/ca.v9i2.29124

Abstract

Lake Toba, located in North Sumatra, is the largest tectonic and volcanic lake in Indonesia. Lake Toba has an equatorial climate characterized by abundant rainfall throughout the year. High rainfall, coupled with annual increases due to climate change, results in a vulnerability to the unpredictable extreme weather, causing harm to the surrounding communities. Consequently, a rainfall prediction model is needed to anticipate the impacts of such extreme rainfall. One of the rainfall prediction models used is ERA5-Land. However, this prediction model has biases that can be avoided. A method that can be used is the statistical bias correction using the quantile delta mappings (QDM) by correcting ERA5-Land model data against BMKG observation data. The QDM method used in this study employs two types of methods: monthly and full distribution. The results shows that both methods can improve biases at Silaen, Laguboti, and Doloksanggul stations, as well as improve the model during the equatorial dry seasons in May, June, July, and August. However, the first method improves the model distribution more in Silaen and Laguboti, while the second method improves the model distribution more in Doloksanggul.
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. 
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