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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 Zero : Jurnal Sains, Matematika, dan Terapan Teorema: Teori dan Riset Matematika Jurnal Telematika 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 Journal of Mathematics: Theory and Applications 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 International Journal of Computing Science and Applied Mathematics-IJCSAM
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 Chemistry 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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Comparing Five Machine Learning-Based Regression Models for Predicting the Study Period of Mathematics Students at IPB University Nurdiati, Sri; Najib, Mohamad Khoirun
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 6, No 3 (2022): July
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v6i3.8408

Abstract

Grade point average (GPA) is initial information for supervisors to characterize their supervised students. One model that can be used to predict a student's study period based on GPA is a machine learning-based regression model so that supervisors can apply the right strategy for their students. Therefore, this study aims to implement and select a machine learning-based regression model to predict a student's study period based on GPA in semesters 1-6. Several regression models used are least-square regression, ridge regression, Huber regression, quantile regression, and quantile regression with l_2-regularization provided by Machine Learning in Julia (MLJ). The model is evaluated and selected based on several criteria such as maximum error, RMSE, and MAPE. The results showed that the least-square regression model gave the worst evaluation results, although the calculation method was easy and fast. Meanwhile, the quantile regression model provided the best evaluation results. The quantile regression model without regularization gives the smallest RMSE (2.31 months) and MAPE (3.56%), while the quantile regression model with l_2-regularization has a better maximum error (4.9 months). The resulting model can be used by supervisors to predict the study period of their supervised students so that supervisors can characterize their students and can design appropriate strategies. Thus, the student's study period is expected to be accelerated with a high-quality final project.
GENERALIZED NESTED COPULA REGRESSION TO UNVEIL THE IMPACT OF EXCHANGE RATES AND NIKKEI 225 ON BANK MANDIRI STOCK PRICE Khairiati, Alfi; Budiarti, Retno; Najib, Mohamad Khoirun
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 2 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss2pp1167-1184

Abstract

Fluctuations in exchange rates and foreign stock indices strongly influence domestic stock performance, particularly in the banking sector, which is highly sensitive to global economic dynamics. Traditional financial models often fail to capture the complex, non-linear dependencies between these variables, underscoring the need for more advanced approaches. This study examines the effectiveness of copula-based regression models in predicting Bank Mandiri’s (BMRI) stock price using exchange rates and the Nikkei 225 Index as predictors. Conventional regression methods, such as Linear Regression, cannot adequately capture nonlinear relationships and tail dependencies in financial time series. To address this, we compare Elliptical Copula, Symmetric Archimedean Copula, Asymmetric Archimedean Copula, and Generalized Nested Copula models. Results show that the Generalized Nested Copula Regression achieves the lowest Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Weighted MAPE (wMAPE), effectively modeling asymmetric and tail dependencies that are crucial in financial forecasting. While Elliptical Copula (t-Copula) also provides strong predictive accuracy, Archimedean copulas perform poorly, failing to improve upon linear regression. These findings highlight the importance of flexible statistical models in financial prediction, demonstrating that copula-based regression offers a superior alternative to traditional methods. Unlike prior research that often relied on simpler copula families or linear models, this study introduces a Generalized Nested Copula Regression in the context of the Indonesian banking sector, addressing a gap in emerging market literature. The study assumes correctly specified marginal distributions and a stable dependency structure, which may limit applicability under rapidly changing market conditions. Future work should consider dynamic copula structures and additional economic indicators to further enhance predictive accuracy.
Sentimen Publik Terhadap Kebijakan Pemindahan Ibu Kota Indonesia di X Menggunakan Model BiLSTM-CNN Nugraha, Wanda; Tito Julianto, Mochamad; Khoirun Najib, Mohamad; Khatizah, Elis
Jurnal Telematika Vol. 20 No. 2 (2025)
Publisher : Yayasan Petra Harapan Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61769/telematika.v20i2.796

Abstract

Pembangunan ibu kota baru Indonesia, Ibu Kota Nusantara (IKN), merupakan kebijakan pemerintah yang inovatif dan telah memicu respons publik yang beragam. Studi ini bertujuan untuk menganalisis tren sentimen di platform media sosial X guna memahami persepsi publik terhadap kebijakan tersebut. Selain itu, model klasifikasi sentimen yang menggabungkan bidirectional long short-term memory (BiLSTM) dan convolutional neural network (CNN) dikembangkan dan dioptimalkan melalui penyesuaian hiperparameter. Analisis eksploratori menunjukkan bahwa sentimen positif mendominasi 46%, diikuti oleh sentimen negatif 30% dan netral 24%. Model klasifikasi mencapai akurasi uji sebesar 78% dan akurasi rata-rata 81% pada 10-fold cross-validation, dengan simpangan baku 0,006. Akurasi yang dicapai, bersama dengan simpangan baku cross-validation yang rendah, menunjukkan bahwa model BiLSTM-CNN menunjukkan kinerja yang stabil dan andal.
Comparison of Non-linear Autoregressive Neural Network (NARNN) and Holt–Winters Methods for Antam Gold PricePrediction Akbar, Raihan; Saputra, Rika Ardiansyah; Najib, Mohamad Khoirun; Khatizah, Elis; Nurdiari, Sri
Desimal Vol. 9 No. 1 (2026): Desimal
Publisher : Universitas Islam Negeri Raden Intan Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/djm.v9i1.30260

Abstract

The high volatility and nonlinear dynamics of Antam gold prices present significant challenges for accurate time series forecasting, particularly within emerging financial markets. This study aims to develop and evaluate a comparative forecasting framework by examining the performance of the Nonlinear Autoregressive Neural Network (NARNN) and the Holt–Winters exponential smoothing method. A quantitative approach was applied using daily gold price data from January 4, 2010, to January 4, 2025. Data preprocessing included linear interpolation for missing values, Box–Cox transformation for variance stabilization, and time series decomposition to identify structural patterns. The dataset was partitioned into training and testing sets using an 80:20 ratio. Model performance was assessed using the Mean Absolute Percentage Error (MAPE). The results demonstrate that the NARNN model significantly outperforms the Holt–Winters approach, achieving a MAPE of 0.44%, compared to 11.43% and 11.90% for the additive and multiplicative variants, respectively. These findings highlight the limitations of classical linear smoothing methods in capturing abrupt structural changes and confirm the superiority of nonlinear neural network models in modeling complex financial time series. This study provides a robust empirical contribution by establishing a comparative modeling framework that enhances forecasting accuracy in volatile commodity markets.
Simulasi Propagasi Sinyal Wi-Fi Menggunakan Metode Elemen Hingga pada Ruangan Kompleks dengan Variasi Posisi Router Nadhira Maulida Hayani; Harley Dearmanson Girsang; Nur Nabila; Khairuna Putri Gunawan; Nerissa Patrice Manuella; Maliha Qonita; Aaron August Vincent Soelaiman; Mochamad Tito Julianto; Sri Nurdiati; Mohamad Khoirun Najib; Syukri Arif Rafhida
Techno.Com Vol. 25 No. 1 (2026): February 2026
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/tc.v25i1.15769

Abstract

Wi-Fi merupakan teknologi komunikasi nirkabel yang banyak digunakan untuk mendukung aktivitas sehari-hari, baik di lingkungan rumah maupun perkantoran. Kualitas sinyal Wi-Fi di dalam ruangan sangat dipengaruhi oleh geometri bangunan dan posisi router, terutama pada bangunan dengan bentuk kompleks seperti rumah berbentuk L. Penelitian ini bertujuan untuk menyusun model matematis propagasi sinyal Wi-Fi menggunakan persamaan Helmholtz pada domain dua dimensi, menerapkan Metode Elemen Hingga (Finite Element Method/FEM) untuk menyelesaikan model tersebut pada geometri ruangan berbentuk L, serta menganalisis pengaruh variasi posisi router terhadap pola distribusi medan listrik dan terbentuknya area pelemahan sinyal (dead zone). Data dan parameter yang digunakan meliputi frekuensi Wi-Fi sebesar 2,4 GHz, bilangan gelombang yang dihitung berdasarkan kecepatan cahaya, serta domain komputasi yang direkonstruksi dari denah rumah nyata berbentuk L. Penyelesaian numerik dilakukan menggunakan perangkat lunak Mathematica dengan pendekatan FEM dan diskritisasi domain menggunakan mesh segitiga. Hasil simulasi divisualisasikan dalam skala logaritmik (dB) untuk menggambarkan distribusi intensitas sinyal secara jelas. Hasil penelitian menunjukkan bahwa penempatan router di ruang tengah menghasilkan distribusi sinyal yang paling merata dan meminimalkan dead zone, sedangkan penempatan di sudut atau ujung ruangan menyebabkan redaman signifikan akibat pemantulan dan difraksi gelombang oleh dinding dan lorong. Penelitian ini menunjukkan bahwa FEM efektif untuk memodelkan propagasi sinyal Wi-Fi pada geometri ruangan kompleks dan dapat digunakan sebagai dasar pengembangan simulasi yang lebih realistis, seperti pemodelan tiga dimensi, variasi material dinding, serta optimasi penempatan router pada bangunan nyata.   Kata Kunci - Metode Elemen Hingga; Persamaan Helmholtz; Propagasi Sinyal; Rumah Berbentuk L; Wi-Fi
Modeling Monthly Rainfall Data Using the Alpha Power Transformed X-Lindley Distribution in the Toba Lake Region Mohamad Khoirun Najib; Sri Nurdiati; Elis Khatizah; Aulia Rizki Firdawanti; Hendri Irwandi; Mirza Farhan Azhari; David Vijanarco Martal; Nicholas Abisha
ZERO: Jurnal Sains, Matematika dan Terapan Vol 9, No 3 (2025): Zero: Jurnal Sains Matematika dan Terapan
Publisher : UIN Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/zero.v9i3.25692

Abstract

Modeling rainfall is crucial for hydrological studies and climate adaptation, especially in regions with complex topography such as the Toba Lake area, North Sumatra. Classical probability distributions often struggle to represent skewness, heavy tails, and variability observed in tropical rainfall. This study explores APTXL distribution as a flexible two-parameter model. Through the alpha power transformation, APTXL extends the X-Lindley distribution by introducing an additional shape parameter, allowing better accommodation of asymmetrical and extreme values while maintaining analytical tractability. Statistical properties are derived, and parameters are estimated using maximum likelihood. The model is applied to a long-term dataset from 13 meteorological stations, covering 408 monthly observations per station. Comparative analysis against Gamma, Lognormal, and Generalized Extreme Value distributions using multiple goodness-of-fit criteria indicates that APTXL provides consistently improved performance. These results suggest APTXL as a practical tool for rainfall modeling and water-resource applications in climate-sensitive regions.
Implementasi Metode Random Forest dan Support Vector Regression dalam Memprediksi Harga Cryptocurrency Ethereum Firdhasari, Azizah Aulia; Sriwahyuni, Lilis; Nurdiati, Sri; Najib, Mohamad Khoirun
Journal of Mathematics: Theory and Applications Vol. 8 No. 1 (2026): Volume 8 Nomor 1 Tahun 2026
Publisher : Program Studi Matematika Universitas Sulawesi Barat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31605/jomta.v8i1.6189

Abstract

Perkembangan cryptocurrency menjadikan Ethereum (ETH) sebagai salah satu aset digital penting, namun pergerakan harganya sangat volatil karena dipengaruhi oleh berbagai faktor fundamental dan eksternal. Kondisi tersebut menyebabkan prediksi harga close ETH menjadi permasalahan utama karena akurasi peramalan sangat menentukan analisis dan pengambilan keputusan berbasis data. Penelitian ini bertujuan membangun serta membandingkan model prediksi harga close Ethereum menggunakan Random Forest dan Support Vector Regression (SVR) untuk forecasting 30 hari ke depan. Data yang digunakan berupa harga harian Ethereum periode 1 Januari 2020 hingga 30 Desember 2024 dari Yahoo Finance, kemudian dilakukan pra-pemrosesan, standarisasi, dan pembagian data train-test 80:20 dengan menjaga urutan waktu. Feature engineering dibagun dari harga close melalui MA 7, EMA 7, dan lag return 7, serta diterapkan exponential smoothing untuk mengurangi noise. Model Random Forest dan SVR dioptimasi menggunakan Grid Search CV, kemudian dievaluasi menggunakan metrik MAPE. Hasil tuning menunjukkan konfigurasi terbaik Random Forest adalah max depth = 10 dan total estimator = 90. Konfigurasi terbaik SVR adalah kernel linear dengan C = 10, ε = 0.5, dan γ = scale. Evaluasi MAPE menunjukkan Random Forest lebih unggul dengan MAPE train 1,37% dan test 2,04%, sedangkan SVR menghasilkan MAPE train 5,83% dan test 2,22%. Secara keseluruhan, kedua model memberikan akurasi prediksi yang sangat baik, namun Random Forest menunjukkan kinerja lebih stabil dan akurat pada data pengujian. Model Random Forest kemudian digunakan untuk forecasting harga close ETH 30 hari ke depan sebagai estimasi jangka pendek yang cenderung stabil dan mengikuti tren data pengujian.
Sentiment Analysis of Indonesia’s Free Nutritious Meal Program on X Using SVM and Random Forest Ferdy Aliansyah Hasyim; Talenta Parfaibya Mahenindra; Lilis Sriwahyuni; Alika Azka Shapira; Wigawijayanti Wigawijayanti; Nadhifa Zahra Ghaisani; Mirlan Sujana; Sri Nurdiati; Mohamad Khoirun Najib
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 11, No 1 (2026): 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/cauchy.v11i1.40717

Abstract

The Free Nutritious Meal (Makan Bergizi Gratis/MBG) Program was introduced to address stunting in Indonesia, yet its implementation has sparked diverse public debate. This study aims to map public perception on social media X and compare the performance of Support Vector Machine (SVM) and Random Forest algorithms in sentiment classification. Utilizing a large-scale dataset of 7,452 tweets collected via stratified random sampling from January to October 2025, this research applies TF-IDF feature extraction and SMOTE data balancing. The analysis reveals that positive sentiment dominates at 47.62%, while negative sentiment accounts for 39.8\%, and neutral for 12.57%. In model comparison, SVM without SMOTE achieved the best performance with 80.66% accuracy and an F1-Score of 79.79%, outperforming Random Forest, which only reached a maximum accuracy of 72.23% after SMOTE application. These findings provide an objective overview of MBG policy acceptance and methodological insights into the effectiveness of SVM in handling high-dimensional text data.
Short- and Long-Run Relationships Between Observed and Model 1 Output Rainfall Data in Majalengka Regency Sri Nurdiati; Mohamad Khoirun Najib; Fathia Rahmaisty
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 11, No 1 (2026): 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/cauchy.v11i1.40902

Abstract

This study examines the short-run and long-run relationships between observed monthly rainfall and CMIP6 climate model projections in Majalengka Regency, Indonesia. Monthly rainfall observations from the BMKG Kertajati Meteorological Station are analyzed using the Autoregressive Distributed Lag (ARDL) framework, which enables simultaneous assessment of short-term dynamics and long-term equilibrium relationships. Stationarity and cointegration are evaluated using the Augmented Dickey–Fuller test and ARDL bounds testing, respectively, while model performance is assessed through out-of-sample validation for the period 2015–2017 under three CMIP6 emission scenarios: SSP1-2.6, SSP2-4.5, and SSP5-8.5. The results indicate a positive and statistically significant short-run relationship between observed rainfall and CMIP6 projections across all scenarios, suggesting that climate models capture local scale monthly rainfall variability reasonably well. In contrast, the long-run relationship is weak and negative, highlighting limitations in representing long-term local rainfall dynamics. Model performance is highest under the low-emission SSP1-2.6 scenario and decreases under higher-emission scenarios. These findings suggest that CMIP6 outputs are more reliable for short-term rainfall analysis than for long-term local assessments without bias correction or downscaling.
Heterogeneous Correlation Mapping between Rainfall Variability in Lake Toba and Indian Ocean Sea Surface Temperature Mohamad Khoirun Najib; Sri Nurdiati
(IJCSAM) International Journal of Computing Science and Applied Mathematics Vol. 12 No. 1 (2026)
Publisher : LPPM Institut Teknologi Sepuluh Nopember

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

Abstract

Rainfall variability in the Lake Toba watershed of North Sumatra is influenced by large-scale ocean–atmosphere in-teractions, particularly those involving sea surface temperatures (SST) in the Indian Ocean. This study applies Heterogeneous Correlation Mapping (HCM) to examine the spatially varying relationship between monthly rainfall at 13 meteorological stations and SST over the Indian Ocean warm pool (5°S–10°N, 60°E–80°E) during 1981–2014. Singular Value Decomposition (SVD) is employed to extract dominant coupled modes of SST–rainfall variability. Results indicate that a six-month lag yields the strongest coupling, with the leading mode explaining 88.5% of the total variance. A clear spatial heterogeneity is observed: stations such as Lumban Julu and Silaen exhibit stronger SST–rainfall correlations, while others show weaker responses, likely due to topographic and local climatic modulation. These findings underscore the importance of accounting for spatial and temporal structures in hydroclimatic teleconnection analysis and offer insights for improving seasonal rainfall prediction in mountainous tropical regions
Co-Authors Aaron August Vincent Soelaiman Abisha, Nicholas Ade Irawan Ade Irawan Akbar, Raihan Alifah, Nayla Nur Alifah, Rifdah Nur Alika Azka Shapira Amalia, Rizki Nurul Andriani, Rizka D. Annisa Permata Sari, Annisa Permata Antika, Ester Ardhana, Muhammad Reza Ardhasena Sopaheluwakan Ardhasena Sopaheluwakan Ardiyani, Evi Aufa Ghifada Aulia Rizki Firdawanti Aziz, Muhammad Farhan Blante, Trianty Putri Chairunisa, Ghevira David Vijanarco Martal Dezvini Muthmainnati Vidia Ekaputri, Dhea Elis Khatizah Endar Hasafah Nugrahani Ester Antika Fahren Bukhari Fahren Bukhari Fahren Bukhari Faiqul Fikri Farah Annisa Tri Sundari Fathia Rahmaisty Fatmawati, Linda Leni Fauzan, Muhammad Daryl Ferdy Aliansyah Hasyim Firdhasari, Azizah Aulia Ginting, Dini Tri Putri Br Handoyo, Sapto Mukti Harley Dearmanson Girsang Hasafah Nugrahani, Endar Hendri Irwandi Henriyansah Herlambang, Karen Hilmi, Kautsar I Wayan Mangku Iftar Hendry Imni, Salsabila F. Kasiyah Junus Kautsar Hilmi Khairiati, Alfi Khairuna Putri Gunawan Khatizah, Elis Khoerunnisa, Nazwa Lilis Sriwahyuni Linda Leni Fatmawati Lizzilmi Syarifatuz Zaimah Maliha Qonita Martal, David Vijanarco Maulia, Syammira Dhifa Mirlan Sujana Mirza Farhan Azhari Mochamad Tito Julianto Mochamad Tito Julianto Muhammad Adam Tripranoto Muhammad Reza Ardhana Muhammad Tito Julianto Muhammad Zidane Bayu Muliawan Sebastian, Denny Nadhifa Zahra Ghaisani Nadhira Maulida Hayani Nadiyah, Fadilah Karamun Nisaa Nandika Safiqri Naura Dalta Indriyani Nerissa Patrice Manuella NGAKAN KOMANG KUTHA ARDHANA Nicholas Abisha Noval Nugraha, Wanda Nur Fallahi, Putri Afia Nur Nabila Nurdiari, Sri 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 Saputra, Rika Ardiansyah Setyawati, Suci Nur Sopaheluwakan, Ardhasena Sri Nurdiati Sriwahyuni, Lilis Sukmana, Ihwan SYAHID AHMAD MUKRIM Sya’adah, Syifa Noer Syukri Arif Rafhida Talenta Parfaibya Mahenindra Tito Julianto, Mochamad Trianty Putri Blante Triwulandari, Raden Roro Carissa Valentdio, Ruben Harry Wigawijayanti Wigawijayanti Yoga Abdi Pratama Yulianty, Sherly