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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 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 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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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.
Simulasi Propagasi Sinyal Wi-Fi Menggunakan Metode Elemen Hingga pada Ruangan Kompleks dengan Variasi Posisi Router Hayani, Nadhira Maulida; Girsang, Harley Dearmanson; Nabila, Nur; Gunawan, Khairuna Putri; Manuella, Nerissa Patrice; Qonita, Maliha; Soelaiman, Aaron August Vincent; Julianto, Mochamad Tito; Nurdiati, Sri; Najib, Mohamad Khoirun; Rafhida, Syukri Arif
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
Short- and Long-Run Relationships Between Observed and Model 1 Output Rainfall Data in Majalengka Regency Nurdiati, Sri; Najib, Mohamad Khoirun; Rahmaisty, Fathia
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.
Comparison of Nonlinear 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: Jurnal Matematika Vol. 9 No. 1 (2026): Desimal: Jurnal Matematika
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.
Co-Authors Abisha, Nicholas Ade Irawan Ade Irawan Akbar, Raihan 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 Girsang, Harley Dearmanson Gunawan, Khairuna Putri Handoyo, Sapto Mukti Hasafah Nugrahani, Endar Hayani, Nadhira Maulida Henriyansah Herlambang, Karen Hilmi, Kautsar I Wayan Mangku Imni, Salsabila F. Kasiyah Junus Kautsar Hilmi Khairiati, Alfi Khatizah, Elis Khoerunnisa, Nazwa Linda Leni Fatmawati Manuella, Nerissa Patrice Martal, David Vijanarco Maulia, Syammira Dhifa Mochamad Tito Julianto Muhammad Adam Tripranoto Muhammad Reza Ardhana Muhammad Tito Julianto Muhammad Zidane Bayu Muliawan Sebastian, Denny Nabila, Nur Nadiyah, Fadilah Karamun Nisaa Nandika Safiqri NGAKAN KOMANG KUTHA ARDHANA Noval Nugraha, Wanda Nur Fallahi, Putri Afia Nurdiari, Sri Nuzhatun Nazria Pratama, Yoga Abdi Putri, Renda S. P. Qonita, Maliha Rafhida, Syukri Arif Rahmaisty, Fathia 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 Soelaiman, Aaron August Vincent Sopaheluwakan, Ardhasena Sri Nurdiati Sriwahyuni, Lilis Sukmana, Ihwan SYAHID AHMAD MUKRIM Sya’adah, Syifa Noer Tito Julianto, Mochamad Trianty Putri Blante Triwulandari, Raden Roro Carissa Valentdio, Ruben Harry Yoga Abdi Pratama Yulianty, Sherly