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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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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: Jurnal Matematika 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.
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, Maulana Malik Ibrahim State Islamic University of 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, Maulana Malik Ibrahim State Islamic University of 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 | DOI: 10.12962/j24775401.ijcsam.v12i1.7665

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
Penerapan Finite Element Method (FEM) Persamaan Difusi Pada Ruangan Yang Memiliki Air Conditioner Aufa Ghifada; Lizzilmi Syarifatuz Zaimah; Iftar Hendry; Farah Annisa Tri Sundari; Dezvini Muthmainnati Vidia; Naura Dalta Indriyani; Mochamad Tito Julianto; Sri Nurdiati; Mohamad Khoirun Najib; Syukri Arif Rafhida
Techno.Com Vol. 25 No. 2 (2026): May 2026
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/tc.v25i2.15990

Abstract

Kenyamanan termal merupakan salah satu aspek penting dalam perancangan bangunan karena kondisi suhu berpengaruh langsung terhadap produktivitas dan kenyamanan penghuni ruangan. Salah satu sistem yang umum digunakan untuk mengatur suhu ruangan adalah air conditioner (AC). Namun demikian, distribusi suhu di dalam ruangan tidak langsung menjadi seragam ketika AC dinyalakan. Penelitian ini bertujuan untuk menganalisis distribusi suhu pada ruangan ber-AC menggunakan metode elemen hingga (Finite Element Method). Fenomena fisik dimodelkan menggunakan persamaan panas dua dimensi yang menggambarkan proses difusi suhu terhadap ruang dan waktu. Domain simulasi merepresentasikan denah rumah sederhana yang terdiri dari beberapa ruangan dengan sumber pendingin berada di ruang tengah. Model matematis yang digunakan melibatkan kondisi awal suhu ruangan sebesar 25°C serta kondisi batas yang merepresentasikan sumber pendingin dan dinding ruangan yang terisolasi. Simulasi numerik dilakukan menggunakan perangkat lunak Wolfram Mathematica untuk memperoleh distribusi suhu terhadap waktu. Hasil simulasi menunjukkan bahwa pendinginan awalnya terjadi di sekitar sumber AC kemudian secara bertahap menyebar ke bagian ruangan lainnya. Keberadaan dinding dan sekat ruangan mempengaruhi pola difusi suhu sehingga menghasilkan laju pendinginan yang berbeda pada setiap ruangan. Seiring berjalannya waktu, sistem mendekati kondisi tunak di mana distribusi suhu menjadi lebih seragam. Hasil penelitian ini menunjukkan bahwa metode elemen hingga efektif digunakan untuk memodelkan penyebaran suhu pada ruangan dengan geometri yang kompleks.   Kata Kunci - Distribusi Suhu, Metode Elemen Hingga, Pendingin Ruangan, Persamaan Panas, Simulasi Numerik
Pemodelan Curah Hujan Bulanan Jakarta Pusat Menggunakan Neural Network Long Short-Term Memory (LSTM) Syukri Arif Rafhida; Mohamad Khoirun Najib
Jurnal Sains Matematika dan Statistika Vol. 12 No. 2 (2026): JSMS Juli 2026
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/t1b6xb48

Abstract

Jakarta, sebagai pusat pemerintahan dan ekonomi Indonesia, mengalami urbanisasi pesat yang memengaruhi pola curah hujan serta ketersediaan air, terutama di tengah perubahan iklim global. Model Long Short-Term Memory (LSTM), yang efektif dalam menangani data sekuensial, digunakan untuk menangkap pola curah hujan jangka panjang di Jakarta. Penelitian ini bertujuan untuk membangun model prediksi curah hujan menggunakan LSTM dengan berbagai konfigurasi lapisan dan teknik regulasi. Tiga skenario model diuji: LSTM sederhana, LSTM dengan dropout, dan LSTM dengan kombinasi dropout serta early stopping. Hasil evaluasi menunjukkan bahwa model dengan konfigurasi 1 LSTM layer, 2 dense layer, dan dropout menghasilkan nilai Root Mean Squared Error (RMSE) terendah pada data uji (146,41), mengungguli model dengan konfigurasi lain. Dropout terbukti efektif dalam mengurangi overfitting dan meningkatkan generalisasi model. Namun, model ini masih menghadapi kesulitan dalam memprediksi puncak curah hujan ekstrem. Penggunaan early stopping tidak memberikan peningkatan signifikan dalam performa. Penelitian ini menyimpulkan bahwa kombinasi dropout pada model LSTM dapat menghasilkan prediksi curah hujan bulanan yang baik, dengan potensi peningkatan melalui penyesuaian hyperparameter dan arsitektur model yang lebih kompleks. Model LSTM dapat menghasilkan prediksi curah hujan bulanan yang andal, yang berpotensi mendukung pengambilan keputusan dalam mitigasi risiko banjir, pengelolaan sumber daya air, dan perencanaan infrastruktur untuk adaptasi iklim.
Time-varying Distribution Analysis for Rainfall and Air Temperature Data in Jakarta in Response to Future Climate Change Suci Nur Setyawati; Sri Nurdiati; I Wayan Mangku; Mohamad Khoirun Najib
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 10, No 2 (2025): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Maulana Malik Ibrahim State Islamic University of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/cauchy.v10i2.32780

Abstract

AbstractIndonesia is vulnerable to climate change (rainfall and air temperature), which can increase the chances of climatic disasters. An organized risk analysis is a strategic plan to minimize the impact. The purpose of this research is to estimate time-varying distribution parameters for normal, generalized extreme value (GEV), and lognormal distributions using fminsearch and MLE algorithms on rainfall and air temperature data in Jakarta, as well as visualize and analyze the best time-varying distribution. The maximum likelihood estimation (MLE) method is used for stationary distribution parameter estimation. The fminsearch algorithm is used for stationary and nonstationary distribution parameter estimation. The highest difference value of stationary distribution parameter results from both methods is 5.3768 mm for rainfall data and 0.2670°C for air temperature data. The results of the best distribution based on the AIC value are the 3-parameter lognormal distribution for rainfall data and the 4-parameter GEV distribution for air temperature data. Over time, the variance of rainfall increases, and the average air temperature increases with a fixed variance.
Evaluation of Best-Fit Probability Distribution Models for Monthly Rainfall in the Lake Toba Region Syukri Arif Rafhida; Sri Nurdiati; Retno Budiarti; Mohamad Khoirun Najib
ZERO: Jurnal Sains, Matematika dan Terapan Vol 9, No 2 (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.v9i2.25688

Abstract

Understanding rainfall's statistical distribution is crucial for effective water resource management, disaster mitigation, and climate adaptation in tropical regions. This study identifies the best-fit probability distributions for monthly rainfall in the Lake Toba region, Indonesia, based on long-term data from 34 rain gauge stations. Ten commonly used probability distributions were evaluated, with parameters estimated via Maximum Likelihood Estimation (MLE). The Kolmogorov-Smirnov (KS) test was applied to assess model goodness-of-fit at each station and month. Results indicate that the Generalized Extreme Value (GEV), Gamma, and Weibull distributions consistently provide the best fit for most stations and regencies, while Exponential and Inverse Gaussian distributions perform poorly. Spatial analysis reveals notable variation in best-fit models among regencies, emphasizing the influence of local topography and microclimate. These results highlight the need to select flexible probability models for hydrological planning and climate risk assessment in complex tropical regions. The findings provide valuable references for rainfall modeling and bias correction elsewhere.
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 Azizah Aulia Firdhasari Blante, Trianty Putri Chairunisa, Ghevira David Vijanarco Martal Dezvini Muthmainnati Vidia Ekaputri, Dhea Elis Khatizah Elis Khatizah Endar Hasafah Nugrahani Ester Antika Fahren Bukhari Fahren Bukhari Fahren Bukhari Faiqul Fikri Faiza Mayla Sabita Farah Annisa Tri Sundari Fathia Rahmaisty Fatmawati, Linda Leni Fauzan, Muhammad Daryl Ferdy Aliansyah Hasyim Foky Michelin 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 M. Junus Kautsar Hilmi 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 Mualim Arya Ilyas Wiradinata Muhamad Adzka Rizkia 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 Najwaa Alifya Azka Nandika Safiqri Naura Dalta Indriyani Nerissa Patrice Manuella NGAKAN KOMANG KUTHA ARDHANA Nicholas Abisha Noval Nur Fallahi, Putri Afia Nur Nabila Nurdiari, Sri Nuzhatun Nazria Pratama, Yoga Abdi Putri, Renda S. P. Rafhida, Syukri Arif Rahma Alya Zahrani 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 Sopaheluwakan, Ardhasena Sri Nurdiati Sriwahyuni, Lilis Suci Nur Setyawati Sukmana, Ihwan SYAHID AHMAD MUKRIM Sya’adah, Syifa Noer Syukri Arif Rafhida Syukri Arif Rafhida Syukri Arif Rafhida Talenta Parfaibya Mahenindra Trianty Putri Blante Triwulandari, Raden Roro Carissa Valentdio, Ruben Harry Wanda Nugraha Wigawijayanti Wigawijayanti Yoga Abdi Pratama Yulianty, Sherly Zelisha Pitriatuz Zahra Fauzi