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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Agromet IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Techno.Com: Jurnal Teknologi Informasi CAUCHY: Jurnal Matematika Murni dan Aplikasi Lingua Jurnal Bahasa dan Sastra Jurnal Ilmu Komputer dan Agri-Informatika Journal of the Indonesian Mathematical Society Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Aplikasi Bisnis dan Manajemen (JABM) E-Journal Seminar Nasional Informatika (SEMNASIF) Widyariset Indonesian Journal of Science and Technology Jurnal Sains Matematika dan Statistika Al-Jabar : Jurnal Pendidikan Matematika JOIV : International Journal on Informatics Visualization JURNAL SIMETRIK Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Matematika: MANTIK MAJALAH ILMIAH GLOBE Desimal: Jurnal Matematika BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) Zero : Jurnal Sains, Matematika, dan Terapan Teorema: Teori dan Riset Matematika Jambura Journal of Mathematics Jambura Geoscience Review SALINGKA Jurnal Matematika UNAND Building of Informatics, Technology and Science Sains, Aplikasi, Komputasi dan Teknologi Informasi Indonesian Journal of Electrical Engineering and Computer Science InPrime: Indonesian Journal Of Pure And Applied Mathematics Widyariset Jambura Journal of Biomathematics (JJBM) Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Journal of Mathematics: Theory and Applications Jurnal Pijar MIPA Jurnal Sains Terapan : Wahana Informasi dan Alih Teknologi Pertanian Journal of Applied Agricultural Science and Technology Milang Journal of Mathematics and Its Applications Jurnal Sintak Jurnal Matematika Integratif Indonesian Journal of Mathematics and Applications Jurnal Pendidikan Progresif Indonesian Journal of Mathematics and Natural Sciences MILANG Journal of Mathematics and Its Applications Majalah Ilmiah Bahasa dan Sastra International Journal of Computing Science and Applied Mathematics-IJCSAM
Sri Nurdiati
Department Of Mathematics, Faculty Of Science And Mathematics, IPB University, Jl. Meranti, Kampus IPB Dramaga, Bogor 16680
Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering 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 Environmental Science Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Neuroscience Physics Social Sciences Transportation Veterinary Other

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Dynamic optimization using long short-term memory and genetic algorithms for predicting marine data Mukhlis Mukhlis; Indra Jaya; Sri Nurdiati; Karlisa Priandana; Irman Hermadi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2826-2837

Abstract

This study aims to develop an accurate and efficient ocean data prediction model to tackle the challenges posed by climate change and complex oceanographic dynamics. The main goal is to use long short-term memory (LSTM) networks along with genetic algorithms (GA) to predict four key ocean factors at once: sea surface temperature (SST), sea surface height (SSH), sea surface salinity (SSS), and chlorophyll-a (Chl-a). An experimental quantitative approach is employed, utilizing satellite data from the Banda Sea region. This approach involves time series modeling using LSTM, which is optimized by GA for hyperparameters such as the number of neurons and batch size. The results show that the combined LSTM-GA model greatly improves prediction accuracy and successfully identifies seasonal trends and irregular changes in all variables, even when there is a lot of noise. Tests reveal that the optimal configuration varies for each variable, and the GA optimization process can expedite model convergence by as little as 10 epochs. These findings underscore the effectiveness of integrating evolutionary techniques in training deep learning (DL) models for ocean data. The implications of this research include potential applications in adaptive ocean monitoring systems, early warning initiatives, and data-driven planning in marine resource management.
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
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.
Implementasi Metode Random Forest dan Support Vector Regression dalam Memprediksi Harga Cryptocurrency Ethereum Azizah Aulia Firdhasari; Lilis Sriwahyuni; Sri Nurdiati; Mohamad Khoirun Najib
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.
Numerical Solution of 2D Advection-Diffusion for River Pollutant Transport using the Finite Element Method Muhamad Adzka Rizkia; Rahma Alya Zahrani; Zelisha Pitriatuz Zahra Fauzi; Foky Michelin; Najwaa Alifya Azka; Faiza Mayla Sabita; Mualim Arya Ilyas Wiradinata; Ferdy Aliansyah Hasyim; Mochamad Tito Julianto; Sri Nurdiati; Mohamad Khoirun Najib; Syukri Arif Rafhida
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 11, No 2 (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.v11i2.42814

Abstract

Pollutant dispersion in rivers is governed by advection, diffusion, and the physical characteristics of the channel. This paper models two-dimensional pollutant transport using the advection-diffusion equation and solves it numerically with the Finite Element Method (FEM) under five scenarios: constant flow with a single pollutant source, flow that follows a meandering channel, constant flow with two sources, the presence of a rock obstacle, and an irregular river domain. Simulations are implemented in Mathematica through domain construction, mesh generation, and a Finite Element-based numerical solution. The results show that flow velocity is the primary driver of plume movement, while diffusion smooths concentration gradients. Comparative analysis across the five scenarios demonstrates that obstacle-containing and irregular domains produce the widest plume spreading and the strongest concentration deformation compared to the straight-channel case. Peak concentrations also decrease more rapidly in multi-source and irregular-flow scenarios due to enhanced mixing and plume interaction. Physical obstacles and channel irregularities generate loacal recirculation zones and plume deviation, producing more realistic pollutant transport behavior than simplified channer models. These findings highlight the importance of geometry-aware flow representations for understanding river pollutant transport in numerical modelling studies.
Co-Authors AA Gede Rai Gunawan Aaron August Vincent Soelaiman Abisha, Nicholas Ade Irawan Ade Irawan Agah D. Garnadi Agung Widyo Utomo Agus Buono Aldri Frinaldi Alifah, Nayla Nur Alifah, Rifdah Nur Alika Azka Shapira Amalia, Rizki Nurul Amanah, Ayu Anak Agung Gede Rai Gunawan Andriani, Rizka D. Annisa Annisa Permata Sari, Annisa Permata Antika, Ester Ardhana, Muhammad Reza Ardhasena Sopaheluwakan Ardhasena Sopaheluwakan Ardhasena Sopaheluwakan Ardiyani, Evi Aufa Ghifada Aulia Rizki Firdawanti Ayu Amanah Aziz, Muhammad Farhan Azizah Aulia Firdhasari Bib Paruhum Silalahi Blante, Trianty Putri Cece Sumantri Chairunisa, Ghevira David Vijanarco Martal Deni Suwardhi DEWI RAHMAWATI Dezvini Muthmainnati Vidia Edi Santosa Ekaputri, Dhea Elis Khatizah Endar Hasafah Nugrahani Eragilang Muhammad Hastapatria Ester Antika Fahren Bukhari Fahren Bukhari Fahren Bukhari Faiqul Fikri Faiza Mayla Sabita Fajar Delli Wihartiko Farah Annisa Tri Sundari Fathia Rahmaisty Fatmawati, Linda Leni Fauzan, Muhammad Daryl Ferdy Aliansyah Hasyim Foky Michelin Ginting, Dini Tri Putri Br Hanief, Hafzal Hany Savitry Harley Dearmanson Girsang Hasafah Nugrahani, Endar Heliza Rahmania Hatta, Heliza Rahmania Hendri Irwandi Henny Nuraini Henriyansah Herlambang, Karen Hilmi, Kautsar I Wayan Mangku Iftar Hendry Imni, Salsabila F. Indra Jaya Irman Hermadi Irmanida Batubara Jauhari, Muhammad Fakhri Karlisa Priandana Kasiyah Junus Kautsar Hilmi Khairuna Putri Gunawan Khatizah, Elis Khoerunnisa, Nazwa Komariah . Lana Syakina LILIK BUDIPRASETYO Lilis Sriwahyuni Linda Leni Fatmawati Lizzilmi Syarifatuz Zaimah M. Syamsul Maarif Maliha Qonita Maman Turjaman Marimin Marimin Mas’oed, Teduh W. Maulia, Syammira Dhifa Mirlan Sujana Mirza Farhan Azhari Mochamad Tito Julianto Mochamad Tito Julianto Mohamad Khoirun Najib Mualim Arya Ilyas Wiradinata Muhamad Adzka Rizkia Muhamad Syukur Muhammad Adam Tripranoto Muhammad Fikri Isnaini Muhammad Ilyas Muhammad Reza Ardhana Muhammad Tito Julianto Muhammad Zidane Bayu Mukhlis Mukhlis Muliawan Sebastian, Denny Nadhifa Zahra Ghaisani Nadhira Maulida Hayani Nadiyah, Fadilah Karamun Nisaa Najib, Mohamad K. Najib, Mohamad Khoirun Najwaa Alifya Azka Nandika Safiqri Naura Dalta Indriyani Nerissa Patrice Manuella NGAKAN KOMANG KUTHA ARDHANA Nicholas Abisha Niswati, Za'imatun Noval Nur Fallahi, Putri Afia Nur Nabila Nurwegiono, Muhammad Nuzhatun Nazria Pandu Septiawan Pratama, Yoga Abdi Prihasuti Harsani Putri, Renda S. P. Rachma Fauziah Krismayanti Rafhida, Syukri Arif Rahma Alya Zahrani Redytadevi, Tita Putri REFI REVINA Retno Budiarti Rika Kusumawati Rohimahastuti, Fadillah Ruben Harry Valentdio Salsabila, Fitra Nuvus Salsabilla Rahmah Salsabilla, Fitra Nuvus Sanjaya, Wardah Septian Dhimas Shelvie Nidya Neyman Sitanggang, Imas S. Solikin Sony Hartono Wijaya Sopaheluwakan, Ardhasena Sri Hartati Sri Mulatsih Srihadi Agungpriyono Sriwahyuni, Lilis Suci Nur Setyawati SUHARINI, YUSTINA SRI Sukmana, Ihwan SYAHID AHMAD MUKRIM Sya’adah, Syifa Noer Syukri Arif Rafhida Syukri Arif Rafhida Talenta Parfaibya Mahenindra Tri Rapani Febbiyanti Trianty Putri Blante Triwulandari, Raden Roro Carissa Usmany, Rendy Valentdio, Ruben Harry Verry Riyanto Vicky Zilvan Wigawijayanti Wigawijayanti Wisnu Ananta Kusuma Yandra Arkeman Yasin Yusuf Yoga Abdi Pratama Zelisha Pitriatuz Zahra Fauzi