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All Journal Jurnal Sains Matematika dan Statistika BAREKENG: Jurnal Ilmu Matematika dan Terapan Jurnal Matematika UNAND
REFI REVINA
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Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Energy Engineering Mathematics Mechanical Engineering Physics Transportation

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SOLUSI PERSAMAAN DIFERENSIAL ORDE DUA DENGAN SYARAT BATAS FRACTIONAL (KASUS AKAR RIIL BERBEDA) REFI REVINA; BUDI RUDIANTO
Jurnal Matematika UNAND Vol 10, No 1 (2021)
Publisher : Jurusan Matematika FMIPA Universitas Andalas Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jmu.10.1.99-107.2021

Abstract

Dalam artikel ini dikaji solusi persamaan diferensial linier orde dua dengan syarat batas fractional, dimana turunan fractional pada syarat batas berbentuk turunan fractional Riemann-Liouville kiri. Beberapa contoh yang mengilustrasikan hasil disajikan untuk setiap kasus.Kata Kunci: Persamaan diferensial linier orde dua, syarat batas, turunan fractional Riemann-Liouville kiri.
PERFORMANCE COMPARISON OF GRADIENT-BASED CONVOLUTIONAL NEURAL NETWORK OPTIMIZERS FOR FACIAL EXPRESSION RECOGNITION Nurdiati, Sri; Najib, Mohamad Khoirun; Bukhari, Fahren; Revina, Refi; Salsabila, Fitra Nuvus
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 16 No 3 (2022): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1086.562 KB) | DOI: 10.30598/barekengvol16iss3pp927-938

Abstract

A convolutional neural network (CNN) is one of the machine learning models that achieve excellent success in recognizing human facial expressions. Technological developments have given birth to many optimizers that can be used to train the CNN model. Therefore, this study focuses on implementing and comparing 14 gradient-based CNN optimizers to classify facial expressions in two datasets, namely the Advanced Computing Class 2022 (ACC22) and Extended Cohn-Kanade (CK+) datasets. The 14 optimizers are classical gradient descent, traditional momentum, Nesterov momentum, AdaGrad, AdaDelta, RMSProp, Adam, Radam, AdaMax, AMSGrad, Nadam, AdamW, OAdam, and AdaBelief. This study also provides a review of the mathematical formulas of each optimizer. Using the best default parameters of each optimizer, the CNN model is trained using the training data to minimize the cross-entropy value up to 100 epochs. The trained CNN model is measured for its accuracy performance using training and testing data. The results show that the Adam, Nadam, and AdamW optimizers provide the best performance in model training and testing in terms of minimizing cross-entropy and accuracy of the trained model. The three models produce a cross-entropy of around 0.1 at the 100th epoch with an accuracy of more than 90% on both training and testing data. Furthermore, the Adam optimizer provides the best accuracy on the testing data for the ACC22 and CK+ datasets, which are 100% and 98.64%, respectively. Therefore, the Adam optimizer is the most appropriate optimizer to be used to train the CNN model in the case of facial expression recognition.
Analisis Numerik Model Aliran Lapisan Batas dan Perpindahan Panas dari Nanofluida NEPCMPL Deswita, Leli; Syamsudhuha, Syamsudhuha; Rustam, Rustam; Asral, Asral; Revina, Refi; Saleh, Habibis
Jurnal Sains Matematika dan Statistika Vol 12, No 1 (2026): JSMS Januari 2026
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/jsms.v12i1.38260

Abstract

Penelitian ini memodelkan dan menganalisis aliran lapisan batas dan perpindahan panas nanofluida berbasis nano-encapsulated phase change material power-law (NEPCMPL) pada permukaan pelat yang meregang kontinu dengan suhu permukaan bervariasi. Model matematis dikembangkan menggunakan pendekatan hukum daya untuk menggambarkan karakteristik fluida non-Newtonian serta mekanisme penyimpanan panas laten yang terjadi selama transisi fasa partikel NEPCM. Persamaan tak berdimensi yang diperoleh diselesaikan secara numerik untuk mengevaluasi pengaruh indeks hukum daya, konsentrasi kapsul, dan suhu fusi terhadap distribusi kecepatan, suhu, rasio kapasitas panas, serta bilangan Nusselt. Hasil penelitian menunjukkan bahwa peningkatan konsentrasi kapsul mempercepat aliran, meningkatkan suhu sistem, serta memperluas wilayah transisi fase, dengan pengaruh yang paling dominan teramati pada fluida dilatan. Selain itu, laju perpindahan panas meningkat signifikan seiring penambahan konsentrasi kapsul, dengan peningkatan tertinggi pada nanofluida dilatan dibandingkan Newtonian maupun pseudo-plastik.
Co-Authors Asral, Asral Fahren Bukhari Habibis Saleh Leli Deswita Najib, Mohamad Khoirun Rustam Rustam Salsabila, Fitra Nuvus Sri Nurdiati Syamsudhuha Syamsudhuha