M. Khalil, A. Khalil, and A. Ngom, “A Comprehensive Study of Vision Transformers in Image Classification Tasks.” 2023. [Online]. Available: http://arxiv.org/abs/2312.01232

N. F. Abubacker and M. Raheem, “Handwritten Digit Recognition Using Machine Learning,” J. Theor. Appl. Inf. Technol., vol. 102, no. 5, pp. 2172–2180, 2024, doi: 10.54254/2755-2721/5/20230613.

S. Dewi, F. Ramadhani, and S. Djasmayena, “Klasifikasi Jenis Jerawat Berdasarkan Gambar Menggunakan Algoritma CNN (Convolutional Neural Network),” Hello World J. Ilmu Komput., vol. 3, no. 2, pp. 68–73, 2024, doi: 10.56211/helloworld.v3i2.518.

M. Kevin Santosa, M. Hanindia Prami Swari, and A. Nugroho Sihananto, “Implementasi Arsitektur Alexnet Dan Resnet34 Pada Klasifikasi Citra Penyakit Daun Kentang Menggunkan Transfer Learning,” JATI (Jurnal Mhs. Tek. Inform., vol. 7, no. 5, pp. 3293–3301, 2024, doi: 10.36040/jati.v7i5.7337.

W. Setiawan, A. Ghofur, F. Hastarita Rachman, and R. Rulaningtyas, Deep Convolutional Neural Network AlexNet and Squeezenet for Maize Leaf Diseases Image Classification. KINETIK: Game Technology, Information System, Computer Network, Computing, Electronics and Control, 2021. doi: 10.22219/kinetik.v6i4.1335.

B. Gope, S. Pande, N. Karale, S. Dharmale, and P. Umekar, Handwritten digits identification using mnist database via machine learning models, vol. 1022, no. 1. PURPOSE-LED PUBLISHING, 2021. doi: 10.1088/1757-899X/1022/1/012108.

L. Ming Seng, B. Bang Chen Chiang, Z. Arabee Abdul Salam, G. Yih Tan, and H. Tong Chai, “MNIST handwritten digit recognition with different CNN architectures,” J. Appl. Technol. Innov., vol. 5, no. 1, pp. 2600–7304, 2021.

Y. Chychkarov, A. Serhiienko, I. Syrmamiikh, and A. Kargin, Handwritten digits recognition using SVM, KNN, RF and deep learning neural networks, vol. 2864. CEUR-WS.org, 2021. doi: 10.32782/cmis/2864-44.

A. A. Yahya, J. Tan, and M. Hu, “A novel handwritten digit classification system based on convolutional neural network approach,” Sensors, vol. 21, no. 18, 2021, doi: 10.3390/s21186273.

F. Panjaitan, R. S. Simbolon, and J. Batubara, “Handwritten Digit Recognition Using Support Vector Machine (SVM) with Radial Basis Function (RBF) Kernel,” J. Basic Sci. Technol., 2025.

R. Zhu et al., MNIST classification using Neuromorphic Nanowire Networks. Association for Computing Machinery, 2021. doi: 10.1145/3477145.3477162.

Susilawati, “Algoritma Restricted Boltzmann Machines (RBM) untuk Pengenalan Tulisan Tangan Angka,” Semin. Nas. Teknol. Inform., pp. 140–148, 2017.

T. Soupizet, Z. Jouni, S. Wang, A. Benlarbi-Delai, and P. M. Ferreira, “Analog Spiking Neural Network Synthesis for the MNIST,” J. Integr. Circuits Syst., vol. 18, no. 1, 2023, doi: 10.29292/jics.v18i1.663.

F. Adams, R. A. Dwi Anggoro, M. B. Satria, A. W. Oktavia, and N. Chamidah, “Perbandingan Normalisasi Data untuk Klasifikasi Wine Menggunakan Algoritma Naïve Bayes, Decision Tree, dan Support Vector Machine,” Semin. Nas. Mhs. Ilmu Komput. dan Apl., vol. 2, no. 2, pp. 260–268, 2021, [Online]. Available: https://conference.upnvj.ac.id/index.php/senamika/article/view/1744

Gde Agung Brahmana Suryanegara, Adiwijaya, and Mahendra Dwifebri Purbolaksono, “Peningkatan Hasil Klasifikasi pada Algoritma Random Forest untuk Deteksi Pasien Penderita Diabetes Menggunakan Metode Normalisasi,” in Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), 2021, pp. 114–122. doi: 10.29207/resti.v5i1.2880.

B. Qiang et al., “SqueezeNet and Fusion Network-Based Accurate Fast Fully Convolutional Network for Hand Detection and Gesture Recognition,” IEEE Access, vol. 9, pp. 77661–77674, 2021, doi: 10.1109/ACCESS.2021.3079337.

P. Theerthagiri, A. U. Ruby, J. G. C. Chandran, T. H. Sardar, and A. Shafeeq B. M, “Deep SqueezeNet learning model for diagnosis and prediction of maize leaf diseases,” J. Big Data, vol. 11, no. 1, 2024, doi: 10.1186/s40537-024-00972-z.

M. Sipper, “High Per Parameter: A Large-Scale Study of Hyperparameter Tuning for Machine Learning Algorithms,” Algorithms, vol. 15, no. 9, 2022, doi: 10.3390/a15090315.

Afis Julianto, Andi Sunyoto, and Ferry Wahyu Wibowo, “Optimasi Hyperparameter Convolutional Neural Network Untuk Klasifikasi Penyakit Tanaman Padi,” Tek. Teknol. Inf. dan Multimed., vol. 3, no. 2, pp. 98–105, 2022, doi: 10.46764/teknimedia.v3i2.77.