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All Journal Journal of Deep Learning, Computer Vision and Digital Image Processing Jurnal MediaTIK
Ihlasul Amal
Universitas Negeri Makassar
Computer Science & IT Education Social Sciences Other

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Identifikasi Identifikasi Tulisan Tanda Tangan Menggunakan Algoritma Convolutional Neural Network Ihlasul Amal; Ishak; Muh.Devan Fahresi; Maulana Muhammad
Journal of Deep Learning, Computer Vision, and Digital Image Processing Vol 1 No 2 (2023): Volume 1 Issue 2 September 2023
Publisher : CV. Sakura Digital Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61255/decoding.v1i2.157

Abstract

Signature is the outcome of a writing process with distinct characteristics, serving as one of the proofs of the validity of an agreement conducted by two or more parties as evidence of identity verification. This study aims to design a system capable of identifying an individual based on inputted signature images into the system. The rapid development of knowledge can certainly be leveraged to facilitate and address issues in human daily life, one of which is the application of expertise in the field of pattern recognition, enabling the creation of a signature identification system. There are five main stages employed in this research, namely image acquisition, image augmentation, system architecture design, training process, and testing process. The research results demonstrate that the applied method proves to be effective in designing a signature identification system. This is substantiated by the accuracy level of the system testing reaching 98.148%.
Sistem Pendeteksi Kematangan Buah Tomat Berbasis Pengolahan Citra Digital Menggunakan Metode Jaringan Syaraf Tiruan Ishak; Ihlasul Amal; Maulana Muhammad; Andi Baso Kaswar
Jurnal MediaTIK Volume 5 Issue 1, Januari (2022)
Publisher : Jurusan Teknik Informatika dan Komputer

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Abstract

Penelitian ini bertujuan untuk merancang sistem yang mampu mendeteksi tingkat kematangan buah tomat dengan memanfaatkan pesatnya perkembangan ilmu pengetahuan khususnya di bidang artificial intelligence yang dikolaborasikan dengan pengolahan citra digital. Terdapat lima tahapan metode yang digunakan dalam penelitian ini yakni, tahap akuisisi citra, tahap preprocessing, tahap segmentasi, tahap morfologi dan tahap klasifikasi. Hasil penelitian menunjukkan bahwa metode yang digunakan peneliti sangat cocok untuk merancang sistem pendeteksi tingkat kematangan buah tomat yakni mentah, mengkal, dan matang. Hal tersebut dibuktikan dengan tingkat akurasi pengujian sistem yang mencapai 100%. Jumlah dataset yang digunakan sebanyak 90 citra tomat yaitu 30 citra tomat mentah, 30 citra tomat mengkal, dan 30 citra tomat matang.
Co-Authors Andi Baso Kaswar Andi Baso Kaswar Ishak Ishak Maulana Muhammad Maulana Muhammad Muh.Devan Fahresi