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All Journal ELKHA : Jurnal Teknik Elektro JPM17: Jurnal Pengabdian Masyarakat Jurnal IPTEK SMARTICS Journal JMM (Jurnal Masyarakat Mandiri) CYCLOTRON Prosiding Seminar Nasional Sains dan Teknologi Terapan Adimas : Jurnal Pengabdian Kepada Masyarakat JE-Unisla Indonesian Journal of Electrical Engineering and Computer Science Mechonversio: Mechanical Engineering Journal Jurnal Tika SinarFe7 Prosiding Seminar Nasional Teknik Elektro, Sistem Informasi, dan Teknik Informatika (SNESTIK) PELS (Procedia of Engineering and Life Science) Sewagati
Muharom, Syahri
Institut Teknologi Adhi Tama Surabaya
Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mechanical Engineering Public Health Social Sciences Other

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Journal : Jurnal Tika

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Klasifikasi Jenis Tanaman Rempah Rhizoma Zingiberaceae dengan Metode CNN dan VGG 19 Haris Abdullah Firmasnsyah; Kurnaiwan Muchamad; Citra Nurina Prabiantissa; Syahri Muharom
Jurnal Tika Vol 9 No 1 (2024): Jurnal Teknik Informatika Aceh
Publisher : Fakultas Ilmu Komputer Universitas Almuslim Bireuen - Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51179/tika.v9i1.2557

Abstract

The need for identification of spice plant species is very important to achieve accuracy levels accurately and efficiently. Previous researchers have demonstrated the success of this CNN method in classifying various spice plant species. However, only three species of Zingiberaceae (also known as ginger) spice plants were studied in this research: ginger, turmeric, and galangal. There has not been much previous research on these plant species. To ensure label accuracy, this study compares the performance of two popular CNN optimizers, Adam and SGD. A dataset of spice plant images obtained from Internet websites was then diagnosed by experts. To prepare for training the CNN model with VGG19, the image data is pre-processed. The pre-trained VGG19 architecture is used as the basis for spice plant classification. The classification accuracy is used to evaluate the performance of the model. The results of the study show that in the classification of spice plants, the use of the pre-trained VGG19 architecture is used, providing research results that also show that the architectural CNN method successfully classifies Zingiberaceae spice plant species. Consistently, the use of Adam's optimizer resulted in higher accuracy than SGD. This suggests that Adam's optimizer may be more effective in optimizing VGG19 model parameters for spice plant classification
Klasifikasi Jenis Tanaman Rempah Rhizoma Zingiberaceae dengan Metode CNN dan VGG 19 Firmasnsyah, Haris Abdullah; Muchamad, Kurnaiwan; Prabiantissa, Citra Nurina; Muharom, Syahri
Jurnal Tika Vol 9 No 1 (2024): Jurnal Teknik Informatika Aceh
Publisher : Fakultas Ilmu Komputer Universitas Almuslim Bireuen - Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51179/tika.v9i1.2557

Abstract

The need for identification of spice plant species is very important to achieve accuracy levels accurately and efficiently. Previous researchers have demonstrated the success of this CNN method in classifying various spice plant species. However, only three species of Zingiberaceae (also known as ginger) spice plants were studied in this research: ginger, turmeric, and galangal. There has not been much previous research on these plant species. To ensure label accuracy, this study compares the performance of two popular CNN optimizers, Adam and SGD. A dataset of spice plant images obtained from Internet websites was then diagnosed by experts. To prepare for training the CNN model with VGG19, the image data is pre-processed. The pre-trained VGG19 architecture is used as the basis for spice plant classification. The classification accuracy is used to evaluate the performance of the model. The results of the study show that in the classification of spice plants, the use of the pre-trained VGG19 architecture is used, providing research results that also show that the architectural CNN method successfully classifies Zingiberaceae spice plant species. Consistently, the use of Adam's optimizer resulted in higher accuracy than SGD. This suggests that Adam's optimizer may be more effective in optimizing VGG19 model parameters for spice plant classification
Co-Authors Abdul Hamid Achmad Efendi Setiawan Ade Rachmawan Ade Rachmawan Adelina Purba Adji Ramadhan Adjie Ramadhan Affan Bachri Akbar, Zakky Rezky Aloysius Gonzaga Kristianto Juje Andy Suryowinoto Antonius Ari Kunto Antonius Ari Kunto Bagus Priyo Raharjo Bagus Yudit Laksono Bernado Da Costa Ximenes Bintang Ramadhani Candrarani, Diva Choirul Anam AM Diponegoro Citra Nurina Prabiantissa Denaldan Tabarui Landu Praing Devy Kuswidiastuti Dimas Aditya Putra Wardhana Djoko Purwanto Firmansyah, Riza Agung Firmansyah, Vegal Firmasnsyah, Haris Abdullah Firnanda, Rony Haris Abdullah Firmasnsyah Haris Rachmansyah Heru Suseno Heru Suseno, Heru Huda, Mochammad Syamsul Ilham Surya Saputra Ilmiatul Masfufiah Imam taufik Imam Taufik Karisma Trinanda Putra Kurnaiwan Muchamad Laksono, Bagus Yudit Mamat Septyan Marcelinus Amalia Lamanele Masfufiah, Ilmiatul Misbahul Munir Moch. Kalam Mollah Muchamad, Kurnaiwan Muh Luay Bagus Pamungkas Muhammad Attamimi Muhammad Hasyim, Muhammad Muhammad Rivai Muhammad Shofiyullah Odinanto, Tjahya Prabowo, Yulianto Agung Prakoso, Asril Rahmat Pratama, Sandy Putra Dharma Rachmawan, Ade Raharjo, Bagus Priyo Riza Agung Romadhon, Sandi Romadon, Moch Syahrul Fajar Ronny Mardiyanto, Ronny Rudy Dikairono Ruth Johana Hutagalung Saiful Asnawi Sasmito Oetomo Septyan, Mamat Setiawan, Achmad Efendi Setyawan, Surya Adhi Surya Adhi Setyawan Surya Adi Surya Adi Titiek Suheta Titiek Suheta Titiek Suheta Tjahya Odinanto Totok Mujiono, Totok Trisna Wati Trisna Wati, Trisna Tukadi Tukadi, Tukadi Wahyu Setyo Pambudi Yuliyanto Agung Prabowo Yunardi, Riky Tri