Article Per Year (5 Year)
p-Index From 2019 - 2024
5.208
P-Index
This Author published in this journals
All Journal JUITA : Jurnal Informatika ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika JOURNAL OF ELECTRICAL AND SYSTEM CONTROL ENGINEERING MIND (Multimedia Artificial Intelligent Networking Database) Journal TESLA: Jurnal Teknik Elektro Jurnal Tekinkom (Teknik Informasi dan Komputer) Jurnal Teknik Informatika (JUTIF) Almufi Jurnal Pengabdian Kepada Masyarakat (AJPKM) eProceedings of Engineering
Nur Ibrahim
Telkom University
Religion Humanities Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Social Sciences Other

Published : 54 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search
Journal : JOURNAL OF ELECTRICAL AND SYSTEM CONTROL ENGINEERING

 1 
TEA LEAVES GMB SERIES CLASIFFICATION USING CONVOLUTIONAL NEURAL NETWORK Rizal, Syamsul; Pratiwi, Nor Kumalasari Caecar; Ibrahim, Nur; Vidya, Hurianti; Saidah, Sofia; Fu'adah, R Yunendah Nur
JESCE (JOURNAL OF ELECTRICAL AND SYSTEM CONTROL ENGINEERING) Vol 3, No 2 (2020): Journal Of Electrical And System Control Engineering Februari
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (860.876 KB) | DOI: 10.31289/jesce.v3i2.3320

Abstract

This study classifies GMB series tea leaves by using a convolutional neural network as a classification system. GMB series tea are the superior tea seeds in Indonesia. Gambung series, namely: GMB 1 to GMB 11, are planting material seeds that have been recommended by the Ministry of Agriculture. The potential of these tea series yield of 4,000 - 5,800 kg / ha of dried tea. The morphological similarity level of GMB 1 to GMB 11 is very high, because many elders from the clones are from the same crossing parents. During this time, the process of identifying GMB clones 1 through GMB 11 is done manually using the visual eye of an experts at PPTK Gambung. These experts are limited to be able to identify each tea series. This process is susceptible to errors in the reading of clone types, and is very dependent on the presence of the experts. If an error occurs in the process of identifying the type of clone, it will interfere with the nursery process. Errors in the selection of recommended clones will harm the process of a long period of time, because the economic age of tea plants can reach until 50 years. The potential loss of production due to misuse of plant material can reach 1,200 kg / ha per year. Against the background of these problems, it is very necessary to have a system to identify the GMB series clone. Continuous studies has been conducted to build an automation system for the identification and classification of GMB series tea clones. The system is designed using the Convolutional Neural Network (CNN) method. The results obtained from this system output in the form of accuracy with a value of 85%.
KLASIFIKASI DAUN TEH GAMBUNG VARIETAS ASSAMICA MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK DENGAN ARSITEKTUR LENET-5 Abdul Hafiz Suherman; Nur Ibrahim; Heri Syahrian; Vitria Puspitasari Rahadi; Muhammad Khais Prayoga
JOURNAL OF ELECTRICAL AND SYSTEM CONTROL ENGINEERING Vol 4, No 2 (2021): Journal of Electrical And System Control Engineering
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jesce.v4i2.4136

Abstract

Indonesia merupakan salah satu pengolahan produk teh gambung terbesar. Produk teh gambung dihasilkan dengan jenis teh yang berbeda. Namun, kualitas system pengolahan produk mengalami penurunan dikarenakan pekebun sulit membedakan jenis daun teh produksi dengan daun teh unggul dan masih menggunakan prosedur pengolahan daun secara manual. Diketahui, daun teh gambung memiliki 11 klon jenis. Daun teh GMB (1-11) merupakan klon unggul jenis teh dari jenis assamica maupun jenis sinensis dari hasil riset Pusat Penelitian Teh dan Kina (PPTK). Oleh karena itu, diperlukan teknologi pengenalan jenis daun teh sebagai peningkatan kualitas produk. Penelitian ini membuat metode klasifikasi, yaitu dengan menggunakan metode Convolutional Neural Network (CNN) sebagai algoritma klasifikasi. Proses klasifikasi data citra daun akan diuji dengan kelas sebanyak 11 jenis daun klon dan jumlah dataset diaugmentasi sebesar 4400 data. Arsitektur LeNet-5 akan digunakan pada pengujian model klasifikasi. Proses klasifikasi memperoleh hasil terbaik dengan nilai akurasi sebesar 94.55% dengan parameter optimizer Adam dan learning rate yang digunakan sebesar 0.001.
TEA LEAVES GMB SERIES CLASIFFICATION USING CONVOLUTIONAL NEURAL NETWORK Syamsul Rizal; Nor Kumalasari Caecar Pratiwi; Nur Ibrahim; Hurianti Vidya; Sofia Saidah; R Yunendah Nur Fu'adah
JOURNAL OF ELECTRICAL AND SYSTEM CONTROL ENGINEERING Vol 3, No 2 (2020): Journal Of Electrical And System Control Engineering Februari
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jesce.v3i2.3320

Abstract

This study classifies GMB series tea leaves by using a convolutional neural network as a classification system. GMB series tea are the superior tea seeds in Indonesia. Gambung series, namely: GMB 1 to GMB 11, are planting material seeds that have been recommended by the Ministry of Agriculture. The potential of these tea series yield of 4,000 - 5,800 kg / ha of dried tea. The morphological similarity level of GMB 1 to GMB 11 is very high, because many elders from the clones are from the same crossing parents. During this time, the process of identifying GMB clones 1 through GMB 11 is done manually using the visual eye of an experts at PPTK Gambung. These experts are limited to be able to identify each tea series. This process is susceptible to errors in the reading of clone types, and is very dependent on the presence of the experts. If an error occurs in the process of identifying the type of clone, it will interfere with the nursery process. Errors in the selection of recommended clones will harm the process of a long period of time, because the economic age of tea plants can reach until 50 years. The potential loss of production due to misuse of plant material can reach 1,200 kg / ha per year. Against the background of these problems, it is very necessary to have a system to identify the GMB series clone. Continuous studies has been conducted to build an automation system for the identification and classification of GMB series tea clones. The system is designed using the Convolutional Neural Network (CNN) method. The results obtained from this system output in the form of accuracy with a value of 85%.
Co-Authors Abdul Hafiz Suherman ADHI IRIANTO MASTUR Afifah Amatulla Suaib Andrean David Chrismadandi Anindita Fitriani Annisa Adlina Mulyaningrum Annisa Bianca Hayuningtyas Ari Ashari Jaelani Asyraf Fakhri AZIZAH AULIA RAHMAN BACHERAMSYAH, TASYA FIKRIYAH Bambang Hidayat BAMBANG HIDAYAT Bambang Hidayat Begita Wahyuningtyas Citra Marshela Danish Ario Wirawan Denis Ramadana Efri Suhartono Eka Wulandari Fajar Dwi Septria FANIESA SAUFANA HANAFI Fanny Oksa Salindri Faturachman Faturachman FAUZI FRAHMA TALININGSING Fiky Yosef Supratman Frisnanda Aditya Galuh Lintang Permatasari Gelar Budiman GITA AYU LESTARY HAFIZHANA, YASQI HERI SYAHRIAN Heri Syahrian HERLAMBANG YOGASWARA Hurianti Vidya IBNU DAWAN UBAIDULLAH Ibnu Da’wan Salim Ubaidah Ilva Herdayanti Inung Wijayanto Iqbal Afriadi Irma Safitri Iwan Iwut Tritoasmoro Iwan Iwut Tritosmoro JANGKUNG RAHARJO Kevin Aglianry KHAERUDIN SALEH Koredianto Usman Krisma Asmoro Ledya Novamizanti MOCHAMMAD HALDI WIDIANTO Muh, Ipnu Udjie Hasiru Muh. Gazali Saleh MUHAMMAD ADNAN PRAMUDITHO Muhammad Khais Prayoga Muhammad Rizqi Rahmawan MUTHIA SYAFIKA HAQ Nabila Herman Nasywan Azrial Fariqin NIDAAN KHOFIYA SY NOR CAECAR KUMALASARI Nor Kumalasari Caecar Pratiwi NOR KUMALASARI CAESAR PRATIWI R. Yunendah Nur Fu’adah Rahma Nur Auliasari Ramadhan Prasetya Dahlan Ramdhan Nugraha Ramdhan Nugraha Reyhan Ivandhani Reza Yudistira Rezki Diar Amelia Rita Magdalena Rita Purnamasari Rustam Satrio Ardhimasetyo SISLY DESTRI AGUSTIN Sjafril Darana Sjafril Darana SJAFRIL DARANA SOFIA SAIDAH SOFIA SA’IDAH Syamsul Rizal Syamsul Rizal Syifa Maliah Rachmawati TASYA FIKRIYAH BACHERAMSYAH Vidiya Rossa Atfira Vidya, Hurianti VITRIA PUSPITASARI RAHADI Vitria Puspitasari Rahadi WIDIANTO, MOCHAMMAD HALDI YASQI HAFIZHANA YOGASWARA, HERLAMBANG