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All Journal ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Kultivasi Jurnal Sains Teh dan Kina
Adhi Irianto Mastur, Adhi Irianto
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Agriculture, Biological Sciences & Forestry Computer Science & IT Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Mechanical Engineering

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Prunning technique and foliar fertilizer application to improve yield of pecco in fourth prunning year of tea plant haq, muthia syafika; Mastur, Adhi Irianto; Karyudi, Dr H
Jurnal Penelitian Teh dan Kina Vol 19, No 1 (2016)
Publisher : Research Institute for Tea and Cinchona

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (283.734 KB) | DOI: 10.22302/pptk.jur.jptk.v19i1.54

Abstract

Effect of prunning technique and foliar fertilizer application had been conducted to improve yield of pecco in the Research Institute for Tea and Cinchona experiment station, Gambung, Indonesia with altitude of 1.350 m above sea level, and with andysol soil type. The experiment was held from July to October 2015 in tea production field area containing 480 plants. The clone was GMB 7and was in the fourth prunning year, interval of plucking pecco was seven days. The experiment was arranged in a randomized block design with four treatments, replicated six times. Foliar fertilizer application was performed following every plucking. The results indicated that breaking apical dominance of tea short by 5 cm–10 cm of prunning above plucking table combined with foliar fertilizer application of N 1% and ZnSo4 2% + 0,1% of humic acid, could increase weight of pecco per plot in the first nine weeks of the prunning. This treatment was better than the other three. But the percentage of pecco shoots was low, lower than 50%, the weight of one pecco was also very low 0,7 g, indicating that the treatment of harvesting pecco in the fourth prunning year was not recomended to be practiced in improving yield of pecco.
Review: The harvesting process and recent advances on health benefits of white tea Mastur, Adhi Irianto; Karuniawan, Agung; Ernah, Ernah
Kultivasi Vol 22, No 3 (2023): Jurnal Kultivasi
Publisher : Universitas Padjadjaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24198/kultivasi.v22i3.50075

Abstract

The meticulous harvesting process and appropriate post-harvest techniques play pivotal roles in preserving the quality and health benefits of white tea. This careful approach maintains the bioactive compounds such as polyphenols, caffeine, gallic acid, Epigallocationchin (EGC), Epigallocationchin gallate (EGCG), and Epicatechin gallate (ECG), integral to white tea's health benefits. The stability of catechin content in tea plants is greatly influenced by the environment (clone, plant age, leaf age, altitude, temperature, humidity, processing, and pH when storing dry tea). In Indonesia, the raw materials used to produce white tea are mostly pecco from the superior GMB clone Assamica variety which has high polyphenol content (14.83 – 15.43% dry weight). To increase polyphenol levels, the treatment that needs to be considered is the provision of optimum and appropriate fertilizer. The highest catechin content comes from plucking in summer and spring season. Subsequently, controlled post-harvest processes, including controlled withering and drying, safeguard the integrity of active compounds like catechins as antioxidants in white tea, mitigating free radicals and cellular damage.  The highest antioxidant showed from 23 hours whithered. The storage time for white tea also has an impact on quality. The content of catechins and amino acids showed a tendency to decrease with storage time. On the other hand, gallic acid increases with the length of storage. The combined effect of these phases, from harvesting through post-harvesting, contributes significantly to white tea's health benefits, encompassing cardioprotective effects, anti-diabetic potential, prevention of anticarcinogenic and antimutagenic activity, neuroprotective properties, and antimicrobial attributes.
Klasifikasi Tingkat Kematangan Pucuk Daun Teh menggunakan Metode Convolutional Neural Network IBRAHIM, NUR; LESTARY, GITA AYU; HANAFI, FANIESA SAUFANA; SALEH, KHAERUDIN; PRATIWI, NOR KUMALASARI CAECAR; HAQ, MUTHIA SYAFIKA; MASTUR, ADHI IRIANTO
ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Vol 10, No 1: Published January 2022
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/elkomika.v10i1.162

Abstract

ABSTRAKIndonesia merupakan salah satu negara besar pengekspor teh di dunia. Luasnya area perkebunan teh menyebabkan setiap blok tanam daun teh memiliki waktu petik dan tingkat kematangan yang berbeda. Sehingga salah satu upaya untuk mengetahui tingkat kematangan daun teh yaitu menerapkan sistem otomatisasi menggunakan pengolahan citra digital. Pada penelitian ini dirancang sebuah sistem klasifikasi tingkat kematangan pucuk daun teh dari jenis sampel varietas Assamica Klon (GMB 7) yang yang berada pada Pusat Penelitian Teh dan Kina Gambung. Penelitian ini menerapkan metode pengolahan citra digital dengan algoritma Convolutional Neural Network (CNN) menggunakan Arsitektur VGGNET19 dan ResNet50. Hasil pengujian sistem memperoleh nilai akurasi terbaik sebesar 97.5% dengan menggunakan arsitektur VGGNET19, Optimizer RMSprop, learning rate 0.01, batch size 32 dan epoch 100.Kata kunci: teh, klasifikasi, Convolutional Neural Network, VGGNET19, ResNet50 ABSTRACTIndonesia is one of the major tea exporting countries in the world. The large area of tea plantations causes each tea leaves planting block to have a different picking time and maturity level. So that one of the efforts to determine the maturity level of tea leaves is to apply an automation system using digital image processing. In this study, a classification system for the maturity level of tea leaves design from the Assamica Klon (GMB 7) variety sample located at the Gambung Tea and Quinine Research Center. This study applies a digital image processing method with the Convolutional Neural Network (CNN) algorithm using VGGNET19 and ResNet50 Architecture. The results of the system test obtained the best accuracy value of 97.5% using the VGGNET19 architecture, RMSprop Optimizer, learning rate 0.01, batch size 32 and epoch 100.Keywords: tea, classification, Convolutional Neural Network, VGGNET19, ResNet50
Co-Authors Agung Karuniawan Dr H Karyudi, Dr H Ernah, Ernah HANAFI, FANIESA SAUFANA KHAERUDIN SALEH LESTARY, GITA AYU Muthia Syafika Haq, Muthia Syafika Nor Kumalasari Caecar Pratiwi Nur Ibrahim