Article Per Year (5 Year)
p-Index From 2019 - 2024
0.444
P-Index
This Author published in this journals
All Journal JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Proceeding Applied Business and Engineering Conference
Ajulio Padly Sembiring
Unknown Affiliation
Religion Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Applications For Detecting The Rate Of Fruit In Mangrove Plants Sharfina Faza; Meyatul Husna; Ajulio Padly Sembiring; Rina Anugrahwaty; Silmi; Romi Fadillah Rahmat; Rhama Permadi Ahmad
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 6 No. 2 (2023): Issues January 2023
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v6i2.8379

Abstract

Mangrove plants are one of the plants that really help aquatic ecosystems between the sea, coast, and land. Mangrove plants provide many ecological, social, and economic benefits. In Indonesia, mangrove plants have 202 species with the same anatomy as other plants in general, consisting of roots, fruits, stems and leaves. Nowadays, the location of mangrove plants in Indonesia has experienced the fastest damage in the world due to conversion to ponds, settlements, industry and plantations. One of the efforts to restore aesthetic value and restore the ecological function of mangrove forest areas is rehabilitation using mangrove fruit. In the rehabilitation process, farmers generally use the manual method with the naked eye to determine fruit ripeness on mangrove plants, so the resulting level of accuracy is not optimal. To overcome this problem, an application is needed that can facilitate farmers in determining fruit maturity in mangrove plants so that it can help determine the maturity level of mangrove fruit. The development of this application utilizes the Deep Learning method as well as the utilization of digital image processing techniques with Grayscaling, Adaptive Threshold, Sharpening and Smoothing techniques. The results of this study are an application that can detect the level of fruit maturity in mangrove plants with an accuracy of 99.11%. With this application, determining the maturity level of fruit on mangrove plants can be easily done.
Klasifikasi Kecambah Mangrove Menggunakan Multi Layer Perceptron Sharfina Faza; Ajulio Padly Sembiring
ABEC Indonesia Vol. 9 (2021): 9th Applied Business and Engineering Conference
Publisher : Politeknik Caltex Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Data processing in the field of machine learning and its application to environment is still an interesting field until now. It is because there have been a lot of research related to computer science and agriculture especially mangroves, so there are still many research gaps that can be executed in the future. The long-term goal of this research is to apply machine learning techniques to the data and problem domains of mangrove plants. This study aims to obtain a classification of three classes of mangrove sprouts, namely: Avicennia Marina, Sonneratia Caseolaris and Ceriops Tagal, using the Multi Layer Perceptron (MLP) method, where MLP is one of the methods in the field of Machine Learning and Artificial Intelligence. The results of this study are using the number of neurons in the hidden layer more than the number of neurons in the input layer resulting in an optimal accuracy value at the 1000th epoch with an accuracy value of 97.7% for data testing, and an accuracy value of 99% for testing data. Keywords: Machine Learning, Classification, Mangrove Sprouts, Multi-Layer Perceptron.
Co-Authors Meyatul Husna Rhama Permadi Ahmad Rina Anugrahwaty Romi Fadillah Rahmat Sharfina Faza Silmi