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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Sinergi InComTech: Jurnal Telekomunikasi dan Komputer Indonesian Journal of Electrical Engineering and Computer Science Journal of Integrated and Advanced Engineering (JIAE)
Mirna Yunita
Beijing Institute of Technology
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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Journal : Sinergi

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Implementation of Bayesian inference MCMC algorithm in phylogenetic analysis of Dipterocarpaceae family Mirna Yunita; Rachmat Muwardi; Zendi Iklima
SINERGI Vol 27, No 1 (2023)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2023..1.004

Abstract

Dipterocarpaceae is one of the most prominent plant families, with more than 500 members of species. This family mostly used timber plants for housing, making ships, decking, and primary materials for making furniture. In Indonesia, many Dipterocarpaceae species have morphological similarities and are challenging to recognize in the field. As a result, the classification process becomes difficult and even results are inconsistent when viewed only from the morphology. This research will analyze the phylogenetic tree of Dipterocarpaceae based on the chloroplast matK gene. The aim of the research is to classify the phylogenetics tree of Dipterocarpaceae family using Bayesian inference algorithm. This research used the chloroplast gene instead of morphological characters which has more accurate. The analysis steps are collecting data, modifying the structure sequence name, sequence alignment, constructing tree by using Markov Chain Monte Carlo (MCMC) from Bayesian Inference, and evaluating and analyzing the phylogenetic tree. The results showed that the tree constructed based on the gene is different from the tree based on morphology. Based on the morphological, Dipterocarpus should be in the Dipterocarpeae tribe but based on the similarity of its genes, Dipterocarpus is more similar to the Shoreae tribe.   
Multilabel image analysis on Polyethylene Terephthalate bottle images using PETNet Convolution Architecture Khoirul Aziz; Inggis Kurnia Trisiawan; Kadek Dwi Suyasmini; Zendi Iklima; Mirna Yunita
SINERGI Vol 27, No 2 (2023)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2023.2.003

Abstract

Packaging is one of the important aspects of the product. Good packaging can increase the competitiveness of a product. Therefore, to maintain the quality of the packaging of a product, it is necessary to have a visual inspection. Furthermore, an automatic visual inspection can reduce the occurrence of human errors in the manual inspection process. This research will use the convolution network to detect and classify PET (Polyethylene Terephthalate) bottles. The Convolutional Neural Network (CNN) method is one approach that can be used to detect and classify PET bottle packaging. This research was conducted by comparing seven network architecture models, namely VGG-16, Inception V3, MobileNet V2, Xception, Inception ResNet V2, Depthwise Separable Convolution (DSC), and PETNet, which is the architectural model proposed in this study. The results of this study indicate that the PETNet model gives the best results compared to other models, with a test score of 96.04%, by detecting and classifying 461 of 480 images with an average test time of 0.0016 seconds.
Co-Authors Andika Arrizki Danang Widya Pratama Diah Septiyana Firman Andika Galang P. N. Hakim Galang Persada Nurani Hakim Galatia Erica Yehezkiel Harun Usman Ghifarsyam Harun Usman Ghifarsyam Hendy Juliyanto Heri Santoso, Makmur Hongmin Gao Hongmin Gao Inggis Kurnia Trisiawan Joe Mada Ranseda Permana Julpri Andika Kadek Dwi Suyasmini Khoirul Aziz Lenni Lenni Mhd Adanan Purba Mustain Rhozaly Rachmat Muwardi Septiyana, Diah Yuliza Yuliza Yuliza Yuliza Zendi Iklima