Indonesia ancient temple classification using convolutional neural network

Danukusumo, Kefin Pudi; Pranowo, Pranowo; Maslim, Martinus

doi:10.1109/iccerec.2017.8226709

Cited by 12 publications

(9 citation statements)

References 5 publications

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“…Penggunaan library pada python menggunakan Tensorflow untuk mengekpresikan suatu permaslahan matematika pada Deep Learning. Deep learning merupakan salah satu bidang dari Machine Learning yang memanfaatkan jaringan syaraf tirutambahkan saran untuk implementasi permasalahan dengan dataset yag besar [5].…”

Section: Dalam Proses Face Recognition Ini Juga Digunakan Algoritmaunclassified

Face Recognition Untuk Akses Pegawai Bank Menggunakan Deep Learning Dengan Metode CNN

Arsal

Wardijono

Anggraini

2020

TEKNOSI

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Perkembangan teknologi berkembang dengan sangat cepat, sehingga memberikan banyak manfaat khususnya dalam bidang Teknologi Informasi. Permintaan layanan dengan penggunaan teknologi semakin banyak dibutuhkan oleh perusahaan. Salah satu perusahaan yang membutuhkannya adalah perusahaan perbankan. Perkembangan teknologi memudahkan perusahaan untuk bisa menyelesaikan masalah, salah satunya masalah dalam sistem keamanan. Sistem kemanan dibutuhkan dalam setiap perusahaan dalam segala aspek. Sistem keamanan untuk pintu akses pegawai merupakan permintaan sebuah perusahaan bank yang dibuat dalam penelitian ini. Pada penelitian ini, dilakukan sistem keamanan pintu akses pegawai bank dengan menggunakan face recognition. Teknologi Face Recognition menggunakan pembahasan Deep Learning. Pembuatan aplikasi ini menggunakan algoritma Convolutional Neural Network (CNN). Bahasa pemrograman yang digunakan dalam pembuatan program ini yaitu python. Proses pembuatan aplikasi ini dengan tahapan pembuatan Face Recognition yaitu akuisisi gambar, preprocessing, ektraksi, klasifikasi, dan identifikasi data gambar. Penelitian ini berhasil menggunakan Face Recognition oleh 5 orang dataset wajah pegawai bank yang terdiri dari 70 data wajah pada masing-masing orang. Sehingga total data wajah yang digunakan 350 data wajah. Dataset tersebut dipisahkan menjadi 3 tahapan data yaitu data train, data validasi, dan data uji. Hasil dari pengujian ketiga dataset tersebut berhasil mengidentifikasi wajah yang ditangkap oleh kamera dengan persentase keakuratan 95%. Program pada penelitian ini berhasil digunakan bank untuk pintu akses ruangan perkantoran oleh pegawai bank.

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Section: Dalam Proses Face Recognition Ini Juga Digunakan Algoritmaunclassified

Face Recognition Untuk Akses Pegawai Bank Menggunakan Deep Learning Dengan Metode CNN

Arsal

Wardijono

Anggraini

2020

TEKNOSI

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“…The use of CNN with SGD optimization conducted by [6] is used to classify temple imagery. There are 6 classes with 100 epoch training that get 86.28% accuracy.…”

Section: Related Workmentioning

confidence: 99%

Landmark Classification Service Using Convolutional Neural Network and Kubernetes

Aji¹

2020

IJATCSE

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The use of Deep Learning for the image classification process has developed rapidly, one of which is in a method called Convolutional Neural Network (CNN). One use of the CNN method is to classify images. This study uses the CNN method to classify image datasets that have landmark categories. The results show that each model has an accuracy level with the highest value of 95% and the lowest value of 92%. The method for this research to produce a service that can be used by other services and or can be implemented in other resources so that the classification function can be used or developed. We show that our deployment method has good results seen with ResNet50 Model using 1200 concurrent users which response rates were smaller by 4,92% using autoscaling pods with one minimum pod and 6 maximum pods that had an error value of 26,58% compared with the best result from the other method.

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“…Pooling Layer tends to use functions with Feature Map as its input, subsequently processing with various statistical operations available, using nearby pixel values [29]. In addition, the application often requires the use of (1) Max Pooling with size 2x2 and Stride 2, with a value taken for each shift of this filter, and the largest in the 2x2 area, (2) Average Pooling, which takes the average value [30].…”

Section: Fig 1 Convolution Layer [30]mentioning

confidence: 99%

The Javanese Letters Classifier with Mobile Client-Server Architecture and Convolution Neural Network Method

Harjoseputro

Handarkho

Adie

2019

Int. J. Interact. Mob. Technol.

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<p class="0abstract">the rapid development of mobile technologies allows platform devices to perform sophisticated tasks, including character recognition. These identification systems are notable techniques that required high computation cost, in order to achieve acceptable accuracy resulting from diversity in alphabet shape and method of writing, especially for the non-Latin alphabet, e.g., Javanese letter. In addition, numerous studies have attempted to address these issues by employing a Convolution Neural Network (CNN) due to its ability to provide high accuracy in character detection. However, the performance on mobile devices is possibly faced with problems resulting from the limitation of computation resource on the platform that also affect computation cost. This study, therefore, proposes a 2-tier architecture by placing the mobile app as a client that invokes a Javanese letters classifier service, which is based on CNN, and implemented in the web-server through the Application Program Interface (API). The results show that the letter classification was successfully implemented in a mobile platform, with an accuracy rate of 86.68%, utilizing training for 50 epochs, and an average time of 1935 ms.</p>

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Indonesia ancient temple classification using convolutional neural network

Cited by 12 publications

References 5 publications

Face Recognition Untuk Akses Pegawai Bank Menggunakan Deep Learning Dengan Metode CNN

Face Recognition Untuk Akses Pegawai Bank Menggunakan Deep Learning Dengan Metode CNN

Landmark Classification Service Using Convolutional Neural Network and Kubernetes

The Javanese Letters Classifier with Mobile Client-Server Architecture and Convolution Neural Network Method

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