scholarly journals Deep Learning dalam Mengindetifikasi Jenis Bangunan Heritage dengan Algoritma Convolutional Neural Network

2021 ◽  
Vol 5 (3) ◽  
pp. 831
Author(s):  
Sri Winiarti ◽  
Mochammad Yulianto Andi Saputro ◽  
Sunardi Sunardi
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Confusion Matrix ◽  
Architectural Style ◽  
The Public ◽  
Heritage Buildings ◽  
Heritage Building ◽  
History Of ◽  
Digital Documentation ◽  
New Buildings

A heritage building is a building that has a distinctive style or tradition from a culture whose activities are carried out continuously until now and are used as a characteristic of that culture. The problems that occur in the community are the lack of knowledge to recognize the types of heritage buildings and the lack of digital documentation. Another problem that occurs in identifying heritage buildings is that there are similarities between heritage buildings and new buildings that imitate the architectural style of heritage buildings from ornaments. This can raise doubts in the information related to the original history of heritage buildings for the public or visitors. This study aims to apply the Convolutional Neural Network (CNN) to identify the types of heritage buildings. The benefits of this research can be found in the characteristics of a building based on ornaments so that it can be used to obtain information about the types of heritage buildings in Indonesia. A dataset of 7184 images of ornaments from heritage buildings were used which were taken directly at the Yogyakarta location, namely; Mataram Grand Mosque, Taqwa Wonokromo Mosque, Kalang House, Joglo KH Ahmad Dahlan and Ketandan. It is necessary to identify the heritage building because the object of the building can become extinct at any time, so to maintain it, documentation is needed as an effort to preserve culture and for education. Based on the evaluation of the performance of the tests carried out using the confusion matrix method from 391 ornamental images, the results obtained are 98% accuracy

Single Channel EEG signal for Automatic Detection of Absence Seizure using Convolutional Neural Network

2019 ◽  
Vol 13 ◽  
Author(s):  
Niha Kamal Basha ◽  
Aisha Banu Wahab
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Convolutional Neural Network ◽  
Monitoring System ◽  
Single Channel ◽  
Confusion Matrix ◽  
Sudden Change ◽  
Automatic Detection ◽  
Support Vector ◽  
Absence Seizure

: Absence seizure is a type of brain disorder in which subject get into sudden lapses in attention. Which means sudden change in brain stimulation. Most of this type of disorder is widely found in children’s (5-18 years). These Electroencephalogram (EEG) signals are captured with long term monitoring system and are analyzed individually. In this paper, a Convolutional Neural Network to extract single channel EEG seizure features like Power, log sum of wavelet transform, cross correlation, and mean phase variance of each frame in a windows are extracted after pre-processing and classify them into normal or absence seizure class, is proposed as an empowerment of monitoring system by automatic detection of absence seizure. The training data is collected from the normal and absence seizure subjects in the form of Electroencephalogram. The objective is to perform automatic detection of absence seizure using single channel electroencephalogram signal as input. Here the data is used to train the proposed Convolutional Neural Network to extract and classify absence seizure. The Convolutional Neural Network consist of three layers 1] convolutional layer – which extract the features in the form of vector 2] Pooling layer – the dimensionality of output from convolutional layer is reduced and 3] Fully connected layer–the activation function called soft-max is used to find the probability distribution of output class. This paper goes through the automatic detection of absence seizure in detail and provide the comparative analysis of classification between Support Vector Machine and Convolutional Neural Network. The proposed approach outperforms the performance of Support Vector Machine by 80% in automatic detection of absence seizure and validated using confusion matrix.

scholarly journals PENENTUAN EMOSI PADA VIDEO DENGAN CONVOLUTIONAL NEURAL NETWORK

2020 ◽  
Vol 5 (1) ◽  
pp. 23
Author(s):  
Daru Prasetyawan ◽  
Shofwatul 'Uyun
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Confusion Matrix ◽  
Video Model

Emosi seseorang dapat ditunjukan melalui ekspresi wajah. Ekspresi wajah manusia dapat berubah-ubah secara dinamis tanpa disadari oleh orang tersebut. Penelitian ini melakukan penentuan emosi dengan melakukan pengenalan ekspresi wajah manusia dan melakukan perekaman untuk setiap perubahan ekspresi wajah tersebut. Metode dalam penelitian ini adalah dengan melakukan klasifikasi terhadap 6 ekspresi dasar wajah manusia ditambah ekspresi netral dengan Convolutional Neural Network (CNN). Pemerataan distribusi data dilakukan untuk meningkatkan kinerja model. Dari pemodelan tersebut, dihasilkan model klasifikasi yang dapat diterapkan pada sebuah video. Model tersebut diuji menggunakan data yang terpisah dari data latih dan dievaluasi menggunakan confusion matrix. Sebagai hasil evaluasi, diperoleh akurasi 74%, rata-rata presisi 75,05%, dan rata-rata recall 74%. Di akhir penelitian ini, peneliti melakukan percobaan dengan menerapkan model klasifikasi tersebut pada beberapa video yang mewakili ekspresi seseorang di dalam video tersebut. Setiap perubahan ekspresi akan direkam dan dianalisis sehingga ditemukan emosi yang paling dominan.

scholarly journals AlexNet convolutional neural network to classify the types of Indonesian coffee beans

2021 ◽  
Vol 905 (1) ◽  
pp. 012059
Author(s):  
Y Hendrawan ◽  
B Rohmatulloh ◽  
F I Ilmi ◽  
M R Fauzy ◽  
R Damayanti ◽  
...  
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Sensitivity Analysis ◽  
Convolutional Neural Network ◽  
Confusion Matrix ◽  
Learning Rate ◽  
Agricultural Products ◽  
Coffee Bean ◽  
Coffee Beans ◽  
Non Destructive

Abstract Various types of Indonesian coffee are already popular internationally. Recently, there are still not many methods to classify the types of typical Indonesian coffee. Computer vision is a non-destructive method for classifying agricultural products. This study aimed to classify three types of Indonesian Arabica coffee beans, i.e., Gayo Aceh, Kintamani Bali, and Toraja Tongkonan, using computer vision. The classification method used was the AlexNet convolutional neural network with sensitivity analysis using several variations of the optimizer such as SGDm, Adam, and RMSProp and the learning rate of 0.00005 and 0.0001. Each type of coffee used 500 data for training and validation with the distribution of 70% training and 30% validation. The results showed that all AlexNet models achieved a perfect validation accuracy value of 100% in 1,040 iterations. This study also used 100 testing-set data on each type of coffee bean. In the testing confusion matrix, the accuracy reached 99.6%.

scholarly journals OIL PATTERN IDENTIFICATION ANALYSIS USING SEMANTIC DEEP LEARNING METHOD FROM PLEIADES-1B SATELIITE IMAGERY WITH ARCGIS PRO SOFTWARE (Case Study: Village “A”)

2021 ◽  
Vol 936 (1) ◽  
pp. 012021
Author(s):  
Novi Anita ◽  
Bangun Muljo Sukojo ◽  
Sondy Hardian Meisajiwa ◽  
Muhammad Alfian Romadhon
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
High Resolution ◽  
Land Cover ◽  
Convolutional Neural Network ◽  
Local Community ◽  
Confusion Matrix ◽  
Test Method ◽  
Oil Refinery ◽  
Thermal Data

Abstract There are many petroleum mining activities scattered in developing countries, such as Indonesia. Indonesia is one of the largest oil-producing countries in Southeast Asia with the 23rd ranking. Since the Dutch era, Indonesia has produced a very large amount of petroleum. One of the oil producing areas is “A” Village. There is an old well that produces petroleum oil which is still active with an age of more than 100 years, for now the oil well is still used by the local community as the main source of livelihood. With this activity, resulting in an oil pattern around the old oil refinery, which over time will absorb into the ground. This study aims to analyze and identify the oil pattern around the old oil refinery in the “A” area. The data used is in the form of High-Resolution Satellite Imagery (CSRT), namely Pleiades-1B with a spatial resolution of 1.5 meters. Data were identified using the Deep Learning Semantic method. For the limitation of this research is the administrative limit of XX Regency with a scale of 1: 25,000 as supporting data when cutting the image. The method used is the Deep Learning Convolutional Neural Network series. This research is based on how to wait for the method of the former oil spill which is the consideration of the consideration used. This study produced a land cover map that was classified into 3 categories, namely oil patterns area, area not affected by oil and vegetation. As a supporting value to show the accuracy of the classification results, an accuracy test method is used with the confusion matrix method. To show the accuracy of this study using thermal data taken from the field. Thermal data used in the form of numbers that show the temperature of each land cover. Based on the above reference, a research related to the analysis of very high-resolution image data (Pleiades-1B) will be conducted to examine the oil pattern. This research uses the deep learning series convolutional neural network (CNN) method. With this research, it is hoped that it can help agencies in knowing the right method to identify oil in mainland areas.

scholarly journals ModCOVNN: a convolutional neural network approach in COVID-19 prognosis

2021 ◽  
Vol 7 (2) ◽  
pp. 125
Author(s):  
Ahmed Wasif Reza ◽  
Jannatul Ferdous Sorna ◽  
Md. Momtaz Uddin Rashel ◽  
Mir Moynuddin Ahmed Shibly
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Primary Concern ◽  
Neural Network Approach ◽  
Good Efficiency ◽  
X Ray ◽  
Proposed Model ◽  
Driven System ◽  
History Of ◽  
Pcr Test

COVID-19 is a devastating pandemic in the history of humankind. It is a highly contagious flu that can spread from human to human. For being so contagious, detecting patients with it and isolating them has become the primary concern for healthcare professionals. However, identifying COVID-19 patients with a Polymerase chain reaction (PCR) test can sometimes be problematic and time-consuming. Therefore, detecting patients with this virus from X-ray chest images can be a perfect alternative to the de-facto standard PCR test. This article aims at providing such a decision support system that can detect COVID-19 patients with the help of X-ray images. To do that, a novel convolutional neural network (CNN) based architecture, namely ModCOVNN, has been introduced. To determine whether the proposed model works with good efficiency, two CNN-based architectures – VGG16 and VGG19 have been developed for the detection task. The experimental results of this study have proved that the proposed architecture has outperformed the other two models with 98.08% accuracy, 98.14% precision, and 98.4% recall. This result indicates that proper detection of COVID-19 patients with the help of X-ray images of the chest is possible using machine learning methods with high accuracy. This type of data-driven system can help us to overcome the current appalling situation throughout the world.

Deteksi Masker Pencegahan Covid19 Menggunakan Convolutional Neural Network Berbasis Android

2021 ◽  
Vol 5 (3) ◽  
pp. 576-583
Author(s):  
Purnama Nyoman ◽  
Putu Kusuma Negara
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Gradient Descent ◽  
Detection Method ◽  
World Health ◽  
The Public ◽  
Network Method ◽  
Public Areas ◽  
The Government ◽  
Health Organization

Masks are an important part of preventing Covid19 disease.The World Health Organization (WHO) have also recommended  the community use masks when doing activities in public areas. There are many types of masks that are used to cover the nose and mouth.  In general, there are about 3 types of masks that are commonly used by the public today, namely medical masks, N95 and cloth masks. This study aims to detect the type of mask used by the community. So that it can make easier for the government to apply discipline in COVID-19 health protocol. The detection method used in this study is a convolutional neural network (CNN). The first step is acquisition of knowledge, which first collects the types of masks on the market, followed by the representation of that knowledge before being modeled into a mathematical calculation formula, which will then be processed using the Convolutional Neural Network method. The system will be carried out by analyzing the recall value, its precision and accuracy.Testing process is carried out on an Android-based device  and the mobilenetV2 framework. In this study, the accuracy value is 90% using ADAM Optimization and 80 % using Gradient descent optimization.

scholarly journals Assessment of Adaptive Reuse Practices through User Experiences: Traditional Houses in the Walled City of Nicosia

2019 ◽  
Vol 11 (2) ◽  
pp. 540 ◽  
Author(s):  
Kağan Günçe ◽  
Damla Mısırlısoy
Keyword(s):  
Case Studies ◽  
Adaptive Reuse ◽  
Holistic Approach ◽  
Buffer Zone ◽  
User Experiences ◽  
The Public ◽  
Residential Architecture ◽  
Heritage Buildings ◽  
Heritage Building ◽  
The City

The conservation of traditional residential architecture is crucial in terms of sociocultural continuity. When the traditional houses are no longer used for residential purposes, new functions should be assigned to them for the continuity of the heritage buildings. However, new functions should respect the originality of the heritage building. This research focuses on the conservation and reuse of traditional houses located in the walled city of Nicosia. The walled city is divided into two parts as north and south with a buffer zone between the two. This paper includes case studies of re-functioned traditional houses from the two parts. The study questions the appropriateness of the new functions that have been assigned to the traditional houses both in the northern and southern parts of the city. This research aims to measure and compare the success of the adaptive reuse practices through user experiences. As the method of study, the literature survey was carried out to identify different aspects of adaptive reuse projects. Then, selected buildings were observed through site surveys in order to discover the current condition of the adaptive reuse projects. The third step was to complete the questionnaires with different users in order to question the success of the adaptive reuse projects through the user experience. Finally, the collected data were evaluated and discussed. The respondents were asked to answer questions about each building, which are organized under the three categories of sociocultural, economic, and physical aspects of the heritage buildings. As observed with the evaluated case studies, heritage buildings that are re-functioned with the public use, such as commercial, cultural, and educational use, are more successful in contributing to the sociocultural and economic development of the city. The preservation and reuse of abandoned traditional houses in the walled city contribute to the continuity and livability of the city. For the continuity of the heritage buildings, sociocultural, economic, and physical aspects should be taken into consideration with a holistic approach.

scholarly journals Blister Defect Detection Based on Convolutional Neural Network for Polymer Lithium-Ion Battery

2019 ◽  
Vol 9 (6) ◽  
pp. 1085 ◽  
Author(s):  
Liyong Ma ◽  
Wei Xie ◽  
Yong Zhang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Lithium Ion Battery ◽  
Defect Detection ◽  
Confusion Matrix ◽  
Cell Sheet ◽  
Lithium Ion ◽  
Classification Performance ◽  
Field Application ◽  
Cell Sheets

To ensure the quality and reliability of polymer lithium-ion battery (PLB), automatic blister defect detection instead of manual detection is developed in the production of PLB cell sheets. A convolutional neural network (CNN) based detection method is proposed to detect blister in cell sheets employing cell sheet images. An improved architecture for dense block and a learning method based on optimization of learning rate are discussed. The proposed method was superior to other machine learning based methods when the classification performance and confusion matrix were compared in experiments. The proposed CNN method had the best defect detection performance and real-time performance for industry field application.

scholarly journals Interpretasi Konsep Interior pada Tiga Ruang Museum House of Sampoerna Surabaya Berdasarkan Visualisasi Ruangan

2021 ◽  
Vol 1 (1) ◽  
pp. 121-136
Author(s):  
Rania Erin Oktiara ◽  
Lilik Indrawati ◽  
Swastika Dhesti Anggriani
Keyword(s):  
Data Collection ◽  
Cultural Heritage ◽  
Document Analysis ◽  
Educational Media ◽  
The Public ◽  
Heritage Buildings ◽  
Historical Objects ◽  
History Of ◽  
And Function ◽  
Collection Methods

Abstract: A museum is an institution that collects and looks after historical objects to showcase and function them as educational media for the public. The realization of those functions depends on the interior concept through visualization in each room. One museum that is particularly attractive to the researcher to analyze is Surabaya House of Sampoerna Museum. This museum is recorded to be one of Surabaya’s cultural heritage buildings. It displays the history of the establishment and the development of Sampoerna company thematically in each room in the building, therefore, there are different themes even in one room. The implementation of the mentioned visualization concept has become the basis of interpretation for the researcher with the focus on room visualization. House of Sampoerna Museum consists of five showcase rooms; however, this research only interprets three rooms that do not undergo significant alteration since 2018. The three rooms are referred to as room 1, room 2, room 3. The data collection methods of this research are observation, interview, and document analysis that involves the researcher’s interpretation. Based on the results of this research, the interior concepts of room 1, room 2, and room 3 have been discovered. Keywords: concept, interior, museum, House of Sampoerna, visualization Abstrak: Museum merupakan lembaga yang mengumpulkan dan merawat benda-benda yang memiliki nilai sejarah untuk dipamerkan dan difungsikan sebagai sarana edukasi kepada masyarakat umum. Penyampaian fungsi tersebut dipengaruhi oleh konsep interior melalui visualisasi pada setiap ruangannya. Salah satu museum yang menarik peneliti untuk menginterpretasi penerapan konsepnya yaitu Museum House of Sampoerna Surabaya. Museum ini tercatat sebagai salah satu bangunan cagar budaya di Kota Surabaya. Museum ini menampilkan sejarah pendirian dan berkembangnya perusahaan Sampoerna yang bersifat tematik pada masing-masing ruangannya, sehingga terdapat tema yang berbeda-beda meskipun masih dalam satu ruangan. Adanya penerapan visualisasi tersebut yang melandasi tujuan penelitian ini untuk menginterpretasi konsep interior yang diterapkan berdasarkan visualisasi ruangannya. Museum House of Sampoerna terdiri atas 5 ruang pamer, akan tetapi pada penelitian ini hanya menginterpretasi 3 ruang pamer yang tidak mengalami perubahan interior secara signifikan sejak tahun 2018, yang disebutkan sebagai ruang 1, ruang 2, ruang 3. Penelitian ini menggunakan metode pengumpulan data observasi, wawancara, serta analisis dokumen yang melibatkan interpretasi peneliti. Berdasarkan hasil penelitian ini dapat diketahui konsep interior yang diterapkan pada ruang 1, ruang 2, dan ruang 3.  Kata kunci: konsep, interior, museum, House of Sampoerna, visualisasi

scholarly journals Memanfaatkan Potensi dan Keindahan Bangunan Kolonial melalui Alih Fungsi Bangunan Studi Kasus: Heritage the Factory Outlet di Jl Riau Bandung

2020 ◽  
Vol 4 (3) ◽  
pp. 133-144
Author(s):  
Nurtati - Soewarno
Keyword(s):  
Cultural Heritage ◽  
Government Support ◽  
Architectural Style ◽  
Natural Lighting ◽  
Heritage Buildings ◽  
Heritage Building ◽  
Research Goal ◽  
Furniture Layout ◽  
Functional Shift ◽  
The Right

ABSTRAKBangunan peninggalan kolonial merupakan warisan budaya yang saat ini banyak dialih fungsikan terutama untuk fungsi komersial. Bangunan ini mempunyai gaya arsitektur yang unik dan beradaptasi terhadap iklim tropis dengan penerapan bukaan lebar, plafond tinggi dan atap bersudut tajam. Penelitian ini bertujuan untuk mengeksplorasi bagaimana memanfaatkan potensi bangunan peninggalan kolonial. Dengan melakukan observasi diperoleh data bahwa keindahan gaya arsitektur menjadi daya tarik pengunjung dan dengan tata letak furniture yang tepat akan diperoleh kenyamanan termal dan pencahayaan alami yang optimal. Heritage the Factory Outlet dipilih sebagai kasus studi karena alih fungsi terbilang sukses, tidak menghilangkan keaslian gaya arsitekturnya bahkan menjadikannya daya tarik tersendiri. Bangunan tambahan di bangun tidak lebih menonjol dari bangunan utama sehingga keberhasilan alih fungsi ini diharapkan dapat diterapkan pada bangunan cagar budaya lainnya. Diperlukan dukungan Pemerintah Daerah dalam pengawasan pelaksanaan perubahan agar tidak melanggar aturan konservasi dan menindak tegas segala bentuk pelanggaran yang dapat merusak bangunan sebagai warisan budaya.Kata kunci: bangunan peninggalan kolonial, alih fungsi, adaptasi gaya arsitektur, bangunan cagar budaya, adaptive reuseABSTRACTColonial heritage buildings are cultural heritages that nowadays many of them are undergoing functional shift, mainly into commercial function. These buildings have an architectural style that adapt to tropical climate by applying wide openings, high ceilings with sharp angeled roof. This research goal is to explore how to benefit the potential of colonial heritage buildings. Observation results showed that beauty of the architectural style is the attraction for visitors together with the right furniture layout, thermal comfort and optimal natural lighting. “Heritage” Factory Outlet was selected as a case study because of the function shift was successful, does not eliminate the beauty of the architectural style and in fact it becomes its unique attraction. The additional building does not become more prominent of the main building so that the succes of function shift is expected to be applied to other cultural heritage building. Local Government support is required in monitoring the implementation of changes that do not violate the rules of conservation and take firm action against any violation that may damage the building as a cultural heritage.Keywords: Colonial heritage building, building function shift, architecture style addaptation, cultural heritage building, adaptive reuse

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