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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Register: Jurnal Ilmiah Teknologi Sistem Informasi JOIV : International Journal on Informatics Visualization Knowledge Engineering and Data Science WIDYA LAKSANA Logic : Jurnal Rancang Bangun dan Teknologi Jurnal Praksis dan Dedikasi Sosial Journal of Applied Data Sciences Pusaka : Journal of Tourism, Hospitality, Travel and Business Event Dharma: Jurnal Pengabdian Masyarakat
I Nyoman Gede Arya Astawa
Politeknik Negeri Bali
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Engineering Environmental Science Languange, Linguistic, Communication & Media Social Sciences Other

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The Impact of Color Space and Intensity Normalization to Face Detection Performance I Nyoman Gede Arya Astawa; I Ketut Gede Darma Putra; I Made Sudarma; Rukmi Sari Hartati
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 15, No 4: December 2017
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v15i4.6718

Abstract

In this study, human face detection have been widely conducted and it is still interesting to be research. In this research, strong impact of color space for face i.e., many and multi faces detection by using YIQ, YCbCr, HSV, HSL, CIELAB, and CIELUV are proposed. In this experiment, intensity normality method in one of the color space channel and tested the faces using Android based have been developed. The faces multi image datasets came from social media, mobile phone and digital camera. In this experiment, the color space YCbCr percentage value with the image initial value detection before processing are 67.15%, 75.00%, and 64.58% have been reached. Then, after the normalization process are 83.21%, 87.12%, and 80.21% have been increased. Furthermore, this study showed that color space of YCbCr have reached improvement percentage
Face Images Classification using VGG-CNN I Nyoman Gede Arya Astawa; Made Leo Radhitya; I Wayan Raka Ardana; Felix Andika Dwiyanto
Knowledge Engineering and Data Science Vol 4, No 1 (2021)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um018v4i12021p49-54

Abstract

Image classification is a fundamental problem in computer vision. In facial recognition, image classification can speed up the training process and also significantly improve accuracy. The use of deep learning methods in facial recognition has been commonly used. One of them is the Convolutional Neural Network (CNN) method which has high accuracy. Furthermore, this study aims to combine CNN for facial recognition and VGG for the classification process. The process begins by input the face image. Then, the preprocessor feature extractor method is used for transfer learning. This study uses a VGG-face model as an optimization model of transfer learning with a pre-trained model architecture. Specifically, the features extracted from an image can be numeric vectors. The model will use this vector to describe specific features in an image.  The face image is divided into two, 17% of data test and 83% of data train. The result shows that the value of accuracy validation (val_accuracy), loss, and loss validation (val_loss) are excellent. However, the best training results are images produced from digital cameras with modified classifications. Val_accuracy's result of val_accuracy is very high (99.84%), not too far from the accuracy value (94.69%). Those slight differences indicate an excellent model, since if the difference is too much will causes underfit. Other than that, if the accuracy value is higher than the accuracy validation value, then it will cause an overfit. Likewise, in the loss and val_loss, the two values are val_loss (0.69%) and loss value (10.41%).
PERBANDINGAN METODE JARINGAN SARAF TIRUAN PADA PERAMALAN CURAH HUJAN I Putu Sutawinaya; I Nyoman Gede Arya Astawa; Ni Kadek Dessy Hariyanti
Logic : Jurnal Rancang Bangun dan Teknologi Vol 17 No 2 (2017): July
Publisher : Pusat Penelitian dan Pengabdian kepada Masyarakat (P3M) Politeknik Negeri Bali

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (625.547 KB) | DOI: 10.31940/logic.v17i2.542

Abstract

Intensitas curah hujan dikatakan besar apabila hujan lebat dan kondisi ini sangat berbahaya karena dapat menimbulkan banjir dan longsor, untuk itu perlu dilakukan peramalan untuk memperkirakan seberapa besar curah hujan yang akan datang. Metode Jaringan Saraf Tiruan (JST) adalah paradigma pengolahan informasi yang terinspirasi oleh sistem saraf secara biologis, seperti proses informasi pada otak manusia. Metode JST yang digunakan dalam meramal curah hujan pada penelitian ini adalah metode Backpropagation dan Adaline. Hasil peramalan dengan tingkat kesalahan yang lebih kecil dari kedua metode JST tersebut akan menunjukkan bahwa metode tersebut baik digunakan untuk peramalan. Berdasarkan pengujian yang telah dilakukan pada iterasi 1000 dihasilkan Root Mean Square Error (RMSE) dengan metode Backpropagation sebesar 0.0435, sedangkan Adaline sebesar 0.0674. Berdasarkan perbandingan nilai RMSE metode Backpropagation lebih baik dibandingkan dengan metode Adaline
Roboswab: A Covid-19 Thermal Imaging Detector Based on Oral and Facial Temperatures I Nyoman Gede Arya Astawa; I.D.G Ary Subagia; Felipe P. Vista IV; IGAK Cathur Adhi; I Made Ari Dwi Suta Atmaja
JOIV : International Journal on Informatics Visualization Vol 7, No 1 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.1.1505

Abstract

The SARS-CoV-2 virus has been the precursor of the coronavirus disease (COVID-19). The symptoms of COVID-19 begin with the common cold and then become very severe, such as those of Middle East Respiratory Syndrome (MERS) and Severe Acute Respiratory Syndrome (SARS). Currently, polymerase chain reaction (PCR) is used to detect COVID-19 accurately, but it causes some side effects to the patient when the test is performed. Therefore, the proposed "Roboswab" was developed that uses thermal imaging to measure non-contact facial and oral temperature. This study focuses on the performance of the proposed equipment in measuring facial and oral temperature from various distances. Face detection also involves checking whether the subject is wearing a mask or not. Image processing methods with thermal imaging and robotic manipulators are integrated into a contact-free detector that is inexpensive, accurate, and painless. This research has successfully detected masked or non-masked faces and accurately detected facial temperature. The results showed that the accurate measurement of facial temperature with a mask is 90% with an error of +/- 0.05%, while it was 100% without a mask. On the other hand, the oral temperature was measured with 97% accuracy and an error of less than 5%. The optimal distance of the Roboswab to the face for measuring temperature is an average of 60 cm. The Roboswab tool equipped with masked or non-masked face detection can be used for early detection of COVID-19 without direct contact with patients.
Social Media Mining with Fuzzy Text Matching: A Knowledge Extraction on Tourism After COVID-19 Pandemic Ida Bagus Putra Manuaba; I Wayan Budi Sentana; I Nyoman Gede Arya Astawa; I Wayan Suasnawa; I Putu Bagus Arya Pradnyana
Knowledge Engineering and Data Science Vol 5, No 2 (2022)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um018v5i22022p143-149

Abstract

Social media mining is an emerging technique for analyzing data to extract valuable knowledge related to various domains. However, traditional text matching techniques, such as exact matching, are not always suitable for social media data, which can contain spelling mistakes, abbreviations, and variations in the use of words. Fuzzy matching is a text matching technique that can handle such variations and identify similarities between two texts, even if there are differences in spelling or phrasing. The gap in existing research is the limited use of fuzzy matching in social media mining for tourism recovery analysis. By applying fuzzy matching to social media data related to COVID-19 and tourism recovery, this research seeks to bridge this gap and extract valuable insights related to the impact of the pandemic on tourism recovery. We manually retrieved 19,462 Twitter records and differentiated the data sources using four diver parameters to indicate data related to the impact of COVID-19 on the tourism industry, such as the economy, restrictions, government policies, and vaccination. We conducted text mining analysis on the collected 7,352 words and identified 25 highly recommended words that indicated COVID-19 recovery from a tourism perspective. We separated the four words representing the tourism perspective to perform fuzzy matching as a dataset. We then used the inbound dataset on the fuzzy matching process, with the 7,352-word data collected from the text mining process. The matching process resulted in 18 words representing COVID-19 recovery from a tourism perspective.
TKJ and Graphic Design Training for Student Strengthening Facing UKK at SMK PGRI Amlapura Putu Gde Sukarata; I Nyoman Gede Arya Astawa; Gusti Nyoman Ayu Sukerti; I Wayan Suasnawa; I Putu Bagus Arya Pradnaya
Dharma: Jurnal Pengabdian Masyarakat Vol 4, No 1 (2023): Mei
Publisher : Universitas Pembangunan Nasional "Veteran" Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/dlppm.v4i1.8409

Abstract

The Vocational High School of the Indonesian Teachers Association (SMK PGRI) Amlapura is a vocational school at the upper middle level that has four majors namely, hospitality, catering, computer network and multimedia engineering. Located in Amlapura City, Karangasem Regency, Bali Province. Based on the vocational education that is carried out, all students studying in vocational schools are required to take the expertise competency test (UKK) of each field that is in demand in accordance with the majors they choose. This UKK is carried out nationally and is a national practical test. This is what distinguishes an educational model that exists at the high school level. As for facing the National Practice Examination, schools generally provide assistance for their students.Bali State Polytechnic in this case implementing Tri Dharma Higher Education such as teaching, research and service. One of the Tri Dharma of the tertiary institution is the Commander of the Commander, where the community service aims to play a role and participate in building the welfare of the community. This service activity is carried out in accordance with existing academic culture.Bali State Polytechnic Department of Electrical Engineering Information Management Study Program is willing to accompany the students of SMK PGRI Amlapura in terms of preparing themselves to take part in UKK. This mentoring activity is a service activity. Assistance provided specifically to UKK Computer Network Engineering Program and UKK Multimedia Program. Activities in the form of exposure in theory and direct practice. The continuation of assistance is also done using WhatsApp Group media.
IMPLEMENTASI CCTV ONLINE UNTUK MENINGKATKAN PEMANTAUAN FASILITAS WARGA BANJAR SAMPALAN Rudiastari, Elina; Atmaja, I Made Ari Dwi Suta; Bawa, I Gusti Ngurah Bagus Catur; Indah, Komang Ayu Triana; Sukerti, Gusti Nyoman Ayu; Astawa, I Nyoman Gede Arya
Jurnal Praksis dan Dedikasi Sosial (JPDS) Vol 7, No 1 (2024)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um032v7i1p77-84

Abstract

IMPLEMENTATION OF ONLINE CCTV TO IMPROVE MONITORING OF BANJAR SAMPALAN RESIDENTS' FACILITIESSampalan Hamlet is a tourist destination where many facilities and infrastructure have begun to be built. The problems faced by the Sampalan Traditional Banjar are that there is no supervision of the routes around the Banjar area, there is no documentary evidence that can be used to analyze accidents that occur around the Banjar, there is no supervision to avoid acts of theft at the temple and there is no supervision of the social activities of residents which was carried out in Banjar. The solution offered in this community service activity for the problems experienced is the installation of CCTV. This service activity aims to help access monitoring and supervision of the Banjar area to increase the security and comfort of residents and visiting tourists. This method of implementing community service activities is implemented into three main stages, namely preparation, implementation and evaluation. The results of the evaluation through a questionnaire resulted in 71.4 percent of residents stating that the installed CCTV was very useful, and 50 percent of residents stated that the presence of CCTV greatly facilitated monitoring and supervision around the Banjar.Dusun Sampalan merupakan destinasi wisata dimana fasilitas serta infrastruktur sudah mulai banyak dibangun. Permasalahan yang dihadapi Banjar Adat Sampalan yaitu belum adanya pengawasan terhadap jalur disekitar areal Banjar, tidak adanya bukti dokumentasi yang dapat digunakan untuk menganalisa peristiwa kecelakaan yang terjadi di sekitar Banjar, tidak adanya pengawasan untuk menghindari tindakan pencurian di pura serta tidak adanya pengawasan terhadap aktivitas sosial warga yang dilakukan di Banjar. Solusi yang ditawarkan dalam kegiatan pengabdian kepada masyarakat ini untuk permasalahan yang dialami adalah pemasangan CCTV. Tujuan dari kegiatan pengabdian ini adalah membantu akses monitoring dan pengawasan areal Banjar sehingga meningkatkan keamanan dan kenyamanan warga serta wisatawan yang berkunjung. Metode pelaksanaan kegiatan pengabdian kepada masyarakat ini diimplementasikan menjadi tiga tahapan utama yaitu persiapan, pelaksanaan dan evaluasi. Hasil evaluasi melalui kuisioner menghasilkan 71,4 persen warga menyatakan CCTV yang terpasang tersebut sangat bermanfaat serta 50 persen warga menyatakan bahwa adanya CCTV sangat mempermudah monitoring dan pengawasan di sekitar Banjar.
Comparison of the Packet Wavelet Transform Method for Medical Image Compression Atmaja, I Made Ari Dwi Suta; Triadi, Wilfridus Bambang; Astawa, I Nyoman Gede Arya; Radhitya, Made Leo
JOIV : International Journal on Informatics Visualization Vol 7, No 4 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.7.4.1732

Abstract

Medical images are often used for educational, analytical, and medical diagnostic purposes. Medical image data requires large amounts of storage on computers. Three types of codecs, namely Haar, Daubechies, and Biorthogonal, were used in this study. This study aims to find the best wavelet method of the three tested wavelet methods (Haar, Daubechies, and Biorthogonal). This study uses medical images representing USG and CT-scan images as testing data. The first test is carried out by comparing the threshold ratio. Three threshold values are used, namely 30, 40, and 50. The second test looks for PSNR values with different thresholds. The third test looks for a comparison of the rate (image size) to the PSSR value. The final test is to find each medical image's compression and decompression times. The first compression ratio test results on both medical images showed that CT scan images on Haar and Biorthogonal wavelets were the best, with an average compression ratio of 40.76% and a PSNR of 33.77. The PSNR obtained is also getting more significant for testing with a larger image size. The average compression time is 0.52 seconds, and the decompression time is 2.27 seconds. Based on the test results, this study recommends that the Daubechies wavelet method is very good for compression, which is 0.51 seconds, and the Biorthogonal wavelet method is very good for medical image decompression, which is 1.69 seconds.
A Novel Approach to Defect Detection in Arabica Coffee Beans Using Deep Learning: Investigating Data Augmentation and Model Optimization Ardian, Yusriel; Irawan, Novta Danyel; Sutoko, Sutoko; Astawa, I Nyoman Gede Arya; Purnama, Ida Bagus Irawan; Dwiyanto, Felix Andika
Knowledge Engineering and Data Science Vol 7, No 1 (2024)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um018v7i12024p117-127

Abstract

Arabica coffee beans have valuable market worth because of their taste and quality, and there are defects like wholly and partially black beans that can lower the standards of a product, especially in the premium coffee sector. However, the manual processes used to detect the defects take an inordinate amount of time and are inefficient. This study aims to bridge the knowledge gap on the automated detection and recognition of the defects present in the Arabica coffee beans by creating and optimizing a CNN model based on a modified VGG16 architecture. The model applies data augmentation, rotation, cropping, and Bayesian hyperparameter optimization to improve defect detectability and expedite the training period. During testing, the defined model demonstrated excellent efficiency in defect detection, with a 97.29% confidence level, which was higher than that of the modified VGG16 and Slim-CNN models. The goal of the second optimization was an improvement of the practical application of the model. In terms of the time it takes for a model to be trained, approximately 30% of the time was saved. These findings present a consistent and effective way for the mass production processes of coffee to have quality control procedures automated. The model's ability to detect defects in other agricultural items makes it attractive, thus serving as a practical example of how AI can impact effective management in the inspection processes. The research further enriches the study of deep learning applications in agriculture by demonstrating how to efficiently address specific defect detection problems through an optimized convolutional neural network model.
Comparison of MobileNet and VGG16 CNN Architectures for Web-based Starfish Species Identification System Latumakulita, Luther Alexander; Paat, Frangky J.; Saroyo, Saroyo; Karim, Irwan; Astawa, I Nyoman Gede Arya; Sirait, Hasanuddin
Journal of Applied Data Sciences Vol 5, No 4: DECEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i4.456

Abstract

Bunaken Marine Park (BMP) is famous for its rich marine biodiversity. BMP is an asset for the marine tourism industry of the Manado city government, and the North Sulawesi Province of Indonesia needs to be strengthened. This research aims to build a web-based intelligent system using a convolutional neural network (CNN) to identify starfish species to initiate developing a media center marine biota identification system of BMP. Two CNN architectures, namely MobileNet and VGG16, were conducted to produce identification models. The first stage carried out a training process on 1800 starfish image data and then evaluated using the 5-fold cross-validation technique. Validation results show that MobileNet is superior to the VGG16 architecture by achieving validation accuracy of 100% for each fold while VGG16 produces validation accuracy in the range of 94% to 100%. On the other hand, in the second stage of model testing, it was found that VGG16 worked better than MobileNet in identifying 200 new data. The Best Model produced by VGG16 achieved testing accuracy of 100% while MobileNet produced 99.5%. However, stability analysis of the identification models produced by both architectures shows that MobileNet has relatively small loss values ranging from 0.00069325 to 0.00214802 as well as smaller standard deviation values of 0.27 compared to 0.61 produced by VGG16. These findings indicate MobileNet is more stable in carrying out identification work compared to VGG16 of, thus the best model provided by MobileNet is taken to deploy in the web platform which is created using the Python flask framework. The proposed system can be used to strengthen the marine tourism industry as a media center of educational marine biota using deep learning approaches.
Co-Authors Aji Prasetya Wibawa Andika Dwiyanto, Felix Ardina, Cening Atmaja, I Made Ari Dwi Suta Atmaja, I Made Ari Dwi Suta Atmaja Bawa, I Gusti Ngurah Bagus Catur Dessy Hariyanti, NK Didik Dwi Prasetya Dwiyanto, Felix Andika Fachrul Kurniawan Fadhli Almu’iini Ahda Fajar Purnama Felipe P. Vista IV Felix Andika Dwiyanto Frangky J. Paat Gede Sukadarmika Gusti Nyoman Ayu Sukerti Gusti Nyoman Ayu Sukerti, Gusti Nyoman Ayu I Dewa Gede Ary Subagia I ketut Gede Darma Putra I Made Oka Widyantara I MADE SUDARMA I Putu Astawa I Putu Bagus Arya Pradnaya I Putu Bagus Arya Pradnyana I Putu Sutawinaya I Wayan Budi Sentana I Wayan Raka Ardana I Wayan Suasnawa I Wayan Suasnawa Ida Bagus Putra Manuaba IGAK Cathur Adhi Irawan, Novta Danyel Islam, Noorul Jabari, Nida Kamarudin, Nur Diyana Karim, Irwan Komang Ayu Triana Indah Linawati Linawati Luther Latumakulita Mairi, Vitrail Gloria Ngr. Putu Raka Novandra Asta Purnama, Ida Bagus Irawan Putu Gde Sukarata Radhitya, Made Leo Rudiastari, Elina Rukmi Sari Hartati Saroyo Saroyo Sirait, Hasanuddin Sukarata, I Putu Gde Sulistyo, Danang Arbian Sutoko, Sutoko Triadi, Wilfridus Bambang Yusriel Ardian