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All Journal Perfecting a Video Game with Game Metrics 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 Dharma: Jurnal Pengabdian Masyarakat
I Nyoman Gede Arya Astawa
Politeknik Negeri Bali
Computer Science & IT Education Engineering 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
Combination of Feature Extractions for Classification of Coral Reef Fish Types Using Backpropagation Neural Network Luther Alexander Latumakulita; I Nyoman Gede Arya Astawa; Vitrail Gloria Mairi; Fajar Purnama; Aji Prasetya Wibawa; Nida Jabari; Noorul Islam
JOIV : International Journal on Informatics Visualization Vol 6, No 3 (2022)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.3.1082

Abstract

Feature extraction is important to obtain information in digital images, where feature extraction results are used in the classification process. The success of a study to classify digital images is highly dependent on the selection of the feature extraction method used, from several studies providing a combination of feature extraction solutions to produce a more accurate classification.  Classifying the types of marine fish is done by identifying fish based on special characteristics, and it can be through a description of the shape, fish body pattern, color, or other characteristics. This study aimed to classify coral reef fish species based on the characteristics contained in fish images using Backpropagation Neural Network (BPNN) method. Data used in this research was collected directly from Bunaken National Marine Park (BNMP) in Indonesia. The first stage was to extract shape features using the Geometric Invariant Moment (GIM) method, texture features using Gray Level Co-occurrence Matrix (GLCM) method, and color feature extraction using Hue Saturation Value (HSV) method. The third value of feature extraction was used as input for the next stage, namely the classification process using the BPNN method. The test results using 5-fold cross-validation found that the lowest test accuracy was 85%, the highest was 100%, and the average was 96%. This means that the intelligent model derived from the combination of the three feature extraction methods implemented in the BPNN training algorithm is very good for classifying coral reef fish.
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 : Politeknik Negeri Padang

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.
Facemask Detection using the YOLO-v5 Algorithm: Assessing Dataset Variation and R esolutions Fachrul Kurniawan; I Nyoman Gede Arya Astawa; I Made Ari Dwi Suta Atmaja; Aji Prasetya Wibawa
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 9 No 2 (2023): July (In Progress)
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v9i2.3249

Abstract

The Covid-19 pandemic has made it imperative to prioritize health standards in companies and public areas with a large number of people. Typically, officers oversee the usage of masks in public spaces; however, computer vision can be employed to facilitate this process. This study focuses on the detection of facemask usage utilizing the YOLO-v5 algorithm across various datasets and resolutions. Three datasets were employed: the face with mask dataset (M dataset), the synthetic dataset (S dataset), and the combined dataset (G dataset), with image resolutions of 320 pixels and 640 pixels, respectively. The objective of this study is to assess the accuracy of the YOLO-v5 algorithm in detecting whether an individual is wearing a mask or not. In addition, the algorithm was tested on a dataset comprising individuals wearing masks and a synthetic dataset. The training results indicate that higher resolutions lead to longer training times, but yield excellent prediction outcomes. The system test results demonstrate that face image detection using the YOLO-v5 method performs exceptionally well at a resolution of 640 pixels, achieving a detection rate of 99.2 percent for the G dataset, 98.5 percent for the S dataset, and 98.9 percent for the M dataset. These test results provide evidence that the YOLO-v5 algorithm is highly recommended for accurate detection of facemask usage.
APLIKASI DIGITALISASI LAYANAN SURAT-MENYURAT UNTUK MENINGKATAN LAYANAN ADMINISTRASI KANTOR DESA I Nyoman Gede Arya Astawa; Ida Bagus Putra Manuaba; I Made Ari Dwi Suta Atmaja Atmaja; I Putu Gde Sukarata
JURNAL WIDYA LAKSANA Vol 12 No 2 (2023)
Publisher : Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/jwl.v12i2.61871

Abstract

Pelayanan surat-menyurat merupakan bagian dari kualitas kinerja pemerintah desa, dengan memberikan pelayanan surat-menyurat yang efektif dan efisien mampu meningkatkan kepuasan masyarakat terhadap administrasi desa. Saat ini pengelolaan pengajuan pembuatan surat-menyurat khususnya surat keterangan di Kantor Desa Adat Sibetan masih dilakukan dengan cara konvensional, sehingga menyebabkan beberapa permasalahan yang muncul. antara lain: proses pengajuan yang memerlukan waktu relatif lama, pengecekan status surat yang tidak efektif, data penduduk yang tidak terdokumentasi dengan baik. Untuk mengatasi permasalahan tersebut kegiatan pengabdian difokuskan pada pembuatan aplikasi digitalisasi surat-menyurat pada Kantor Desa Sibetan. Setelah aplikasi siap dan dihosting pada website maka dilakukan sosialisasi kepada warga Desa Sibetan. Evaluasi dari pengabdian masyarakat ini dilakukan dengan dua cara, pertama adalah mengevaluasi website menggunakan aplikasi tool yaitu Pagespeed Insights dan evaluasi kedua adalah evaluasi kebermanfaatan dan output hasil surat-menyurat menggunakan kuisioner. Hasil evaluasi pertama menunjukkan nilai diagnosa kinerja bila dijalankan pada dekstop rata-rata sebesar 92,25 dan pada perangkat mobile/smartphone adalah rata sebesar 81,5. Evaluasi kedua menggunakan kuisioner yang diisi oleh peserta dalam sosialisasi menunjukkan bahwa aplikasi layanan surat-menyurat sangat bermanfaat dan bila diimplementasikan sangat mudah digunakan dan sangat efesien dalam mengurus surat-surat di Kantor Desa Sibetan.
Co-Authors Aji Prasetya Wibawa Atmaja, I Made Ari Dwi Suta Dessy Hariyanti, NK Fachrul Kurniawan Fajar Purnama Felipe P. Vista IV Felix Andika Dwiyanto Gusti Nyoman Ayu Sukerti I Dewa Gede Ary Subagia I ketut Gede Darma Putra I MADE SUDARMA I Putu Bagus Arya Pradnaya I Putu Bagus Arya Pradnyana I Putu Gde Sukarata I Putu Sutawinaya I Wayan Budi Sentana I Wayan Raka Ardana I Wayan Suasnawa I Wayan Suasnawa Ida Bagus Putra Manuaba IGAK Cathur Adhi Luther Latumakulita Nida Jabari Noorul Islam Putu Gde Sukarata Radhitya, Made Leo Rukmi Sari Hartati Vitrail Gloria Mairi