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All Journal International Journal of Electrical and Computer Engineering Jurnal Teknik Elektro Lontar Komputer: Jurnal Ilmiah Teknologi Informasi Perfecting a Video Game with Game Metrics Journal of Education and Learning (EduLearn) Journal of Telematics and Informatics Jurnal Teknologi Informasi dan Ilmu Komputer International Journal of Advances in Intelligent Informatics Jurnal Sains Teknologi dan Lingkungan (JSTL) Journal of Computer Science and Informatics Engineering (J-Cosine) EDUMATIC: Jurnal Pendidikan Informatika Jurnal Teknologi Informasi, Komputer, dan Aplikasinya (JTIKA ) Dielektrika : Jurnal Ilmiah Kajian Teori dan Aplikasi Teknik Elektro JATI EMAS (Jurnal Aplikasi Teknik dan Pengabdian Masyarakat) Jurnal Abdi Insani Indonesian Journal of Electrical Engineering and Computer Science Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Begawe Teknologi Informasi

I Gede Pasek Suta Wijaya

Informatics Engineering Dept., University Of Mataram, Jl. Majapahit 62 Mataram, Mataram INDONESIA

Author-ID : 417288

Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Public Health Social Sciences Other

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Pengenalan Citra Sidik Jari Berbasis Transformasi Wavelet dan Jaringan Syaraf Tiruan Suta Wijaya, I Gede Pasek; Kanata, Bulkis
Jurnal Teknik Elektro Vol 4, No 1 (2004): MARET 2004
Publisher : Institute of Research and Community Outreach

Image recognition is a mechanism to recognize an image that is not recognized by eyes, using certain method. This research was fingerprint recognition based on wavelet transforms and neural network. The aims of this research are to find the best wavelet and to know what the performance of this method is. Fingerprint recognition algorithms start from extracting an image to find image signature by choosing a little wavelet transforms coefficients that have the biggest magnitude value and neural network was used to select the best match (likeness) to original images in the collection. The test were carried out in three kind of wavelets viz Coiflet 6, Daubechies 8, dan Symlet 8 and 5 types of query images (pure, blur, noise, pencil sketch, and edge) and each query image has 30 samples. Query's success rates were determined by using one percent threshold value times size of databases. The result show that this method has good performance, which the average of success rate over 90% and need a little time query. The Symlet 6 can be considered to be the best wavelet for fingerprint image recognition, with success rate 96.36%. With respect to the elapsed query time, of about 0.11 second, the above method is sufficiently efficient for the database size of 1500 records. Abstract in Bahasa Indonesia : Pengenalan citra merupakan suatu mekanisme untuk mengenali kembali citra yang secara signifikan oleh mata tidak dapat dikenali lagi, namun dengan metode dan teknik tertentu citra tersebut masih dapat dikenali. Penelitian ini merupakan pengenalan citra sidik jari berbasis transformasi wavelet sebagai pengolah awal (pre-processing) dan jaringan syaraf tiruan sebagai elemen pengenal (metrika). Tujuan dari penelitian ini untuk menentukan wavelet yang terbaik untuk pengenalan citra sidik jari dan mengetahui performance dari metode pengenalan ini. Algoritma pengenalan citra sidik jari dimulai dengan mengekstrak citra menjadi ciri-ciri citra dengan cara memilih sejumlah kecil (m) koefisien hasil transformasi wavelet yang memiliki magnitude terbesar dan dilanjutkan dengan menghitung tingkat kemiripan antara ciri-ciri citra query dengan citra pustaka digunakan digunakan metode jaringan syaraf tiruan jenis backpropagation. Pengujian dilakukan pada 3 jenis wavelet, yaitu Coiflet 6, Daubechies 6, dan Symlet 6; dan 5 tipe citra query yaitu asli, blur, berderau, sketsa pencil, dan tepi sisi dengan setiap tipe query memiliki 30 buah sampel. Untuk mengetahui tingkat kesuksesan pengenalan, digunakan nilai ambang 1% x ukuran basis data citra. Hasil penelitian menunjukkan bahwa pengenalan citra sidik jari menggunakan transformasi wavelet dan jaringan syaraf tiruan memberikan hasil yang baik, hal ini ditunjukkan dengan tingkat kesuksesan pengenalan diatas 90% dan waktu pengenalan yang singkat. Dari ketiga jenis wavelet yang diuji ternyata ketiga-tiganya memberikan hasil yang baik. Namun jenis wavelet Symlet 6 merupakan wavelet yang terbaik untuk pengenalan citra sidik jari, dengan tingkat kesuksesan pengenalan 96,36%. Sistem pengenalan ini memerlukan waktu pengenalan relatif kecil, yaitu sekitar 0,11 detik untuk ukuran basis data 1500 rekord. Kata kunci: Citra sidik jari, pengenalan citra, transformasi wavelet, jaringan syaraf tiruan dan citra pustaka dan query.

PENGENALAN CITRA PORNO BERBASIS KANDUNGAN INFORMASI CITRA (IMAGE CONTENT) Gede Pasek Suta Wijaya, I; B K Widiartha, I
Jurnal Teknik Elektro Vol 5, No 2 (2005): SEPTEMBER 2005
Publisher : Institute of Research and Community Outreach

Pornographic image recognition is a matching process between an image signature of pornographic training image and an image signature of query image. The aims of this research were to build pornographic image recognition system based on image content that can classify an image that is porno or not and to know how the performance of this method is. Image content that was used in this research was color content and image signature. The image content was taken by color histogram and by extracting an image using wavelet transform, next choosing a little wavelet transforms coefficients that have the biggest magnitude value and moment was used to sharpness the image information content. The tests were carried out in Daubechies 8 wavelets type, 25 heterogeneous pornographic images for training and 500 query images that consist 125 heterogeneous pornographic images and 375 non-pornographic images. Screening success rates were determined by using number images that can be screening per number images on database in percentage, and screening time was determined by the time that needed for an image to classify as pornographic image. The results show that this method give average of screening success rate about 67.02% (it means can classify 84 images as pornographic from 125 pornographic images in database) and can classify about 36 images as pornographic from 375 non-pornographic images in database. This system also needs a little time for screening process that is about 0.29 second every image and screening time is linear to size of database. This method is good enough as pornographic image screening but need some improvement for increasing the performance. Abstract in Bahasa Indonesia : Pengenalan citra merupakan proses pencocokan antara ciri-ciri citra citra query dengan ciri-ciri citra pelatihan yang tersimpan dalam basis data (citra pustaka). Penelitian ini merupakan pengenalan citra porno berbasis kandungan informasi (image content) citra yang bertujuan untuk membangun sistem yang dapat melakukan pengenalan apakah suatu citra berkategori porno atau bukan dan untuk mengetahui keefektifan teknik ini. Kandungan informasi yang dimaksud adalah informasi warna dan ciri-ciri citra (image signature) yang di peroleh dengan cara teknik histogram warna dan mentransformasi-wavelet-kan citra dan memilih sebagian kecil (m) koefisien hasil transformasi yang memiliki nilai magnitude terbesar yang selanjutnya ciri-ciri citra dikenakan proses penghitungan momen untuk penajaman informasi citra. Informasi ini yang digunakan sebagai basis pengenalan. Pengenalan citra porno menjadi sulit karena citra porno memiliki tingkat heterogen yang tinggi seperti memiliki pose, warna latar belakang, diambil dari sudut kamera, warna kulit, dan etnis yang berbeda. Sistem ini diujikan pada wavelet jenis Daubechies 8, 25 citra porno heterogen untuk pelatihan, dan 500 sample citra query yang terdiri dari 125 citra porno heterogen dan 375 citra non-porno. Tingkat kesuksesan pengenalan dihitung dengan menggunakan nilai persentase dari jumlah citra porno yang dikenali terhadap jumlah total citra porno yang terdapat dalam basis data dan waktu pengenalan merupakan waktu yang diperlukan oleh sistem untuk mengklasifikasikan sebuah citra sebagai citra porno atau bukan. Hasil pengujian menunjukkan bahwa tingkat kesuksesan pengenalan citra porno menggunakan metode ini sebesar 67.02% (dapat mengenali sebanyak 84 citra sebagai citra porno dari 125 citra porno yang diuji) dan mendeteksi citra non-porno menjadi porno sebanyak 36 citra dari 375 cita non porno (9.06 %), Waktu pengenalan bersifat linear terhadap ukuran data basis data dan waktu pengenalan relatif pendek yaitu rata-rata untuk setiap citra sebesar 0.29 detik. Hasil ini menunjukkan transformasi wavelet cukup baik digunakan sebagai pemroses-awal (pre-processing) citra dan moment sebagai penajam informasi untuk pengenalan citra porno atas kandungan informasi (image content) dari citra, namun perlu diteliti lebih lanjut sehingga tingkat kesuksesan lebih baik. Katakunci: sistem pengenalan, citra porno, transformasi wavelet, momen, dan ciri-ciri citra.

Pencarian Citra Menggunakan Metode Transformasi Wavelet dan Metrika Histogram Terurut Bagus K. Widiartha, Ida; Gede Pasek Suta Wijaya, I
Jurnal Teknik Elektro Vol 6, No 1 (2006): MARET 2006
Publisher : Institute of Research and Community Outreach

Image retrieval was a matching process between feature of query image and feature of target image. Image retrieval was a very interesting project because many institutions such as artistic gallery and police used it. This paper explained about image retrieval method that was used to retrieve image from a collection of images. The method was sorted wavelet histogram. The method was a result of development of previous image retrieval which implements histogram to wavelet transforms coefficients for feature extraction. The retrieval application just runs in the computer when the test was carried out. The result showed that this method gave good performance which the average of succeed rate was 100% for original image, 99,83% for the image of Blur, 90,25% for edge image and 73,57% for the 50% salt & paper noise image and the querying time is about 0.2 second. Abstract in Bahasa Indonesia : Pencarian citra merupakan proses pencocokan antara ciri-ciri citra query dengan ciri-ciri citra target (citra pustaka). Pencarian citra merupakan materi yang sangat menarik karena banyak digunakan oleh pihak-pihak yang memerlukan seperti galeri-galeri seni dan kepolisian. Paper ini menjelaskan tentang suatu teknik yang digunakan untuk melakukan pencarian citra dari setumpukan citra, teknik yang dimaksud adalah teknik histogram wavelet terurut. Teknik ini merupakan pengembangan teknik pencarian citra sebelumnya dengan menerapkan teknik histogram pada koefisien hasil transformasi wavelet untuk menentukan ciri-ciri citra. Pengujian dilakukan dengan hanya menjalan aplikasi pencarian.pada komputer Hasil pengujian menunjukkan bahwa metode memberikan hasil yang memuaskan dengan tingkat kesuksesan pencarian rata-rata 100% untuk citra Asli, 99,83% untuk citra Blur, 90,25% untuk citra tepi dengan 73,57% untuk citra berderau 50% salt & paper dan waktu pencarian dengan kisaran 0.2 detik Kata kunci : pencarian citra, wavelet, histogram, ciri-ciri citra, citra query dan pustaka.

Traffic Light Signal Parameters Optimization using Modification of Multielement Genetic Algorithm I Gede Pasek Suta Wijaya; Keeichi Uchimura; Gou Koutaki
International Journal of Electrical and Computer Engineering (IJECE) Vol 8, No 1: February 2018
Publisher : Institute of Advanced Engineering and Science

A strategy to optimize traffic light signal parameters is presented for solving traffic congestion problem using modification of the Multielement Genetic Algorithm (MEGA). The aim of this method is to improve the lack of vehicle throughput (FF ) of the works called as traffic light signal parameters optimization using the MEGA and Particle Swarm Optimization (PSO). In this case, the modification of MEGA is done by adding Hash-Table for saving some best populations for accelerating the recombination process of MEGA which is shortly called as H-MEGA. The experimental results show that the H-MEGA based optimization provides better performance than MEGA and PSO based methods (improving the FF of both MEGA and PSO based optimization methods by about 10.01% (from 82,63% to 92.64%) and 6.88% (from 85.76% to 92.64%), respectively). In addition, the H-MEGA improve significantly the real FF of Ooe Toroku road network of Kumamoto City, Japan about 21.62%.

Media Pembelajaran Pengenalan Alat Transportasi dan Rambu Lalu Lintas Berbasis Android untuk Sekolah Dasar Nadiyasari Agitha; Romi Saefudin; I Gede Pasek Suta Wijaya
Jurnal Pendidikan Informatika (EDUMATIC) Vol 5, No 2 (2021): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v5i2.4100

Transportation equipment and traffic signs are one of the important lessons for elementary school students. However, many elementary school students only know the means of transportation without knowing the traffic signs. The purpose of this study is to build and create learning media to make it easier for students to recognize transportation tools and traffic signs using the Android platform. The method used in building this application is the waterfall method, which starts with making observations to testing the system. The tests used are black box testing and Mean Opinion Score (MOS). The results of the black box testing test are that the menu has been able to run according to their respective functions, while for MOS, it produces about 98% results stating that the application is running well. So that this application is used in accordance with the needs of elementary school students in helping to recognize means of transportation and traffic signs.

Real Time Face Recognition Based on Face Descriptor and Its Application I Gede Pasek Suta Wijaya; Ario Yudo Husodo; I Wayan Agus Arimbawa
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 16, No 2: April 2018
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v16i2.7418

This paper presents a real time face recognition based on face descriptor and its application for door locking. The face descriptor is represented by both local and global information. The local information, which is the dominant frequency content of sub-face, is extracted by zoned discrete cosine transforms (DCT). While the global information, which also is the dominant frequency content and shape information of the whole face, is extracted by DCT and by Hu-moment. Therefore, face descriptor has rich information about a face image which tends to provide good performance for real time face recognition. To decrease the dimensional size of face descriptor, the predictive linear discriminant analysis (PDLDA) is employed and the face classification is done by kNN. The experimental results show that the proposed real time face recognition provides good performances which indicated by 98.30%, 21.99%, and 1.8% of accuracy, FPR, and FNR respectively. In addition, it also needs short computational time (1 second).

Face Recognition Using Holistic Features and Linear Discriminant Analysis Simplification I Gede Pasek Suta Wijaya; Keiichi Uchimura; Gou Koutaki
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 10, No 4: December 2012
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v10i4.866

This paper proposes an alternative approach to face recognition algorithm that is based on global/holistic features of face image and simplified linear discriminant analysis (LDA). The proposed method can overcome main problems of the conventional LDA in terms of large processing time for retraining when a new class data is registered into the training data set. The holistic features of face image are proposed as dimensional reduction of raw face image. While, the simplified LDA which is the redefinition of between class scatter using constant global mean assignment is proposed to decrease time complexity of retraining process. To know the performance of the proposed method, several experiments were performed using several challenging face databases: ORL, YALE, ITS-Lab, INDIA, and FERET database. Furthermore, we compared the developed algorithm experimental results to the best traditional subspace methods such as DLDA, 2DLDA, (2D)2DLDA, 2DPCA, and (2D)22DPCA. The experimental results show that the proposed method can be solve the retraining problem of the conventional LDA indicated by requiring shorted retraining time and stable recognition rate.

Pornographic Image Recognition Based on Skin Probability and Eigenporn of Skin ROIs Images I Gede Pasek Suta Wijaya; IBK Widiartha; Sri Endang Arjarwani
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 13, No 3: September 2015
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v13i3.1476

The paper proposed a pornographic image recognition using skin probability and principle component analysis (PCA) on YCbCr color space. The pornographic image recognition is defined as a process to classify the image containing and showing genital elements of human body from any kinds of images. This process is hard to be performed because the images have large variability due to poses, lighting, and background variations. The skin probability and holistic feature, which is extracted by YCbCr skin segmentation and PCA, is employed to handle those variability problems. The function of skin segmentation is to determine skin ROI image and skin probability. While the function of PCA is to extract eigenporn of the skin ROIs images and by using the eigenporns the holistic features are determined. The main aim of this research is to optimize the accuracy and false rejection rate of the skin probability and fusion descriptor based recognition system. The experimental result shows that the proposed method can increase the accuracy by about 12% and decrease the FPR and FNR by about 16%, respectively. The proposed method also works fast for recognition, which requires 1.3.second per image.

Learning media for the transliteration of Latin letters into Bima script based on android applications Arik Aranta; I Gede Pasek Suta Wijaya; Ario Yudo Husodo; Gibran Satya Nugraha; Ramaditia Dwiyansaputra; Fitri Bimantoro; I Putu Teguh Putrawan
Journal of Education and Learning (EduLearn) Vol 15, No 2: May 2021
Publisher : Intelektual Pustaka Media Utama

Preservation of Indonesian culture is an important thing that must be considered. One of the efforts that have been made in preserving culture is technological development implementation. In cultural preservation, based on data obtained from 45 respondents from the Bima community in Indonesia, 34.9% of the community does not understand the Bima script. It means that only 65.1% of the community can understand Bima script. This condition will continue to grow if there is no effort made to preserve the Bima script. Because people should have a comprehensive knowledge of the Bima script to understand its usage in writing and reading activities, people tend to have less desire to learn the Bima script. Mataram University is trying to develop creative and innovative learning products to learn the Bima script learning model in an interactive application using a smartphone to translate Latin letters into the script. This development aims to facilitate the process of learning Bima characters for the community. The method that has been used is to apply a string replacement algorithm based on the Bima script rules in the reference book containing the ancient Bima script. According to the experiment result, the alpha test value from 31 respondents is 99.36%, and the Beta test value from 45 respondents Bima society is 91.50%. It can be concluded that this application is feasible as a learning medium.

Beef Quality Classification based on Texture and Color Features using SVM Classifier Rani Farinda; Zul Rijan Firmansyah; Chaerus Sulton; I Gede Pasek Suta WIJAYA; Fitri Bimantoro
Journal of Telematics and Informatics Vol 6, No 3: September 2018
Publisher : Universitas Islam Sultan Agung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/jti.v6i3.

Beef quality can be examined visually by observing the beef color or texture using human eyes. This manual method is very simple yet very subjective because of differences in knowledge about fresh or defective beef characteristics and differences in accuracy. Therefore, a system that can automatically classify beef quality whether it is still fresh or already defective is needed. In this research, we developed a system that can classify beef quality based on its color and texture features using Support Vector Machines classifier. Statistical approach and Gray Level Co-Occurrence Matrix (GLCM) methods were used for the feature extraction process. The total of data used in this research was 480 images, divided into training and testing datasets. The highest accuracy was 97% for cold beef when the system was tested using color features of HSI color space.

Co-Authors Adi Sugita Pandey Aditya Perwira Joan Dwitama Afwani, Royana Agitha, Nadiyasari Ahmad Zafrullah Mardiansyah Aldian Wahyu Septiadi Andy Hidayat Jatmika Anita Rosana MZ Annisa Mujahidah Robbani Arik Aranta Arik Aranta Ario Yudo Husodo Ario Yudo Husodo Ario Yudo Husodo Ario Yudo Husodo Ario Yudo Husodo Ario Yudo Husodo Ariyan Zubaidi Ariyan Zubaidi Asno Azzawagaam Firdaus Ayu Rezki Azizah Arif Paturrahman Belmiro Razak Setiawan Budi Irmawati Budi Irmawati Bulkis Kanata Chaerus Sulton Chandra Adiguna Chandra Adiguna Cipta Ramadhani David Arizaldi Muhammad Dedi Ermansyah Ditha Nurcahya Avianty Eet Widarini Eka Dina Juliani U M Fadilah . Fahmi Syuhada Farhan Yakub Bawazir Fiena Efliana Alfian Fitri Bimantoro Gibran Satria Nugraha Gibran Satya Nugraha Gibran Satya Nugraha Gibran Satya Nugraha Gibran Satya Nugraha Gou Koutaki Gunawan Haidra Rahman Hendy Marcellino Heri Wijayanto Heri Wijayanto I B K Widiartha I Gde Putu Wirarama Wedaswhara W. I Made Budi Suksmadana I Made Budii i Suksmadana I Made Subiantara Putra I Putu Teguh Putrawan I Wayan Agus Arimbawa I Wayan Agus Arimbawa IBK Widiartha Ida Bagus K. Widiartha Ida Bagus Ketut Widiartha Ida Bagus Ketut Widiartha Ida Bagus Ketut Widiartha Ida Bagus Ketut Widiartha Ida Nyoman Tegeh Adnyana Imam Arief Putrajaya Keeichi Uchimura Keiichi Uchimura Keiichi Uchimura L. A. Syamsul Irfan Lalu Sweta Arif Lalu Zulfikar Muslim Lidia Ardhia Wardani Marlia Zuhraini Mayzar Anas Mega Laely Moh Ali Albar Moh. Ali Albar Muhamad Syamsu Iqbal Muhammad Daden Kasandi Putra Wesa Muhammad Husnul Ramdani Muhammad Khaidar Rahman Muhammad Naufal Rizqullah Muhammad Syulhan Al Ghofany Mustiari Mustiari Ni Nyoman Kencanawati Novian Maududi Novita Nurul Fakhriyah Nurhalimah Nurhalimah Pandu Deski Prasetyo Ramaditia Dwiyansaputra Ramaditia Dwiyansaputra Ramlah Nurlaeli Rani Farinda Reza Rismawandi Rina Lestari Riska Yulianti Ristirianto Adi Romi Saefudin Salsabila Putri Rajani Said Santi Ika Murpratiwi Siti Faria Astari Sri Endang Anjarwani Sri Endang Anjarwani Sri Endang Arjarwani Sulfan Akbar Suthami Ariessaputra Syaifullah Syaifullah Topan Khrisnanda Tri Erna Suharningsih Ulil Amri Wahyu Alfandi Wirarama Wedashwara Yogi Permana Zakiyah Rahmiati Zul Rijan Firmansyah

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