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All Journal JOIV : International Journal on Informatics Visualization Jurnal Sains Teknologi dan Lingkungan (JSTL) PROCESSOR Jurnal Ilmiah Sistem Informasi, Teknologi Informasi dan Sistem Komputer Dielektrika : Jurnal Ilmiah Kajian Teori dan Aplikasi Teknik Elektro INFOKUM Prosiding Konferensi Nasional PKM-CSR Jurnal Pijar MIPA Jurnal Bakti Nusa Jurnal Pepadu Jurnal Pendidikan dan Pengabdian Masyarakat Jurnal Rekayasa elektrika Jurnal Pengabdian Inovasi Masyarakat Indonesia JEITECH (JOURNAL OF ELECTRICAL ENGINEERING AND INFORMATION TECHNOLOGY)
Ariessaputra, Suthami
Universitas Mataram
Religion Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Library & Information Science Mathematics Nursing Physics Public Health Social Sciences Other

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Journal : JOIV : International Journal on Informatics Visualization

 1 
Application of Gray Scale Matrix Technique for Identification of Lombok Songket Patterns Based on Backpropagation Learning Sudi Mariyanto Al Sasongko; Erni Dwi Jayanti; Suthami Ariessaputra
JOIV : International Journal on Informatics Visualization Vol 6, No 4 (2022)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.4.1532

Abstract

Songket is a woven fabric created by prying the threads and adding more weft to create an embossed decorative pattern on a cotton or silk thread woven background. While songket from many places share similar motifs, when examined closely, the motifs of songket from various regions differ, one of which is in the Province of West Nusa Tenggara, namely Lombok Island. To assist the public in recognizing the many varieties of Lombok songket motifs, the researchers used digital image processing technology, including pattern recognition, to distinguish the distinctive patterns of Lombok songket. The Gray Level Co-occurrence Matrix (GLCM) technique and Backpropagation Neural Networks are used to build a pattern identification system to analyze the Lombok songket theme. Before beginning the feature extraction process, the RGB color image has converted to grayscale (grayscale), which is resized. Simultaneously, a Backpropagation Neural Network is employed to classify Lombok songket theme variations. This study used songket motif photos consisting of a sample of 15 songket motifs with the same color theme that was captured eight times, four of which were used as training data and kept in the database. Four additional photos were utilized as test data or data from sources other than the database. When the system’s ability to recognize the pattern of Lombok songket motifs is tested, the maximum average recognition percentage at a 0° angle is 88.33 percent. In comparison, the lowest average recognition percentage at a 90° angle is 68.33 percent.
Classification of Lombok Songket and Sasambo Batik Motifs Using the Convolution Neural Network (CNN) Algorithm Suthami Ariessaputra; Viviana Herlita Vidiasari; Sudi Mariyanto Al Sasongko; Budi Darmawan; Sabar Nababan
JOIV : International Journal on Informatics Visualization Vol 8, No 1 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.1.1386

Abstract

Sasambo batik is a traditional batik from the West Nusa Tenggara province. Sasambo itself is an abbreviation of three tribes, namely the Sasak (sa) in the Lombok Islands, the Samawa (sam), and the Mbojo (bo) tribes in Sumbawa Island. Classification of batik motifs can use image processing technology, one of which is the Convolution Neural Network (CNN) algorithm. Before entering the classification process, the batik image first undergoes image resizing. After that, proceed with the operation of the convolution, pooling, and fully connected layers. The sample image of Lombok songket motifs and Sasambo batik consists of 20 songket fabric data with the same motif and color and 14 songket data with the same motif but different colors. In addition, there are 10 data points on songket fabrics with other motifs and colors. In addition, there are 5 data points on Sasambo batik fabrics with the same motif and color and 5 data points on Sasambo batik fabrics with the same motif but different colors. The training data rotates the image by 150 as many as 20 photos. Testing with motifs with the same color shows that the system's success rate is 83.85%. The highest average recognition for Sasambo batik cloth is in testing motifs with the same color for data in the database at 93.66%. The CNN modeling classification results indicate that the Sasambo batik cloth can be a reference for developing songket categorization using a website platform or the Android system.
Mel Frequency Cepstral Coefficients (MFCC) Method and Multiple Adaline Neural Network Model for Speaker Identification Sasongko, Sudi Mariyanto Al; Tsaury, Shofian; Ariessaputra, Suthami; Ch, Syafaruddin
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.7.4.1376

Abstract

Speech recognition technology makes human contact with the computer more accessible. There are two phases in the speaker recognition process: capturing or extracting voice features and identifying the speaker's voice pattern based on the voice characteristics of each speaker. Speakers consist of men and women. Their voices are recorded and stored in a computer database. Mel Frequency Cepstrum Coefficients (MFCC) are used at the voice extraction stage with a characteristic coefficient of 13. MFCC is based on variations in the response of the human ear's critical range to frequencies (linear and logarithmic). The sound frame is converted to Mel frequency and processed with several triangular filters to get the cepstrum coefficient. Meanwhile, at the speech pattern recognition stage, the speaker uses an artificial neural network (ANN) Madaline model (many Adaline/ which is the plural form of Adaline) to compare the test sound characteristics. The training voice's features have been inputted as training data. The Madaline Neural Network training is BFGS Quasi-Newton Backpropagation with a goal parameter of 0,0001. The results obtained from the study prove that the Madaline model of artificial neural networks is not recommended for identification research. The results showed that the database's speech recognition rate reached 61% for ten tests. The test outside the database was rejected by only 14%, and 84% refused testing outside the database with different words from the training data. The results of this model can be used as a reference for creating an Android-based real-time system.
Co-Authors Abdullah Zainuddin aiza_22 Akbar, Lalu A. Syamsul Irfan Budi Darmawan Budi Darmawan Budi Darmawan Cipta Ramadhani Danang Januar Dani Dedi Ramdan Erni Dwi Jayanti Fathurrahman . Giri Wahyu Wiriasto I Gede Pasek Suta Wijaya I Made Sulastra I Made Sulastra, I Made Indra Sastra Jamal, Venny Aprilia Jumadi Jumadi Jurnal Pepadu Khalifatul Syuhada Kurniawan Yuniarto Kurniawan Yuniarto lalu A. Syamsul Irfan Akbar lu Ahmad Syam`sul Irfan Akbar Made Sutha Yadnya Muhammad Afrizal G. Rasyda Muhammad Alfarizi Putra Muhammad Arwan Rosyadi Muhammad Fajri Muhammad Juaeni Muna Aulia Muvianto, Cahyo Mustiko Okta Muvianto, Cahyo Mustiko Okta Muvianto Natasya Dewi Astuti Novianung, Lalu Triawanri Nurfajrin Pahrurrozi Pahrurrozi Paniran Paniran Paniran Putra, Rayasa Puringgar Prasadha Rasyda, Muhammad Afrizal G. Sabar Nababan Saprini Hamdiani Saprizal Hadisaputra Sudi Mariyanto Al Sasongko Suksmadana, I Made Budi Syafaruddin Ch Syafaruddin Syafaruddin Syarifuddin Syarifuddin Thauty, Achmad Gildas Tsaury, Shofian Ulul Khairi Zuryati Viviana Herlita Vidiasari Warindi D