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Journal articles on the topic 'Classification des motifs'

Author: Grafiati

Published: 2 August 2022

Last updated: 31 July 2025

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1

Nalle, Derwi Rainord, Luh Gede Astuti, I. Gede Santi Astawa, Luh Arida Ayu Rahning Putri, AAIN Eka Karyawati, and I. Wayan Supriana. "Implementasi Metode Convolutional Neural Network Untuk Pengenalan Pola Motif Kain Tenun Rote Ndao Berbasis Android." JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) 11, no. 1 (2022): 157. http://dx.doi.org/10.24843/jlk.2022.v11.i01.p17.

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Rote ndao Ikat Weaving has interesting characteristics in each fabric motif because it has different motifs which indicate the ethnic differences contained in each of the resulting motifs. Rote Ndao weaving has a variety of motifs that are still unknown to many people, so in this study a classification of motifs of rote ndao woven fabrics was carried out using the Convolutional Neural Network method. Weaving motif classification uses 3 motifs with a total of 1050 data including 70 data for Ai Bunak, Dula Kakaik and Lafa Langgak motifs each. Data for 3 fabric motifs is divided into 80% training

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Meranggi, Dewa Gede Trika, Novanto Yudistira, and Yuita Arum Sari. "Batik Classification Using Convolutional Neural Network with Data Improvements." JOIV : International Journal on Informatics Visualization 6, no. 1 (2022): 6. http://dx.doi.org/10.30630/joiv.6.1.716.

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Batik is one of the Indonesian cultures that UNESCO has recognized. Batik has a variety of unique and distinctive patterns that reflect the area of origin of the batik motif. Batik motifs usually have a 'core motif' printed repeatedly on the fabric. The entry of digitization makes batik motif designs more diverse and unique. However, with so many batik motifs spread on the internet, it is difficult for ordinary people to recognize the types of batik motifs. This makes an automatic classification of batik motifs must continue to be developed. Automation of batik motif classification can be assi

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Prayoga, Arya, Maimunah, Pristi Sukmasetya, Muhammad Resa Arif Yudianto, and Rofi Abul Hasani. "Arsitektur Convolutional Neural Network untuk Model Klasifikasi Citra Batik Yogyakarta." Journal of Applied Computer Science and Technology 4, no. 2 (2023): 82–89. http://dx.doi.org/10.52158/jacost.v4i2.486.

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Batik is an Indonesian culture that has been recognized as a world heritage by UNESCO. Indonesian batik has a variety of different motifs in each region. One area that is famous for its batik motifs is Yogyakarta. Yogyakarta has a variety of batik motifs such as ceplok, kawung, and parang which can be differentiated based on the pattern. Yogyakarta batik motifs need to be preserved so they do not experience extinction, one way is by introducing Yogyakarta batik motifs. The recognition of Yogyakarta batik motifs can utilize technology to classify images of Yogyakarta batik motifs based on patte

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Perdana, Am Akbar Mabrur, Muhammad Fajar B, and Abdul Muis Mappalotteng. "Enhancing Batik Classification Leveraging CNN Models and Transfer Learning." JOIV : International Journal on Informatics Visualization 9, no. 3 (2025): 1033. https://doi.org/10.62527/joiv.9.3.2535.

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Batik is a traditional art originating from Indonesia and recognized by UNESCO. Batik motifs vary depending on the region of origin. The diverse batik motifs reflect the rich cultural heritage and unique traditions owned by each region in Indonesia. From Sabang to Merauke, each motif features a different story and values, depicting the beauty and diversity of nature and the lives of diverse local people. However, in the context of the modern era that continues to develop, batik motifs also experience renewal and creativity that always adapts to the times. As a result, the diversity of batik mo

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Sinaga, Daurat, Cahaya Jatmoko, Suprayogi Suprayogi, and Novi Hedriyanto. "Multi-Layer Convolutional Neural Networks for Batik Image Classification." Scientific Journal of Informatics 11, no. 2 (2024): 477–84. https://doi.org/10.15294/sji.v11i2.3309.

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Purpose: The purpose of this study is to enhance the classification of batik motifs through the implementation of a novel approach utilizing Multi-Layer Convolutional Neural Networks (CNN). Batik, a traditional Indonesian textile art form, boasts intricate motifs reflecting rich cultural heritage. However, the diverse designs often pose challenges in accurate classification. Leveraging advancements in deep learning, this research proposes a methodological framework employing Multi-Layer CNN to improve classification accuracy. Methods: The methodology integrates Multi-Layer CNN architecture wit

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Sulastri, Endang, Ana Yuniasti Retno Wulandari, Dwi Bagus Rendy Astid Putera, and Vita Dwi Darmawati. "EXPLORATION OF SCIENCE CONCEPTS IN KLAMPAR PAMEKASAN BATIK MOTIFS AS A SCIENCE LEARNING RESOURCE." Jurnal Pendidikan Matematika dan IPA 16, no. 2 (2025): 206–21. https://doi.org/10.26418/jpmipa.v16i2.87928.

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Science learning fosters logical and critical thinking by integrating real word contexts, such as the Klampar Pamekasan batik motif, to enhance students understanding of biological classification. Therefore, the objective of this research is to examine the concept of science in the Pamekasan Klampar batik motif as a source for learning science. This study employs an exploratory descriptive method, which involves field observations, interviews with batik artisans and educators, and documentation analysis to comprehensively examine the classification of living organisms depicted in Klampar Pamek

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Elvitaria, Luluk, Ezak Fadzrin Ahmad, Noor Azah Samsudin, Shamsul Kamal Ahmad Khalid, Salamun, and Zul Indra. "An Improved Okta-Net Convolutional Neural Network Framework for Automatic Batik Image Classification." JOIV : International Journal on Informatics Visualization 9, no. 1 (2025): 115. https://doi.org/10.62527/joiv.9.1.2591.

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Batik is one of Indonesia's most important cultural arts and has received recognition from UNESCO. Batik has high artistic and historical value with a variety of patterns. Currently, Indonesia has 5,849 batik motifs which are generally classified based on shape, color, motif and symbolic meaning. The diversity of batik motifs makes it difficult for ordinary people to fully recognize them. This paper intends to develop an automatic framework for classifying batik motifs as a solution to overcome this issue. To develop this classification automation framework, the paper proposes a new architectu

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Ariessaputra, Suthami, Viviana Herlita Vidiasari, Sudi Mariyanto Al Sasongko, Budi Darmawan, and Sabar Nababan. "Classification of Lombok Songket and Sasambo Batik Motifs Using the Convolution Neural Network (CNN) Algorithm." JOIV : International Journal on Informatics Visualization 8, no. 1 (2024): 38. http://dx.doi.org/10.62527/joiv.8.1.1386.

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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

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Sriani, Sriani, Muhammad Siddik Hasibuan, and Rizkika Ananda. "Classification of Batu Bara Songket Using Gray-Level Co-Occurrence Matrix and Support Vector Machine." Jurnal Riset Informatika 5, no. 1 (2022): 481–90. http://dx.doi.org/10.34288/jri.v5i1.469.

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Songket is a traditional woven cloth from the Malay and Minangkabau tribes. Songket can also be classified from the brocade woven family and woven with gold or silver thread. Songket cloth's beauty is the Indonesian people's wealth and preservation. Batu Bara Regency is one of Indonesia's regions with several Songket motifs characteristics. Public knowledge of Batu Bara Songket motifs is still minimal, and the differences between one motif and another are still unknown. This research provides information about the variety of Songket fabrics by classifying six types of Batu Bara Songket motifs,

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Sutresno, Stephen Aprius. "The Classification of Batik Besurek Fabric Motifs in Indonesia Utilizing YOLOv8 for Enhanced Cultural Preservation." Journal of Computer System and Informatics (JoSYC) 6, no. 1 (2024): 86–95. https://doi.org/10.47065/josyc.v6i1.6123.

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Batik Besurek is an Indonesian cultural heritage that presents a variety of motifs reflecting the richness of creativity and symbolic meanings. A significant challenge in this field is accurately and efficiently identifying and classifying batik Besurek motifs, known for their intricate designs and cultural significance. In efforts towards cultural preservation and development, a combination of modern technology and local wisdom is required. One technology that can be utilized is object detection technology using You Only Look Once (YOLO), specifically the latest version, YOLOv8, for the class

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Nepomnyashchikh, N. A. "Hagiographicals Plots and Motives on the Modern Period: On Issue of Research and Classification." Studies in Theory of Literary Plot and Narratology, no. 1 (2019): 123–38. http://dx.doi.org/10.25205/2410-7883-2019-1-123-138.

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Studying the hagiographic traditions in the literature the modern period, one faces several issues. Firstly, it is assumed that a scholar who compares the hagiographic motifs and plots to the motifs and plots of the modern period, deals with the a hypothetical complex of the already identified and described the hagiographic motifs and plots and all he has to do is to compare them to the motifs and plots of the literature works written in the later times. However, there is no yet such a thing as a complete research on all hagiographic motifs and plots. There are studies of the certain hagiograp

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Reddy, Ayaluri Mallikarjuna, Vakulabharanam Venkata Krishna, Lingamgunta Sumalatha, and Avuku Obulesh. "Age Classification Using Motif and Statistical Features Derived On Gradient Facial Images." Recent Advances in Computer Science and Communications 13, no. 5 (2020): 965–76. http://dx.doi.org/10.2174/2213275912666190417151247.

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Background: Age estimation using face images has become increasingly significant in the recent years, due to diversity of potentially useful applications. Age group feature extraction, the local features, has received a great deal of attention. Objective: This paper derived a new age estimation operator called “Gradient Dual-Complete Motif Matrix (GD-CMM)” on the 3 x 3 neighborhood of gradient image. The GD-CMM divides the 3 x 3 neighborhood in to dual grids of size 2 x 2 each and on each 2 x 2 grid complete motif matrices are derived. Methods: The local features are extracted by using Motif C

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Kangkachit, Thanapat, Kitsana Waiyamai, and Philippe Lenca. "Enzyme classification using reactive motifs." International Journal of Functional Informatics and Personalised Medicine 4, no. 3/4 (2014): 243. http://dx.doi.org/10.1504/ijfipm.2014.068173.

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Ariani, Dian Dwi, Sitti Zuhriyah, Eva Yulia Puspaningrum, and Mahabintang Pallawabonang. "Identification of Papua Cenderawasih Batik Motifs using Local Binary Pattern and K-Nearest Neighbor." SISTEMASI 14, no. 2 (2025): 623. https://doi.org/10.32520/stmsi.v14i2.5008.

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Papua Island has natural and cultural richness wich is reflected in its batik motifs, such as the Cenderawasih and Tifa motifs. Although batik recognition technology has developed, systems capable of automatically identifying Papua batik motifs are still limited. This research aims to develop a texture recognition system using the Local Binary Pattern (LBP) feature extraction method and K-Nearest Neighbor (KNN) classification. The Cenderawasih motif dataset consists of 115 images, and the Tifa motif dataset consists of 120 images with an 80:20 composition for training and testing data. We test

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Alika, Aka, ⁠Haidar Mirza, Andri, and ⁠Ferdiansyah. "Classification Of South Sumatra Songket Woven Fabric Motifs Using Deep Learning." Data : Journal of Information Systems and Management 2, no. 2 (2024): 24–35. http://dx.doi.org/10.61978/data.v2i3.313.

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The application of a Deep Learning model to classify songket woven cloth motifs from South Sumatra reflects the richness of local culture through its diverse motifs. The main challenge faced is the difficulty in distinguishing various songket motifs due to their complexity and wide variety of designs. This research aims to: (1) develop an effective Deep Learning model for classifying songket woven fabric motifs, (2) measure the accuracy and performance of the model, and (3) assess the implications of this model for cultural preservation and the textile industry. The research method employs the

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Xie, Wen-Jie, Rui-Qi Han, and Wei-Xing Zhou. "Time series classification based on triadic time series motifs." International Journal of Modern Physics B 33, no. 21 (2019): 1950237. http://dx.doi.org/10.1142/s0217979219502370.

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It is of great significance to identify the characteristics of time series to quantify their similarity and classify different classes of time series. We define six types of triadic time-series motifs and investigate the motif occurrence profiles extracted from the time series. Based on triadic time series motif profiles, we further propose to estimate the similarity coefficients between different time series and classify these time series with high accuracy. We validate the method with time series generated from nonlinear dynamic systems (logistic map, chaotic logistic map, chaotic Henon map,

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Handayani, Meli, Rika Rosnelly, and Hartono Hartono. "Classification of Basurek Batik Using Pre-Trained VGG-16 and Support Vector Machine." International Conference on Information Science and Technology Innovation (ICoSTEC) 2, no. 1 (2023): 40–44. http://dx.doi.org/10.35842/icostec.v2i1.34.

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By introducing Indonesian batik motifs, we know that the island of Sumatra, especially Bengkulu and Jambi provinces, has a distinctive batik called Basurek batik. This research aims to classify the two batik motifs using the Support Vector Machine (SVM) algorithm. First, we extract the image of the batik motif with a pre trained VGG-16 model and then use them as a dataset for the SVM classification process. The classification process itself uses linear, polynomial, and sigmoid kernels. We divided the data 90:10 and used 10-fold cross-validation to analyze each training and testing data classif

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Agus, Eko Minarno, Septya Maulani Ayu, Kurniawardhani Arrie, Bimantoro Fitri, and Suciati Nanik. "Comparison of Methods for Batik Classification Using Multi Texton Histogram." TELKOMNIKA Telecommunication, Computing, Electronics and Control 16, no. 3 (2018): 1358–66. https://doi.org/10.12928/TELKOMNIKA.v16i3.7376.

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Batik is a symbol reflecting Indonesian culture which has been acknowledged by UNESCO since 2009. Batik has various motifs or patterns. Because most regions in Indonesia have their own characteristic of batik motifs, people find difficulties to recognize the variety of Batik. This study attempts to develop a system that can help people to classify Batik motifs using Multi Texton Histogram (MTH) for feature extraction. Meanwhile, k-Nearest Neighbor (k-NN) and Support Vector Machine (SVM) algorithm were employed for classification. The performance of those classifications is then compared to see

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Kurniawan, Muhammad Bayu, and Ema Utami. "COMPARATIVE ANALYSIS OF CONTRAST ENHANCEMENT METHODS FOR CLASSIFICATION OF PEKALONGAN BATIK MOTIFS USING CONVOLUTIONAL NEURAL NETWORK." Jurnal Teknik Informatika (Jutif) 5, no. 6 (2024): 1779–87. https://doi.org/10.52436/1.jutif.2024.5.6.2621.

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Batik artists in Pekalongan have freedom in determining motifs, creating a diversity of distinctive batik motifs. However, this diversity often makes it difficult for people to recognize the different motifs, as visual identification requires in-depth knowledge. The lack of understanding about Pekalongan batik is a challenge in recognizing these motifs. To overcome this challenge, an efficient and accurate method of motif identification is needed. This study aims to analyze the efficacy of contrast enhancement methods in improving the classification results of Pekalongan batik motifs using con

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Minarno, Agus Eko, Indah Soesanti, and Hanung Adi Nugroho. "Batik Nitik 960 Dataset for Classification, Retrieval, and Generator." Data 8, no. 4 (2023): 63. http://dx.doi.org/10.3390/data8040063.

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Batik is one of the traditional heritages of Indonesia, with each motif of batik having a profound cultural and philosophical significance. This article introduces Batik Nitik 960 dataset from Yogyakarta, Indonesia. The dataset was extracted from a piece of fabric with 60 Nitik patterns. The dataset was supplied by the Paguyuban Pecinta Batik Indonesia (PPBI) Sekar Jagad Yogyakarta collection of Winotosasto Batik and the data were extracted from the APIPS Gallery. Each of the 60 categories in the collection contains 16 photographs, for a total of 960 images. The photographs were acquired with

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Alya, Riqqah Fadiyah, Merlinda Wibowo, and Paradise Paradise. "CLASSIFICATION OF BATIK MOTIF USING TRANSFER LEARNING ON CONVOLUTIONAL NEURAL NETWORK (CNN)." Jurnal Teknik Informatika (Jutif) 4, no. 1 (2023): 161–70. http://dx.doi.org/10.52436/1.jutif.2023.4.1.564.

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The number of batik motifs in Indonesia is not comparable to the knowledge possessed by the Indonesian people about batik motifs. The diversity of batik motifs can be a problem because classifying them can only be done by those who are familiar with batik in depth, both the pattern and the philosophy behind the motif, most of which are elderly people. To classify batik accurately and quickly is to use image classification technology. In this study, data were obtained from the previous researchers' GitHub repository, google images, and camera shots with a total dataset of 3,534 images. The data

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Kari, Rabiatuadawiyah, Mohd Azhar Samin, and Rafeah Legino. "The Flora Motif as Design Identity in Local Traditional Block Batik." Environment-Behaviour Proceedings Journal 5, SI3 (2020): 123–27. http://dx.doi.org/10.21834/ebpj.v5isi3.2542.

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This study discusses that floral motifs as the dominant traditional motifs in Malaysian block batik. In the 20th century, the block makers created any motif with purpose—the traditional block batik motifs not revealed due to lack of secure identity upon the development of high-tech modernisation. Based on the sequence of this issue, this study aims to classify the various types of local block motifs and designs. The classification base on their features using a suitable procedure. The crucial outcomes where the motifs of block batik still show the elements and innovation of the local motif ide

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Lamasigi, Zulfrianto Yusrin, and Andi Bode. "Influence of gray level co-occurrence matrix for texture feature extraction on identification of batik motifs using k-nearest neighbor." ILKOM Jurnal Ilmiah 13, no. 3 (2021): 322–33. http://dx.doi.org/10.33096/ilkom.v13i3.1025.322-333.

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Batik is one type of fabric that is unique because it has a special motif, in Indonesia itself batik is unique because it has certain motifs that are made based on the culture from which batik was made. This study aims to examine the effect of the texture feature extraction method on the identification of batik motifs from five major islands in Indonesia. The method used in this study is the Gray Level Co-occurrence Matrix as the texture feature extraction of batik motifs to obtain good batik motif identification accuracy results and to determine the value of the proximity of the training data

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Octaviani, Adelia, and Muhammad Pajar Kharisma Putra. "COMPARISON OF K-NEAREST NEIGHBOR AND SUPPORT VECTOR MACHINE CLASSIFICATION ALGORITHMS IN PATTERN RECOGNITION OF TAPIS FABRIC MOTIFS USING NON-GRAYSCALE LBP FEATURE EXTRACTION." Jurnal Teknik Informatika (Jutif) 5, no. 6 (2024): 1621–31. https://doi.org/10.52436/1.jutif.2024.5.6.2720.

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Tapis fabric is a traditional garment of the Lampung people, made from cotton threads and adorned with silver or gold thread motifs. Tapis fabric is an important cultural heritage for the people of Lampung, Indonesia, with its motifs holding deep historical and symbolic meanings. The aim of this research is to develop a classification model for Tapis fabric patterns using K-Nearest Neighbors (KNN) and Support Vector Machine (SVM) algorithms. This involves utilizing Local Binary Pattern (LBP) without converting the images to grayscale, thereby preserving the color in Tapis fabric motifs. The go

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Petrov, A. I., C. L. Zirbel, and N. B. Leontis. "Automated classification of RNA 3D motifs and the RNA 3D Motif Atlas." RNA 19, no. 10 (2013): 1327–40. http://dx.doi.org/10.1261/rna.039438.113.

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Handayani, Sri, Ahmad Zuhdi, and Ratna Shofiati. "Implementation of Gray Level Co-occurrence Matrix (GLCM) and Support Vector Machine (SVM) Methods for Recognition of Batik Bekasi Motifs Implementation of Gray Level Co-occurrence Matrix (GLCM) and Support Vector Machine (SVM) Methods for Recognition of B." Intelmatics 2, no. 2 (2022): 67–72. http://dx.doi.org/10.25105/itm.v2i2.14423.

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Batik is the identity and wealth of the Indonesian people in the field of culture, whose existence has been recognized by the world. However, many of the Indonesian people themselves are still unable to distinguish and recognize the motifs found on batik cloth. This is due to the large variety of motifs and patterns that are owned in different batik from each region in Indonesia. Therefore, we need a merchine leraning model that can be used to identify and classify the motifs contained in batik cloth. In this research, the method that will be used is the gray level co-occurrence matrix method

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Ardyani, Salma Shafira Fatya, and Christy Atika Sari. "A Web-Based for Demak Batik Classification Using VGG16 Convolutional Neural Network." Advance Sustainable Science Engineering and Technology 6, no. 4 (2024): 0240406. http://dx.doi.org/10.26877/asset.v6i4.771.

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The diversity of Demak batik motifs presents challenges in classification and identification. This research aims to develop a Demak batik motif classification system using deep learning and VGG16 convolutional network. A dataset of Demak batik images is collected and processed to train the model. The VGG16 architecture is modified by fine-tuning to optimize the classification performance. Results show that the modified VGG16 model achieved a classification accuracy of 98.72% on the test dataset, demonstrating its potential application in preserving and digitizing Demak batik cultural heritage.

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Hendrix, Donna K., Steven E. Brenner, and Stephen R. Holbrook. "RNA structural motifs: building blocks of a modular biomolecule." Quarterly Reviews of Biophysics 38, no. 3 (2005): 221–43. http://dx.doi.org/10.1017/s0033583506004215.

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1. Introduction 2222. What is an RNA motif? 2222.1 Sequence vs. structural motifs 2222.2 RNA structural motifs 2232.3 RNA structural elements vs. motifs 2232.4 Specific recognition motifs 2242.5 Tools for identifying and classifying elements and motifs 2263. Types of RNA structural motifs 2283.1 Helices 2283.2 Hairpin loops 2283.3 Internal loops 2303.4 Junction loops/multiloops 2303.5 Binding motifs 2323.5.1 Metal binding 2323.5.2 Natural and selected aptamers 2343.6 Tertiary interactions 2344. Future directions 2365. Acknowledgments 2396. References 239RNAs are modular biomolecules, composed

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Wahyuni, Masri, Rika Rosnelly, and Wanayumini Wanayumini. "Combination of Pre-Trained CNN Model and Machine Learning Algorithm on Pekalongan Batik Motif Classification." International Conference on Information Science and Technology Innovation (ICoSTEC) 2, no. 1 (2023): 23–28. http://dx.doi.org/10.35842/icostec.v2i1.31.

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Pekalongan is a region in Indonesia well-known for its batik production. The Pekalongan batik is rich in varieties of motifs, such as the Jlamprang, Liong, Terang Bulan, and Tujuh Rupa. The difficulty of distinguishing Pekalongan batik motifs for ordinary people causes the need for a model that can help recognize these motifs automatically based on input from digital images. This research aims to classify the Pekalongan batik motifs using a pre-trained Convolutional Neural Network (CNN), the Inception V3, and machine learning, the K-Nearest Neighbors (K-NN) algorithm. First, we extract the fea

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Degtyarenko, K. "Bioinorganic motifs: towards functional classification of metalloproteins." Bioinformatics 16, no. 10 (2000): 851–64. http://dx.doi.org/10.1093/bioinformatics/16.10.851.

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Cobanoglu, M. C., Y. Saygin, and U. Sezerman. "Classification of GPCRs Using Family Specific Motifs." IEEE/ACM Transactions on Computational Biology and Bioinformatics 8, no. 6 (2011): 1495–508. http://dx.doi.org/10.1109/tcbb.2010.101.

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Nguyen, Hai-Long, Wee-Keong Ng, and Yew-Kwong Woon. "Closed motifs for streaming time series classification." Knowledge and Information Systems 41, no. 1 (2013): 101–25. http://dx.doi.org/10.1007/s10115-013-0662-6.

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ZAMAN, BADROE, and Khoirudin Khoirudin. "Klasifikasi Citra Batik Menggunakan Co-Occurrence Matrices Berbasis Wavelet Filter." Jurnal Pengembangan Rekayasa dan Teknologi 5, no. 2 (2021): 123. http://dx.doi.org/10.26623/jprt.v17i2.4594.

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<em><span lang="IN">Batik is the result of cultural arts that contains a philosophical meaning in each of its motifs. Various types of batik motifs create complexity in the recognition of batik image patterns. Classification of images into certain classes is also a problem in the field of pattern recognition. Machine learning is a method that is very developed at this time. Machine learning method is used to identify batik motifs through batik image classification. This study focuses on the image dataset of written batik which has two motifs, namely classical motifs and contemporar

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Szigeti, Balázs, Ajinkya Deogade, and Barbara Webb. "Searching for motifs in the behaviour of larval Drosophila melanogaster and Caenorhabditis elegans reveals continuity between behavioural states." Journal of The Royal Society Interface 12, no. 113 (2015): 20150899. http://dx.doi.org/10.1098/rsif.2015.0899.

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We present a novel method for the unsupervised discovery of behavioural motifs in larval Drosophila melanogaster and Caenorhabditis elegans . A motif is defined as a particular sequence of postures that recurs frequently. The animal's changing posture is represented by an eigenshape time series, and we look for motifs in this time series. To find motifs, the eigenshape time series is segmented, and the segments clustered using spline regression. Unlike previous approaches, our method can classify sequences of unequal duration as the same motif. The behavioural motifs are used as the basis of a

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Khoirunnisa, Emila, Farrikh Alzami, Ricardus Anggi Pramunendar, et al. "Enhanced Semarang Batik Classification using MobileNetV2 and Data Augmentation." sinkron 9, no. 1 (2025): 43–54. https://doi.org/10.33395/sinkron.v9i1.14308.

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Batik, an Indonesian cultural heritage recognized by UNESCO, faces challenges in pattern identification and documentation, particularly for the younger generation. Previous studies on batik classification have shown limitations in handling small datasets and maintaining accuracy with limited computational resources. This research proposes an enhanced classification approach for Semarang Batik motifs using MobileNetV2 architecture combined with strategic data augmentation techniques. The study utilizes a dataset of 3,020 images comprising 10 distinct Semarang Batik motifs, implementing horizont

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Toyib Hosen, Ida I'aza, and Saadi Ahmad Kamaruddin. "Batik Pattern Classification Using Machine Learning Approaches." Applied Mathematics and Computational Intelligence (AMCI) 13, no. 3 (2024): 186–219. http://dx.doi.org/10.58915/amci.v13i3.690.

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Batik is one of Malaysia traditional textile art form that is well known. By using Artificial Neural Network (ANN), k-Nearest Neighbors (k-NN) and Decision Tree methods, this study aims to classify Kelantan batik designs according to flora, fauna and geometry motifs. 133 images of the three categories were collected from social media and underwent pre-processing techniques. Image augmentation was done to enhance the diversity and quality of available training data for machine learning models. Digital transformation on the images based on colour features was developed to classify the three type

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Zhu, Qiyao, Louis Petingi, and Tamar Schlick. "RNA-As-Graphs Motif Atlas—Dual Graph Library of RNA Modules and Viral Frameshifting-Element Applications." International Journal of Molecular Sciences 23, no. 16 (2022): 9249. http://dx.doi.org/10.3390/ijms23169249.

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RNA motif classification is important for understanding structure/function connections and building phylogenetic relationships. Using our coarse-grained RNA-As-Graphs (RAG) representations, we identify recurrent dual graph motifs in experimentally solved RNA structures based on an improved search algorithm that finds and ranks independent RNA substructures. Our expanded list of 183 existing dual graph motifs reveals five common motifs found in transfer RNA, riboswitch, and ribosomal 5S RNA components. Moreover, we identify three motifs for available viral frameshifting RNA elements, suggesting

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Baso, Budiman, Risald Risald, and Ramaulvi Muhammad Akhyar. "Analisis Pengaruh Data Augmentasi Pada Klasifikasi Tenun Menggunakan Deep Learning Berbasis Convolutional Neural Network." Journal of Information and Technology 5, no. 1 (2025): 20–24. https://doi.org/10.32938/jitu.v5i1.9209.

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This research develops a classification model of Timorese weaving motifs using Deep Learning method based on Convolutional Neural Network (CNN). Timor's diverse weaving motifs reflect the richness of local culture, but manual classification takes a long time and is prone to subjectivity. To improve model performance, Data Augmentation techniques, such as flipping, rotation, and zooming,, are applied to enrich the variety of pre-processed Timor weaving image datasets. In addition, the CNN model was developed using Transfer Learning techniques to improve training efficiency. Experimental results

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Wang, Yin, Rudong Li, Yuhua Zhou, et al. "Motif-Based Text Mining of Microbial Metagenome Redundancy Profiling Data for Disease Classification." BioMed Research International 2016 (2016): 1–11. http://dx.doi.org/10.1155/2016/6598307.

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Background. Text data of 16S rRNA are informative for classifications of microbiota-associated diseases. However, the raw text data need to be systematically processed so that features for classification can be defined/extracted; moreover, the high-dimension feature spaces generated by the text data also pose an additional difficulty.Results. Here we present a Phylogenetic Tree-Based Motif Finding algorithm (PMF) to analyze 16S rRNA text data. By integrating phylogenetic rules and other statistical indexes for classification, we can effectively reduce the dimension of the large feature spaces

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Muhartini, Sitti, Andi Sunyoto, and Alva Hendi Muhammad. "Implementation of the CNN Deep Learning Method in Tajong (Sarung) Samarinda Classification." Journal of Applied Sciences, Management and Engineering Technology 5, no. 2 (2024): 58–66. http://dx.doi.org/10.31284/j.jasmet.2024.v5i2.6406.

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Samarinda sarongs are one of Indonesia's traditional fabrics that are famous for their beautiful motifs and textures. This fabric is made using traditional weaving techniques using non-machine looms (ATBMs), resulting in a unique and distinctive diversity of textures. The difference between the loom, namely the machine and the non-machine, resulting in a difference in the texture of the Samarinda sarong. This difference can be seen from the thread density, texture smoothness, and sharpness of the motif. On certain Samarinda sarong motifs that do not require special details. This study aims to

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Herman, An'nisa Pratama Putri, Megat Norulazmi Megat Mohamed Noor, et al. "A Comperative Study on Efficacy of CNN VGG-16, DenseNet121, ResNet50V2, And EfficientNetB0 in Toraja Carving Classification." Indonesian Journal of Data and Science 6, no. 1 (2025): 123–33. https://doi.org/10.56705/ijodas.v6i1.220.

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Passura', or Toraja carvings, are a significant part of the culture in Toraja and have deep symbolic significance and beautiful patterns. Inspired by folklore and natural elements, these motifs capture the harmonious coexistence of nature, the divine, and humans. In order to classify Toraja carving motifs, this study uses Convolutional Neural Networks (CNN), specifically the architectures VGG-16, DenseNet121, ResNet50V2, and EfficientNetB0. Seven motif types were represented in the 700 photos that made up the dataset, which was split between 80% training and 20% validation data. Despite showin

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Popenda, Mariusz, Joanna Miskiewicz, Joanna Sarzynska, Tomasz Zok, and Marta Szachniuk. "Topology-based classification of tetrads and quadruplex structures." Bioinformatics 36, no. 4 (2019): 1129–34. http://dx.doi.org/10.1093/bioinformatics/btz738.

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Abstract Motivation Quadruplexes attract the attention of researchers from many fields of bio-science. Due to a specific structure, these tertiary motifs are involved in various biological processes. They are also promising therapeutic targets in many strategies of drug development, including anticancer and neurological disease treatment. The uniqueness and diversity of their forms cause that quadruplexes show great potential in novel biological applications. The existing approaches for quadruplex analysis are based on sequence or 3D structure features and address canonical motifs only. Result

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Leontis, Neocles B., and Eric Westhof. "The Annotation of RNA Motifs." Comparative and Functional Genomics 3, no. 6 (2002): 518–24. http://dx.doi.org/10.1002/cfg.213.

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The recent deluge of new RNA structures, including complete atomic-resolution views of both subunits of the ribosome, has on the one hand literally overwhelmed our individual abilities to comprehend the diversity of RNA structure, and on the other hand presented us with new opportunities for comprehensive use of RNA sequences for comparative genetic, evolutionary and phylogenetic studies. Two concepts are key to understanding RNA structure: hierarchical organization of global structure and isostericity of local interactions. Global structure changes extremely slowly, as it relies on conserved

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Taylor, Dewey T., John W. Cain, Danail G. Bonchev, Stephen S. Fong, Advait A. Apte, and Lauren E. Pace. "Toward a classification of isodynamic feed-forward motifs." Journal of Biological Dynamics 4, no. 2 (2009): 196–211. http://dx.doi.org/10.1080/17513750903144461.

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Debroutelle, Teddy, Sylvie Treuillet, Aladine Chetouani, Matthieu Exbrayat, Lionel Martin, and Sebastien Jesset. "Automatic classification of ceramic sherds with relief motifs." Journal of Electronic Imaging 26, no. 2 (2017): 023010. http://dx.doi.org/10.1117/1.jei.26.2.023010.

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Vens, Celine, Marie-Noëlle Rosso, and Etienne G. J. Danchin. "Identifying discriminative classification-based motifs in biological sequences." Bioinformatics 27, no. 9 (2011): 1231–38. http://dx.doi.org/10.1093/bioinformatics/btr110.

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Oktarina, Dwi. "KEBERAGAMAN MOTIF DALAM CERITA RAKYAT ULAR RENGGIONG DAN PUTRI GUNUNG LABU DARI BELITUNG TIMUR: ANALISIS MOTIF MODEL STITH THOMPSON." Sirok Bastra 8, no. 1 (2020): 35–46. http://dx.doi.org/10.37671/sb.v8i1.199.

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Kabupaten Belitung Timur yang masuk ke wilayah Provinsi Kepulauan Bangka Belitung me miliki kekayaan budaya sastra lisan, khususnya cerita rakyat yang belum banyak dikaji. Selain cerita rakyat, wilayah ini juga kaya akan pantun, syair, mantra, juga peribahasa. Penelitian ini bertujuan untuk mendeskripsikan keberagaman motif cerita rakyat dalam dua legenda, yakni “Ular Renggiong” dan “Putri Gunung Labu” berdasarkan klasifikasi Motif Indeks Stith Thompson. Kajian ini termasuk ke dalam jenis penelitian kualitatif. Metode yang digunakan adalah metode deskriptif analisis. Kedua cerita menggambarkan

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Andono, Pulung Nurtantio, and Eko Hari Rachmawanto. "Evaluasi Ekstraksi Fitur GLCM dan LBP Menggunakan Multikernel SVM untuk Klasifikasi Batik." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 5, no. 1 (2021): 1–9. http://dx.doi.org/10.29207/resti.v5i1.2615.

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Batik as one of Indonesia's cultural heritages has various types, motifs and colors. A batik may have almost the same motif with a different color or vice versa, therefore it requires a classification of batik motifs. In this study, a printed batik was used with various coastal batik motifs in Central Java. The algorithm for classification is selected Support Vector Machine (SVM) with feature extraction of the Gray Level Co-Occurrence Matrix (GLCM) and Local Binary Pattern (LBP). SVM has the advantage of grouping data with small amounts and short operation times. GLCM as an extractive feature

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Abdullah, Taufik, Kana Saputra S, Hermawan Syahputra, Zulfahmi Indra, and Dinda Kartika. "Comparative Analysis of Model Architectures Using Transfer Learning Approach in Convolutional Neural Networks for Traditional Ulos Fabric Classification." Journal of Artificial Intelligence and Engineering Applications (JAIEA) 4, no. 2 (2025): 628–38. https://doi.org/10.59934/jaiea.v4i2.719.

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Ulos cloth is a traditional woven fabric of the Batak tribe in North Sumatra, valued for its aesthetic and symbolic significance in various ceremonies. The diversity of ulos motifs presents challenges in preservation due to their unique patterns and functions. This study aims to develop an accurate method for classifying ulos motifs using Transfer Learning on Convolutional Neural Network (CNN) architectures. Five popular models—VGG16, VGG19, MobileNetV3, Inception-V3, and EfficientNetV2—were evaluated on a dataset of 962 ulos images across six motif categories.The results show that Inception-V

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Silvia, Joseph, Hipiny Irwandi, and Ujir Hamimah. "Iban plaited mat motif classification with adaptive smoothing." International Journal of Artificial Intelligence (IJ-AI) 12, no. 2 (2023): 840–50. https://doi.org/10.11591/ijai.v12.i2.pp840-850.

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Decorative mats plaited by the Iban communities in Borneo contains motifs that reflect their traditional beliefs. Each motif has its own special meaning and taboos. A typical mat motif contains multiple smaller patterns that surround the main motif hence is likely to cause misclassification. We introduce a classification framework with adaptive sampling to remove smaller features whilst retaining larger (and discriminative) image structures. Canny filter and probabilistic hough transform are gradually applied to a clean greyscale image until a threshold value pertaining to the image’s st

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