Aplikasi untuk Deteksi dan Klasifikasi Motif Kain Tenun Timor Tengah Selatan berbasis ANFIS pada Platform Mobile di Provinsi NTT Application for Detection and Classification of South Central Timor Woven Fabric Motifs Based on ANFIS on a Mobile Platform in NTT Province
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Page Numbers:
2293-2311
Digital Object Identifier:
10.58578/masaliq.v5i5.7052
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Authors:
Okthen Orlanda Naitboho1*, Ni Ketut Kertiasih2, Agus Aan Jiwa Permana3 
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Main Article Content
Abstract
The woven fabric from South Central Timor (TTS), East Nusa Tenggara Province, holds significant cultural and symbolic value; however, the influx of similar-patterned fabrics from outside the region presents challenges in authenticity identification, particularly among the general public and younger generations. This study aims to develop a mobile application based on the Adaptive Neuro-Fuzzy Inference System (ANFIS) to detect and classify motifs of TTS woven fabric. Texture feature extraction was conducted using the Gray Level Co-occurrence Matrix (GLCM) method, applying six key parameters: contrast, dissimilarity, homogeneity, energy, correlation, and ASM. The ANFIS model was trained for two types of classification: fabric authenticity (authentic vs. non-authentic), achieving an average accuracy of 87.50%, and regional motif classification (Amanatun, Amanuban, and Mollo), with an accuracy of 78.00%. The application was developed using a prototyping method and integrated with the classification system via FastAPI services. Black-box testing confirmed that all application features functioned as designed, while usability testing using the Usability Metric for User Experience (UMUX) yielded a score of 87.92, indicating a high level of user comfort and ease of use. The study concludes that the ANFIS-based mobile application is effective as a supporting tool in preserving TTS woven fabric through the application of intelligent technology.
Keywords: Mobile Application; TTS Woven Fabric; ANFIS; GLCM Texture Extraction; Image Classification
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References
Amrulloh, A. R. F. M. L. M. F. (2022). Klasifikasi jenis tanaman kelengkeng berdasarkan ciri tekstur daun menggunakan metode Adaptive Neuro Fuzzy Inference System (AFIS). Jurnal Explore IT, 14(36), 29–38. https://doi.org/10.35891/explorit
Andalusia, N. (2022). Perlindungan Hukum Terhadap Pengusaha Kerajinan Tenun Sebagai Pendorong Perekonomian Sumatera Barat. Tanjungpura Law Journal, 5(1), 93. https://doi.org/10.26418/tlj.v5i1.46224
Ardiyansah, D., Pahlevi, O., & Santoso, T. (2021). Implementasi Metode Prototyping Pada Sistem Informasi Pengadaan Barang Cetakan Berbasis Web. Hexagon Jurnal Teknik Dan Sains, 2(2), 17–22. https://doi.org/10.36761/hexagon.v2i2.1083
Ayumi, V., Nurhaida, I., & Haji, W. H. (2023). Perancangan Aplikasi Web Untuk Deteksi Motif Batik Indonesia Berbasis Image Processing dan Machine Learning. JSAI : Journal Scientific and Applied Informatics, 6(3), 499–504. https://doi.org/10.36085/jsai.v6i3.6240
Azzahrah, N., Putri, U. M., Informasi, S., Islam, U., Sultan, N., Saifuddin, T., & Galery, Y. (2024). Perancangan E-Commerce Pada Yhara Galery Muaro Jambi. 8(6), 12291–12297. https://doi.org/10.36040/jati.v8i6.11893
Damayanti, A., & Agustina, D. (2024). Implementasi Metode Adaptive Neuro Fuzzy Inference System (ANFIS) dalam Prediksi Harga Saham X. Euler : Jurnal Ilmiah Matematika, Sains Dan Teknologi, 12(1), 71–76. https://doi.org/10.37905/euler.v12i1.25278
Darma, U. B. (2021). Penerapan Metode Prototype Pada Perancangan Sistem Pengarsipan Dokumen Kantor Kecamatan Lais. 2(1), 16–27. https://doi.org/10.51519/journalsea.v2i1.89
Hardiyanto, D., Kristiyana, S., Kurniawan, D., & Sartika, D. A. (2019). Klasifikasi Motif Citra Batik Yogyakarta Menggunakan Metode Adaptive Neuro Fuzzy Inference System. Setrum : Sistem Kendali-Tenaga-Elektronika-Telekomunikasi-Komputer, 8(2), 229. https://doi.org/10.36055/setrum.v8i2.6545
Hawibowo, M. S., & Muhimmmah, I. (2024). Aplikasi Pendeteksi Tingkat Kematangan Pepaya menggunakan Metode Convolutional Neural Network (CNN) Berbasis Android. Jurnal Edukasi Dan Penelitian Informatika (JEPIN), 10(1), 162. https://doi.org/10.26418/jp.v10i1.77819
Herdiansah, A., Nurnaningsih, D., & Rusdianto, H. (2022). Pemanfaatan Flutter Pada Pengembangan Aplikasi Mobile Ebisnis Penyediaan Bahan Baku Bisnis Katering. Jurnal Teknoinfo, 16(2), 291. https://doi.org/10.33365/jti.v16i2.1937
Ichwani, A., Anwar, N., Karsono, K., & Alrifqi, M. (2021). Sistem Informasi Penjualan Berbasis Website dengan Pendekatan Metode Prototype. Prosiding SISFOTEK, 5(1), 1–6. https://scholar.google.com/citations?view_op=view_citation&hl=en&user=FOwZ8hUAAAAJ&pagesize=100&citation_for_view=FOwZ8hUAAAAJ:F9fV5C73w3QC
Kurniawan, I., Ulfah, S., Sholikah, M., Mubais, A., Ayu Ramadhani, N., & Muhaimin, M. (2024). Implementasi Metode Prototype Pada Pembuatan Website Destination Branding Pariwisata “Pantaitelukawur.Id” Di Desa Teluk Awur. Journal of Information System and Computer, 4(1), 20–26. https://doi.org/10.34001/jister.v4i1.981
Meisak, D., Hendri, & Agustini, S. R. (2022). Penerapan Metode Prototype Pada Perancangan Sistem Informasi Penjualan Mediatama Solusindo Jambi. STORAGE: Jurnal Ilmiah Teknik Dan Ilmu Komputer, 1(4), 1–11. https://doi.org/10.55123/storage.v1i4.1066
Muhammad Arofiq, N., Ferdo Erlangga, R., Irawan, A., & Saifudin, A. (2023). OKTAL : Jurnal Ilmu Komputer dan Science Pengujian Fungsional Aplikasi Inventory Barang Kedatangan Dengan Metode Black Box Testing Bagi Pemula. Ilmu Komputer Dan Science, 2(5), 1322–1330. https://journal.mediapublikasi.id/index.php/oktal
Nenometa, E. A., Marlin, & Kaesmetan, yampi R. (2024). Identifikasi Citra Warna Pada Kain Tenun Lotis Timor Tengah Selatan(TTS) Menggunakan Metode Convolution Network(CNN). Jurnal Sistem Informasi Dan Aplikasi, 1(1). https://ejournal.upnvj.ac.id/jsia/article/view/7692
Nopriansyah, U. (2020). Pengembangan Aplikasi Kesehatan Berbasis Mobile Untuk Pemantauan Deteksi Dini Tumbuh Kembang ( Ddtk ) Anak Usia 4-6 Tahun Karakter Bangsa ( Udu Et Al ., 2019 ). Hasil Riset Kesehatan Dasar ( Riskesdas ) 2010 Tentang Penyebaran Status Gizi Balita Pada Prov. Al Athfaal: Jurnal Ilmiah Pendidikan Anak Usia Dini, 3(1), 98–111. https://doi.org/10.24042/ajipaud.v3i1.6912
Permana, A. A. J., Setemen, K., & Pracasitaram, I. G. M. S. B. (2024). D Evelopment of Balinese Puppet Educational Game and Usability Testing Using Sus , Umux , and Umux Lite. 53–70. https://revistas.unilasalle.edu.br/index.php/conhecimento_diversidade/article/view/11815
Sartika, D. A., Arrosida, H., & Hardiyanto, D. (2022). Implementasi Teknik Klasifier Adaptive Neuro Fuzzy Inference System Untuk Mengklasifikasi Motif Citra Batik Jawa Timur. Setrum : Sistem Kendali-Tenaga-Elektronika-Telekomunikasi-Komputer, 11(1), 126–134. https://doi.org/10.36055/setrum.v11i1.14872
Simatupang, J., & Sianturi, S. (2019). Perancangan_Sistem_Informasi_Pemesanan_T. Jurnal Intra-Tech, 3(2). https://doi.org/10.37030/jit.v3i2.56
Trisudarmo, R. (2022). Penerapan Metode Prototype dalam Sistem E-Government pada Pelayanan Administrasi Kependudukan. Jurnal Informatika Dan Teknologi Pendidikan, 2(2), 64–71. https://doi.org/10.25008/jitp.v2i2.35