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All Journal Jurnal Algoritma Jurnal Algoritma Jurnal Media Pengabdian Komunikasi
Ilhami, Azriel Noer
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Computer Science & IT Education Languange, Linguistic, Communication & Media Social Sciences

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Pengenalan Alfabet Sistem Isyarat Bahasa Indonesia (SIBI) Menggunakan Convolutional Neural Network Thira, Indra Jiwana; Riana, Dwiza; Ilhami, Azriel Noer; Dwinanda, Brama Rizky Setia; Choerunisya, Hana
Jurnal Algoritma Vol 20 No 2 (2023): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.20-2.1480

Abstract

Deaf is fourth in the list of persons with disabilities in Indonesia at 7.03%. Deaf people communicate using sign language both when communicating with fellow deaf people and with normal people. The problem that arises is that few normal people master sign language, especially the Indonesian Sign System (SIBI) so that it becomes an obstacle when they have to communicate with deaf people. This study aims to classify the alphabet in SIBI except the letters J and Z with a total of 24 classes. Classification is done by comparing three CNN architectures, namely MobileNetV2, MobileNetV3Small and MobileNetV3Large to get the best model. The results showed that the MobileNetV3Small architecture produced the best model at batch size 32 and the number of epochs 30 with an accuracy of 98.81% for testing data.
Perancangan dan Pemanfaatan Website Dalam Membangun Ketahanan Masyarakat Desa Padaawas Pasirwangi Garut Gusdiana, Ridian; Hadiati, Hadiati; Firmansyah, Firmansyah; Alisha, Fathia; Ilhami, Azriel Noer
Jurnal Media Pengabdian Komunikasi Vol 4, No 2 (2024): Jurnal Media Pengabdian Komunikasi
Publisher : Universitas Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52434/medikom.v4i2.306

Abstract

Desa Padaawas Kecamatan Pasirwangi Kabupaten Garut memiliki potensi yang cukup bervariasi, antara lain potensi seni budaya, wisata alam, hasil perkebunan, hasil pertanian, yang membutuhkan promosi dan pemasaran yang lebih luas. Dari segi mekanisme pemerintahan diinstruksikan untuk melaksanakan pemerintahan bersih, transparan, serta mampu menjawab tuntutan perubahan secara efektif dengan menggunakan teknologi komunikasi dan informasi berbasis jaringan (e-government). Desa Padaawas, dilengkapi sarana komunikasi dan informasi berbasis jaringan, namum belum memiliki media yang efektif dan efisien. Kehadiran Website Desa merupakan sarana yang penting dan strategis dibutuhkan oleh masyarakat dan pemerintahan Desa Padaawas guna mengakumulasi kebutuhan media informasi, promosi dan komunikasi interaktif dan pemerintahan berbasis jaringan (e-government). Metode observasi, diskusi dan partisipasi dalam bentuk pelatihan dan pendampingan banyak digunakan dalam kegiatan pengabdian ini. Luaran yang ditargetkan melalui kegiatan ini adalah: (1) Terbangun Website Desa Padaawas (https://padaawas.org) sebagai sarana e-government, sarana promosi dan pemasaran potensi desa, sarana komunikasi dan informasi, (2) Aparat desa mampu mengelola dan memanfaatkan website desa secara berkesinambungan
Pengenalan Alfabet Sistem Isyarat Bahasa Indonesia (SIBI) Menggunakan Convolutional Neural Network Thira, Indra Jiwana; Riana, Dwiza; Ilhami, Azriel Noer; Dwinanda, Brama Rizky Setia; Choerunisya, Hana
Jurnal Algoritma Vol 20 No 2 (2023): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.20-2.1480

Abstract

Deaf is fourth in the list of persons with disabilities in Indonesia at 7.03%. Deaf people communicate using sign language both when communicating with fellow deaf people and with normal people. The problem that arises is that few normal people master sign language, especially the Indonesian Sign System (SIBI) so that it becomes an obstacle when they have to communicate with deaf people. This study aims to classify the alphabet in SIBI except the letters J and Z with a total of 24 classes. Classification is done by comparing three CNN architectures, namely MobileNetV2, MobileNetV3Small and MobileNetV3Large to get the best model. The results showed that the MobileNetV3Small architecture produced the best model at batch size 32 and the number of epochs 30 with an accuracy of 98.81% for testing data.
Co-Authors Alisha, Fathia Choerunisya, Hana Dwinanda, Brama Rizky Setia Dwiza Riana Firmansyah Firmansyah Hadiati, Hadiati Ridian Gusdiana Thira, Indra Jiwana