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All Journal EMITTER International Journal of Engineering Technology Padjadjaran Journal of Dentistry JOIV : International Journal on Informatics Visualization INOVTEK Polbeng - Seri Informatika Jurnal Informatika: Jurnal Pengembangan IT The Indonesian Journal of Computer Science Seminar Nasional Riset dan Teknologi (SEMNAS RISTEK)
Riyanto Sigit, Riyanto
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Biochemistry, Genetics & Molecular Biology Civil Engineering, Building, Construction & Architecture Computer Science & IT Dentistry Education Electrical & Electronics Engineering Engineering Health Professions Immunology & microbiology Medicine & Pharmacology Public Health Other

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Journal : The Indonesian Journal of Computer Science

 1 
Online Terrain Classification Using Neural Network for Disaster Robot Application Sanusi, Muhammad Anwar; Dewantara, Bima Sena Bayu; Setiawardhana; Sigit, Riyanto
The Indonesian Journal of Computer Science Vol. 12 No. 1 (2023): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v12i1.3132

Abstract

A disaster robot is used for crucial rescue, observation, and exploration missions. In the case of implementing disaster robots in bad environmental situations, the robot must be equipped with appropriate sensors and good algorithms to carry out the expected movements. In this study, a neural network-based terrain classification that is applied to Raspberry using the IMU sensor as input is developed. Relatively low computational requirements can reduce the power needed to run terrain classification. By comparing data from the Accelerometer, Gyroscope, and combined Accelero-Gyro using the same neural network architecture, the tests were carried out in a not moving position, indoors, on asphalt, loose gravel, grass, and hard ground. In its implementation, the mobile robot runs over the field at a speed of about 0,5 m/s and produces predictive data every 1,12s. The prediction results for online terrain classification are above 93% for each input tested.
Deteksi Kondisi Gigi Manusia pada Citra Intraoral Menggunakan YOLOv5 Makarim, Ahmad Fauzi; Karlita, Tita; Sigit, Riyanto; Dewantara, Bima Sena Bayu; Brahmanta, Arya
The Indonesian Journal of Computer Science Vol. 12 No. 4 (2023): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v12i4.3355

Abstract

Proses identifikasi dan pencatatan rekam medis pada praktik kedokteran gigi masih dilakukan secara manual. Akibatnya, proses tersebut memakan waktu yang cukup lama. Pada penelitian ini metode deteksi objek dimanfaatkan untuk membantu dokter melakukan identifikasi pada gigi pasien. YOLOv5 dipilih untuk dilatihkan pada dataset citra intraoral dengan lima kelas kondisi gigi (normal, karies, tumpatan, sisa akar, dan impaksi). Dataset yang digunakan berjumlah 1.767 data citra intraoral yang diambil dan dilabeli oleh dokter gigi. Dataset dibagi menjadi tiga bagian, 10% digunakan untuk data testing dan 90% digunakan untuk data training dan validation. Dilakukan komparasi performa berdasarkan nilai metrik evaluasi terhadap tiga jenis model YOLOv5 (S, M, L). Dari hasil pelatihan, YOLOv5 M sebagai model terbaik mendapatkan nilai mAP sebesar 84%, dan 82% nilai akurasi testing. Penelitian ini telah memenuhi tujuan utama untuk membangun sebuah model deep learning yang robust untuk mendeteksi dan mengklasifikasi beberapa kondisi gigi pada manusia.
Co-Authors ., Justiawan Achmad Basuki Ali Ridho Barakbah Anwar Ari Setiawan Arna Fariza Arya Brahmanta, Arya Aulia Dwi Maharani Bima Sena Bayu Dewantara Fadhillah, Excel Daris Fakhri, Haidar Huda, Achmad Torikul iwan Syarif Jovan Josafat Jaenputra Kemalasari Madyono, Madyono Makarim, Ahmad Fauzi Maulana, Firja Hanif Maulana, Rifqi Affan Mochamad Mobed Bachtiar, Mochamad Mobed Nabilah, Anisah Pamenang, Muhammad Unggul Perdanasari, Lukie Rahagiyanto, Angga Ratna Kusumawati Rika Rokhana Rochmad, Moch. Rokhana, Rika Romadhon, Nur Rizky Ryando, Catoer Sanusi, Muhammad Anwar Sena Bayu Dewantara, Bima Setiawardhana Setiawardhana, Setiawardhana Taufiq Hidayat Tita Karlita Wibowo, Galih Hendra Yuniarti, Heny Zainal Arief Zamzami, Amran