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All Journal EMITTER International Journal of Engineering Technology JOIV : International Journal on Informatics Visualization Technomedia Journal Journal of Applied Data Sciences El-Mujtama: Jurnal Pengabdian Masyarakat The Indonesian Journal of Computer Science Riau Jurnal Teknik Informatika
Tita Karlita
Politeknik Elektronika Negeri Surabaya
Religion Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Social Sciences

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Mobile Application to Identify Indonesian Flowers on Android Platform Karlita, Tita; Basuki, Achmad; Makarti, Lakmi
EMITTER International Journal of Engineering Technology Vol 1 No 1 (2013)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v1i1.10

Abstract

Although many people love flowers, they do not know their name. Especially, many people do not recognize local flowers. To find the flower image, we can use search engine such as Google, but it does not give much help to find the name of local flower. Sometimes, Google cannotshow the correct name of local flowers. This study proposes an application to identify Indonesian flowers that runs on the Android platform for easy use anywhere. Flower recognition is based on the color features using the Hue-Index, shape feature using Centroid Contour Distance (CCD), and the similarity measurement using Entropy calculations. The outputs of this application are information about inputted flower image including Latinname, local name, description, distribution and ecology. Based on tests performed on 44 types of flowers with 181 images in the database, the best similarity percentage is 97.72%. With this application, people will be expected to know more about Indonesia flowers.Keywords: Indonesian flowers, android, hue-index, CCD, entropy
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
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 Achmad Basuki Alde, Muhammad Riski Alfi Fadliana Ali Ridho Barakbah ANITA DAMAYANTI Arna Fariza Arya Brahmanta, Arya Asmara, Rengga Azahro Choirunisa, Nadia Bima Sena Bayu Dewantara Edelani, Renovita Edi Satriyanto Eko Mulyanto Yuniarno Fatihia, Wifda Muna Fauzi Nafi'Ubadah, Kriza Febrianto, Ardiansyah Indra Fitrah Maharani Humaira Haikal Yuniarta Krisgianto, Ricko I Ketut Eddy Purnama I Made Gede Sunarya Insani, Fawzan Isbat Uzzin Nadhori, Isbat Uzzin Lakmi Makarti, Lakmi Makarim, Ahmad Fauzi Maulana, Firja Hanif Maulana, Wahyu Ikbal Mauridhi Hery Purnomo Ni'Ma, Najma Akmalina Nikma Prastita, Valentia Priambodo, Joko Rachmawati, Oktavia Citra Resmi Ratna Kusumawati Ridho, Bistiana Syafina Riyanto Sigit, Riyanto Rokhana, Rika Saputra, Muhammad Krisnanda Vilovan Susetyoko, Ronny Tri Hadiah Muliawati, Tri Hadiah Yuniarti, Heny