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All Journal Jurnal Masyarakat Informatika PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Jurnal Algoritma Jurnal Teknologi Informasi dan Ilmu Komputer REKAYASA Jurnal Ecodemica : Jurnal Ekonomi Manajemen dan Bisnis Jurnal Informatika Jurnal Pilar Nusa Mandiri Forum Ekonomi : Jurnal Ekonomi, Manajemen dan Akuntansi JURNAL MANAJEMEN Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) INTI Nusa Mandiri Jurnal Teknik Informatika (JUTIF) Journal of Applied Data Sciences IAIC Transactions on Sustainable Digital Innovation (ITSDI) Tridharmadimas: Jurnal Pengabdian Kepada Masyarakat Jayakarta International Journal of Informatics, Information System and Computer Engineering (INJIISCOM) JUSTIN (Jurnal Sistem dan Teknologi Informasi) Jurnal Algoritma International Journal of Advanced Science Computing and Engineering Jurnal Pengabdian Ibnu Sina Journal Medical Informatics Technology SmartComp
Dwiza Riana
Institut Teknologi Bandung
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Physics Public Health Social Sciences Other

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Optimization the Naive Bayes Classifier Method to diagnose diabetes Mellitus Susilawati, Desi Susilawati; Riana, Dwiza
IAIC Transactions on Sustainable Digital Innovation (ITSDI) Vol 1 No 1 (2019): October
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/itsdi.v1i1.21

Abstract

World Health Organization (WHO) states that Diabetes Mellitus is the world's top deadly disease. several studies in the health sector including diabetes mellitus have been carried out to detect diseases early. In this study optimization of naive bayes classifier using particle swarm optimization was applied to the data of patients with 2 classes namely positive diabetes mellitus and negative diabetes mellitus and data on patients with 3 classes, those who tested positive for diabetes mellitus type 1, diabetes mellitus type 2 and negative diabetes mellitus. After testing, the algorithm of Naive Bayes Classifier and Naive Bayes Classifier based on Particle Swarm Optimization, the results obtained are the Naive Bayes Classifier method for 2 classes and 3 classes each producing an accuracy value of 78.88% and 68.50%. but after adding Particle Swarm Optimization the value of accuracy increased respectively to 82.58% and 71, 29%. The classification results for 2 classes have an accuracy value higher than 3 classes with a difference of 11.29%
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 Abbas, Moch Anwar Ahmad Azhari Anton Anton Arif Budiarto Astuti, Rachmawati Darma Aziz, Faruq Choerunisya, Hana Dwi Saputra, Heru Dwinanda, Brama Rizky Setia Edi Sabara Efendi, Ade Irfan Efendi eka rahmawati Ferda Ernawan Gata, Windu Hakim, Valianda Farradillah Hasanah, Riyan Latifahul Herwanto, Teguh Hewiz , Alya Shafira Hewiz, Alya Shafira Ilhami, Azriel Noer Jamal Maulana Hudin Jamil, Muh. Jumadi, J Kahfi, Ahmad Hafidzul Kodri, Wan Ahmad Gazali Kurniawantoro Laela Kurniawati, Laela Luh Putu Ratna Sundari Mahendra Mahendra, M Muhamad Hasan Muhammad Qomaruddin Na'am, Jufriadif Nur Sulistyowati, Daning Nurhasanah, Fitri Yani Octaviantara, Adi Pardede, Hilman F Pardede, Hilman Ferdinandus Rahman, Irfan Fadholur Ramanudin, Eko Rudini, Edwin Rumintarsih, Ade Sahidillah Nurdin Saputra, Ridwan Saputra, Surya Fajar Sri Hadianti Sri Hardianti Sulistyowati, Daning Nur Susilawati, Desi Susilawati Thira, Indra Jiwana Waeisul Bismi Wati, Elga Watmah, Sri Widagdo, Galih Widanto, Frantina Andri Widianto, Tetra Wildan Wiguna WITRIANA ENDAH PANGESTI Zevana, Alda