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All Journal Pendidikan Ekonomi Jurnal Online Mahasiswa (JOM) Bidang Teknik dan Sains J-PAI: Jurnal Pendidikan Agama Islam Journal of Governance and Public Policy Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) INVOTEK: Jurnal Inovasi Vokasional dan Teknologi Jurnal Geografi Jurnal Hukum Respublica Politik Indonesia: Indonesian Political Science Review JURNAL EDUCATION AND DEVELOPMENT JURNAL PENDIDIKAN TAMBUSAI Journal of Information Technology and Computer Engineering Jurnal Sains Sosio Humaniora Ensiklopedia of Journal Jurnal Review Pendidikan dan Pengajaran (JRPP) Pena Justisia: Media Komunikasi dan Kajian Hukum AL-HIKMAH (Jurnal Pendidikan dan Pendidikan Agama Islam) JOM FTK UNIKS : Jurnal Online Mahasiswa Fakultas Tarbiyah dan Keguruan Universitas Islam Kuantan Singingi Community Development Journal: Jurnal Pengabdian Masyarakat Suluah Bendang: Jurnal Ilmiah Pengabdian Kepada Masyarakat AEEJ : Journal of Automotive Engineering and Vocational Education Jurnal Gagasan Hukum AKADEMIK: Jurnal Mahasiswa Humanis JASNA : Journal For Aswaja Studies Bhakti Nagori (Jurnal Pengabdian kepada Masyarakat) Jurnal Karya Abdi Masyarakat Literasi: Jurnal Pengabdian Masyarakat dan Inovasi Jurnal Pengabdian Kompetitif Jotika Research in Business Law JAPEPAM, Jurnal Pengabdian kepada Masyarakat
Andrizal Andrizal
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Journal : Journal of Information Technology and Computer Engineering

 1 
Identifikasi Halitosis Berdasarkan Tingkatan Berbasis Sensor Gas Menggunakan Metode Learning Vector Quantization Dodon Yendri; Anisa Irviana; Andrizal Andrizal
JITCE (Journal of Information Technology and Computer Engineering) Vol 1 No 01 (2017): Journal of Information Technology and Computer Engineering
Publisher : Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1227.274 KB) | DOI: 10.25077/jitce.1.01.35-47.2017

Abstract

Diabetes mellitus and gastric infections can be detected through bad breath bad breath (halitosis). Halitosis is a condition where the smell of bad breath occurs when a person exhales (usually smells when talking). This study aims to create an oral health identification and classification system (halitosis). TGS-2602 gas sensor will detect gas levels in the mouth of the patient, and send data in the form of an analog signal to the ATmega 328 microcontroller. By programming the data read on the Raspberry Pi, the data from the microcontroller is stored in a file and then the data is processed using the Fast Fourier Transform method. (FFT) so that the desired data pattern is obtained. The data pattern of the Fast Fourier Transform (FFT) output will be used as input data on the Learning Vector Quantization (LVQ) neural network method. System testing is done to people with halitosis and not halitosis bad breath. The results showed that the percentage success rate of sensor responses to mild halitosis samples was 25%, moderate halitosis samples were 50%, acute Halitosis samples were 50% and non-halitosis samples were 100%.
Identifikasi Halitosis Berdasarkan Tingkatan Berbasis Sensor Gas Menggunakan Metode Learning Vector Quantization Yendri, Dodon; Irviana, Anisa; Andrizal, Andrizal
JITCE (Journal of Information Technology and Computer Engineering) Vol. 1 No. 01 (2017)
Publisher : Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jitce.1.01.35-47.2017

Abstract

Diabetes mellitus and gastric infections can be detected through bad breath bad breath (halitosis). Halitosis is a condition where the smell of bad breath occurs when a person exhales (usually smells when talking). This study aims to create an oral health identification and classification system (halitosis). TGS-2602 gas sensor will detect gas levels in the mouth of the patient, and send data in the form of an analog signal to the ATmega 328 microcontroller. By programming the data read on the Raspberry Pi, the data from the microcontroller is stored in a file and then the data is processed using the Fast Fourier Transform method. (FFT) so that the desired data pattern is obtained. The data pattern of the Fast Fourier Transform (FFT) output will be used as input data on the Learning Vector Quantization (LVQ) neural network method. System testing is done to people with halitosis and not halitosis bad breath. The results showed that the percentage success rate of sensor responses to mild halitosis samples was 25%, moderate halitosis samples were 50%, acute Halitosis samples were 50% and non-halitosis samples were 100%.
Identifikasi Penyakit Diabetes Mellitus Melalui Nafas Berbasis Sensor Gas Dengan Metode Fast Fourier Transform dan Backpropagation Hersyah, Mohammad Hafiz; Andrizal, Andrizal; Revinessia, Revinessia
JITCE (Journal of Information Technology and Computer Engineering) Vol. 2 No. 02 (2018)
Publisher : Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jitce.2.02.85-91.2018

Abstract

The purpose of this research is to detect whether a person has diabetes mellitus or not. In people with diabetes mellitus uncontrolled will result in a decline in the rate of saliva that results in bad breath. The system uses the sensor TGS 2602 and MQ 4. It's function is to detect the levels of Hydrogen Sulfide and Methan in a person’s breath. The decision is made by using the neural network with a backpropagation method. The result for 5 (five) tests of diabetes mellitus samples can be detected with a success rate of 80%, whereas using random samples, the test detected with detected with a success rate of 80% samples that didn’t contain diabetes mellitus. This system could provide a solution for testing if a person is suffering from diabetes mellitus
Co-Authors 3Mareta Kemala Sari A Mualif A. Mualif Ahmad Arif Akbar, Helbi Akbar, Helbi Alexsander Yandra, Alexsander Alhairi Alhairi Alhairi, Alhairi Amun Amri Angraini, Tuti Anisa Irviana Anton Anton Anton Hidayat Ardiansah, Ardiansah Aslati Aslati Asmayarni Asmayarni Bambang Hermanto Budhi Bakhtiar Bustanur Bustanur Daulay, Waddi Elman Syah Putra Dian Tri Lestari Dodon Yendri Donny Fernandez Eddy Asnawi Ekis Yulanda Erman Gani Faisal Ismet Faridhi, Adrian Fauzun Aisyah Febi Pransiska Hakim, Bambang Nur Hakim, Putra Oktari Hasan Maksum Helbi Akbar Hendra Dani Saputra Hersyah, Mohammad Hafiz Indriati Adni Irma Yulia Basri Irviana, Anisa Irwandi Irwandi Jasmawan Jasmawan Jumni Nelli Junil Adri Kosariza, Kosariza Koto, Rahmat Desman Lasmiadi, Lasmiadi Lesi Yunita Mailani, Ikrima Martoni, Martoni Meri Yarni Misri Susanti Muhammad Ashari Mulza Rois Muslim Muslim Mustikomah Mustikomah Mutaqin , Awaludin Nadia Alfitri Nazario, Rafael Nelawati Nelawati Neni Minarti Nova Ayu Wulandari Novianti Novianti Nunung Setiani Nurchotimah, Aulia Sholichah Iman Oky Saputra, Rachmad Popilaya, Iwan Prima Bakti, Irfan Purwanto, Wawan Putra, Rizky Kurnia Putra, Taufan Dyusandra Putri, Riska Dwi Rahmi Berlianti Rampirta Rampirta Revinessia, Revinessia Rifdarmon, Rifdarmon Rivanol Chadry Rizky MM SE Natassia Rohmad Wandy Satriawan Rosidi, Imron Roza Susanti Rusfandi Rusfandi Sarmidin Sarmidin Satria, Yovi Setiawan, Husni simamora, birman Sopiatun Nahwiyah Suci Lestari Suhermi, Suhermi Suyito, Suyito Syamsu Herman Tuti Angraini Tuti Azra Ulfa Novrilla Veithzal Rivai Zainal Vidiarti, Erni Wagino Waskito Waskito, Waskito Wigati Iswandhiari Yandra, Alexander Yefriadi Yefriadi Yuhelvi Novera Yul Antonisfia Yulia Basri, Irma Yundari, Yundari zalman, alex sandra Zas Ressy Aidha Zulhaini Zulhaini Zulhaini, Zulhaini