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All Journal Indonesian Journal of Tropical and Infectious Disease
Franky Arisgraha, Franky
Biomedical Engineering - Faculty of Science and Technology, Universitas Airlangga
Earth & Planetary Sciences Health Professions Medicine & Pharmacology Public Health

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DIGITAL DETECTION SYSTEM DESIGN OF MYCOBACTERIUM TUBERCULOSIS THROUGH EXTRACTION OF SPUTUM IMAGE USING NEURAL NETWORK METHOD Arisgraha, Franky; Widiyanti, Prihartini; Apsari, Retna
Indonesian Journal of Tropical and Infectious Disease Vol 3, No 1 (2012)
Publisher : Institute of Topical Disease

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (154.924 KB)

Abstract

Tuberculosis (TBC) is an dangerous disease and many people has been infected. One of many important steps to control TBC effectively and efficiently is by increasing case finding using right method and accurate diagnostic. One of them is to detect Mycobacterium Tuberculosis inside sputum. Conventional detection of Mycobacterium Tuberculosis inside sputum can need a lot of time, so digitallydetection method of Mycobacterium Tuberculosis was designed as an effort to get better result of detection. This method was designed by using combination between digital image processing method and Neural Network method. From testing report that was done, Mycobacterium can be detected with successful value reach 77.5% and training error less than 5%.
APPLICATION OF NEURAL NETWORKS ON BLOOD SERUM IMAGE FOR EARLY DETECTION OF TYPHUS Purnamasari, Betty; Arisgraha, Franky; Astuti, Suryani Dyah
Indonesian Journal of Tropical and Infectious Disease Vol 4, No 4 (2013)
Publisher : Institute of Topical Disease

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (970.71 KB)

Abstract

Background: Typhus is a disease caused by Salmonella typhi, Salmonella paratyphi A Salmonella parathypi B, dan Salmonella paratyphi C bacteria that attacks digestive tract and caused infection in small intestine. The common test that performed in the laboratory is widal test. The result reading of the widal test still processed manually with looking the turbidity caused by the agglutination. Aim: The research was made to decrease human error by creating a program based on artificial neural network (ANN) with learning vector quantization (LVQ) method. Method: Input of this program is image of blood serum that has reacted with widal reagen. Image procesing start with grayscaling, filtering, and thresholding. Result: Output of this program is divided into two classes, normal and typhus detected. Conclusion: From this experiment result that using 24 testing data, gives the accuracy of this program 95.833% with 1 error result from 24 testing data.
BRADYCARDIA AND TACHYCARDIA DETECTION SYSTEM WITH ARTIFICIAL NEURAL NETWORK METHOD S, Delima Ayu; Arisgraha, Franky; Apsari, Retna
Indonesian Journal of Tropical and Infectious Disease Vol 3, No 2 (2012)
Publisher : Institute of Topical Disease

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (546.931 KB)

Abstract

Heart disease is one disease with high mortality rate in the world. Based on WHO records from 112 countries at 2004, the rate is 29% of all deaths each year. Medical devices are necessary to diagnose ones health as an indication of a disease. Nowadays, Indonesia still imports medical devices, for the diagnosis of heart failure, from abroad. This research aims to assist the monitoring of cardiac patients with bradycardia and tachycardia appearances of message condition patient’s heart rate at the same time. The results were displayed with the output of bradycardia condition of the heart rate (heart rate less than 60 beats per minute) or tachycardia (heart rate over 100 beats per minute). The system displayed the data read from the heart to the PC embedded system to monitor the condition of the patients under decisions based on backpropagation neural network. Classification system could be performed quite well, training data and by testing the 10 pieces, the optimal weight gain was 1727 iteration, the learning rate was 0.1122, and the error was below 0.001 (0.0009997).
DIGITAL DETECTION SYSTEM DESIGN OF MYCOBACTERIUM TUBERCULOSIS THROUGH EXTRACTION OF SPUTUM IMAGE USING NEURAL NETWORK METHOD Arisgraha, Franky; Widiyanti, Prihartini; Apsari, Retna
Indonesian Journal of Tropical and Infectious Disease Vol. 3 No. 1 (2012)
Publisher : Institute of Topical Disease Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (154.924 KB) | DOI: 10.20473/ijtid.v3i1.200

Abstract

Tuberculosis (TBC) is an dangerous disease and many people has been infected. One of many important steps to control TBC effectively and efficiently is by increasing case finding using right method and accurate diagnostic. One of them is to detect Mycobacterium Tuberculosis inside sputum. Conventional detection of Mycobacterium Tuberculosis inside sputum can need a lot of time, so digitallydetection method of Mycobacterium Tuberculosis was designed as an effort to get better result of detection. This method was designed by using combination between digital image processing method and Neural Network method. From testing report that was done, Mycobacterium can be detected with successful value reach 77.5% and training error less than 5%.
BRADYCARDIA AND TACHYCARDIA DETECTION SYSTEM WITH ARTIFICIAL NEURAL NETWORK METHOD S, Delima Ayu; Arisgraha, Franky; Apsari, Retna
Indonesian Journal of Tropical and Infectious Disease Vol. 3 No. 2 (2012)
Publisher : Institute of Topical Disease Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (546.931 KB) | DOI: 10.20473/ijtid.v3i2.206

Abstract

Heart disease is one disease with high mortality rate in the world. Based on WHO records from 112 countries at 2004, the rate is 29% of all deaths each year. Medical devices are necessary to diagnose one's health as an indication of a disease. Nowadays, Indonesia still imports medical devices, for the diagnosis of heart failure, from abroad. This research aims to assist the monitoring of cardiac patients with bradycardia and tachycardia appearances of message condition patient's heart rate at the same time. The results were displayed with the output of bradycardia condition of the heart rate (heart rate less than 60 beats per minute) or tachycardia (heart rate over 100 beats per minute). The system displayed the data read from the heart to the PC embedded system to monitor the condition of the patients under decisions based on backpropagation neural network. Classification system could be performed quite well, training data and by testing the 10 pieces, the optimal weight gain was 1727 iteration, the learning rate was 0.1122, and the error was below 0.001 (0.0009997).
APPLICATION OF NEURAL NETWORKS ON BLOOD SERUM IMAGE FOR EARLY DETECTION OF TYPHUS Purnamasari, Betty; Arisgraha, Franky; Astuti, Suryani Dyah
Indonesian Journal of Tropical and Infectious Disease Vol. 4 No. 4 (2013)
Publisher : Institute of Topical Disease Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (970.71 KB) | DOI: 10.20473/ijtid.v4i4.234

Abstract

Background: Typhus is a disease caused by Salmonella typhi, Salmonella paratyphi A Salmonella parathypi B, dan Salmonella paratyphi C bacteria that attacks digestive tract and caused infection in small intestine. The common test that performed in the laboratory is widal test. The result reading of the widal test still processed manually with looking the turbidity caused by the agglutination. Aim: The research was made to decrease human error by creating a program based on artificial neural network (ANN) with learning vector quantization (LVQ) method. Method: Input of this program is image of blood serum that has reacted with widal reagen. Image procesing start with grayscaling, filtering, and thresholding. Result: Output of this program is divided into two classes, normal and typhus detected. Conclusion: From this experiment result that using 24 testing data, gives the accuracy of this program 95.833% with 1 error result from 24 testing data.
Co-Authors Betty Purnamasari, Betty Delima Ayu S, Delima Ayu Prihartini Widiyanti Retna Apsari Suryani Dyah Astuti