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All Journal Heuristic EMITTER International Journal of Engineering Technology Aiti: Jurnal Teknologi Informasi International Journal on Advanced Technology, Engineering, and Information System (IJATEIS) Jurnal Taguchi : Jurnal Ilmiah Teknik dan Manajemen Industri Jurnal Pengabdian Masyarakat dan aplikasi Teknologi The Indonesian Journal of Computer Science
Zainal Arief
Fakultas Teknik Untag Surabaya
Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Other

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Journal : EMITTER International Journal of Engineering Technology

 1 
Development of Healthcare Kiosk for Checking Heart Health Sigit, Riyanto; Arief, Zainal; Bachtiar, Mochamad Mobed
EMITTER International Journal of Engineering Technology Vol 3, No 2 (2015)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (455.371 KB)

Abstract

The main problem encountered nowadays in the health field, especially in health care is the growing number of population and the decreasing health facilities. In this regard, healthcare kiosk is used as an alternative to the health care facilities. Heart disease is a dangerous one which could threaten human life. Many people have died due to heart disease and the surgery itself is still very expensive. To analyze heart diseases, doctor usually takes a video of the heart movement using ultrasound equipment to distinguish between normal and abnormal case. The results of analysis vary depending on the accuracy and experience of each doctor so it is difficult to determine the actual situation. Therefore, a method using healthcare kiosk to check the heart health is needed to help doctor and improve the health care facilities. The aim of this research is to develop healthcare kiosk which can be used to check the heart health. This research method is divided into three main parts: firstly, preprocessing to clarify the quality of the image.In this section, the writers propose a Median High Boost Filter method which is a combined method of Median Filtering and High Boost Filtering. Secondly, segmentation is used to obtain local cavities of the heart. In this part, the writers propose using Triangle Equation that is a new method to be developed. Thirdly, classification using Partial Monte Carlo method and artificial neural network method; these methods are used to measure the area of the heart cavity and discover the possibility of cardiac abnormalities. Methods for detecting heart health are placed in the kiosk. Therefore, it is expected to facilitate and improve the healthcare facilities.Keywords: Healthcare kiosk, heart health, reprocessing, segmentation, classification.
LOVETT Scaling with Flex Sensor and MYO Armband for Monitoring Finger Muscles Therapy of Post-Stroke People Alfian Hidayat, Achmad; Arief, Zainal; Happyanto, Dedid Cahya
EMITTER International Journal of Engineering Technology Vol 3, No 2 (2015)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (339.317 KB)

Abstract

LOVETT scale is a common parameter used by the doctor or therapist to determine the muscle strength of the patient’s hands, especially patients with post-stroke. As a result of previous work of our group, a sensory glove for monitoring finger muscle therapy for post-stroke people with the name of Electronic Therapy Gloves (ETG) was proposed. With the flex sensor that embedded to the gloves we can measure the LOVETT scale of the post-stroke people. This sensory glove can help the patient doing their rehabilitation fast so that they don’t have to go to the hospital every week to check up their progress. In this work, we combine the data of sensory glove and the MYO armband for LOVETT scaling that has never been done before. The output of the Electronic Therapy Gloves can be optimized by 25%. All the LOVETT grade can be identify by the gloves, then it can help the doctor monitor the patient’s rehabilitation just by looking the patient’s record data with ETG.Keyword: LOVETT scale, flex sensor, MYO armband, post-stroke, rehabilitation.
Reduction of Total Harmonic Distortion (THD) on Multilevel Inverter with Modified PWM using Genetic Algorithm Raharja, Lucky Pradigta Setiya; Q., Ony Asrarul; Arief, Zainal; Windarko, Novie Ayub
EMITTER International Journal of Engineering Technology Vol 5, No 1 (2017)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (3130.646 KB) | DOI: 10.24003/emitter.v5i1.174

Abstract

In this research, modified PWM has been applied to the multilevel inverter (MLI) single-phase three-level diode clamp full bridge. Modified PWM is performed to produce minimum Total Harmonic Distortion (THD) the voltage because the quality of the good voltage is indicated by small THD. The THD indicates the quality of AC voltage source. The THD standard by the IEEE STD 519-1992 Harmonic Voltage Limits is 5% and the Pacific Corp standard is 8%, if the THD value is greater than the THD standard it can cause the electronic load to be damaged due to the damaged waveform. Modified PWM is applied by adding a 50 Hz sinusoidal reference signal with a sinusoidal signal which has a certain amplitude, frequency and phase shift angle. The frequency of the adder signal is the frequency at which the value of the individual harmonic voltage appears (n harmonic). To get maximum result, optimization using Genetic Algorithm (GA) method to determinate amplitude & phase shift angle done. The result of implementation hardware with modified PWM shows smaller THD voltage compared to the THD voltage with Sinusoidal Pulse Width Modulation (SPWM) switching up to 0.19 or decrease 65,51 % for modified PWM of harmonic injection n = 7 with GA optimization ma= 0.8 (A=0.0936 and ø = 0 rad) and up to 0.08 or decrease 12,30 % for modified PWM of harmonic injection n = 22 with GA optimization ma = 0.4 (A=0.1221 and ø = 0 rad).
Application of Artificial Neural Networks in Modeling Direction Wheelchairs Using Neurosky Mindset Mobile (EEG) Device Siswoyo, Agus; Arief, Zainal; Sulistijono, Indra Adji
EMITTER International Journal of Engineering Technology Vol 5, No 1 (2017)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (4277.212 KB) | DOI: 10.24003/emitter.v5i1.165

Abstract

The implementation of Artificial Neural Network in prediction the direction of electric wheelchair from brain signal input for physical mobility impairment.. The control of the wheelchair as an effort in improving disabled person life quality. The interaction from disabled person is helping in relation to social life with others. Because of the mobility impairment, the wheelchair with brain signal input is made. This wheel chair is purposed to help the disabled person and elderly for their daily activity. ANN helps to develop the mapping from input to target. ANN is developed in 3 level: input level, one hidden level, and output level (6-2-1). There are 6 signal from Neurosky Mindset sensor output, Alpha1, Alpha2, Raw signal, Total time signal, Attention Signal, and Meditation signal. The purpose of this research is to find out the output value from ANN: value in turning right, turning left, and forward. From those outputs, we can prove the relevance to the target. One of the main problem that interfering with success is the problem of proper neural network training. Arduino uno is chosen to implement the learning program algorithm because it is a popular microcontroller that is economic and efficient. The training of artificial neural network in this research uses 21 data package from raw data, Alpha1, Aplha2, Meditation data, Attention data, total time data. At the time of the test there is a value of Mean square Error(MSE) at the end of training amounted to 0.92495 at epoch 9958, value a correlation coefficient of 0.92804 shows that accuracy the results of the training process good.  Keywords: Navigation, Neural network, Real-time training, Arduino 
Development of Healthcare Kiosk for Checking Heart Health Arief, Zainal; Bachtiar, Mochamad Mobed; Sigit, Riyanto
EMITTER International Journal of Engineering Technology Vol 3, No 2 (2015)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (455.371 KB) | DOI: 10.24003/emitter.v3i2.49

Abstract

The main problem encountered nowadays in the health field, especially in health care is the growing number of population and the decreasing health facilities. In this regard, healthcare kiosk is used as an alternative to the health care facilities. Heart disease is a dangerous one which could threaten human life. Many people have died due to heart disease and the surgery itself is still very expensive. To analyze heart diseases, doctor usually takes a video of the heart movement using ultrasound equipment to distinguish between normal and abnormal case. The results of analysis vary depending on the accuracy and experience of each doctor so it is difficult to determine the actual situation. Therefore, a method using healthcare kiosk to check the heart health is needed to help doctor and improve the health care facilities. The aim of this research is to develop healthcare kiosk which can be used to check the heart health. This research method is divided into three main parts: firstly, preprocessing to clarify the quality of the image.In this section, the writers propose a Median High Boost Filter method which is a combined method of Median Filtering and High Boost Filtering. Secondly, segmentation is used to obtain local cavities of the heart. In this part, the writers propose using Triangle Equation that is a new method to be developed. Thirdly, classification using Partial Monte Carlo method and artificial neural network method; these methods are used to measure the area of the heart cavity and discover the possibility of cardiac abnormalities. Methods for detecting heart health are placed in the kiosk. Therefore, it is expected to facilitate and improve the healthcare facilities.Keywords: Healthcare kiosk, heart health, reprocessing, segmentation, classification.
Tooth Color Detection Using PCA and KNN Classifier Algorithm Based on Color Moment ., Justiawan; Sigit, Riyanto; Arief, Zainal
EMITTER International Journal of Engineering Technology Vol 5, No 1 (2017)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1114.623 KB) | DOI: 10.24003/emitter.v5i1.171

Abstract

Matching the suitable color for tooth reconstruction is an important step that can make difficulties for the dentists due to the subjective factors  of color selection. Accurate color matching system is mainly result based on images analyzing and processing techniques of recognition system.  This system consist of three parts, which are data collection from digital teeth color images, data preparation for taking color analysis technique and extracting the features, and data classification involve feature selection for reducing the features number of this system. The teeth images which is used in this research are 16 types of teeth that are taken from RSGM UNAIR SURABAYA. Feature extraction is taken by the characteristics of the RGB, HSV and LAB based on the color moment calculation such as mean, standard deviation, skewness, and kurtosis parameter. Due to many formed features from each color space, it is required addition method for reducing the number of features by choosing the essential information like Principal Component Analysis (PCA) method. Combining the PCA feature selection technique to the clasification process using K Nearest Neighbour (KNN) classifier  algorithm can be improved the accuracy performance of this system. On the experiment result, it showed that only using  KNN classifier achieve accuracy percentage up to 97.5 % in learning process and 92.5 % in testing process while combining PCA with KNN classifier can reduce the 36 features to the 26 features which can improve the accuracy percentage up to 98.54 % in learning process and  93.12% in testing process. Adding PCA as the feature selection method can be improved the accuracy performance of this color matching system with little number of features. 
LOVETT Scaling with Flex Sensor and MYO Armband for Monitoring Finger Muscles Therapy of Post-Stroke People Alfian Hidayat, Achmad; Arief, Zainal; Happyanto, Dedid Cahya
EMITTER International Journal of Engineering Technology Vol 3, No 2 (2015)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (339.317 KB) | DOI: 10.24003/emitter.v3i2.45

Abstract

LOVETT scale is a common parameter used by the doctor or therapist to determine the muscle strength of the patient’s hands, especially patients with post-stroke. As a result of previous work of our group, a sensory glove for monitoring finger muscle therapy for post-stroke people with the name of Electronic Therapy Gloves (ETG) was proposed. With the flex sensor that embedded to the gloves we can measure the LOVETT scale of the post-stroke people. This sensory glove can help the patient doing their rehabilitation fast so that they don’t have to go to the hospital every week to check up their progress. In this work, we combine the data of sensory glove and the MYO armband for LOVETT scaling that has never been done before. The output of the Electronic Therapy Gloves can be optimized by 25%. All the LOVETT grade can be identify by the gloves, then it can help the doctor monitor the patient’s rehabilitation just by looking the patient’s record data with ETG.Keyword: LOVETT scale, flex sensor, MYO armband, post-stroke, rehabilitation.
Co-Authors ., Justiawan Achmad Alfian Hidayat, Achmad Daffa Syah Alam Dedid Cahya Happyanto Dian Mursyitah Erma Yuliaty, Erma Harijanto, Setijanen Djoko Herlina Herlina I Nyoman Lokajaya, I Nyoman Indra Adji Sulistijono Istantyo Yuwono, Istantyo Jufrizel , Jufrizel Lucky Pradigta Setiya Raharja Mochamad Mobed Bachtiar, Mochamad Mobed Novie Ayub Windarko Q., Ony Asrarul Ridhoi, Ahmad Riyanto Sigit, Riyanto Rokhana, Rika Singgih, Mochammad Siswoyo, Agus Siti Muhimatul Khoiroh Siti Mundari Wirawan, Andika Puji Zamroni, Moh Shiddiq Zarory, Hilman