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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)

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.

An Augmented Reality Application for the Community Learning about the Risk of Earthquake in a Multi-storey Building Area Pamenang, Muhammad Unggul; Basuki, Achmad; Sigit, Riyanto
EMITTER International Journal of Engineering Technology Vol 5, No 2 (2017)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

The earthquake comes with great risks, especially in urban areas where many multi-storey buildings exist. These risks have not been understood well yet by the people of the urban area. Socialization, simulation, and learning media need to be provided continuously to improve people awareness on the importance of knowledge about the earthquake risks. An interesting learning media is not only contain informations but also a 3D animation and an interaction with the user. For a more immersive interaction, this application is equipped with augmented reality technology that gives more real visual representation like the actual condition. The evaluation result shows that 82% respondent appreciates this application, at first common users do not know the risk of earthquakes on multi-storey building, with this application users can understand the importance of earthquake risk in buildings.

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)

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)

Matching the suitable color for tooth reconstruction is an important step that can make difficulties for the dentists due to the subjective factorsÂ of color selection. Accurate color matching system is mainly result based on images analyzing and processing techniques of recognition system. Â 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) classifierÂ algorithm can be improved the accuracy performance of this system. On the experiment result, it showed that only usingÂ 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 Â 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.Â

Javanese Character Feature Extraction Based on Shape Energy Wibowo, Galih Hendra; Sigit, Riyanto; Barakbah, Aliridho
EMITTER International Journal of Engineering Technology Vol 5, No 1 (2017)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Javanese character is one of Indonesias noble culture, especially in Java. However, the number of Javanese people who are able to read the letter has decreased so that there need to be conservation efforts in the form of a system that is able to recognize the characters. One solution to these problem lies in Optical Character Recognition (OCR) studies, where one of its heaviest points lies in feature extraction which is to distinguish each character. Shape Energy is one of feature extraction method with the basic idea of how the character can be distinguished simply through its skeleton. Based on the basic idea, then the development of feature extraction is done based on its components to produce an angular histogram with various variations of multiples angle. Furthermore, the performance test of this method and its basic method is performed in Javanese character dataset, which has been obtained from various images, is 240 data with 19 labels by using K-Nearest Neighbors as its classification method. Performance values were obtained based on the accuracy which is generated through the Cross-Validation process of 80.83% in the angular histogram with an angle of 20 degrees, 23% better than Shape Energy. In addition, other test results show that this method is able to recognize rotated character with the lowest performance value of 86% at 180-degree rotation and the highest performance value of 96.97% at 90-degree rotation. It can be concluded that this method is able to improve the performance of Shape Energy in the form of recognition of Javanese characters as well as robust to the rotation.

Moment Invariant Features Extraction for Hand Gesture Recognition of Sign Language based on SIBI Rahagiyanto, Angga; Basuki, Achmad; Sigit, Riyanto
EMITTER International Journal of Engineering Technology Vol 5, No 1 (2017)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Myo Armband became an immersive technology to help deaf people for communication each other. The problem on Myo sensor is unstable clock rate. It causes the different length data for the same period even on the same gesture. This research proposes Moment Invariant Method to extract the feature of sensor data from Myo. This method reduces the amount of data and makes the same length of data. This research is user-dependent, according to the characteristics of Myo Armband. The testing process was performed by using alphabet A to Z on SIBI, Indonesian Sign Language, with static and dynamic finger movements. There are 26 class of alphabets and 10 variants in each class. We use min-max normalization for guarantying the range of data. We use K-Nearest Neighbor method to classify dataset. Performance analysis with leave-one-out-validation method produced an accuracy of 82.31%. It requires a more advanced method of classification to improve the performance on the detection results.

Analysis on Handwritten Document Text to Identify Human Personality Characteristics by Using Preprocessing and Feature Extraction Perdanasari, Lukie; Sigit, Riyanto; Basuki, Achmad
EMITTER International Journal of Engineering Technology Vol 6, No 2 (2018)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

It is important that a company uses the right means to recruit employees with certain personal characteristics as needed. Nowadays, the techniques to respond to psychological tests on peopleâs characteristics have been widely understood by most job applicants, so that it is difficult to know their true personality. Graphology is a way to identify a personâs characteristics by analyzing the handwriting from the document text made by the applicant. The two types of text document of each applicant are obtained from people of different ages and different writing times. The methods of graphology used in this research for identifying the handwriting are preprocessing and feature extraction. The preprocessing method uses projection integrals, shear transformations, and template matching. While the feature extraction process applies 10 features, they are, margins, line spacing, space between words, size of writing, style, zone, direction of writing, slope of writing, width of writing and shape of the letter. The result of the experiment from five writers shows the accuracy of writing identification equals to 82%, while personality identification equals to 67,4%.

Implementation of Portable Ultrasound for Heart Disease Detection Using Cloud Computing-Based Machine Learning Sigit, Riyanto; Rika Rokhana; Setiawardhana; Taufiq Hidayat; Anwar; Jovan Josafat Jaenputra
EMITTER International Journal of Engineering Technology Vol 12 No 2 (2024)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v12i2.904

Heart disease remains one of the leading causes of death globally, including in Indonesia. Cardiovascular disease is the leading cause of death worldwide, resulting in a significant number of fatalities. In Indonesia, access to specialized heart examination services is limited, requiring patients to visit large hospitals equipped with specialized facilities. Echocardiographic examinations using ultrasound can measure various heart parameters, such as hemodynamics, heart mass, and myocardial deformation. Portable ultrasound devices have emerged, enabling flexible and effective heart examinations. These devices capture video data of the patient's heart condition. The data undergoes image preprocessing involving median filtering, high-boost filtering, morphological operations, thresholding, and Canny filtering. Segmentation is performed using region filters, collinear filters, and triangle equations. Tracking utilizes the Optical Flow Lucas-Kanade method, and feature extraction employs Euclidean distance and trigonometric equations. The classification stage uses Support Vector Machine (SVM). Video data is transmitted via a mobile application to the cloud, where all stages from preprocessing to classification are conducted on cloud servers. The classification results are then sent back to the mobile application. The proposed model achieved an accuracy rate of 86% with a standard deviation of 0.09, indicating that the detection system performs effectively.

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