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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%.

Klasifikasi Tumor Otak Menggunakan Convolutional Neural Network Fakhri, Haidar; Setiawardhana, Setiawardhana; Syarif, Iwan; Sigit, Riyanto
Jurnal Inovtek Polbeng Seri Informatika Vol 9, No 1 (2024)
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/isi.v9i1.3908

Metode klasifikasi citra MRI otak yang digunakan pada penelitian ini adalah Deep Learning dengan Convolutional Neural Network (CNN) dengan 2 model skema arsitektur CNN. Model skema 1 terdapat 2 max pooling layer dan 2 hidden layer, sedangkan model skema 2 terdapat 3 max pooling layer dan 4 hidden layer.Â Dataset yang digunakan memuat citra MRI otak manusia dengan total 7023 citra, dengan rincian 1621 Glioma, 1645 Meningioma, 1757 Pituitary, dan 2000 Notumor. Evaluasi F1-Score model skema 1 dan skema 2 berturut-turut: 96% dan 97%, Sedangkan untuk nilai Accuracy yaitu 98%. Hal ini menunjukkan bahwa nilai F1-Score dan Accuracy, model skema 2 lebih baik. Untuk menguji dataset digunakan 10 fold cross-validation menghasilkan nilai rata-rata Accuracy, F1-Score, Precision, dan Recall berturut-turut 0,8520, 0,8470, 0,8493 dan 0,8504, dengan standar deviasi yang kecil, yaitu berturut-turut 0,0352; 0,0346; 0,0337 dan 0,0353 yang menunjukkan bahwa penyimpangan sebaran nilai semakin mendekati nilai rata-ratanya. nilai metrik F1-score dan accuracy berturut-turut, 97,47% dan 97,39%. Hasil accuracy penelitian ini lebih tinggi dibandingkan dengan beberapa penelitian sebelumnya, yakni dari [1], [2], [3], [5], [7], dan [8], berturut-turut: 94.39%, 97.54%, 97.18%, 96.08%, 96,36%, dan 95.55%.

Deep Learning Models for Dental Conditions Classification Using Intraoral Images Makarim, Ahmad Fauzi; Karlita, Tita; Sigit, Riyanto; Bayu Dewantara, Bima Sena; Brahmanta, Arya
JOIV : International Journal on Informatics Visualization Vol 8, No 3 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.3.1914

This paper presents the digitalization of dentistry medical records to support the dentist in the patient examination process. A dentist uses manual input to fill out the evaluation form by drawing and labeling each patient’s tooth condition based on their observations. Consequently, it takes too long to finish only one examination. For time efficiency, using AI-based digitalization technology can be a promising solution. To address the problem, we made and compared several classification models to recognize human dental conditions to help doctors analyze patient teeth. We apply the YOLOv5, MobileNet V2, and IONet (proposed CNN model) as deep learning models to recognize the five common human dental conditions: normal, filling, caries, gangrene radix, and impaction. We tested the ability of YOLO classification as an object detection model and compared it with classification models. We used a dataset of 3.708 intraoral dental images generated by various augmentation methods from 1.767 original images. We collected and annotated the dataset with the help of dentists. Furthermore, the dataset is divided into three parts: 90% of the total dataset is used as training and validation data, then divided again into 80% training data and 20% validation data. 10% of the total dataset will be used as testing data to compare classification performance. Based on our experiments, YOLOv5, as an object detection model, can classify dental conditions in humans better than the classification model. YOLOv5 produces an 82% accuracy testing value and performs better than the classification model. MobileNet V2 and IONet only get 80% and 70% testing accuracy. Although statistically, there is not much of a difference between the test accuracy values for YOLOv5 and MobileNet v2, the speed in classifying dental objects using YOLOv5 is more efficient, considering that YOLOv5 is an object detection model. There are still challenges with the deep learning technique used in this research, but these can be addressed in further development. A more complex model and the enlargement of more data, ensuring it is varied and balanced, can be used to address the limitations.

Environmental Monitoring System using Wireless Multi-Node Sensors based Communication System on Volcano Observations Drones Huda, Achmad Torikul; Setiawardhana, Setiawardhana; Dewantara, Bima Sena Bayu; Sigit, Riyanto
JOIV : International Journal on Informatics Visualization Vol 8, No 2 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.2.1961

Indonesia is on the Ring of Fire and has the world's most active volcanoes. Volcanic activity has a significant effect on the landscape and on the people who live there. The difficulty of evacuating and helping victims requires hard work and sometimes even the safety of the rescue team itself. For this reason, high-tech tools are needed. Unmanned aerial vehicles (UAVs), also called drones, have become a hopeful tool for remote environmental monitoring in recent years. The system design has a monitoring platform, gateway, and sensor nodes attached to the UAV, which monitors the content of toxic gas contamination in the air. Using IoT technology, sensor data is sent wirelessly to a central monitoring station for a thorough and accurate volcanic activity study. This system is a flexible and complete way to monitor volcanic activity, learn more about it, and make it easier to respond to disasters. Tests are also done to measure system speed, including latency, and determine network service quality. The results show that data is successfully sent in real-time from the sensor nodes to the monitoring system. The average Round-Trip time for the payload transmission is 446.046226 ms. This shows how well the system works to send data from the sensors connected to the UAV to the monitoring station. The UAV has sensor nodes and a monitoring system platform. These can be used to build and optimize disaster mitigation systems.

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