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All Journal EMITTER International Journal of Engineering Technology Padjadjaran Journal of Dentistry JOIV : International Journal on Informatics Visualization INOVTEK Polbeng - Seri Informatika Jurnal Informatika: Jurnal Pengembangan IT The Indonesian Journal of Computer Science Seminar Nasional Riset dan Teknologi (SEMNAS RISTEK)

Riyanto Sigit, Riyanto

Unknown Affiliation

Author-ID : 559648

Biochemistry, Genetics & Molecular Biology Civil Engineering, Building, Construction & Architecture Computer Science & IT Dentistry Education Electrical & Electronics Engineering Engineering Health Professions Immunology & microbiology Medicine & Pharmacology Public Health Other

Published : 28 Documents Claim Missing Document

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Articles

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Face Recognition Using Convolution Neural Network Method with Discrete Cosine Transform Image for Login System Setiawan, Ari; Sigit, Riyanto; Rokhana, Rika
JOIV : International Journal on Informatics Visualization Vol 7, No 2 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.2.1546

These days, the application of image processing in computer vision is becoming more crucial. Some situations necessitate a solution based on computer vision and growing deep learning. One method continuously developed in deep learning is the Convolutional Neural Network, with MobileNet, EfficientNet, VGG16, and others being widely used architectures. Using the CNN architecture, the dataset consists primarily of images; the more datasets there are, the more image storage space will be required. Compression via the discrete cosine transform technique is a method to address this issue. We implement the DCT compression method in the present research to get around the system's limited storage space. Using DCT, we also compare compressed and uncompressed images. All users who had been trained with each test 5 times for a total of 150 tests were given the test. Based on testing findings, the size reduction rate for compressed and uncompressed images is measured at 25%. The case study presented is face recognition, and the training results indicate that the accuracy of compressed images using the DCT approach ranges from 91.33% to 100%. Still, the accuracy of uncompressed facial images ranges from 98.17% to 100%. In addition, the accuracy of the proposed CNN architecture has increased to 87.43%, while the accuracy of MobileNet has increased by 16.75%. The accuracy of EfficientNetB1 with noisy-student weights is measured at 74.91%, and the accuracy of EfficientNetB1 with imageNet weights can reach 100%. Facial biometric authentication using a deep learning algorithm and DCT-compressed images was successfully accomplished with an accuracy value of 95.33% and an error value of 4.67%.

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)

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.

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

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.

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 Indonesia's 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.

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 peopleâ€™s 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 personâ€™s 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%.

Multi-Distance Veins Projection Based on Single Axis Camera and Projector System Gunawan, I Putu Adi Surya; Sigit, Riyanto; Gunawan, Agus Indra
EMITTER International Journal of Engineering Technology Vol 7 No 2 (2019)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v7i2.367

Every person has different location of veins, some veins are easily detected because it is visible due to thin tissue, and the other are invisible. This different location of veins causes intravenous access procedures and the procreas of intravenous therapy become longer. Multi-distance vein projections aim to simplify the measurement process where the device and object do not have to be at a certain distance. Some research that has been done especially for real-time vein projection does not conduct how the characteristics of projection at different distances. In this paper, we propose a method for performing multi-distance real-time back-projection by using the intersection between camera and projector. This method equiped with an ultrasonic distance sensor to identify the projection characteristic in any distance. In its implementation, this method is able to project at a distance of 20-40 cm with a maximum projection error of 0.6 mm. The measurement angle tolerance between the object and the device is Â±5 degrees with a maximum error of 0.7 mm.

Co-Authors ., Justiawan ., Justiawan Achmad Basuki Ali Ridho Barakbah Anwar Ari Setiawan Arna Fariza Arya Brahmanta, Arya Aulia Dwi Maharani Bima Sena Bayu Dewantara Fadhillah, Excel Daris Fakhri, Haidar Gunawan, Agus Indra Gunawan, I Putu Adi Surya Huda, Achmad Torikul iwan Syarif Jovan Josafat Jaenputra Kemalasari Madyono, Madyono Makarim, Ahmad Fauzi Maulana, Firja Hanif Maulana, Rifqi Affan Mochamad Mobed Bachtiar, Mochamad Mobed Nabilah, Anisah Pamenang, Muhammad Unggul Pamenang, Muhammad Unggul Perdanasari, Lukie Perdanasari, Lukie Rahagiyanto, Angga Rahagiyanto, Angga Ratna Kusumawati Rika Rokhana Rochmad, Moch. Rokhana, Rika Romadhon, Nur Rizky Ryando, Catoer Sanusi, Muhammad Anwar Sena Bayu Dewantara, Bima Setiawardhana Setiawardhana, Setiawardhana Taufiq Hidayat Tita Karlita Wibowo, Galih Hendra Wibowo, Galih Hendra Yuniarti, Heny Zainal Arief Zamzami, Amran

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