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All Journal Techno.Com: Jurnal Teknologi Informasi TELKOMNIKA (Telecommunication Computing Electronics and Control) EMITTER International Journal of Engineering Technology KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Proceeding of the Electrical Engineering Computer Science and Informatics INOVTEK Polbeng - Seri Informatika JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Informatika dan Rekayasa Elektronik Indonesian Journal of Electrical Engineering and Computer Science

Riyanto Sigit

Politeknik Elektronika Negeri Surabaya

Author-ID : 2453636

Computer Science & IT Electrical & Electronics Engineering Engineering

Published : 18 Documents Claim Missing Document

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Improved echocardiography segmentation using active shape model and optical flow Riyanto Sigit; Calvin Alfa Roji; Tri Harsono; Son Kuswadi
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 17, No 2: April 2019
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v17i2.11821

Heart disease is one of the most dangerous diseases that threaten human life. The doctor uses echocardiography to analyze heart disease. The result of echocardiography test is a video that shows the movement of the heart rate. The result of echocardiography test indicates whether the patient’s heart is normal or not by identifying a heart cavity area. Commonly it is determined by a doctor based on his own accuracy and experience. Therefore, many methods to do heart segmentation is appearing. But, the methods are a bit slow and less precise. Thus, a system that can help the doctor to analyze it better is needed. This research will develop a system that can analyze the heart rate-motion and automatically measure heart cavity area better than the existing method. This paper proposes an improved system for cardiac segmentation using median high boost filter to increase image quality, followed by the use of an active shape model and optical flow. The segmentation of the heart rate-motion and auto measurement of the heart cavity area is expected to help the doctor to analyze the condition of the patient with better accuracy. Experimental result validated our approach.

DETEKSI KUALITAS BERAS MENGGUNAKAN SEGMENTASI CITRA BERDASARKAN PECAHAN BULIR DAN SEBARAN WARNA Eko Supriyadi; Achmad Basuki; Riyanto Sigit
Jurnal Informatika dan Rekayasa Elektronik Vol. 3 No. 1 (2020): JIRE April 2020
Publisher : LPPM STMIK Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36595/jire.v3i1.210

Ada banyak kasus penipuan dalam pemalsuan beras dengan mencampur beras berkualitas baik dengan beras berkualitas rendah untuk kenaikan harga. Untuk melindungi masyarakat dari pemalsuan, kami melakukan penelitian untuk mendeteksi kualitas beras yang nantinya dapat membantu masyarakat untuk dapat membedakan kualitas baik dan buruk. Penelitian ini menyajikan sistem pemrosesan citra beras berbiaya rendah untuk menilai kualitas beras. Banyak faktor yang mempengaruhi kualitas beras seperti fragmen biji-bijian, warna yang tidak seragam, bau dan faktor lainnya. Penelitian ini menggunakan prosentase butiran beras pecah dan keseragaman warna untuk menentukan kualitas beras. Kami mengusulkan fitur tekstur dengan segmentasi Otsu untuk menentukan jumlah butiran pecah dan distribusi warna untuk menentukan seragam warna. Hasil klasifikasi menggunakan validasi K Folddengan k=10 pada data asli menunjukkan hasil K-Nearest Neigbour memiliki akurasi 99,87%.

Development of Healthcare Kiosk for Checking Heart Health Riyanto Sigit; Zainal Arief; Mochamad Mobed Bachtiar
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 Muhammad Unggul Pamenang; Achmad Basuki; Riyanto Sigit
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 .; Riyanto Sigit; Zainal Arief
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 Angga Rahagiyanto; Achmad Basuki; Riyanto Sigit
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 Galih Hendra Wibowo; Riyanto Sigit; Aliridho Barakbah
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 Lukie Perdanasari; Riyanto Sigit; Achmad Basuki
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%.

HAND GESTURE RECOGNITION FOR INDONESIAN SIGN LANGUAGE INTERPRETER SYSTEM WITH MYO ARMBAND USING SUPPORT VECTOR MACHINE Aditiya Anwar; Achmad Basuki; Riyanto Sigit
KLIK- KUMPULAN JURNAL ILMU KOMPUTER Vol 7, No 2 (2020)
Publisher : Lambung Mangkurat University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/klik.v7i2.320

Hand gestures are the communication ways for the deaf people and the other. Each hand gesture has a different meaning. In order to better communicate, we need an automatic translator who can recognize hand movements as a word or sentence in communicating with deaf people. This paper proposes a system to recognize hand gestures based on Indonesian Sign Language Standard. This system uses Myo Armband as hand gesture sensors. Myo Armband has 21 sensors to express the hand gesture data. Recognition process uses a Support Vector Machine (SVM) to classify the hand gesture based on the dataset of Indonesian Sign Language Standard. SVM yields the accuracy of 86.59% to recognize hand gestures as sign language.Keywords: Hand Gesture Recognition, Feature Extraction, Indonesian Sign Language, Myo Armband, Moment Invariant

Implementation of Myo Armband on Mobile Application for Post-stroke Patient Hand Rehabilitation Tri Bintang Dewantoro; Riyanto Sigit; Heny Yuniarti; Yudith Dian Prawitri; Fridastya Andini Pamudyaningrum; Mahaputra Ilham Awal
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 5: EECSI 2018
Publisher : IAES Indonesia Section

Medical rehabilitation is one of the efforts to restore motor function of post-stroke patients, but the biggest factor that makes patients quickly restore motor function by active patient movement exercises. The movement in question is the movement carried out every day outside medical rehabilitation at the hospital. On the other hand, patients are reluctant to do therapy independently outside the hospital, because there is no tool that supports patients to do so. So, we need a device that helps patients to do therapy independently. The device is connected to Myo Armband to read the gestures of the patient by looking at the EMG signal from the patient's hand. Then the system performs matching gestures during therapy with EMG signal data that has been trained. The motion matching is done by calculating the Euclidean distance between the two EMG signal data obtained from the Myo Armband device. From the results of the tests carried out, the accuracy of movement matching results obtained an average accuracy of 89.67 percent for flexion-extension gestures and 82 percent for pronation-supination gestures. It can be concluded that Myo Armband in the Mobile Application can be used for Rehabilitation of post stroke patient hands.

Co-Authors Achmad Basuki Aditiya Anwar Agus Indra Gunawan Ahmad Fauzi Makarim Ali Ridho Barakbah Angga Rahagiyanto Anwar Anwar Anwar Arya Brahmanta Aziz, Adam Shidqul Bima Sena Bayu Dewantara Calvin Alfa Roji Eko Supriyadi Farah Devi Isnanda Fildzah Aure Gehara Zhafirah Fridastya Andini Pamudyaningrum Galih Hendra Wibowo Heny Yuniarti I Putu Adi Surya Gunawan Indra Adji Sulistijono Justiawan . Lukie Perdanasari Mahaputra Ilham Awal Moch. Rochmad Mochamad Mobed Bachtiar, Mochamad Mobed Mohamad Walid Asyhari Muhammad Anwar Sanusi Muhammad Aunur Rofiq Muhammad Unggul Pamenang Prawitri, Yudith Dian Rika Rokhana Setiawardhana Son Kuswadi Taufik Hidayat Tita Karlita Tri Bintang Dewantoro Tri Harsono Zainal Arief

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