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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
Computer Science & IT Electrical & Electronics Engineering Engineering

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Journal : Indonesian Journal of Electrical Engineering and Computer Science

 1 
Veins projection performance based on ultrasonic distance sensor in various surface objects I Putu Adi Surya Gunawan; Riyanto Sigit; Agus Indra Gunawan
Indonesian Journal of Electrical Engineering and Computer Science Vol 17, No 3: March 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v17.i3.pp1362-1370

Abstract

Intravenous therapy aims to inject fluids such as medicine or nutritions into the body via vein vessel. This procedure is needed in various cases whether in an ordinary or emergency. Every person has a different difficulty level thus a nurse usually encountered a problem when locating the position of vein vessel. A visualization device that able to work in realtime and have high mobility is really necessary for an emergency situation to speed up the intravenous access. In this study, a stand-alone veins visualization system was developed. The back-projection method that can adjust based on distance was used to speed up the visualization process. The distance between the device and the object is obtained by an ultrasonic distance sensor. The results of this projection method with a flat surface have maximum shift of 0.48 mm. While on various surfaces, projection shifts under 0.9 mm reach 89% from 140 measurement points. Projection shifts that reach more than 0.9 mm occurred due to the sensor readings are on steep curvature or large angles between segments and sensors.
Hand Wrist Bone Identification Using Quadrant Ballon Snake Riyanto Sigit; Moch. Rochmad; Tri Harsono; Farah Devi Isnanda
Indonesian Journal of Electrical Engineering and Computer Science Vol 8, No 2: November 2017
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v8.i2.pp335-342

Abstract

In this research, hand wrist bone Identification for human forensic is discussed. Hand wrist bone is one of the effective methods used in the forensic science for age identification. There are four techniques used in this research: cropping image, preprocessing, Quadrant Ballon Snake and identification. The first step is to crop an image on metaphysis and epiphysis bone. The second step is preprocessing using morphology and edge detection. The third step is to apply Quadrant Ballon Snake to segment hand wrist bone. The last step is to use ratio metaphysis and epiphysis to identify a person. The performance segmentation for assessment hand wrist bone showed an average age identification 91%, bone age metaphysis 95% and bone age epiphysis 95%. The experiments resulted in the fact that Quadrant Ballon Snake method is able to find and improve the segmentation of hand wrist bone images accurately. This indicates that this method is effective for segmenting hand wrist bone.
Automatic Cardiac Segmentation Using Triangle and Optical Flow Riyanto Sigit; Ali Ridho Barakbah; Indra Adji Sulistijono; Adam Shidqul Aziz
Indonesian Journal of Electrical Engineering and Computer Science Vol 8, No 2: November 2017
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v8.i2.pp315-326

Abstract

Cardiac function assessment plays an important role in daily cardiology and ultrasound. Full automatic cardiac segmentation is a challenging study because cardiac ultrasound imaging has low contrast and irregular moves. In this research, full automatic cardiac segmentation for cardiac diseases is presented. The technique used Initial Center Boundary, Pre-processing, Triangle Segmentation and Optical Flow. The first step is determining the initial center boundary. The second step is using Pre-processing to eliminate noise. The third step is Triangle Segmentation to detect cardiac boundary and reconstruct the accurate border. The last step is applying Optical Flow method to detect and track the border for every frame in a cardiac video. The performance segmentation for assessment errors cardiac cavity obtained an average triangle 8.18%, snake 19.94% and watershed 15.97%. The experiments showed that triangle method is able to find and improve the segmentation of cardiac cavity images with accurate. The result can be seen that error between system and average of users is only less than 5.6%. This indicates that this method is effective to segment and tracking cardiac cavity in a cardiac video.
Implementation of optical flow: good feature definition for tracking of heart cavity Anwar Anwar; Riyanto Sigit; Achmad Basuki; I Putu Adi Surya Gunawan
Indonesian Journal of Electrical Engineering and Computer Science Vol 18, No 2: May 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v18.i2.pp1057-1065

Abstract

Echocardiography is a method of examination using high-frequency sound waves to capture images of the heart organ structure. Echocardiography video is used by a doctor to analyze heart wall cavity movements and identify heart disease. Several points of view including the long axis, the two and four cavities in the left ventricle can be used in the examination of heart function. Cardiac assessment is still performed conventionally, which requires a level of thoroughness. This research proposes a method for tracking the movement of the heart wall. In this method, the good feature was defined only in the first frame. Furthermore, the whole frame will be processed by the optical flow method. Good feature definition consists of image enhancement, segmentation and tracking processes using the optical flow. Furthermore, the calculation of contour similarity from the proposed method by forming contour manually using 24 point initialization to draw the heart cavity. The proposed method shows the calculation results with a sensitivity of 90% and an accuracy of 87.451%.
Cardiac Motions Classification on Sequential PSAX Echocardiogram Adam Shidqul Aziz; Riyanto Sigit; Achmad Basuki; Taufik Hidayat
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 3: December 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v12.i3.pp1289-1296

Abstract

Cardiac wall motions classification on 2-dimensional (2D) echocardiographic images is an important issue for quantitative diagnosiing of heart disease. Unfortunately, the bad quality of echocardiogram cause computationally classification on cardiac wall motions is still become a big homework for many researchers to provide the best result. Echocardiogram is produced by soundwaves which absolutely make its images have speckle noise in different intensity. Therefore, this research improves a set of methodology to classify cardiac wall motion semi-automatically. Raw echocardiogram will be enhanced and segmented to take the boundary of endocardium of left ventricular in PSAX cardiac images. New improvement of Semi-automatically methodology is approach on detecting the contour of endocardium and will be inputed as good features in Lucas-Kanade Optical Flow in all sequential echocargraphic images. On classifying cardiac wall motions, this research proposes two important features including length of displacement and flow direction. New proprosed flow determination algorithm and Euclidean distance is used to calculate those features. All the features will be trained by Neural Network (NN) and validated by Leave One Out (LOO) to get accurate result. NN method, which is validated by LOO, has the best result of 81.82% correctness than the other compared methods.
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