2015 4 th International Conference on Instrumentation, Communications, Information Technology, and

 

Proceedings of

2015 4 ^th International Conference on Instrumentation, Communications, Information Technology, and

Biomedical Engineering

Science and Technology for Future Health

Institut Teknologi Bandung | November 2-3, 2015

Organized By :

IEEE Catalog Number : CFP1587H-ART ISBN : 978-1-4673-7800-0

P ro c e e d in g s o f 2 0 1 5 4

th

In te rn a tio n a l C o n fe re n c e o n In st ru m e n ta tio n , C o m m u n ic a tio n s, In fo rm a tio n T e c h n o lo g y , a n d B io m e d ic a l E n g in e e rin g (IC IC I- B M E )

Sponsored by: Institut Teknologi Bandung

School of Electrical Engineering and Informatics Faculty of Mathematics and Natural Sciences

Technically Co-Sponsored by:

IEEE Indonesia – EMB Chapter IEEE Indonesia – CAS Chapter

Supported by:

School of Life Sciences and Technology, ITB

Center for Instrumentation Technology and Automation (CITA) Center for Health and Technology and Sport Science (CHTSS)

Indonesian Data Mining Society (IndoDM)

Research Center on Information and Communication Technology (RCICT)

Proceedings

2015 4 ^th International Conference on Instrumentation, Communications, Information Technology,

and Biomedical Engineering (ICICI-BME)

November 2-3, 2015

Institut Teknologi Bandung Bandung, Indonesia

Organized by:

Proceedings of

2015 4^th International Conference on Instrumentation, Communications, Information Technology, and Biomedical Engineering (ICICI-BME)

Copyright © 2015 by the Institute of Electrical and Electronics Engineers, Inc.

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ISBN: 978-1-4673-7800-0

IEEE Catalog Number: CFP1587H-ART

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Preface

Wilujeng Sumping,

Welcome to 2015 Fourth International Conference on Instrumentation, Communications, Information Technology, and Biomedical Engineering (ICICI-BME), in Bandung, Indonesia.

On behalf of the organizing committee, we are delighted to welcome all of the participants to the ICICI-BME 2015. This biennial conference is organized under the auspices of the Institut Teknologi Bandung (ITB), Indonesian Sensor and Actuator System Society (ISASS), Indonesian Biomedical Engineering Society (IBES), and sponsored by the School of Electrical Engineering & Informatics, the Faculty of Mathematics & Natural Sciences, together with IEEE Indonesia EMB and CAS Chapters.

ICICI-BME is dedicated to the presentation and discussion of the latest developments and ideas in instrumentation, measurements, communications, information technology, and biomedical engineering, in both theory and application.

This conference also aims to strengthen the collaboration among international researchers, scientists, engineers, and industrial players in the fields of science and engineering. It is designed to be a meeting point for those who are involved, to globally exchange and share their views, ideas, and advances in science, technology, and industrial aspects.

Our gratitude to many people which helped making this conference a reality, to all of our invited speakers and guests, and for all of our committee members for their effort to ensure the success of this conference. Finally, we hope that all of participants will learn new things, make new contacts, get new ideas, and have fruitful discussion while having a pleasant experience during our conference in Bandung.

Hatur Nuhun, Thank You

Mitra Djamal & Tati L. R. Mengko Chairs of ICICI-BME 2015

ii

ICICI-BME 2015 Committee

GENERAL CHAIR

M. Djamal (ITB, Indonesia) T. L. R. Mengko (ITB, Indonesia)

ADVISORY BOARD

K. Suryadi (ITB, Indonesia) H.-D. Liess (UniBw Muenchen, Germany)

E. T. Baskoro (ITB, Indonesia) M. Liess (RheinMain, Germany)

J. Sembiring (ITB, Indonesia) I. Nakajima (Tokai, Japan)

I. N. P. Aryantha (ITB, Indonesia) M. Nuh (ITS, Indonesia)

C. Badri (UI, Indonesia) J. Rodrigues (UBI, Portugal)

E. Y. Chuang (NTU, Taiwan) K. Shimada (JAXA, Japan)

Y. Fujii (Gunma, Japan) S. Soegijoko (ITB, Indonesia)

H. Grain (La Trobe, Australia) A. B. Suksmono (ITB, Indonesia)

M. Horprathum (MST, Thailand) Suprijanto (ITB, Indonesia)

J. Kaewkhao (NPRU, Thailand) E. Supriyanto (UTM, Malaysia)

D. Kurniadi (ITB, Indonesia) G. J. Verkerke (Groningen, Netherlands)

PROGRAM COMMITTEE

D. Abdurrachim (TuE, Netherlands) A. Prakosa (JHU, United States) K. Adi (Undip, Indonesia) I. K. E. Purnama (ITS, Indonesia) W. Adiprawita (ITB, Indonesia) M. H. Purnomo (ITS, Indonesia)

M. K. Agusta (ITB, Indonesia) D. Riana (STMIK NM, Indonesia)

Y. Ansar (ITB, Indonesia) M. I. Rusydi (Unand, Indonesia)

I. Arif (ITB, Indonesia) T. A. Sardjono (ITS, Indonesia)

F. Haryanto (ITB, Indonesia) N. Syakrani (Polban, Indonesia)

E. M. Idris (ITB, Indonesia) Suprijadi (ITB, Indonesia)

A. A. Iskandar (UI, Indonesia) W. Siswomihardjo (UGM, Indonesia) S. N. Khotimah (ITB, Indonesia) H. Zakaria (ITB, Indonesia)

A. I. Mahyuddin (ITB, Indonesia) I. Zakia (ITB, Indonesia)

ORGANIZING COMMITTEE

Astri Handayani Adhelia Ekarani

Agung Wahyu Setiawan Tiper Uniplaita

Donny Danudirdjo Koespriyandito

Muhammad Shiddiq Sayyid Hashuro Rahma Dewi

Habibur Muhaimin Mochamad Irwan Nari

Misuari Sabirin Ilman Himawan Kusumah

Donny Mirza Amanatulhay Pribadi

Rizki Putra Prastio Tuti Adi Tama Nasution

Agwin Fahmi Fahanani Johan Agathon

Dian Rahmawati Raditya Artha

Rebecca Chyntia Aji Suryo

iv

Table of Contents

KEYNOTE ADDRESS

Prognostication and Molecular Regulation of Acute Myeloid Leukemia:

an Integration of Bioinformatics and Clinical Medicine ... 1 Eric Y. Chuang

The Future of Cardiovascular Imaging ... 2 Eko Supriyanto

Safety of Emerging Universal Technologies: Drone and Nano-Bubble Water ... 3 Kazuhito Shimada

Luminescence from Lanthanides Doped Glasses and Applications: A Review ... 4 Patarawagee Yasaka and Jakrapong Kaewkhao

Molecular Imaging of Cancer Cell Response to Chemotherapeutic Drugs

Emphasized on P-glycoprotein Function ... 16 Suchart Kothan

Abuse Prevention of Street Camera Network by Browsing-History Disclosure ... 17 Yusaku Fujii, Noriaki Yoshiura, Naoya Ohta, and Akihiro Takita

PAPERS

Strength and Displacement of Open Cell Designs of Coronary Stent in Responding of

Various Inflated Pressures ... 18 Alva Edy Tontowi, Ivan Pratama, Hariadi Hariawan, Margareta Rinastiti, and

Widowati Siswomihardjo

The Effect of Annealing Temperature on the Physical and Mechanical Properties of

Stainless Steel 316L for Stent Application ... 22 Muhammad Kusumawan Herliansyah, Punto Dewo, Marsetyawan H. N. E. Soesatyo, and

Widowati Siswomihardjo

Evaluation of Various Alloys for Stent on Platelet Aggregation Activity ... 27 Nahar Taufiq, Siti Sunarintyas, and Widowati Siswomihardjo

Changes of Arterial Stiffness during Flow-mediated Dilation Procedure ... 32 Muhammad Shiddiq Sayyid Hashuro, Hasballah Zakaria, and Tati L. R. Mengko

Modelling and Simulation of Blood Circulation Towards Mitral Valve Replacement

Risk Calculation ... 36 Vera Höfflin, Amiliana M. Soesanto, Hananto Andriantoro, Eko Supriyanto, and Jens Haueisen

Electroencephalography (EEG) for Detecting Acute Ischemic Stroke ... 42 Sastra Kusuma Wijaya, Cholid Badri, Jusuf Misbach, Tresna Priyana Soemardi, and V. Sutarmo

Semi-Automatic Determination of Root Canal Length in Dental X-ray Image ... 49 I Ketut Eddy Purnama, Ima Kurniastuti, Margareta Rinastiti, and Mauridhi Hery Purnomo

Iris Image Analysis of Patient Chronic Renal Failure (CRF) using Watershed Algorithm ... 54 Maya A. R. Sitorus, Mauridhi Hery Purnomo, and Adhi Dharma Wibawa

Features Extraction of Palm Vein Image Using Phase Symmetry ... 59 Herry Setiawan and Eko Mulyanto Yuniarno

Extraction and Classification Texture of Inflammatory Cells and Nuclei in Normal

Pap Smear Images ... 65 Dwiza Riana, Dwi H. Widyantoro, and Tati L. Mengko

Automated Detection of Southeast Asian Ovalocytosis (SAO) Obtained from

Thin Blood Smear Microphotographs ... 70 Eunike Sawitning Ayu Setyono, Anto Satriyo Nugroho, Fuad Ughi, Made Gunawan,

Vitria Pragesjvara, Ismail Ekoprayitno Rozi, and Puji Budi Setia Asih

Multi Patch Approach in K-Means Clustering Method for Color Image Segmentation

in Pulmonary Tuberculosis Identification ... 75 Riries Rulaningtyas, Andriyan Bayu Suksmono, Tati Mengko, and Putri Saptawati

Plasmodium Vivax Classification from Digitalization Microscopic Thick Blood Film Using Combination of Second Order Statistical Feature Extraction and K-Nearest

Neighbor (K-NN) Classifier Method ... 79 Farah Zakiyah Rahmanti, Sutojo, Novita Kurnia Ningrum, Niken Kartika Imania, and

Mauridhi Hery Purnomo

Polyphonic Signal Analysis Using Cross-Correlation on Sasando ... 84 Louis Ferdinand Boesday and Yoyon K. Suprapto

Steady-State Visual Evoked Potential based Brain Computer Interface:

Experiment of LED Stimulation in Two-Rooms Condition ... 90 Nugroho Budi Wicaksono, Tati L.R. Mengko, and Suprijanto

Heuristic Steady State Visual Evoked Potential Based Brain Computer Interface

System for Robotic Wheelchair Application ... 94 Stephen Andronicus, Nathaniel Chandra Harjanto, Suprijanto, and Augie Widyotriatmo

Noise and Artifact Reduction Based on EEMD Algorithm for ECG with Muscle Noises,

Electrode Motions, and Baseline Drifts ... 98 Amirinnisa D. Atrisandi, Widyawardana Adiprawita, Tati L. R. Mengko, and Yuan-Hsiang Lin

Automatic Segmentation and Detection of Heart Sound Components S1, S2, S3

and S4 ... 103 Amy H. Salman, Nur Ahmadi, Richard Mengko, Armein Z. R. Langi, and Tati L. R. Mengko

Study about Backhand Short Serve in Badminton Based on the Euler Angle ... 108 Muhammad Ilhamdi Rusydi, Minoru Sasaki, Muhammad Hadi Sucipto, Zaini, and

Noverika Windasari

vi

Fluid Flow Velocity Measurement Using Dual-Ultrasonic Transducer by Means of

Simultaneously Transit Time Method ... 113 K. Amri, L. F. Wiranata, Suprijanto, and D. Kurniadi

Application of Standing Waves as Audible Information in Hypertension Therapy ... 117 Friedhelm Pohl, Yudha Noor Aditya, Christine Pohl, and Eko Supriyanto

Hardware Implementation of Linear Back-Projection Algorithm for

Capacitance Tomography ... 124 Hans Herdian, Imamul Muttakin, Almushfi Saputra, Arbai Yusuf, Wahyu Widada, and

Warsito P. Taruno

Rocket Booster Effect on RKX-200TJ Flight Stability ... 130 Herma Yudhi Irwanto

Development of Mechanical Coupling For Piezoelectric Energy Harvester Adapted

to Low-Frequency Vibration ... 134 Tri Untoro, Suprijanto, and Estiyanti Ekawati

Prototype Instruments for Determination of Aroma and Flavor Quality of

Brewed Black Tea ... 138 Irsyad Waluyo Blit, Adinda Bunga Juwita, Suprijanto, and Nugraha

Development of Laboratory-Scale Electrical Impedance Tomography Imaging System ... 143 Muhammad Hutomo Padmanaba, Faisal Ardhy, Ihsan Hariadi, and Hasballah Zakaria

A Prototype of Compact Digital Holographic Microscope with Mach-Zehnder

Configuration ... 149 Dodi Zulherman, Undiana Bambang, Donny Danudirdjo, and Andriyan B. Suksmono

Design of Root Canal Treatment for Dental Post Application on Maxillary

Central Incisor ... 153 Suci Sansita Susanto, Satrio Wicaksono, Andi Isra Mahyuddin, Tatacipta Dirgantara,

Gantini Subrata, Sandro Mihradi, and Anthony Sugiharta Budiaman

Initial Estimation of Landmark Location for Automated Cephalometric Analysis

Using Template Matching Method ... 159 Indira Pramita Sari, Retno Widayati, Menik Priaminiarti, Donny Danudirdjo, and Tati Latifah Mengko A Comparison of Classification Methods in Vertebral Column Disorder with the

Application of Genetic Algorithm and Bagging ... 163 Rizki Tri Prasetio and Dwiza Riana

Image Based Transesophageal Echocardiography Probe Tip Localization ... 169 Fatin Inani binti Nurullail, Christina Pahl, and Eko Supriyanto

Nailfold Capillaroscopy Image Processing for Morphological Parameters Measurement ... 175 Arya Tama, Tati R. Mengko, and Hasballah Zakaria

Beef Quality Identification using Color Analysis and K-Nearest Neighbor Classification ... 180 Kusworo Adi, Sri Pujiyanto, Oky Dwi Nurhayati, and Adi Pamungkas

The Synthesis of Javanese Woman's Facial Image on Anger Expression Based on

Emotion Regulation ... 185 Ulla Delfana Rosiani, Adi Atmoko, Surya Sumpeno, and Mauridhi Hery Purnomo

3D Point Cloud Data Registration Based on Multiview Image Using SIFT Method

For Djago Temple Relief Reconstruction ... 191 Fawaidul Badri, Eko Mulyanto Yuniarno, and Supeno Mardi Susiki N.

Ontology Driven Knowledge Base for High Risk Pregnancy Management ... 196 Jostinah Lam, Yudha Aditya Noor, and Eko Supriyanto

Mother and Children Health Reporting System: Innovative Information System Application in the Rural West Bandung Area, Indonesia, by Using Multimodal

Communications Systems ... 202 Ahmad Faried, Agung Budi Sutiono, Tono Djuwantono, Muhammad Zafrullah Arifin,

Firman Fuad Wirakusumah, Sony Ari Yuniarto, Hadi Hariyanto, and Yudi Tri Jayadi Validating Video Stimulus for Eliciting Human Emotion: A Preliminary Study for

e-Health Monitoring System ... 208 Lantana Dioren Rumpa, Adhi Dharma Wibawa, Mauridhi Heri Purnomo, and Harmelia Tulak

Text-to-Speech Device for Patients with Low Vision ... 214 Annisa Istiqomah Arrahmah, Aulia Rahmatika, Samantha Harisa, Hasballah Zakaria, and

Richard Mengko

"Learn-to-read" Application for Remediation of Dyslexic Children Based on

Multisensory Approach ... 220 Muhamad Risqi Utama Saputra and Kuntoro Adi Nugroho

Decision Support System Prototype on Obstetrics Ultrasonography for Primary

Service Physicians ... 226 Boy Subirosa Sabarguna, Sastra Kusuma Wijaya, Farian Sakinah, and Atiek Maryati

Mobile Cataract Detection using Optimal Combination of Statistical Texture

Analysis ... 232 Yunendah Nur Fuadah, Agung W. Setiawan, Tati L. R. Mengko, and Budiman

Assessment of Prolonged Tissue Response to Porous-Iron Implant by Radiodensity

Approach ... 237 Sitaria Fransiska Siallagan, Gunanti, Arlita Sariningrum, Devi Paramitha,

Mokhamad Fakhrul Ulum, Hendra Hermawan, and Deni Noviana

The Study of Intracellular Reactive Oxygen Species and Mitochondrial Membrane

Potential in Normal Cells Exposed to Diagnostic X ray ... 241 Montree Tungjai, Jiraporn Kantapan, Yutthana Bunmakat, and Suchart Kothan

Radiation Parameter Study of Bismuth Containing Sodium Calcium Borosilicate

Glasses ... 244 Wasu Cheewasukhanont, Kitipun Boonin, Pruittipol Limkitjaroenporn, and Jakrapong Kaewkhao

Enhanced 1057 nm Luminescence Peak and Radiative Properties of Laser Pump

viii

Fabrication and Characterization of All-Plastic Flexible Microfluidic Chip using

Thermal Lamination of Patterned Sheet ... 254 Willy Wijaya and Henri P. Uranus

Optical Spectroscopy of Dy³⁺ doped Lithium Borate Glasses for Luminescence

Applications ... 260 Benchaphorn Damdee, Keerati Kirdsiri, and Jakrapong Kaewkhao

Optical Properties and Judd-Ofelt Analysis of Dy³⁺ doped MgO-BaO-P2O5 Glass

Systems ... 264 Yaowaluk Tariwong, Natthakridta Chanthima, Jakrapong Kaewkhao, and Narong Sangwaranatee

A Study of Photon Interaction and Radiation Shielding Properties of

BaO:Na2O:SiO2:B2O3 Glasses ... 268 Sunisa Sarachai, Natthakridta Chanthima, Jakrapong Kaewkhao, Nisakorn Sangwaranatee,

and Narong Sangwaranatee

Design of IP Based On-Board Computer for Small Satellite ... 272 Emir Husni, Angga Putra, and Nazmi Febrian

Mobile Monitoring System for Indonesian Volcano ... 278 Maria Evita, Mitra Djamal, Bernd Zimanowski, and Klaus Schilling

Fixed-Mode of Mobile Monitoring System for Indonesian Volcano ... 282 Maria Evita, Mitra Djamal, Bernd Zimanowski, and Klaus Schilling

Coordinated Trajectory Tracking for A Group of Mixed Wheeled Mobile Robots ... 288 Sisdarmanto Adinandra and Dwi Ana Ratnawati

Measurement and Analysis of 3G WCDMA Network Performance –

Case Study: Yogyakarta Indonesia ... 294 Tito Yuwono and Fitrah Ferdiyanto

Comparative Performance Evaluation of DSRC and Wi-Fi Direct in VANET ... 298 Nurain Izzati Shuhaimi, Heriansyah, and Tutun Juhana

Android Smart Home System based on ATmega16 ... 304 Medilla Kusriyanto and Beny Setiawan

Electromyography Microcontroller Leg Brace Processing Software ... 308 Michael Marwono Gondokusumo, Arko Djajadi, and Fuad Ughi

Model Development for RF Ablation Using Bipolar Forceps in Cancer Therapy ... 313 Evan Wijaya Tanotogono, Suprijanto, and Janivita Joto Sudirham

Simulation of Tablet Dissolution Rate on Virtual USP Dissolution Apparatus ... 319 Narendra Kurnia Putra, Suprijanto, Janivita Sudirham, and Saleh Wikarsa

Characterizing the Hearing Comfort in Intensive Care Unit Using Objective and

Subjective Evaluation ... 323 Joko Sarwono, Ezra Tandian, Andika Rizki, Sentagi Utami, and Ressy Yanti

Validated Visible Spectrophotometry for Quantitative Analysis of Pirenoxine in

The Presence of Paraben Preservatives in Eye Drop Preparations ... 327 Hendri Wasito and Chutima Phechkrajang

Effect of Temperature in The Encapsulation of Temulawak Extract with Yeast

Saccharomyces Cereviceae ... 332 Olyvia Sentosa, Katherine, and Asaf Kleopas Sugih

Beef Quality Identification using Color Analysis and K-Nearest Neighbor Classification

Kusworo Adi¹, Sri Pujiyanto², Oky Dwi Nurhayati³ and Adi Pamungkas¹

1Department of Physics, Diponegoro University, Semarang, Indonesia (E-mail: kusworoadi@gmail.com)

2Department of Biology, Diponegoro University, Semarang, Indonesia

3Department of Computer System, Diponegoro University, Semarang, Indonesia

Abstract- Beef is one of the many produce prone to contamination by microorganism. Water and nutrition contents make an ideal medium for the growth and proliferation of microorganism. Contaminated beef will degrade and has less storage duration. Beef is valued by two factors; its price and its quality. The quality itself is measured using four characteristics;

marbling, color of meat, color of fat, and meat density.

Specifically, marbling is the dominant parameter that determines meat’s quality. Determination of meat quality is conducted visually by comparing the actual meat and reference pictures of each meat class. This process is very subjective in nature.

Therefore, this research aims to develop an automated system to determine meat by adopting the Indonesian National Standard requirement on the quality of carcass and beef (SNI 3932:2008) using the image processing technique. Image segmentation is carried out using the thresholding method and classification is conducted using the k-nearest neighbor algorithm. The features used to differentiate beef quality are marbling score, color of meat, and color of fat. Results indicate that the system developed is able to acquire images and identify beef quality as required in the Indonesian National Standard.

Keyword: beef quality, marbling score, image processing, color analysis, k-nearest neighbor.

I. INTRODUCTION

Beef is one of the many produce prone to contamination by microorganism. Water and nutrition contents make an ideal medium for the growth and proliferation of microorganism [1, 2]. Contaminated beef will degrade and has less storage duration. Beef is valued by two factors; its price and its quality.

The quality itself is measured using four characteristics;

marbling, color of meat, color of fat, and meat density.

Specifically, marbling is the dominant parameter that determines meat’s quality [3, 4]. Determination of beef quality is conducted visually by comparing the actual meat and reference pictures of each meat class. This process is very subjective in nature. Therefore, this research aims to develop an automatic system to determine meat quality based on the marbling score using the image processing technique. Some research have shown that image processing can be applied to analyze the color and texture of meat that it can be used as a

segmentation process using the thresholding method and image classification using the k-nearest neighbor algorithm. Some research has already applied image processing with thresholding segmentation to extract features [8, 9, 10, 11].

This method is suitable for the process of meat quality identification based on color and texture. The algorithm developed proves to yield good results, that it can be implemented for the analysis of meat color and texture. Some methods applicable for this research include the thresholding segmentation method that can be used to identify marbling in meat.

II. LITERATURE REVIEW

Some research on the application of image processing for beef quality identification has been conducted earlier [4, 5, and 6]. One of those researches tried to determine the quality of meat using texture analysis with the gray level co-occurrence matrix (GLCM) method [3]. Beef quality is categorized into 12 grades based on the amount of fat it contains. Results show that this method is effective in determining the quality of meat. One research on the application of image texture to classify beef type yielded a correlation up to 0.8 [5]. Yet another research that designed the hardware and software for beef image segmentation using the vision thresholding method has been used as the initial process for meat quality testing [6]. Those researches indicate that image processing can be applied to identify beef quality.

The research conducted here tries to identify beef quality using the image processing technique referring to the Indonesian National Standard (SNI 3932:2008) on carcass and beef quality [12]. Beef quality requirements are classified into three; Class I, II, and III. Beef quality classes based on SNI is given in Table I.

TABLE I

BEEFQUALITY REQUIREMENT (SNI 3932:2008) [12]

No. Type of test

Quality Requirement

I II III

1 Beef

Color

Bright Red Score 1-5

Blackish Red Score 6-7

Dark Red Score 8-9 2015 4th International Conference on Instrumentation, Communications, Information Technology, and Biomedical Engineering (ICICI-BME) Bandung, November 2-3, 2015

Beef color observation is carried out by observing the color of the surface rib tendon with the help of a flashlight and then matching that with the standard color. The color score is based on the standard color score that is closest to the observed color.

The beef standard color consists of nine color scores, ranging from light red to dark red, as depicted in Fig. 1.

Fig. 1. Beef standard color [12].

Fat color observation is conducted by observing the color of thin layers of fat with the help of a flashlight and matching them with the standard color. The color score is based on the standard color score that is closest to the observed color. The fat standard color consists of nine color scores from white to yellow as can be seen in Fig. 2.

Fig. 2. Fat standard color [12].

Marbling observation is done by observing the intensity of marbling on the surface rib tendon with the help of a flashlight and matching it with the standard marbling. The marbling score is based on the nearest score to the surface rib tendon marbling intensity. Standard marbling consists of 12 scores, ranging from practically no marbling to numerous marbling, as described in Fig. 3.

Fig. 3. Marbling standard color [12].

Observation of tendon texture is carried out by observing the softness/coarseness of surface rib tendon with the help of a flashlight and matching it with the standard meat texture. The texture score is based on the closest score to the standard texture. Meat texture standard consists of three scores; soft, medium, and coarse [12].

III. METHOD

The system design for beef quality identification in this research comprises beef image acquisition, image segmentation, features extraction, and beef quality classification. The diagram block for this system design is given in Fig. 4.

Fig. 4. Research block diagram.

Image acquisition is conducted vertically by varying the distance for image acquisition, camera resolution, and angle of acquisition. The distance variations employed are 20 cm and 30 cm. And the resolution is varied from 3.2 MP, 4 MP, and 5 MP. The varied angles are 0⁰, 45⁰, 90⁰, 135⁰, 180⁰, 225⁰, 270⁰, 315⁰, and 360⁰. The image processing starts from image segmentation consisting of two stages. The first is separating the object (meat and fat) from the background. This process begins with thresholding the blue channel of the RGB (Red,

Image Acquisition

Image Segmentation (Thresholding)

Feature Extraction (Color Analysis)

Classification (K-Nearest Neighbor)

181

2015 4th International Conference on Instrumentation, Communications, Information Technology, and Biomedical Engineering (ICICI-BME) Bandung, November 2-3, 2015

Green, and Blue) image to obtain a binary image. Afterwards, the binary image is used as a mask for object cropping. Once the object is separated from the background, the second stage of segmentation i.e. meat and fat separation proceeds. This process itself starts by converting the RGB color image into grayscale. Then, the process of thresholding to separate meat and fat can ensue. The features used to determine beef quality are marbling score, beef color, and fat color. Marbling score is represented by the percentage of fatty area and the area of object (both meat and fat). The meat color and fat color are represented by the hue image on the HSV channel. In this research, marbling score, meat color, and fat color are extracted as features that determines beef quality. The process of beef quality classification is conducted using the k-nearest neighbor algorithm.

IV. RESULT ANDDISCUSSION

Stages of image processing to identify beef quality comprise image acquisition, image segmentation, features extraction, and meat classification.

A. Image Acquisition

Results of beef image acquisition along with its marbling score is given in Fig. 5. It can be seen that there are five marbling scores that match the ones in the market. The marbling scores in this research are 4, 5, 6, 7, and 9.

Fig. 5. Samples of beef image acquisition

Image acquisition is conducted vertically by varying the distance, camera resolution, and angle of acquisition. The varied distances are 20 cm and 30 cm and the varied resolutions are 3.2 MP, 4 MP, and 5 MP. Samples of beef image resulting from distance and resolution variations are given in Table I.

TABLE I

SAMPLES OF BEEFIMAGE RESULTING FROM VARIEDDISTANCE AND RESOLUTION

Resolution

3,2 MP 4 MP 5 MP

Distance 20 cm

30 cm

In order to figure out the effect of angle in image acquisition, the following variations are made; 0⁰, 45⁰, 90⁰, 135⁰, 180⁰, 225⁰, 270⁰, 315⁰, and 360⁰, as depicted in Fig. 6.

Fig. 6. Samples of meat image resulting from angle variation.

B. Image Segmentation

Image processing begins with image segmentation that consists of two stages. The first step is separating the object (meat) from its background. It begins with thresholding the blue channel of the RGB (Red, Green, and Blue) image to obtain a binary image. Afterwards, the binary image is used as a mask for object cropping, as described in Fig. 7.

(a) (b) (c) (d)

Fig. 7. The first stage of image segmentation; (a) RGB image; (b) Blue channel; (c) Binary image; (d) Segmentation result

Once the object is separated from the background, the second stage of segmentation i.e. meat and fat separation proceeds. This process itself starts by converting the RGB color image into grayscale. Then, the process of thresholding to separate meat and fat can ensue, as outlined in Fig. 8.

(a) (b) (c) (d) (e) (f) Fig. 8. The second stage of image segmentation;

(a) RGB image; (b) Grayscale image; (c) Meat binary image;

(d) Meat segmentation result; (e) Fat binary image; (f) Fat segmentation result

C. Features Extraction

The features used to determine beef quality are marbling score, color of meat, and color of fat. Marbling score is represented by the percentage of fatty area and the area of object (both meat and fat). The meat color and fat color are 2015 4th International Conference on Instrumentation, Communications, Information Technology, and Biomedical Engineering (ICICI-BME) Bandung, November 2-3, 2015

TABLE II

SAMPLES OF FEATURES EXTRACTION RESULT OF BEEFIMAGE

Those three extracted features are then used in the process of meat classification.

D. Beef Quality Classification

The classification process uses the k-nearest neighbor algorithm. K-nearest neighbor is an algorithm that classifies objects based on similarities of data to the others.

Geometrically, the nearer a trial data to a test data, the more likeness they have. The value of k in this algorithm states the number of nearest neighbors involved in the determining the class prediction label on test data. From the chosen k neighbors, a class voting from those k-neighbors proceeds.

Then the class with the most neighbors is the class label predicted for that test data. Fig. 9 depicts a graphical representation of k-nearest neighbor distribution for beef quality test by determining the meat and fat color as stated in the Indonesian National Standard.

(a) (b)

Fig. 9. K-nearest neighbor distribution graph;

(a) Color of meat; (b) Color of fat.

Beef quality determination is made by matching the percentage of fat on beef images with that of the Indonesian National Standard. A sample of beef quality classification based on the Indonesian National Standard requirement is given in Table III.

TABLE III

SAMPLES OF BEEFQUALITY CLASSIFICATION

It can be seen in Table III beef quality is classified based on features of color of meat, color of fat, and marbling score as required in the Indonesian National Standard. This shows that requirements for quality beef can be integrated into image processing system.

V. CONCLUSION

Results of this research show that the system developed here is capable of exceptionally acquire images and identify beef quality. This image processing system is designed by adopting the requirements of the Indonesian National Standard on carcass and beef quality (SNI 3932:2008).

ACKNOWLEDGMENT

This research was funding from Indonesian Directorate General of Higher Education Program in 2015.

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