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Jurnal Ilmiah Kursor
Published by Universitas Trunojoyo Madura
ISSN : 02160544     EISSN : 23016914     DOI : https://doi.org/10.21107/kursor
Core Subject : Science,
Computer Science & IT Decision Sciences, Operations Research & Management
Jurnal Ilmiah Kursor is published in January 2005 and has been accreditated by the Directorate General of Higher Education in 2010, 2014, 2019, and until now. Jurnal Ilmiah Kursor seeks to publish original scholarly articles related (but are not limited) to: Computer Science. Computational Intelligence. Information Science. Knowledge Management. Software Engineering. Publisher: Informatics Department, Engineering Faculty, University of Trunojoyo Madura
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 7 Documents
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Search results for , issue "Vol 7 No 2 (2013)" : 7 Documents clear
OPTIMIZING OF BOXING AGENT BEHAVIOR USING ELITISM BASED GENETIC ALGORITHM Anang Kukuh Adisusilo; Mochamad Hariadi; Ahmad Zaini; Supeno Mardi Susiki Nugroho
Jurnal Ilmiah Kursor Vol 7 No 2 (2013)
Publisher : Universitas Trunojoyo Madura

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OPTIMIZING OF BOXING AGENT BEHAVIOR USING ELITISM BASED GENETIC ALGORITHM aAnang Kukuh Adisusilo, bMochamad Hariadi, cAhmad Zaini, d Supeno Mardi Susiki Nugroho aDepartment of Information Engineering, Faculty of Engineering, University of Wijaya Kusuma Surabaya b,c,dIntelligent Network Expertise Multimedia Department of Electrical Engineering, Faculty of Industrial Technology, Sepuluh Nopember Institute of Technology Surabaya Email: anang@anang65.web.id Abstrak Pola perilaku agen tinju pada permainan tinju dipengaruhi oleh beberapa faktor antara lain teknik gerakan bertinju, jenis pukulan tinju, stamina, dan energi pukulan. Pola perilaku agen tinju secara umum menggunakan variabel random dengan distribusi event dari setiap state secara acak. Penelitian dengan menggunakan FSM (Finite State Machine) berbasis algoritma genetika, menghasilkan nilai fitness 0.96, untuk pola perilaku agen cenderung maju kearah lawan, energi pukulan cenderung sedikit, dan menggunakan jenis pukulan dengan objektivitas tinggi. Penelitian ini menggunakan fungsi elitism pada algoritma genetika untuk dapat menghasilkan nilai fitness yang stabil dan pola perilaku agen tinju yang lebih baik dibandingkan tanpa menggunakan fungsi elitism. Nilai fitness yang dihasilkan dari penelitian ini diantara 3.101748 sampai 3.14738 dan nilai fitness optimal diantara 2.78083 sampai 3.167174, dengan siklus algoritma genetika lebih besar sama dengan generasi ke-25. Pola perilaku agen tinju yang dihasilkan berdasarkan nilai fitness adalah menyerang menggunakan satu jenis pukulan uppercut right dan tiga pukulan jab, dengan energi pukulan diantara 48 sampai 52 dan pola permaianan cenderung maju sambil melindungi wajah (covered). Pola perilaku agen tinju dari nilai fitness adalah menyerang menggunakan satu jenis pukulan uppercut right dan tiga pukulan jab dengan energi pukulan diantara 48 sampai 52 dengan pola permaianan cenderung maju dan melindungi wajah (covered). Kata kunci: Perilaku agen, Algoritma Genetika, Optimasi, Permainan Tinju, FSM. Abstract Boxing agent behavior patterns in the game of boxing is affected by several factors, i.e. the technique of boxing movements, type of punch, stamina, and energy of the punch. Boxing agent behavior patterns in general use variable random event where is state distribution randomly. A study using FSM (Finite State Machine) based on genetic algorithms, resulting fitness value 0.96 for boxing agent behavior patterns that tend to move towards the opponent , used energy to blow is likely small, and it uses the kind of blow that has high objectivity. This study will utilize elitism function in genetic algorithms to produce a stable fitness and better boxing agent behavior patterns than the one use genetic algorithms without elitism function. Fitness value result from this study between 3.14738 and 3.101748 and the optimal fitness value between 2.78083 to 3.167174, with a genetic algorithm cycle equal or more than the 25th. The boxing agen behavior patterns generated from fitness value is to attack using single type of blow, right uppercut punch and jab with a three-punch blow energy between 48 to 52 and patterns game that tend to move foward with covered the face. Key words: Agent behavior, Genetic Algorithm, Optimation, Boxing game, FSM
QUALITY IMPROVEMENT OF OBJECT EXTRACTION FOR KEYFRAME DEVELOPMENT BASED ON CLOSED-FORM SOLUTION USING FUZZY CMEANS AND DCT-2D Ruri Suko Basuki; Mochamad Hariadi; Mauridhi Hery Purnomo
Jurnal Ilmiah Kursor Vol 7 No 2 (2013)
Publisher : Universitas Trunojoyo Madura

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QUALITY IMPROVEMENT OF OBJECT EXTRACTION FOR KEYFRAME DEVELOPMENT BASED ON CLOSED-FORM SOLUTION USING FUZZY CMEANS AND DCT-2D aRuri Suko Basuki, bMochamad Hariadi, cMauridhi Hery Purnomo a,b,cFaculty of Industrial Technology, Dept. of Electrical Engineering Institut Teknologi Sepuluh Nopember, Kampus ITS Keputih, Sukolilo, Surabaya, Jawa Timur, Indonesia a Faculty of Computer Science, Dian Nuswantoro University Jalan Imam Bonjol, Semarang, Indonesia E-mail: a rurisb@research.dinus.ac.id Abstrak Penelitian ini bertujuan untuk meningkatkan kualitas ekstraksi obyek pada citra tunggal hasil pemecahan frame dari video sekuensial yang terkompresi. Kualitas hasil ekstraksi obyek dengan algoritma closed-form solution menurun karena adanya beberapa perubahan nilai intensitas pada channel RGB. Sehingga di sekitar batas tepi obyek hasil ekstraksi terlihat kasar baik secara visual maupun hasil pengukuran dengan Mean Squared Error (MSE) antara obyek hasil ekstraksi dengan ground truth. Untuk meningkatkan kualitas hasil ekstraksi objek, nilai threshold pada unknown region ditentukan melalui adaptive threshold yang diperoleh dengan mengaplikasikan algoritma Fuzzy C-Means (FCM). Pemilihan algoritma FCM karena dalam penelitian sebelumnya algoritma ini menunjukkan hasil yang lebih robust dibandingkan algoritma Otsu untuk mendapatkan nilai threshold yang optimal. Sedangkan untuk menghaluskan obyek di sekitar daerah batas tepi digunakan filter Discrete Cosine Transform (DCT) – 2D. Dari 10 obyek yang digunakan dan dievaluasi dengan MSE menunjukkan peningkatan rata-rata sebesar 31.55%. Namun pendekatan ini tidak begitu robust pada citra yang memiliki kemiripan warna. Penggabungan pendekatan ini dengan optimasi cost function dalam alpha region pada basis spectrum diharapkan mampu meningkatkan kinerja algoritma ekstraksi obyek pada penelitian selanjutnya. Kata kunci: Closed-form Solution, Algoritma Fuzzy C-Means, Discrete Cosine Transform-2D. Abstract The research is aimed to improve the quality of the extraction of the object in a single image resulted from frame’s fragmentation of sequential compressed video. The quality of the extracted objects with closed-form solution algorithm decreased due to some changes in the intensity values on the RGB channel. Thus, the extraction result around the boundary edges of objects visually seemed to be rough and when it was measured with the Mean Squared Error (MSE) beween the object extraction results with ground truth. To improve the quality of the extracted object, the threshold value on unknown region was determined by adaptive threshold obtained by applying the Fuzzy C-Means algorithm (FCM). FCM algorithm is chosen since in the previous research this algorithm gives more robust results than Otsu algorithm to obtain the optimal threshold value. Meanwhile, to eliminate noise around the border area, this research applies Discrete Cosine Transform (DCT) - 2D filters. The result of 10 objects used and evaluated with the MSE showed an average increase of 31.55%. However, this approach is not so robust to images having similar color. Combination of this approach with optimization of the cost function on the alpha region based on spectrum is expected improving the performance of object extraction algorithm for the next research. Key words: Closed-form Solution, Fuzzy C-Means Algorithm, Discrete Cosine Transform-2D
MULTIPLE DISCRIMINANT ANALYSIS WITH FUKUNAGA KOONTZ TRANSFOR AND SUPPORT VECTOR MACHINE FOR IMAGE-BASED FACE DETECTION AND RECOGNITION Sri Andriati Asri; Widyadi Setiawan
Jurnal Ilmiah Kursor Vol 7 No 2 (2013)
Publisher : Universitas Trunojoyo Madura

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MULTIPLE DISCRIMINANT ANALYSIS WITH FUKUNAGA KOONTZ TRANSFOR AND SUPPORT VECTOR MACHINE FOR IMAGE-BASED FACE DETECTION AND RECOGNITION a Sri Andriati Asri, bWidyadi Setiawan aElectrical Engineering Dept., Bali State Polytechnic, Bukit Jimbaran, Kuta Selatan, Badung, Bali b Electrical Engineering Dept., Faculty of Engineering Udayana University, Bukit Jimbaran, Kuta Selatan, Badung, Bali 80361 E-Mail: andriati_s@yahoo.com Abstrak Pengenalan wajah dapat diterapkan pada banyak aplikasi potensial, seperti otentikasi identitas, information security, surveillance dan interaksi manusia komputer. Penelitian ini bertujuan membangun perangkat lunak berbasis Matlab untuk deteksi dan pengenalan wajah dengan masukan berupa citra. Sistem yang akan dibangun meliputi deteksi dan pengenalan wajah. Subsistem Deteksi Wajah memakai Principle Component Analysis (PCA) sebagai ekstraksi fitur dan Jaringan Syaraf Tiruan Perambatan Balik sebagai pengklasifikasinya. Pada Subsistem Pengenalan Wajah memakai metode Support Vector Machine salah satu algoritma kecerdasan buatan yang mampu mengklasifikasikan banyak wajah dengan baik. Metode Multiple Discriminant Analysis with Fukunaga Koontz Transform (MDA/FKT) dipakai sebagai ekstraksi fitur. Pelatihan dan pengujian sistem memakai basis data penelitian, dan basis data standar yaitu basis data ORL sebagai pembanding. Rancang bangun Aplikasi Deteksi dan Pengenalan Wajah telah berhasil diselesaikan pada penelitian ini. Subsistem Deteksi Wajah menghasilkan tingkat keakuratan pendeteksian wajah sebesar 99 %. Pada Subsistem Pengenalan Wajah, tingkat pengenalan basis data penelitian (UNUD) 82,76 %, sedangkan tingkat pengenalan pada basis data ORL 97,5%. Kata kunci: Deteksi Wajah, Pengenalan Wajah, Support Vector Machine, Multiple Discriminant Analysis with Fukunaga Koontz Transform. Abstract Face recognition can be applied to many potential applications, such as identity authentication, information security, surveillance and human computer interaction. This research aims to build a Matlab-based software for face detection and recognition application using an image input form. The system consist of face detection and recognition subsystem. Face detection subsystem using PCA as feature extraction and Back Propagation Neural Network as its classifier. In face recognition subsystem using Support Vector Machine as known as one of the good methods in the artificial intelligence algorithm that is able to classify many faces well. Multiple Discriminant Analysis Method with Fukunaga Koontz Transforms (MDA / FKT) is used as feature extraction. Training and Testing database systems using research (UNUD) database, and ORL database as a comparison. Face detection and recognition application has been successfully completed in this research, face detection subsystem produces face detection accuracy rate of 97.95 %, and for face recognition subsystem, the recognition rate is 82.76 % on research (UNUD) database, while the recognition rate on ORL database is 97.5 %. Key words: Face Detection, Face Recognition, Support Vector Machine, Multiple Discriminant Analysis with Fukunaga Koontz Transform
LUNG FIELD SEGMENTATION ON COMPUTED TOMOGRAPHY IMAGE USING ACTIVE SHAPE MODEL Sri Widodo; Wijiyanto Wijayanto
Jurnal Ilmiah Kursor Vol 7 No 2 (2013)
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LUNG FIELD SEGMENTATION ON COMPUTED TOMOGRAPHY IMAGE USING ACTIVE SHAPE MODEL a Sri Widodo, bWijiyanto aMedical Record and Health Informatics Academic of Citra Medika Surakarta Samanhudi, Surakarta a Sekolah Tinggi Manajemen Informatika dan Komputer Duta Bangsa Surakarta Indonesia E-mail: papa_lucky01@yahoo.com Abstrak Metode saat ini yang banyak digunakan untuk segmentasi bidang paru dari citra CT scan adalah thresholding dan Active Contour, yang mengandalkan kontras dari nilai keabuan antara parenkim paru dan jaringan sekitarnya. Kelemahan dari kedua metode tersebut adalah jika nodul tersebut besar dan terletak pada batas tepi paru, maka nodul tersebut tidak akan masuk dalam citra paru (bagian paru yang terdapat kelainan atau nodul akan hilang). Hal ini berarti proses segmentasi bidang paru dianggap gagal, karena citra nodul yang menjadi pokok perhatian akan hilang. Tujuan dari penelitian adalah segmentasi bidang paru yang mengandung kelainan pada citra CT scan dengan menggunakan Active Shape Model. Kontribusi dalam penelitian ini adalah jenis baru dari Active Shape Model dengan variasi tingkat keabuan dari dua batas objek, yaitu, bidang paru kanan dan kiri. Pada penelitian ini, kami mempertimbangkan struktur gambar lokal, yaitu momen dari histogram lokal yang berasal dari pengurangan antara citra asli dengan citra hasil negasi. Kami juga melakukan segmentasi bidang paru dengan thresholding dan Active Contour, sebagai bahan perbandingan dengan metode yang diusulkan. Hasil dari penelitian kami menunjukkan bahwa pendekatan segmentasi dengan Active Shape Model mempunyai akurasi 94.6%, sensitifitas 90.2%, dan spesifisitas 95.9%. Kata kunci: Active Shape Model, Active Contour, Thresholding. Abstract Current method is widely used for lung field segmentation of CT scan image is Thresholding and Active Contour, which relies on contrast of gray values between lung parenchyma and surrounding tissues. Drawback of both methods is that if nodule is large and located on borders of lung, then nodules will not be included in the image of lung (nodules contained will be lost). This means that segmentation of lung field was considered a failure, because image of nodule is object of attention will be lost. Purpose of this research is lung field segmentation that contain abnormalities in CT scan image using Active Shape Model. Contribution in this paper is a new type of Active Shape Model with gray level variation of two object boundaries, namely right lung field and left lung field. In this study, we consider the local image structure, which moments of a local histogram derived from the subtraction of the original image with the negation image. We also perform lung fields segmentation using thresholding and Active Contour, as a comparison with method we proposed. Results of our study show that segmentation with ASM approach has accuracy 94.6%, sensitivity 90.2%, and specificity of 95.9%. Key words: Active Shape Model, Active Contour, Thresholding
VOTING OF ARTIFICIAL NEURAL NETWORK PARTICLE SWARM OPTIMIZATION BICLASSIFIER USING GAIN RATIO FEATURE SELECTION Fetty Tri Anggraeny; Monica Widiasri
Jurnal Ilmiah Kursor Vol 7 No 2 (2013)
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VOTING OF ARTIFICIAL NEURAL NETWORK PARTICLE SWARM OPTIMIZATION BICLASSIFIER USING GAIN RATIO FEATURE SELECTION a Fetty Tri Anggraeny, bMonica Widiasri aTeknik Informatika Universitas Pembangunan Nasional “Veteran” Jawa Timur Jl. Raya Rungkut Madya Gunung Anyar Surabaya Indonesia aUniversitas Surabaya Jawa Timur Jl. Raya Kalirungkut Surabaya Indonesia E-Mail: fetty_ta@yahoo.com Abstrak Seleksi fitur merupakan tahapan penting dalam proses klasifikasi. Proses ini menganalisa data (fitur) sehingga menghasilkan fitur yang berperan atau kurang berperan dalam proses klasifikasi. Fitur yang kurang berperan dapat tidak digunakan dalam proses klasifikasi. Peranan sebuah fitur dalam klasifikasi dapat dikalkulasi dengan suatu rumusan, dalam penelitian ini digunakan metode gain ratio untuk mendapatkan bobot atribut dalam proses klasifikasi. Gain ratio pengembangan dari information gain yang digunakan untuk membangun pohon keputusan (decision tree). Metode seleksi fitur gain ratio menggunakan pendekatan seleksi fitur filter, karena dilakukan terlepas dari mesin klasifikasi. Mesin klasifikasi yang digunakan adalah Artificial Neural Network Particle Swarm Optimization (ANNPSO), dimana mesin ini menggabungkan konsep kecerdasan buatan saraf manusia (neural networks) dengan kecerdasan hewan (particle swarm intelligence). Metode yang diusulkan akan diuji coba terhadap 3 dataset UCI, antara lain iris, breast Wisconsin dan dermatology. Uji coba dengan variasi nilai batas gain ratio fitur menunjukkan nilai akurasi yang cukup tinggi terhadap 3 dataset yaitu 97,6%, 96,41%, dan 99,29%. Kata kunci: Gain Ratio, klasifikasi suara terbanyak, ANNPSO biclassifier.. Abstract Feature selection is an important step in classification process, it analyze the data (features) resulting role each features in the classification process. The role of a feature in the classification can be calculated with a formula, in this research the gain ratio method is used to get the attribute/feature weights. Gain ratio is the development of information gain. Information gain is used to form the induction of decision tree (ID3). Gain ratio feature selection method using the filter feature selection approach, as is done separately from classification engine. Classification engine used is Voting of Artificial Neural Network Particle Swarm Intelligence (ANNPSO) Biclassifier, where this engine combines the concept of artificial intelligence human nerve (neural network) with animal intelligence (particle swarm intelligence). The proposed method is tested on three datasets of UCI, including iris, breast wisconsin and dermatology. Trials with the variation of the boundary gain ratio feature showed a high accuracy of the three datasets are 97.6%, 96.41%, and 99.29%. Keywords: Gain Ratio, Voting Classification, ANNPSO Biclassifier
AN OBJECT ORIENTED APPROACH FOR CREATING WEB SERVICE PRESENCE SYSTEM Fandy Setyo Utomo; Yuli Purwat
Jurnal Ilmiah Kursor Vol 7 No 2 (2013)
Publisher : Universitas Trunojoyo Madura

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AN OBJECT ORIENTED APPROACH FOR CREATING WEB SERVICE PRESENCE SYSTEM aFandy Setyo Utomo, bYuli Purwati a Information System Department, b Informatics Engineering Department a,bSTMIK AMIKOM Purwokerto Watumas, Purwokerto, Jawa Tengah E-Mail: fandy_setyo_utomo@amikompurwokerto.ac.id Abstrak Web Service dengan metode SOAP (Simple Object Access Protocol) berbasis teknologi ASP.NET digunakan sebagai solusi dalam proses integrasi data dan distribusi data. Teknologi web service mampu mengintegrasikan data dari tiap database presensi di ruang kelas, sehingga menghasilkan laporan memonitor kehadiran dosen, karyawan, dan asisten praktikum, yang sebelumnya belum mampu dilakukan oleh sistem aplikasi presensi. Selain itu, web service mampu mendistribusikan data yang dibutuhkan untuk proses presensi dari database akademik menuju database presensi di tiap ruang kelas, serta dengan adanya web service proses backup data presensi dari database presensi di tiap ruang kelas menuju database akademik dapat dilakukan dengan mudah. Kemudahan-kemudahan yang muncul tersebut akibat adanya penerapan teknologi web service, diharapkan mampu meningkatkan layanan dan kinerja dari bagian pengajaran selaku pihak yang bertanggung jawab dalam pengadaan laporan monitoring kehadiran dosen dan mahasiswa, bagian SDM forum asisten selaku pihak yang bertanggungjawab dalam pengadaan laporan memonitor kehadiran asisten praktikum, dan bagian IT selaku pihak yang bertanggung jawab dalam proses distribusi data presensi dan backup data presensi. Langkah penelitian yang dilakukan dimulai dari analisis dan desain sistem berorientasi objek, serta implementasi ASP. NET web service. Kata kunci: Sistem Presensi, Web Service, SOAP, ASP.NET. Abstract Web Service with SOAP method (Simple Object Access Protocol) is used as the ASP.NET technology-based solutions in the process of data integration and data distribution. Web service technology is able to integrate data from each presence database in the classroom, resulting in a monitoring report attendance lecturer, staff, and lab assistant, who previously have not been able to do by the presence of application systems. In addition, the web service is able to distribute the data required for the presence of the database to the database of academic presence in each classroom, as well as with the web service data backup process in the presence of presence of each database classrooms toward academic database can be done easily. Easiness that arise as a result of the application of web services technology, is expected to improve the performance of the service and teaching as part of the responsible parties in the procurement monitoring report the presence of faculty and students, the HRD assistant forum as the party in charge of procurement monitoring report the presence of lab assistant, and IT as part of the responsible parties in the process of data distribution and data backup presence. Stages of study started from object oriented analysis and design, and implementation ASP.NET web. Keywords: Presence System, Web Service, SOAP, ASP.NET
DETERMINING THE ABNORMALITY OF BULL SPERM TAIL MORPHOLOGY USING SUPPORT VECTOR Stevanus Hardiristanto; I Ketut Eddy Purnama,; Adhi Dharma Wibawa; Mira Candra Kirana; Budi Santoso; Munawir .; Slamet Hartono; I Nyoman Tirta Ariana; Dian Ratnawati; Lukman Affandhy
Jurnal Ilmiah Kursor Vol 7 No 2 (2013)
Publisher : Universitas Trunojoyo Madura

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DETERMINING THE ABNORMALITY OF BULL SPERM TAIL MORPHOLOGY USING SUPPORT VECTOR a Stevanus Hardiristanto, b I Ketut Eddy Purnama, cAdhi Dharma Wibawa, dMira Candra Kirana, eBudi Santoso, fMunawir, g Slamet Hartono, h I Nyoman Tirta Ariana, iDian Ratnawati, jLukman Affandhy a,b,c,d,e,fDepartment of Multimedia and Network Engineering, Faculty of Industrial Technology, Institute of Technology Sepuluh Nopember, Surabaya, Indonesia gBalai Pembibitan Ternak Unggul Sapi Bali, Ministry of Agriculture, Republik of Indonesia h Faculty of Animal Science, University of Udayana, Bali, Indonesia i,jLoka Penelitian Sapi Potong Grati, Ministry of Agriculture, Republik of Indonesia E-mail: a hardi@its.ac.id Abstrak Penilaian atas ketidaknormalan spermatozoa bisa dilakukan dari sisi motilitas maupun morfologi (kepala dan ekor). Penelitian ini mengevalusi ketidaknormalan spermatozoa dari sisi morfologi bagian ekor spermatozoa sapi. Data berupa 50 citra mikroskopis spermatozoa yang diperoleh dari Loka Penelitian Sapi Potong Grati, Pasuruan digunakan dalam penelitian ini. Prosedur yang ditetapkan terdiri atas beberapa tahap. Tahap pertama adalah melakukan segmentasi spermatozoa untuk memisahkan spermatozoa dari latar belakang dan memisahkan bagian ekor spermatozoa dari bagian yang lain. Selanjutnya dari hasil segmentasi dicari garis tengah ekor (skeleton) menggunakan metode medial axis transform. Berdasarkan garis tengah yang dihasilkan, dilakukan prosedur ekstraksi fitur menggunakan metode polynomial curve fitting. Kemudian, metode Support Vector Machine (SVM) digunakan untuk menentukan ketidaknormalan bentuk ekor spermatozoa. Untuk pembelajaran digunakan 25 data spermatozoa normal dan 10 data spermatozoa tidak normal. Testing kemudian dilakukan atas 15 data spermatozoa tersisa. Ketelitian SVM dalam menentukan ketidaknormalan bentuk ekor spermatozoa mencapai 73.33%. Dengan demikian ketidaknormalan bentuk ekor spermatozoa dapat ditentukan dengan menggunakan SVM. Kata kunci: Ekor Sperma sapi, Morphology, Polynomial Curve Fitting, SVM. Abstract Determinining the abnormality of spermatozoa can be done by inspecting its motility or morphology (head or tail). This study examined 50 data of sperm microscopic images. The semen was obtained from Loka Penelitian Sapi Potong Grati, Pasuruan. A sequence of procedure consist of several steps were then carried out. The first step was to obtain sperm tails by segmenting the sperms from its background and removing the heads and the necks parts. The skeletons of the tails were then obtained using a method of medial axis transform. The features of the tails were then extracted using polynomial curve fitting. Then, Support Vector Machine (SVM) was used as a classifier. In the training phase, 25 normal sperm and 10 abnormal sperm were utilized. Afterward, the remaining 15 data were used in the testing phase. The accuracy of SVM was 73.33%. Hence, the abnormality of spermatozoa based on the shape of sperm tail can be determined using SVM. Key words: Bull Sperm Tail, Morphology, Polynomial Curve Fitting, SVM

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