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All Journal Jurnal Ilmu Komputer dan Informasi Media Elektrik Jurnal Buana Informatika AlphaMath: Journal of Mathematics Education Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Penjaminan Mutu JTAM (Jurnal Teori dan Aplikasi Matematika) JURNAL MathEdu (Mathematic Education Journal) Jurnal MediaTIK GERVASI: Jurnal Pengabdian kepada Masyarakat Techno Xplore : Jurnal Ilmu Komputer dan Teknologi Informasi Reswara: Jurnal Pengabdian Kepada Masyarakat Jurnal Teknik Informatika (JUTIF) Jurnal Dedikasi Seminar Nasional Pengabdian Kepada Masyarakat Catimore: Jurnal Pengabdian Kepada Masyarakat Jurnal Ilmiah Edutic : Pendidikan dan Informatika Jurnal Penjaminan Mutu Indonesian Journal of Fundamental Sciences IPTEK: Jurnal Hasil Pengabdian kepada Masyarakat Teknovokasi : Jurnal Pengabdian Masyarakat Vokatek : Jurnal Pengabdian Masyarakat Information Technology Education Journal Journal of Embedded Systems, Security and Intelligent Systems Ininnawa: Jurnal Pengabdian Masyarakat
Andi Baso Kaswar Andi Baso Kaswar
Universitas Negeri Makassar
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Journal : Jurnal Buana Informatika

 1 
Segmentasi Citra Ikan Tuna dengan Mahalanobis Histogram Thresholding dan Mahalanobis Fuzzy C-Means Kaswar, Andi Baso; Arifin, Agus Zainal; Wijaya, Arya Yudhi
Jurnal Buana Informatika Vol 7, No 3 (2016): Jurnal Buana Informatika Volume 7 Nomor 3 Juli 2016
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (490.754 KB) | DOI: 10.24002/jbi.v7i3.658

Abstract

Abstract. Fuzzy C-Means segmentation algorithm based on Mahalanobis distance can be utilized to segment tuna fish image. However, initialization of pixels membership value and clusters centroid randomly cause the segmentation process become inefficient in terms of iteration and time of computation. This paper proposes a new method for tuna fish image segmentation by Mahalanobis Histogram Thresholding (M-HT) and Mahalanobis Fuzzy C-Means (MFCM). The proposed method consists of three main phases, namely: centroid initialization, pixel clustering and accuracy improvement. The experiment carried out obtained average of iteration amount is as many as 66 iteration with average of segmentation time as many as 162.03 second. While the average of Accuracy is 98.54%, average of Missclassification Error is 1.46%. The result shows that the proposed method can improve the efficiency of segmentation method in terms of number of iterations and time of segmentation. Besides that, the proposed method can give more accurate segmentation result compared with the conventional method.Keywords: Tuna Fish Image, Segmentation, Fuzzy Clustering, Histogram Thresholding, Mahalanobis Distance. Abstrak. Algoritma segmentasi Fuzzy C-Means berbasis jarak Mahalanobis dapat digunakan untuk mensegmentasi ikan tuna. Namun, inisialisasi derajat keanggotaan piksel dan centroid klaster secara random mengakibatkan proses segmentasi menjadi tidak efisien dalam hal iterasi dan waktu komputasi. Penelitian ini mengusulkan metode baru untuk segmentasi citra ikan tuna dengan Mahalanobis Histogram Thresholding (M-HT) dan Mahalanobis Fuzzy C-Means (MFCM). Metode ini terdiri atas tiga tahap utama, yaitu: inisialisasi centroid, pengklasteran piksel dan peningkatan akurasi. Berdasarkan hasil ekseprimen, diperoleh rata-rata jumlah iterasi sebanyak 66 iterasi dengan rata-rata waktu segmentasi 162,03 detik. Rata-rata Akurasi 98,54% dengan rata-rata tingkat Missclassification Error 1,46%. Hasil yang diperoleh menunjukkan bahwa metode yang diusulkan dapat meningkatkan efisiensi metode segmentasi dalam hal jumlah iterasi dan waktu segmentasi. Selain itu, metode yang diusulkan dapat memberikan hasil segmentasi yang lebih akurat dibandingkan dengan metode konvensional.Kata Kunci: Citra Ikan Tuna, Segmentasi, Fuzzy Clustering, Histogram Thresholding, Jarak Mahalanobis.
Segmentasi Citra Ikan Tuna dengan Mahalanobis Histogram Thresholding dan Mahalanobis Fuzzy C-Means Andi Baso Kaswar; Agus Zainal Arifin; Arya Yudhi Wijaya
Jurnal Buana Informatika Vol. 7 No. 3 (2016): Jurnal Buana Informatika Volume 7 Nomor 3 Juli 2016
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/jbi.v7i3.658

Abstract

Abstract. Fuzzy C-Means segmentation algorithm based on Mahalanobis distance can be utilized to segment tuna fish image. However, initialization of pixels membership value and clusters centroid randomly cause the segmentation process become inefficient in terms of iteration and time of computation. This paper proposes a new method for tuna fish image segmentation by Mahalanobis Histogram Thresholding (M-HT) and Mahalanobis Fuzzy C-Means (MFCM). The proposed method consists of three main phases, namely: centroid initialization, pixel clustering and accuracy improvement. The experiment carried out obtained average of iteration amount is as many as 66 iteration with average of segmentation time as many as 162.03 second. While the average of Accuracy is 98.54%, average of Missclassification Error is 1.46%. The result shows that the proposed method can improve the efficiency of segmentation method in terms of number of iterations and time of segmentation. Besides that, the proposed method can give more accurate segmentation result compared with the conventional method.Keywords: Tuna Fish Image, Segmentation, Fuzzy Clustering, Histogram Thresholding, Mahalanobis Distance. Abstrak. Algoritma segmentasi Fuzzy C-Means berbasis jarak Mahalanobis dapat digunakan untuk mensegmentasi ikan tuna. Namun, inisialisasi derajat keanggotaan piksel dan centroid klaster secara random mengakibatkan proses segmentasi menjadi tidak efisien dalam hal iterasi dan waktu komputasi. Penelitian ini mengusulkan metode baru untuk segmentasi citra ikan tuna dengan Mahalanobis Histogram Thresholding (M-HT) dan Mahalanobis Fuzzy C-Means (MFCM). Metode ini terdiri atas tiga tahap utama, yaitu: inisialisasi centroid, pengklasteran piksel dan peningkatan akurasi. Berdasarkan hasil ekseprimen, diperoleh rata-rata jumlah iterasi sebanyak 66 iterasi dengan rata-rata waktu segmentasi 162,03 detik. Rata-rata Akurasi 98,54% dengan rata-rata tingkat Missclassification Error 1,46%. Hasil yang diperoleh menunjukkan bahwa metode yang diusulkan dapat meningkatkan efisiensi metode segmentasi dalam hal jumlah iterasi dan waktu segmentasi. Selain itu, metode yang diusulkan dapat memberikan hasil segmentasi yang lebih akurat dibandingkan dengan metode konvensional.Kata Kunci: Citra Ikan Tuna, Segmentasi, Fuzzy Clustering, Histogram Thresholding, Jarak Mahalanobis.
Co-Authors A. Mutahharah Mutahharah A.Farha Adella Abd. Rahman Patta Agus Zainal Arifin Agus Zainal Arifin Ahmad Mustofa Hadi Ahmad Mustofa Hadi Ainun Zahra Adistia Alfian Firlansyah Ananta Dwi Prayoga Alwy Andi Akram Nur Risal Andi Nurul Izzah Anggy Heriyanti Anggy Heriyanti Anny Yuniarti Aqsha, Ismail Arinanda Alviansyah Arya Yudhi Wijaya Arya Yudhi Wijaya Ayu Futri Bukhari Naufal Nur A.G Cyahrani Wulan Purnama Della Fadhilatunisa Dirawan, Gufran Darma Dyah Darma Andayani Elva Amalia Eman Wahyudi Kasim Farros Taufiqurrahman Fathahillah Fathahillah Fazli Arif Fhatiah Adiba Fhatiah Adiba Hartanto Tantriawan Herman Ibnu Fikrie Syahputra Ikra Ain Fahwa Indri Pratiwi Ramadhani Irwansyah Suwahyu Israwati Hamsar Jamila Jamila Jamila Jasruddin Daud Malago Jumadi Mabe Parenreng Jusrawati Jusrawati Jusrawati Kurnia Prima Putra Labusab Labusab M. Miftach Fakhri Marwan Eka Ramdhany Marwan Ramdhany Edy Maulana Muhammad Muammar Muammar Muh Aldhy Fatahillah Muh Devan Fahresi Muh Omar Hassan ST Muh. Dirgafa Anugra Rais Muh. Dirgafa Anugrah Rais Muh. Fardika Pratama Putra Muh. Fauzan Arifuddin Muh. Rais Muh. Rasul D Muhammad Agung Muhammad Fajar B Muhammad Nur Yusri Maulidin Yusuf Muhammad Nur Yusri Maulidin Yusuf Muhiddin Palennari Muliaty Yantahin Nur Anny S. Taufieq Nur Fadillah Bustamin Nur Inayah Yusuf Nurhikma Nurhikma Nurhikma Nurjannah Nurjannah Nurjannah Nurjannah Nurjannah Nurul Amanda Pratiwi Hasbullah Nurul Isra Humaira B Nurul Istiqamah Qalbi Nurul Izzah Dwi Patongai, Dian Dwi Putri Ulan Sari Riana T. Mangesa Ridwan Daud Mahande Ridwansyah Ridwansyah Rosidah Rosidah Sahribulan Sahribulan Saiful Bahri Musa Sanatang Sanatang, sanatang Saparuddin Saparuddin Saprina Mamase SATRIYAS ILYAS Silvia Andriani Sri Rahayu St. Fatmah Hiola Suhartono, Suhartono Supria Supria Susiana Sari Tsabita Syalza Billa Tsabita Syalza Billa Irawan Wahyudi Wanda Hamidah