Article Sidebar

Submitted
Oct 25, 2017
Published
Oct 25, 2017
Download
PDF
Statistic
Read Counter : 2346 Download : 1704

Downloads

Download data is not yet available.

Main Article Content

Abstract

Abstract

As the by product of palm oil, palm kernel contains high-quality oil. The manual inspection has low efficiency, subjective and inconsistent results due different perspectives between the buyer and the seller regarding the kernel quality. This research aims to determine the quality of palm kernel using the texture and morphological image analysis. Texture analysis performed on the kernel images separation to obtain the value of the mean, variance, skewness, kurtosis, entropy, energy, contrast, correlation, and homogeneity. Morphology analysis performed on the kernel images separation to obtain the value of the area, perimeter, metrics, and eccentricity. The classification was performed by KNearest Neighbor (KNN) method. Based on a simulation, the classification system could classify the palm kernel into the whole kernels, broken, and shells. The highest accuracy of 66.59 % was obtained by using a combination of mean and morphology when k was 1.

 Abstrak

Sebagai produk samping dari buah kelapa sawit, inti sawit mengandung minyak berkualitas tinggi. Penentuan mutu inti secara manual seringkali mengakibatkan terjadi konflik antar pembeli dan penjual. Proses penentuan mutu secara manual memiliki kekurangan pada rendahnya efisiensi, subjektif, dan tidak konsisten. Penelitian ini bertujuan untuk mempelajari kualitas inti sawit menggunakan analisis tekstur dan morfologi. Analisis tekstur dilakukan terhadap hasil pemisahan untuk mendapatkan nilai mean, variance, skewness, kurtosis, entrophy, energy, contrast, correlation, dan homogenity. Analisis morfologi dilakukan terhadap hasil pemisahan untuk mendapatkan nilai area, perimeter, metric, dan eccentricity. Dalam penelitian ini, metode klasifikasi yang digunakan adalah metode K-Nearest Neighbor (KNN). Berdasarkan simulasi, dapat disimpulkan bahwa sistem dapat diklasifikasikan menurut inti utuh, inti pecah, dan cangkang. Akurasi tertinggi 66.59% diperoleh dengan menggunakan kombinasi mean dan morfologi ketika k adalah 1.

Keywords

palm kernel image processing texture morphology KNN

Article Details

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Authors submitting manuscripts should understand and agree that copyright of manuscripts of the article shall be assigned/transferred to Jurnal Keteknikan Pertanian. This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA) where Authors and Readers can copy and redistribute the material in any medium or format, as well as remix, transform, and build upon the material for any purpose, but they must give appropriate credit (cite to the article or content), provide a link to the license, and indicate if changes were made. If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.

Author Biographies

Okta Danik Nugraheni, Institut Pertanian Bogor

Departemen Pertanian dan Pangan, mahasiswa pascasarjana

I Wayan Astika, Institut Pertanian Bogor,

Departemen Teknik Mesin dan Biosistem

I Dewa Made Subrata, Institut Pertanian Bogor,

Departemen Teknik Mesin dan Biosistem

References

  1. Ahmad U. 2013. Deteksi ujung biji ginko menggunakan pengolahan citra berbasis analisis morfologi. J Keteknikan Pertanian 27(1): 1 – 17.
  2. Arifin A.D. 2012. implementasi algoritma K-Nearest Neighbour yang berdasarkan one pass clustering untuk kategorisasi teks [Tesis].
  3. Teknik Informatika. Institut Teknologi Sepuluh November. Surabaya.
  4. [DSN] Dewan Standarisasi Nasional. 1987. Standar Nasional Indonesia (SNI) Inti Kelapa Sawit 0002:1987. Jakarta (ID): DSN.
  5. Dinar L., A. Suyantohadi, M.A.F. Fallah. 2012. Pendugaan kelas mutu berdasarkan analisa warna dan bentuk biji pala (Myristica fragrans
  6. houtt) menggunakan teknologi pengolahan citra dan jaringan saraf tiruan. J Keteknikan Pertanian (1) : 53 – 59.
  7. Mollazade K,, Omid M., Arefi A. 2012. Comparing data mining classifiers for grading raisins based on visual features. J Comput Electron Agr.
  8. 84:124–13.
  9. Nursalim, Suprapedi, Himawan H. 2014. Klasifikasi bidang kerja lulusan menggunakan algoritma k-nearest neighbor. J Teknolog Informasi
  10. 10(1):31-43.
  11. Santoso I., Christyono Y., Indriani M. 2007. Kinerja Pengenalan Citra Tekstur menggunakan Analisis Tekstur Metode Run Length. Seminar Nasional Aplikasi Teknologi Informasi. 2007 Jun 16; Yogyakarta, Indonesia. hlm 17-25.
  12. Sofi’i I., Astika I.W., Suroso. 2005. Penentuan jenis cacat biji kopi dengan pengolahan citra dan artificial neural network. J Keteknikan Pertanian
  13. 19 (2) : 99 – 108.
  14. Soedibyo D.W., Ahmad U., Seminar K.B., Subrata I.D.M. 2009. Pengembangan pengolahan citra untuk pemutuan kopi beras. J Agro-Tecno 1 (8) : 489 – 499.
  15. Soedibyo D.W., Ahmad U, Seminar K.B., Subrata I.D.M. 2010. Rancang bangun sistem sortasi cerdas berbasis pengolahan citra untuk kopi
  16. beras. J Keteknikan Pertanian 24 (2) : 67 – 74.
  17. [USDA] United States Departement of Agriculture. 2015. Palm Kernel Oil Production by Country in 1000 MT. terdapat pada (http://www.indexmundi.
  18. com/agriculture/?commodity=palm-kerneloil& graph=production-growth-rate).
  19. Wan Y.N. 2002. Kernel handling performance of an automatic grain quality inspection system. ASAE 45(2): 369–377. Transaction of the ASAE. USA.
  20. Weinberger K., Blitzer J., Saul K. 2006. Distance metric learning for large margin nearest neighbor classification. In: Proceeding of the Advances in
  21. Neural Information Processing Systems (NIPS) 1473–1480. USA.