Jurnal pangan nasional "terakreditasi" Kemeristekdikti dari Indonesian Food Technologists® - IFT

DOI: https://doi.org/10.17728/jatp.7434

Sistem Deteksi Cepat Mutu Organoleptik Beras Berbasis Android

*Mulyana Hadipernata orcid scopus  -  Balai Besar Penelitian dan Pengembangan Pascapanen Pertanian, Balitbangtan-Kementerian Pertanian, Bogor, Indonesia
Agus Supriatna Somantri  -  Balai Besar Penelitian dan Pengembangan Pascapanen Pertanian, Balitbangtan-Kementerian Pertanian, Bogor, Indonesia
Maulida Hayuningtyas  -  Balai Besar Penelitian dan Pengembangan Pascapanen Pertanian, Balitbangtan-Kementerian Pertanian, Bogor, Indonesia
Nikmatul Hidayah  -  Balai Besar Penelitian dan Pengembangan Pascapanen Pertanian, Balitbangtan-Kementerian Pertanian, Bogor, Indonesia
Hoerudin Hoerudin  -  Balai Besar Penelitian dan Pengembangan Pascapanen Pertanian, Balitbangtan-Kementerian Pertanian, Bogor, Indonesia

Citation Format:
Abstract

Penelitian ini bertujuan untuk mengembangkan alat deteksi cepat mutu organoleptik beras berbasis pada pemanfaatan aplikasi Android agar pengujian mutu organoleptik beras dapat dilakukan secara cepat dan akurat. Bahan penelitian yang digunakan adalah beras varietas Ciherang dan Tarabas. Metode yang digunakan adalah dengan menggunakan realtime image processing berbasis Android dan Java. Hasil penelitian menunjukkan bahwa lamanya penyimpanan beras sangat mempengaruhi citra beras (Red Green Blue/RGB). Selama penyimpanan beras, nilai Blue menghasilkan nilai perubahan yang nyata dibandingkan nilai Red dan Green. Nilai Blue ini berkorelasi positif terhadap perubahan kadar amilosa selama penyimpanan dan mutu organoleptiknya. Aplikasi deteksi cepat mutu organoleptik beras juga telah berhasil dibuat dan dapat diuji validitasnya dengan memperhatikan perubahan karakateristik citra, perubahan amilosa, dan mutu organoleptiknya. Kesimpulannya, aplikasi deteksi cepat ini berhasil dikembangkan dengan berbasis Android yang dapat digunakan sebagai alat uji mutu organoleptik beras

Rapid Detection System for Organoleptic Quality of Rice using the Android Application

Abstract

The research was aimed at developing rapid detection tool of rice upon organoleptic quality based on the Android application, so the testing may be done quickly and accurately. Ciherang and Tarabas rice varieties were used in this research. Realtime image processing based on Android and Java were used as method in this research. The results showed that the storage affected the rice image value (Red Green Blue/RGB). During storage, the value of the blue (B) produced a proper marked which was positively correlated to the changes in amylose content. Application of rapid detection of organoleptic quality of rice was carried out by observing changes in image characteristics, changes in amylose, and changes in organoleptic properties. As conclusion, the application may functioning properly and can be used as a tool to test the organoleptic quality of rice and its shelf life.


Fulltext View|Download
Keywords: beras; mutu organoleptik; deteksi cepat; image processing; Android; Rice; organoleptic quality; fast detection; image processing; android

Article Metrics:

Article Info
Section: Artikel Penelitian (Research Article)
Language : ID
Recent articles
Pengaruh Substitusi Tepung Terigu dengan Tepung Umbi Dahlia dan Konsentrasi Baking Powder terhadap Karakteristik Fisik Cookies Kaya Serat Karakteristik Fisik dan Total Bakteri Kefir Susu Kerbau yang Diproduksi pada Suhu Ruang Penentuan Umur Simpan Menggunakan Metode Accelerated Shelf Life Test (ASLT) pada Bubuk Minuman Instan Stroberi Foam-Mat Drying Sistem Deteksi Cepat Mutu Organoleptik Beras Berbasis Android Karakteristik Mi Kering Substitusi Tepung Terigu dengan Tepung Labu Kuning dan Tepung Ikan Tuna More recent articles
  1. Ahmed, J., Jafri, M.N., Ahmad, J., Khan, M.I. 2005. Design and implementation of a neural network for real-time object tracking. World Academy of Science, Engineering and Technology 6:209-212. DOI: 10.5281/zenodo.1061154
  2. Aris, B.S., Suma'inna, Maulana, H. 2016. Pengenalan citra wajah sebagai identifier menggunakan metode Principal Component Analysis (PCA). Jurnal Teknik Informatika 9(2):166-175. DOI: 10.15408/jti.v9i2.5608
  3. Blasco, J., Aleixos, N., Go´mez J., Molto, E. 2007. Citrus sorting by identification of the most common defects using multispectral computer vision. Journal of Food Engineering 83: 384–393. DOI: 10.1016/j.jfoodeng.2007.03.027
  4. Blasco, J., Cubero, S., Gómez-Sanchís, J., Mira, P., Moltó, E. 2009. Development of a machine for the automatic sorting of pomegranate (Punica granatum) arils based on computer vision. Journal of Food Engineering 90: 27–34. DOI: 10.1016/j.jfoodeng.2008.05.035
  5. Bhattacharya, K.R. 2011. Rice Quality, A Guide to Rice Properties and Analysis. Woodhead Publishing series in Food Science, Technology and Nutrition 219. Woodhead Publishing Limited, Cambridge, UK
  6. Cabaret, L., Lacassagne, L., Oudni, L. 2014. A Review of world’s fastest connected component labeling algorithms: speed and energy estimation. Proceedings Conference on Design and Architectures for Signal and Image Processing. DOI: 10,3390/jlpea8010007
  7. Chen, S., Xiong, J., Guo, W., Bu, R., Zheng, Z., Chen, Y. 2019. Colored rice quality inspection system using machine vision. Journal of Cereal Science 88:87-95. DOI: 10.1016/j.jcs.2019.05.010
  8. Deaton, A., Dreze, J. 2009. Food and nutrition in India: Facts and interpretations. Economic and Political Weekly 44(7):42–65
  9. Effendi, M., Fitriyah, Effendi, U. 2017. Identifikasi jenis dan mutu teh menggunakan pengolahan citra digital dengan metode jaringan syaraf tiruan. Jurnal Teknotan 11(2):67-76). DOI: 10.24198/jt.vol11n2.7
  10. Gilbert, R.G., Witt, T., Hasjim, J. 2013. What is being learned about starch properties from multiple-level characterization. Cereal Chemistry 90(4):312-325. DOI: 10.1094/CCHEM-11-12-0141-FI
  11. Hadipernata, M., Hidayah, N., Nugraha, S. 2020. Determination of rice expiration time based on microbiological contaminant. IOP Conference Series Earth and Environmental Sciences 518:1-5. DOI: 10.1088/1755-1315/518/1/012053
  12. Hung, J.S., Chen, S.L. 2012. An optical automatic measurement method for the moisture content of rough rice using image processing techniques. Computers and Electronics in Agriculture 85:134–139. DOI: 10.1016/j.compag.2012.04.012
  13. Kuo, T.Y., Chung, C.L., Chen, S.Y., Lin, H.A., Kuo, Y.F. 2016. Identifying rice grains using image analysis and sparse-representation-based classification. Computers and Electronics in Agriculture 127:716-725. DOI: 10.1016/j.compag.2016.07.020
  14. Li, H., Prakash, S., Nicholson, T.M., Fitzgerald, M.A., Gilbert, R.G. 2016. The importance of amylose and amylopectin fine structure for textural properties of cooked rice grains. Food Chemistry 196:702-711. DOI: 10.1016/j.foodchem.2015.09.112
  15. Minten, B., Murshid, K.A.S., Reardon, T. 2013. Food quality changes and implications: Evidence from the rice value chain of Bangladesh. World Development 42:100–113. DOI: 10.1016/j.worlddev.2012.06.015
  16. Narendra, V.G., Hareesh, K.S. 2010. Prospect of computer vision automated grading and sorting systems in agricultural and food products for quality evaluation. International Jounal of Computer Applications 1(4):1-9. DOI: 10.5120/111-226
  17. Neelamegam, P., Abirami, S., Priya, K.V., Valantina, S.R. 2013. Analysis of rice granules using image processing and neural network. IEEE Conference: 879–884. DOI: 10.1109/CICT.2013.6558219
  18. Nuriana, A, Aini, N., Karseno, K. 2019. Formulasi Breakfast Meal Flakes dari Tepung Suweg dan Stabilized Rice Bran Menggunakan Metode Respon Permukaan. Jurnal Aplikasi Teknologi Pangan 8(2). DOI: 10.17728/jatp.3952
  19. Pattel, K.K., Kar, A., Jha, S., Khan, M. 2012. Machine vision system: a tool for quality inspection of food and agricultural products, Journal of Food Science and Technology 49:123–141. DOI: 10.1007/s13197-011-0321-4
  20. Wibawa, I.P.M.P., Gunadnya, I.B.P., Wijaya, I.M.A.S. 2019. Pendugaan umur simpan benih padi (Oryza sativa L) menggunakan metode ASLT (Accelerated Shelf Life Testing) dengan pendekatan model kadar air kritis. Jurnal Biosistem dan Teknik Pertanian 7(2):228. DOI: 1024843/JBETA.2019.v07.i02.p02
  21. Permentan 31. 2017. Peraturan Menteri Pertanian Tentang Kelas Mutu Beras. 31 Agustus 2017
  22. Putranto, B.Y.B., Hapsari, W., Wijana, K. 2010. Segmentasi warna citra dengan deteksi warna HSV untuk mendeteksi objek. Jurnal Informatika 6(2):1-14. DOI: 10.21460/inf.2010.62.81
  23. Robyt, J.F. 2008. Starch: Structure, properties, chemistry, and enzymology. Glycoscience:1437-1472. DOI: 10.1007/978-3-540-30429-6_35
  24. Sari, A.R., Martono, Y., Rondonuwu, F.S. 2020. Identifikasi kualitas beras putih (Oryza Sativa L.) berdasarkan kandungan amilosa dan amilopektin di pasar tradisional dan “selepan” Kota Salatiga. Titian Ilmu: Jurnal Ilmiah Multi Sciences 12(1):24-30. DOI: 10.30599/jti.v12i1.599
  25. SNI 6128:2015. Kelas Mutu Beras. Badan Standardisasi Nasional, Indonesia
  26. Somantri, A.S. 2010. Menentukan klasifikasi mutu beras dengan menggunakan teknologi pengolahan citra digital dan jaringan syaraf tiruan. Jurnal BSN 12(3):162-173. DOI: 10.31153/js.v12i3.154
  27. Somantri, A.S., Miskiyah., Nugraha, S. 2015. Jurnal Standardisasi Volume 17(1):47-58. DOI: 10.35724/mustek.v7i2.908
  28. Supriyadi, E., Basuki, A., Sigit, R. 2020. Deteksi kualitas beras menggunakan segmentasi citra berdasarkan pecahan bulir dan sebaran warna. Jurnal Informatika & Rekayasa Elektronika 3(1):20-29. DOI: 10.36595/jire.v3i1.210
  29. Syamsiah, M., Masliah, M. 2019. Identifikasi kadar amilosa beras pandanwangi dari tujuh kecamatan di Kabupaten Cianjur. Agroscience 9(2):130–136
  30. Timmermans, A.J.M. 1998. Computer vision system for online sorting pot plant based on learning techniques. Acta Horticultura: 421:91-98. DOI: 10.17660/ActaHortic.1998.421.8
  31. Vanna, T., Bundit, L. 2007. Changes in quality of rice (Oryza sativa L.) cv. Khao Dawk Mali 105 during storage. Journal of Food Biochemistry 31(3):415-425. DOI: 10.1111/j.17454514.2007.00125.x
  32. Wardani, N.P.P.P., Adiputro, I.G.K., Suardana, A,A,K. 2020. Efektivitas repelensi serbuk daun pandan wangi (Pandanus amarylilifolius Roxb) terhadap kutu beras (Sitophilus oryzae L) pada beras merah (Oryza nivara). Jurnal Widya Biologi 11(1):30-40. DOI: 10.32795/widyabiologi.v11i01.568
  33. Wright, J., Yang, A.Y., Ganesh, A., Sastry, S.S., Ma, Y. 2009. Robust face recognition via sparse representation. Pattern Analysis and Machine Intelligence. IEEE Transaction on Pattern Analysis and Machine Intelligence 31 (2):210-227. DOI: 10.1109/TPAMI.2008.79

Last update:

No citation recorded.

Last update:

No citation recorded.