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Abstract

Abstrak

The quality of cooking oil sold in the market needs to be checked to ensure its health. cooking oil quality detector is designed to make it easier for the public to know the quality of the cooking oil. The research method is to make tools and conduct testing. The test is carried out by measuring the viscosity and density using the tool made. When the viscosity of 985 fuzzification was "good", and the density was 542.93 Kg/mL of "good" fuzzification, the fuzzification was processed by a fuzzy inference system, then defuzzification occurred in the form of oil quality results. fried "good". When the viscosity of 932 fuzzification is "sufficient", and the density is 618.69 Kg/mL of "moderate" fuzzification, a fuzzy inference system occurs, a defuzzification process is "moderate", when the viscosity of 926 fuzzification is "bad", and a density of 631.31 Kg/mL fuzzification "bad", fuzzy inference system occurs, defuzzification process occurs with "bad" results. To ensure that the results are accurate, the sample is taken to the BPOM which measures free fatty acids. From the BPOM test results converted to viscosity and density. In order to obtain an accurate conversion value between viscosity and density, it is recommended that a large number of samples be tested..

Keywords: viscosity, density, fuzzy logic

Article Details

How to Cite
Surapati, A., Zyaputra, A., & Rinaldi, R. S. (2021). Perancangan Alat Pendeteksi Kualitas Minyak Goreng Dengan Parameter Viskositas Dan Densitas Mengggunakan Metode Fuzzy Logic. JURNAL AMPLIFIER : JURNAL ILMIAH BIDANG TEKNIK ELEKTRO DAN KOMPUTER, 11(1), 22–28. https://doi.org/10.33369/jamplifier.v11i1.17133

References

  1. REFERENSI
  2. V. Nurmalita and P. A. Wibowo, “Analisis Faktor-Faktor Yang Mempengaruhi Ekspor Minyak Kelapa Sawit Indonesia ke India,” Econ. Educ. Anal. J., vol. 8, no. 2, pp. 605–619, 2019, [Online]. Available: https://journal.unnes.ac.id/sju/index.php/eeaj/article/view/31492.
  3. M. Goi, Y. K. Yasin, and Z. I. Mohamad, “Identifikasi Penggunaan Minyak Goreng oleh Pedagang Pisang Goreng di Kecamatan Kota Tengah Kota Gorontalo,” Heal. Nutr. J., vol. 3, no. 1, pp. 28–34, 2017.
  4. B. Nainggolan, N. Susanti, and A. Juniar, “Uji Kelayakan Minyak Goreng Curah dan Kemasan yang Digunakan Menggoreng Secara Berulang,” J. Pendidik. Kim., vol. 8, no. 1, pp. 45–57, 2016.
  5. I. Mustikasari, F. Saktini, and A. R. Gumay, “Pengaruh Frekuensi Penggorengan Minyak Jelantah Terhadap Diameter Dan Gambaran Histopatologi Lumen Aorta Tikus Wistar (Rattus Novergicus),” Diponegoro Med. J. (Jurnal Kedokt. Diponegoro), vol. 8, no. 1, pp. 26–37, 2019.
  6. H. Nasution, “Implementasi Logika Fuzzy pada Sistem Kecerdasan Buatan,” 2012.
  7. H. Herlina, E. Astryaningsih, W. S. Windrati, and N. Nurhayati, “TINGKAT KERUSAKAN MINYAK KELAPA SELAMA PENGGORENGAN VAKUM BERULANG PADA PEMBUATAN RIPE BANANA CHIPS (RBC),” J. AGROTEKNOLOGI, 2018, doi: 10.19184/j-agt.v11i02.6527.
  8. P. T. Sarjana, Perancangan alat pengukur tingkat kemurnian bensin premium menggunakan metode backpropagation. 2017.
  9. S. . Kale, A. Prasad, G. Milind, G. Prakash, and K. Nishant, “Petrochemical Quality Measurement And Adulteration Detection Using Arm Controller,” Int. J. Adv. Res. Electron. Commun. Eng., vol. 4, no. 1, pp. 90–93, 2015, [Online]. Available: http://ijarece.org/wp-content/uploads/2015/01/IJARECE-VOL-4-ISSUE-1-90-93.pdf.
  10. R. A. Sirait, “Penerapan Metode Spektrofotometri Ultraviolet pada Penetapan Kadar Nifedipin dalam Sediaan Tablet,” Fak. Farm. Univ. Sumatra Utara, Medan., 2009.
  11. I. GUTAMA PUTRA, “Perancangan Dan Penerapan Neraca Digital Untuk Percobaanmenentukan Massa Jenis Zat Padat,” Inov. Fis. Indones., vol. 3, no. 03, 2014.
  12. D. Apriani, Gusnedi, and Y. Darvina, “Studi Tentang Nilai Viskositas Madu Hutan dari Beberapa Daerah di Sumatera Barat Untuk Mengetahui Kualitas Madu,” Pillar Phys., vol. 2, pp. 91–98, 2013.
  13. I. M. Mataram, Dasar Komputasi Cerdas. Artificial Neural Network. Denpasar: Universitas Udayana, 2016.
  14. S. Komariyah, R. M. Yunus, and S. F. Rodiansyah, “Logika Fuzzy Dalam Sistem Pengambilan Keputusan Penerimaan Beasiswa,” Proceeding Stima 2.0, pp. 61–68, 2016, [Online]. Available: http://jurnal.unma.ac.id/index.php/ST/article/view/225.
  15. N. Syafitri N, “SIMULASI SISTEM UNTUK PENGONTROLAN LAMPU DAN AIR CONDITIONER DENGAN MENGGUNAKAN LOGIKA FUZZY,” J. Inform., vol. 10, no. 1, pp. 1164–1172, 2016, doi: 10.26555/jifo.v10i1.a3348.
  16. W. Buana, “Penerapan Fuzzy Mamdani Untuk Sistem Pendukung Keputusan Pemilihan Telepon Seluler,” J. Edik Inform., vol. 2, pp. 138–143, 2014.