Implementation of Fuzzy Logic to Determine the Doneness of Beef Steak
DOI:
https://doi.org/10.62535/93z2df58Keywords:
Fuzzy logic, MATLAB, Steak donenessAbstract
This study aims to implement fuzzy logic in determining the doneness level of beef steak based on the inputs of roasting time and roasting temperature. The output of the developed system is in the form of maturity levels in the categories of "rare", "medium rare", "medium", "medium well", and "well done". Data for this study were obtained through interviews with experienced beef steak sellers. This research method includes direct calculations as well as the use of MATLAB software to develop fuzzy logic systems. The results of the interview analysis are used as a basis for the formation of fuzzy rules for determining the degree of doneness of steaks. The results showed that in the example case with a roasting temperature of 300°C and a roasting time of 10 minutes, the resulting output value was 70, which placed the doneness of the steak in the "medium well" position. These results have been verified both through direct calculations and the use of MATLAB software. The study concluded that the implementation of fuzzy logic can help in determining the doneness level of beef steak, based on temperature variations and roasting time.
References
Andani, S. R. (2013). Fuzzy Mamdani Dalammenentukan Tingkat Keberhasilan Dosen mengajar. Seminar Nasional Informatika 2013, 2013(semnasIF), 57–65.
Ayuningtias, L. P., Irfan, M., & Jumadi, J. (2017). Analisa Perbandingan Logic Fuzzy Metode Tsukamoto, Sugeno, Dan Mamdani. Jurnal Teknik Informatika, 10(1), 9–16.
Dary Daffa Haque, M. (2023). Penerapan Logika Fuzzy Mamdani Untuk Optimasi Persediaan Stok Makanan Hewan. Media Online), 4(1), 427–437. https://doi.org/10.30865/klik.v4i1.1160
Dharmayanti, L. (2013). Pengetahuan Bahan Makanan 2. Bahan Ajar SMK Program Keahlian Tata Boga, 021, 178.
Fatwa, M., Rizki, R., Sriwinarty, P., & Supriyadi, E. (2022). Pengaplikasian Matlab pada Perhitungan Matriks. Papanda Journal of Mathematics and Science Research, 1(2), 81–93. https://doi.org/10.56916/pjmsr.v1i2.260
Haerani, E. (2014). Analisa Kendali Logika Fuzzy Dengan Metode Defuzzyfikasi Coa (Center of Area), Bisektor, Mom (Mean of Maximum), Lom (Largest of Maximum), Dan Som (Smallest of Maximum). SITEKIN: Jurnal Sains, Teknologi dan Industri, 10(2), 245–253. https://ejournal.uin-suska.ac.id/index.php/sitekin/article/view/543
Informatika, J. T., & Indonesia, U. I. (2007). ( Taufiq Hidayat ST ^ MCS ).
Izquierdo, S. S., & Izquierdo, L. R. (2018). Mamdani fuzzy systems for modelling and simulation: A critical assessment. Jasss, 21(3). https://doi.org/10.18564/jasss.3660
Kaur, S. (2012). Two Inputs Two Output Fuzzy Controller System Design using MATLAB. 2(3), 209–218.
Maryam, S., Bu’ulolo, E., & Hatmi, E. (2021). Penerapan Metode Fuzzy Mamdani dan Fuzzy Tsukamoto Dalam Menentukan Harga Mobil Bekas. Journal of Informatics, Electrical and Electronics Engineering, 1(1), 10–14. https://djournals.com/jieee/article/view/54%0Ahttps://djournals.com/jieee/article/download/54/164
Mattos-Vela, M. A. (2021). Open access. British Dental Journal, 231(4), 207. https://doi.org/10.1038/s41415-021-3384-2
Nofrianda, H. (2019). Analisis Pengaruh Kualitas Produk, Kualitas Layanan Dan Harga Terhadap Kepuasan Konsumen (Studi Kasus Pada Konsumen Industry/ Toko Bakery di Kota Bengkulu). Managament Insight: Jurnal Ilmiah Manajemen, 13(1), 71–85. https://doi.org/10.33369/insight.13.1.71-85
Paquin, F., Rivnay, J., Salleo, A., Stingelin, N., & Silva, C. (2015). Multi-phase semicrystalline microstructures drive exciton dissociation in neat plastic semiconductors. J. Mater. Chem. C, 3(2), 10715–10722. https://doi.org/10.1039/b000000x
Patriani, P., Hafid, H., Mirwandhono, E., & Wahyuni, T. H. (2020). Teknologi Pengolahan Daging. In Repository.Pertanian.Go.Id (Nomor May). http://repository.pertanian.go.id/handle/123456789/14049%0Ahttp://repository.pertanian.go.id/bitstream/handle/123456789/14049/Teknologi Pengolahan Daging.pdf?sequence=1&isAllowed=y
Pramita, V. D. (2018). Karakterisasi Steak Daging dengan Substitusi Texturized Vegetable Protein (TVP) Modified Legume Flour (Molef) Koro Pedang (Canavalia ensiformis L.). Jawa, 1–119.
Prasetya, B., Boedi Setiawan, A., & Febrinda Hidayatulail, B. (2019). Fuzzy Mamdani Pada Tanaman Tomat Hidroponik (Mamdani Fuzzy on Hydroponics Tomato Plants). JEEE-U (Journal of Electrical and Electronic Engineering-UMSIDA), 3(2), 228.
Priambada, S., Suyadi, I., Yulianto, E., & Susilo, H. (2016). Pentingnya Customer Relationship Management (CRM) Untuk Meningkatkan Loyalitas Pelanggan Di KPRI-UB. Conference: Seminar Nasional Sistem Informasi Indonesia (SESINDO), November, 437–444. https://www.researchgate.net/publication/322500219_Pentingnya_Customer_Relationship_Management_Crm_Untuk_Meningkatkan_Loyalitas_Pelanggan_Di_Kpri-Ub
Putri, A. D., & Maulana, A. (2023). Penerapan Metode Mamdani Fuzzy Logic untuk Menentukan Pembelian Alat Berat dalam Proyek Migas di PT SMOE Indonesia. Jurnal Desain Dan Analisis Teknologi, 2(2), 138–149. https://doi.org/10.58520/jddat.v2i2.32
Radja, M., Londa, M. A., & Sara, K. (2020). Penerapan Metode Logika Fuzzy dalam Evaluasi Kinerja Dosen. Matrix : Jurnal Manajemen Teknologi dan Informatika, 10(2), 78–86. https://doi.org/10.31940/matrix.v10i2.1841
Rangkuti, M. G. (2019). Pengolahan Citra Identifikasi Kematangan Tenderloin Steak Menggunakan Metode Ekstraksi Ciri Statistik. Majalah Ilmiah INTI, 6, 51–54.
Rizky Pahlevi1, Wahyu Oktri Widyarto2, T. A. M. P. (2013). Implementasi Fuzzy Mamdani untuk Penentuan Pengadaan Kartu Operator pada Distributor Kartu Perdana PT . XYZ. Prosiding Seminar Nasional Industrial Services (SNIS) III, 2013–2016.
Santosa, S. H., Hidayat, A. P., & Siskandar, R. (2022). Raw material planning for tapioca flour production based on fuzzy logic approach: a case study. Jurnal Sistem dan Manajemen Industri, 6(1), 67–76. http://dx.doi.org/10.30656/jsmi.v6i1.4594
Schmid, R., & McGee, H. (1989). On Food and Cooking: The Science and Lore of the Kitchen. In Taxon (Vol. 38, Nomor 3). https://doi.org/10.2307/1222284
Simanjuntak, M., & Fauzi, A. (2017). Penerapan Fuzzy Mamdani Pada Penilaian Kinerja Dosen (Studi Kasus STMIK Kaputama Binjai). Jurnal ISD, 2(2), 2528–5114.
Siswoyo, B., & Zaenal, A. (2018). Model Peramalan Fuzzy Logic. Jurnal Manajemen Informatika (JAMIKA), 8(1), 1–14. https://doi.org/10.34010/jamika.v8i1.897
Sitohang, S., & Denson Napitupulu, R. (2017). Fuzzy Logic Untuk Menentukan Penjualan Rumah Dengan Metode Mamdani (Studi Kasus: Pt Gracia Herald). Jurnal ISD, 2(2), 91–101.
Sufarnap, E., & Sudarto, S. (2019). Penerapan Metode Fuzzy Mamdani dalam Penentuan Jumlah Produksi. Seminar Nasional Sains dan Teknologi Informasi (SENSASI), Juli, 379–382. https://s.id/1SHDL
Suryadi, A., Putri, M. V., & Febrianti, E. L. (2022). Pengolahan Citra Digital Dan Logika Fuzzy Dalam Identifikasi Tingkat Kematangan Buah. Journal of Science and Social Research, 5(2), 187. https://doi.org/10.54314/jssr.v5i2.863
Sutikno. (2018). Perbandingan Metode Defuzzyfikasi Sistem Kendali Logika Fuzzy Model Mamdani Pada Motor Dc. Indra Waspada Jurnal Masyarakat Informatika, 2(3), 27–38. https://media.neliti.com/media/publications/112322-ID-perbandingan-metode-defuzzyfikasi-sistem.pdf
Wicaksono Hadi, R., & Setiawan, I. (2011). Perancangan Alat Pendeteksi Kualitas Daging Sapi Berdasar Warna dan Bau Berbasis Mikrokontroler Atmega32 Menggunakan Logika Fuzzy. Jurnal TRANSMISI, 13(1), 21–26. http://ejournal.undip.ac.id/index.php/transmisi