Implementation of fuzzy logic using the tsukamoto method in forecasting the amount of bolu cake production
DOI:
https://doi.org/10.30762/f_m.v7i1.2516Keywords:
Fuzzy Logic , Forecasting, Tsukamoto Method, Bolu Cake, ProductionAbstract
Rumah produksi berkah bolu merupakan salah satu UMKM yang memproduksi kue bolu di Kota Pekanbaru. Pemilik usaha tersebut kesulitan dalam menentukan jumlah produksi kue karena hanya berdasarkan pada jumlah permintaan yang ada. Berdasarkan permasalahan tersebut, maka dipilihlah metode fuzzy Tsukamoto karena menggunakan penalaran monoton dalam setiap aturannya. Terdapat 4 tahapan yang dipakai dalam perhitungan metode Tsukamoto yaitu fuzzifikasi, inferensi, komposisi/Agregasi dan defuzzyfikasi. Hasil MAPE yang diperoleh dengan metode fuzzy Tsakomoto adalah 6,91% dan tingkat keakuratan sebesar 93,01%, yang memiliki arti bahwa metode fuzzy Tsukamoto sangat baik dalam memprediksi jumlah produksi, sehingga dapat digunakan sebagai sistem untuk mendukung keputusan dalam penentuan jumlah produksi kue bolu di rumah produksi Berkah Bolu.
The Berkah Bolu Production House is one of the Small and Medium Enterprise (SME) that produces bolu cakes in Pekanbaru City. The business owner has difficulty in determining the amount of cake production because it is only based on the number of existing requests. Based on these problems, the Tsukamoto fuzzy method was chosen because it uses monotonous reasoning in each rule. There are 4 stages used in the calculation of the Tsukamoto method, namely fuzzification, inference, composition or aggregation, and defuzzification. The MAPE result obtained by the fuzzy Tsukomoto method is 6.91% and the accuracy level is 93.01%, which means that the fuzzy Tsukamoto method is very good at predicting the amount of production, so it can be used as a decision support system in determining the amount of bolu cake production in the Berkah Bolu Production House.
References
Basriati, S., & Safitri, E. (2021). Penerapan Metode Fuzzy Tsukamoto dalam Menentukan Jumlah Produksi Tahu [Application of Tsukamoto Fuzzy Method in Determining the Quantity of Tofu Production]. Jurnal Sains, Teknologi Dan Industri, 18(1), 120. https://doi.org/10.24014/sitekin.v18i1.11022
Beu, L., & Husna, A. (2019). Metode Fuzzy Tsukamoto Untuk Memprediksi Jumlah Produksi Kue Pia [Fuzzy Tsukamoto Method to Predict the Production Amount of Pia Cake]. Jurnal Nasional CosPhi, 3(2), 2597–9329. https://www.cosphijournal.unisan.ac.id/index.php/cosphihome/article/view/82
Biringallo, M., Abapihi, B., . B., . M., . A., & . R. (2022). Perbandingan Akurasi Penggunaan Metode Fuzzy Time Series Markov-Chain dan Cheng Pada Peramalan Jumlah Kecelakaan Lalulintas di Kota Kendari [Comparison of Accuracy of Using Fuzzy Time Series Markov-Chain and Cheng Methods on Forecasting the Number of Traffic Accidents in Kendari City]. Seminar Nasional Sains Dan Terapan VI, 85–98. https://ejournal.unsrat.ac.id/v3/index.php/sinta6/article/view/41874
Chaniago, M. F., Nasution, A. I. L., & Nasution, J. (2023). Potensi Makanan Tradisional Bolu Kemojo Sebagai Daya Tarik Wisata di Pekanbaru [Potential of Bolu Kemojo Traditional Food as a Tourism Attraction in Pekanbaru]. Jurnal Manajemen Akuntansi (JUMSI), 3(2), 987–1000. https://doi.org/10.36987/jumsi.v3i2.4084
Dewi, E., & Berlianti. (2018). Peningkatan Produktivitas Umkm Produk Kue Jajanan Pasar Di Desa Marindal Ii Kecamatan Patumbak Kabupaten Deli Serdang [Productivity Improvement SME Market Snack Cake Products in Marindal Ii Village, Patumbak District, Deli Serdang Regency]. Abdimas Talenta, 3(2), 242–247. http://jurnal.usu.ac.id/abdimas
Kartika, D., Pratiwi, M., & Liana Gema, R. (2019). Fuzzy Logic Applications to Predict Total Production of PKO (Palm Kernel Oil). Journal of Physics: Conference Series, 1339(1). https://doi.org/10.1088/1742-6596/1339/1/012049
Khairudin, M., Refalda, R., Yatmono, S., Pramono, H. S., Triatmaja, A. K., & Shah, A. (2020). The mobile robot controls obstacle avoidance using a fuzzy logic controller. Indonesian Journal of Science and Technology, 5(3), 334–351. https://doi.org/10.17509/ijost.v5i3.24889
Latipah, L., Wahyuningsih, S., & Syaripuddin, S. (2019). Peramalan Pendapatan Asli Daerah Provinsi Kalimantan Timur Menggunakan Model Grey-Markov (1,1) [Forecasting Local Revenue of East Kalimantan Province Using Grey-Markov Model (1,1)]. Jambura Journal of Mathematics, 1(2), 89–103. https://doi.org/10.34312/jjom.v1i2.2347
Muhajirah, A., Safitri, E., Mardiana, T., Hartina, H., & Setiawan, A. (2019). Analisis Tingkat Akurasi Metode Neuro Fuzzy dalam Prediksi Data IPM di NTB [Analysis of the Accuracy Level of Neuro Fuzzy Method in Predicting HDI Data in NTB]. JTAM | Jurnal Teori Dan Aplikasi Matematika, 3(1), 58. https://doi.org/10.31764/jtam.v3i1.769
Napitupulu, S., Nababan, E. B., & Sihombing, P. (2020). Comparative Analysis of Fuzzy Inference Tsukamoto Mamdani and Sugeno in the Horticulture Export Selling Price. MECnIT 2020 - International Conference on Mechanical, Electronics, Computer, and Industrial Technology, 183–187. https://doi.org/10.1109/MECnIT48290.2020.9166587
Ngurah Diksa, I. G. B. (2021). Peramalan Gelombang Covid 19 Menggunakan Hybrid Nonlinear Regression Logistic – Double Exponential Smoothing di Indonesia dan Prancis [Covid 19 Wave Forecasting Using Hybrid Nonlinear Regression Logistic - Double Exponential Smoothing in Indonesia and France]. Jambura Journal of Mathematics, 3(1), 37–51. https://doi.org/10.34312/jjom.v3i1.7771
Nugroho, R. P., Setiawan, B. D., & Furqon, M. T. (2019). Penerapan Metode Fuzzy Tsukamoto untuk Menentukan Harga Sewa Hotel ( Studi Kasus : Gili Amor Boutique Resort , Dusun Gili Trawangan , Nusa Tenggara Barat) [Application of Tsukamoto Fuzzy Method to Determine Hotel Rental Price (Case Study: Gili Amor Boutique Resort, Gili Trawangan Hamlet, West Nusa Tenggara)]. Jurnal Pengembangan Teknologi Lnformasi Dan Llmu Komputer, 3(3), 2581–2588. https://j-ptiik.ub.ac.id/index.php/j-ptiik/article/view/4755
Pradini, E. D., Santoso, E., & Hidayat, N. (2022). Komparasi Hasil Metode Fuzzy Mamdani dan Tsukamoto untuk Prediksi Produksi Benih Padi (Studi Kasus: Kebun Benih Tunjung Kabupaten Bangkalan) [Comparison of the Results of Fuzzy Mamdani and Tsukamoto Methods for Predicting Rice Seed Production (Case Study: Tunjung Seed Farm, Bangkalan Regency)]. Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 6(2), 754–760. https://j-ptiik.ub.ac.id/index.php/j-ptiik/article/view/10589
Prima, P., Laili, U. F., Fanani, A., & Montolalu, B. (2020). Prediksi Hasil Ujian Nasional Siswa Menengah Atas : Pendekatan Data Mining [Prediction of National Examination Results for Senior High School Students: A Data Mining Approach]. Factor M: Focus Action Of Research Mathematic, 3(1), 60–68. https://doi.org/10.30762/f_m.v3i1.2450
Puspitasari, N., Tejawati, A., & Prakoso, F. (2019). Estimasi Stok Penerimaan Bahan Bakar Minyak Menggunakan Metode Fuzzy Tsukamoto [Estimation of Fuel Oil Receipt Stocks Using the Tsukamoto Fuzzy Method]. JRST (Jurnal Riset Sains Dan Teknologi), 3(1), 9. https://doi.org/10.30595/jrst.v3i1.3112
Resti, N. C., & Resti, N. C. (2019). Penerapan Metode Fuzzy Tsukamoto untuk Menentukan Jumlah Produksi Obat Ikan di UD. Indo Multi Fish Tulungagung [Application of the Tsukamoto Fuzzy Method to Determine the Amount of Fish Medicine Production at UD. Indo Multi Fish Tulungagung]. Factor M. https://doi.org/10.30762/f_m.v1i2.1426
Riansyah, A., Mulyono, S., & Roichani, M. (2021). Applying fuzzy proportional integral derivative on the internet of things for figs greenhouse. IAES International Journal of Artificial Intelligence, 10(3), 536–544. https://doi.org/10.11591/ijai.v10.i3.pp536-544
Saepullah, A. (2015). Comparative Analysis of Mamdani, Sugeno and Tsukamoto Method of Fuzzy Inference System for Air Conditioner Energy Saving. Journal of Intelligent Systems, 1(2), 143–147. https://journal.ilmukomputer.org/index.php?journal=jis&page=article&op=view&path%5B%5D=72
Setyono, A., & Aeni, S. N. (2018). Development of decision support system for ordering goods using fuzzy Tsukamoto. International Journal of Electrical and Computer Engineering, 8(2), 1182–1193. https://doi.org/10.11591/ijece.v8i2.pp1182-1193
Surur, A. M., Saputra, N. N., Laili, U. F., Mohamed, H. B., Anggraini, A., Yulita, N., & Kendari, I. (2023). Differential Equations to Predict the Number of Students as a Basis for Accreditation Preparation Study Program Policy. Journal Focus Action Of Research Mathematic (Factor M), 6(02), 125–142. https://doi.org/10.30762/f_m.vi2.2073
Thakkar, H., Shah, V., Yagnik, H., & Shah, M. (2021). Comparative anatomization of data mining and fuzzy logic techniques used in diabetes prognosis. Clinical EHealth, 4(2021), 12–23. https://doi.org/10.1016/j.ceh.2020.11.001
Tundo. (2020). Prediksi Produksi Minyak Kelapa Sawit Menggunakan Metode Fuzzy Tsukamoto Dengan Rule Yang Terbentuk Menggunakan Decision Tree Reptree [Prediction of Palm Oil Production Using the Tsukamoto Fuzzy Method With Rules Formed Using Decision Tree Reptree]. Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI, 9(2), 253–265. https://ejournal.undiksha.ac.id/index.php/janapati/article/view/23868
Tundo, T., Akbar, R., & Sela, E. I. (2020). Analisis Perbandingan Fuzzy Tsukamoto dan Sugeno dalam Menentukan Jumlah Produksi Kain Tenun Menggunakan Base Rule Decision Tree [Comparative Analysis of Fuzzy Tsukamoto and Sugeno in Determining the Production Quantity of Woven Fabrics Using Base Rule Decision Tree]. Jurnal Teknologi Informasi Dan Ilmu Komputer, 7(1), 171–180. https://doi.org/10.25126/jtiik.2020701751
Tundo, T., & Mahardika, F. (2023). Fuzzy Inference System Tsukamoto–Decision Tree C 4.5 in Predicting the Amount of Roof Tile Production in Kebumen. JTAM (Jurnal Teori Dan Aplikasi Matematika), 7(2), 533–544. https://doi.org/10.31764/jtam.v7i2.13034
Tundo, T., & Sela, E. I. (2018). Application of The Fuzzy Inference System Method to Predict The Number of Weaving Fabric Production. IJID (International Journal on Informatics for Development). https://doi.org/10.14421/ijid.2018.07105
Wahyuni, E. G. (2022). Membandingkan Tingkat Efisiensi Metode Tsukamoto dan Sugeno untuk kasus Pneumonia [Comparing the Efficiency Level of Tsukamoto and Sugeno Methods for Pneumonia Cases]. Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer Dan Teknologi Informasi, 7(2), 2021. https://doi.org/10.24014/coreit.v7i2.15085
Wibowo, Y., & Mukammila, R. M. (2022). Perencanaan Produksi Kopi Robusta (Coffea canephora) Menggunakan Metode Logika Fuzzy [Perencanaan Produksi Kopi Robusta (Coffea canephora) Menggunakan Metode Logika Fuzzy]. JOFE : Journal of Food Engineering, 1(4), 191–205. https://doi.org/10.25047/jofe.v1i4.3370
Wu, H., & Xu, Z. S. (2021). Fuzzy Logic in Decision Support: Methods, Applications and Future Trends. International Journal of Computers, Communications and Control, 16(1), 1–28. https://doi.org/10.15837/ijccc.2021.1.4044
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Jabal Yasir Nasution
This work is licensed under a Creative Commons Attribution 4.0 International License.
