Kinerja Logika Fuzzy Sugeno dalam Menangani Prediksi Kain Tenun dengan Kombinasi Random Tree dalam Membangun Rule

Tundo Tundo

doi:10.23887/janapati.v10i2.29081

Authors

Tundo Tundo Universitas Putra Bangsa

DOI:

https://doi.org/10.23887/janapati.v10i2.29081

Keywords:

Logika fuzzy, Metode fuzzy Sugeno, Rule, Random tree, Prediksi.

Abstract

This study describes the performance of Sugeno fuzzy logic in determining the amount of woven fabric production by using a combination of random tree decision trees in forming rules. The criteria used in determining the amount of production, namely, production costs, demand, and stock obtained from woven fabric entrepreneurs in Mlaki Wanarejan Utara Pemalang. The random tree decision tree is used, one of which is to automatically generate rules from the available data without consulting with experts, in addition to introducing random trees in the field of research because there are still few studies using this decision tree. The results of this study, it was found that the accuracy while the prediction results tested obtained an Average Forecasting Error Rate (AFER) of 42% with a value 58% truth after being compared with the actual production data.

Keywords : Fuzzy Logic, Fuzzy Sugeno Method, Rule, Random tree, Prediction.

References

S. Sivagowry and D. M, “An Intelligent System based on Fuzzy Inference System to prophesy the brutality of Cardio Vascular Disease,” Adv. Comput. Sci. an Int. J., vol. 4, no. 6, pp. 119–125, 2015.

J. Hidayati, Sukardi, S. Ani, Sugiharto, and A. M. Fauzi, “Optimization of Business Partners Feasibility for Oil Palm Revitalization Using Fuzzy Approach,” Int. J. Adv. Sci. Eng. Inf. Technol., vol. 3, no. 2, pp. 29–35, 2013.

A. Bahroini, A. Farmadi, and R. A. Nugroho, “Prediksi Permintaan Produk Mie Instan Dengan Metode Fuzzy Takagi-Sugeno,” Klik - Kumpul. J. Ilmu Komput., vol. 3, no. 2, p. 220, 2016.

K. S. Elekar and M. M. Waghmare, “Study of Tree Base Data Mining Algorithms for Network Intrusion Detection,” Int. J. Recent Innov. Trends Comput. Commun., vol. 2, no. 10, pp. 3253–3257, 2014.

A. K. Hamoud, A. S. Hashim, and W. A. Awadh, “Predicting Student Performance in Higher Education Institutions Using Decision Tree Analysis,” Int. J. Interact. Multimed. Artif. Intell., vol. 5, no. 2, p. 26, 2018.

S. S. Alaoui, Y. Farhaoui, and B. Aksasse, “Classification Algorithms in Data Mining : A Survey,” IJSRCSEIT, vol. 3, no. 1, pp. 349–355, 2018.

T. Tundo and S. ’Uyun, “Penerapan Decision Tree J48 dan Reptree dalam Menentukan Prediksi Produksi Minyak Kelapa Sawit menggunakan Metode Fuzzy Tsukamoto,” J. Teknol. Inf. dan Ilmu Komput., vol. 7, no. 3, p. 483, 2020.

Tundo, R. Akbar, and E. I. Sela, “Analisis Perbandingan Fuzzy Tsukamoto Dan Sugeno Dalam Menentukan Jumlah Produksi Kain Tenun Menggunakan Base Rule Decision Tree,” J. Teknol. Inf. dan Ilmu Komput., vol. 7, no. 1, pp. 171–180, 2020.

C. P. P. Maibang and A. M. Husein, “Prediksi Jumlah Produksi Palm Oil Menggunakan Fuzzy Inference System Mamdani,” J. Teknol. dan Ilmu Komput. Prima, vol. 2, no. 2, p. 19, 2019.

M. Djunaidi, “Penentuan Jumlah Produksi Dengan,” J. Ilm. Tek. Insudtri, vol. 4, no. 2, pp. 95–104, 2005.

L. Salisa Setiawati, I. Budiman, and O. Soesanto, “Penerapan Fuzzy Inference System Takagi-Sugeno-Kang pada Sistem Pakar Diagnosa Penyakit Gigi,” J. Ilmu Komput., vol. 04, no. 01, pp. 1–10, 2016.

T. A. Mujahid and E. I. Sela, “Analisis Perbandingan Rule Pakar dan Decision Tree J48 Dalam Menentukan Jumlah Produksi Kain Tenun Menggunakan Metode Fuzzy Tsukamoto,” JURIKOM (Jurnal Ris. Komputer), vol. 6, no. 5, pp. 501–505, 2019.

Tundo, “PREDIKSI PRODUKSI MINYAK KELAPA SAWIT MENGGUNAKAN METODE FUZZY TSUKAMOTO DENGAN RULE YANG TERBENTUK MENGGUNAKAN DECISION TREE REPTREE,” JANAPATI, vol. 9, no. 2, pp. 253–265, 2020.

S. Batubara, “Analisis Perbandingan Metode Fuzzy Mamdani Dan Fuzzy Sugeno Untuk Penentuan Kualitas Cor Beton Instan,” It J. Res. Dev., vol. 2, no. 1, pp. 1–11, 2017.

D. Kartika, R. Sovia, and H. M. Sandawa, “Penerapan Metode Fuzzy Mamdani Untuk Memprediksi Angka Penjualan Token Berdasarkan Persediaan Dan Jumlah,” J. KomTekInfo, vol. 5, no. 1, pp. 81–95, 2018.

T. M. Tuan et al., “M-CFIS-R: Mamdani complex fuzzy inference system with rule reduction using complex fuzzy measures in granular computing,” Mathematics, vol. 8, no. 5, 2020.

Tundo and E. I. Sela, “APPLICATION OF THE FUZZY INFERENCE SYSTEM METHOD TO PREDICT,” Int. J. Informatics Dev., vol. 7, no. 1, pp. 1–9, 2018.

S. D. P. Mustika Sari, H. Ginardi, and C. Fatichah, “Penentuan Harga dengan Menggunakan Sistem Inferensi Fuzzy Tsukamoto Pada Rancang Bangun Aplikasi ‘Finding-Tutor,’” J. Tek. ITS, vol. 6, no. 2, 2017.