fuzzyMain.py

# Fuzzy Inference System Mamdani
# Abdan Subekti  2010501038
# **********Requirements**********
# matplolib, numpy, fuzzy-expert

import matplotlib.pyplot as plt
import numpy as np
from fuzzy_expert.variable import FuzzyVariable
from fuzzy_expert.rule import FuzzyRule
from fuzzy_expert.inference import DecompositionalInference

# Input Himpunan Fuzzy
variables = {
    # INPUT
    "Pelayanan": FuzzyVariable(
        universe_range=(0, 100),                    # Sumbu x
        terms={
            "Buruk": ("trapmf", 0, 0, 15, 40),      # trapmf = Kurva Trapesium
            "Biasa": ("trimf", 25, 50, 75),         # trimf  = Kurva Segitiga
            "Bagus": ("trapmf", 60, 85, 100, 100),
        },
    ),
    "Kerapian": FuzzyVariable(
        universe_range=(0, 100),
        terms={
            "Kurang": ("trapmf", 0, 0, 40, 60),
            "Cukup": ("trimf", 40, 60, 80),
            "Baik": ("trapmf", 60, 80, 100, 100),
        },
    ),
    "Terlambat": FuzzyVariable(
        universe_range=(0, 30),
        terms={
            "Jarang": ("trapmf", 0, 0, 2.5, 7.5),
            "Kadang": ("trimf", 2.5, 10, 17.5),
            "Sering": ("trapmf", 12.5, 17.5, 30, 30),
        },
    ),
    "Produk_Terjual": FuzzyVariable(
        universe_range=(0, 900),
        terms={
            "Sedikit": ("trapmf", 0, 0, 300, 500),
            "Sedang": ("trimf", 350, 500, 650),
            "Banyak": ("trapmf", 500, 700, 900, 900),
        },
    ),
    # OUTPUT
    "Insentif": FuzzyVariable(
        universe_range=(0, 1000),
        terms={
            "Rendah": ("trapmf", 0, 0, 100, 500),
            "Menengah": ("trimf", 100, 500, 900),
            "Tinggi": ("trapmf", 500, 900, 1000, 1000),
        },
    ),
}

# Rules/Inferensi Fuzzy
rules = [
    # 1
    FuzzyRule(
        premise=[
            ("Pelayanan", "Buruk"),
            ("AND", "Kerapian", "Kurang"),
            ("AND", "Terlambat", "Sering"),
            ("AND", "Produk_Terjual", "Sedikit"),
        ],
        consequence=[("Insentif", "Rendah")],
    ),
    # 2
    FuzzyRule(
        premise=[
            ("Pelayanan", "Biasa"),
            ("AND", "Kerapian", "Cukup"),
            ("AND", "Terlambat", "Kadang"),
            ("AND", "Produk_Terjual", "Sedang"),
        ],
        consequence=[("Insentif", "Menengah")],
    ),
    # 3
    FuzzyRule(
        premise=[
            ("Pelayanan", "Bagus"),
            ("AND", "Kerapian", "Baik"),
            ("AND", "Terlambat", "Jarang"),
            ("AND", "Produk_Terjual", "Banyak"),
        ],
        consequence=[("Insentif", "Tinggi")],
    ),
]

# Defuzzyfikasi & Variabelnya
model = DecompositionalInference(
    and_operator="min",
    or_operator="max",
    implication_operator="Rc",
    composition_operator="max-min",
    production_link="max",
    defuzzification_operator="cog",  # Metode Defuzzyfikasi (cog, bisektor, mom, som, lom)
)

# Input data dan visualisasi Fuzzy
"""plt.figure(figsize=(10, 6))         # Ukuran window (Inch)
model.plot(
    variables=variables,
    rules=rules,
    Pelayanan=20,                   # Input Variabel
    Kerapian=10,
    Terlambat=18,
    Produk_Terjual=230,
)
plt.show()"""

# visualisasi input crisp fuzzy
plt.figure(figsize=(6, 2.5))
plt.grid(True)
plt.title("Pelayanan")
variables["Pelayanan"].plot()

plt.figure(figsize=(6, 2.5))
plt.grid(True)
plt.title("Kerapian")
variables["Kerapian"].plot()

plt.figure(figsize=(6, 2.5))
plt.grid(True)
plt.title("Keterlambatan dalam sebulan")
plt.xlabel("Akumulasi waktu (Jam)")
variables["Terlambat"].plot()

plt.figure(figsize=(6, 2.5))
plt.grid(True)
plt.title("Produk Terjual (Unit)")
plt.xlabel("Jumlah Barang")
variables["Produk_Terjual"].plot()

plt.figure(figsize=(6, 2.5))
plt.grid(True)
plt.title("Insentif (Rupiah)")
variables["Insentif"].plot()
plt.show()