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Create thesis anggun.py

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PutriAnggun10 2025-07-16 11:17:58 +07:00
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import time
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.metrics import confusion_matrix, accuracy_score, precision_score, recall_score, f1_score
# ======================= RULES DEFINISI ==========================
rules = [
({"G001", "G002", "G003", "G004", "G011", "G012"}, "Maag Ringan"),
({"G001", "G002", "G003", "G004", "G006", "G007", "G010", "G011", "G012"}, "Maag Sedang"),
({"G001", "G002", "G003", "G004", "G011", "G012", "G006"}, "Maag Sedang"),
({"G001", "G002", "G003", "G004", "G011", "G012", "G007"}, "Maag Sedang"),
({"G001", "G002", "G003", "G004", "G011", "G012", "G010"}, "Maag Sedang"),
({"G001", "G002", "G003", "G004", "G011", "G012", "G006", "G007"}, "Maag Sedang"),
({"G001", "G002", "G003", "G004", "G011", "G012", "G006", "G010"}, "Maag Sedang"),
({"G001", "G002", "G003", "G004", "G011", "G012", "G007", "G010"}, "Maag Sedang"),
({"G001", "G002", "G003", "G004", "G005", "G006", "G007", "G008", "G009", "G010", "G011d", "G012"}, "Maag Kronis"),
({"G001", "G002", "G003", "G004", "G011", "G012", "G006", "G007", "G010", "G005"}, "Maag Kronis"),
({"G001", "G002", "G003", "G004", "G011", "G012", "G006", "G007", "G010", "G008"}, "Maag Kronis"),
({"G001", "G002", "G003", "G004", "G011", "G012", "G006", "G007", "G010", "G009"}, "Maag Kronis"),
({"G001", "G002", "G003", "G004", "G011", "G012", "G006", "G007", "G010", "G005", "G008"}, "Maag Kronis"),
({"G001", "G002", "G003", "G004", "G011", "G012", "G006", "G007", "G010", "G005", "G009"}, "Maag Kronis"),
({"G001", "G002", "G003", "G004", "G011", "G012", "G006", "G007", "G010", "G008", "G009"}, "Maag Kronis")
]
# =================== FUNGSI FORWARD CHAINING =====================
def forward_chaining(rules, initial_facts):
facts = set(initial_facts)
diagnosis = None
new_facts = True
while new_facts:
new_facts = False
for conditions, result in rules:
if conditions.issubset(facts) and result not in facts:
facts.add(result)
diagnosis = result
new_facts = True
return diagnosis
# ======================= MENU ============================
mode = input("PILIH MENU:\n1. Diagnosa \n2. Evaluasi Performa \nPilihan (1/2): ").strip()
if mode == "1":
print("\n=== PREDIKSI ===")
print("Masukkan kode gejala (pisahkan dengan koma), contoh: G001,G002,G003")
input_gejala = input("Gejala: ")
initial_facts = set(g.strip().upper() for g in input_gejala.split(","))
start_time = time.time()
hasil_diagnosa = forward_chaining(rules, initial_facts)
end_time = time.time()
print("\n=== Hasil Diagnosa ===")
print(f"Gejala yang dimasukkan: {initial_facts}")
print(f"Diagnosa: {hasil_diagnosa if hasil_diagnosa else 'Bukan Maag'}")
print(f"Waktu Eksekusi: {end_time - start_time:.6f} detik")
else:
print("\n=== PERFORMA ===")
# ================== DATA EVALUASI ==========================
y_true = np.array([1, 1, 2, 1, 2, 1, 1, 2, 1, 1, 2, 2, 1, 2, 1, 2, 2, 2, 1, 1, 1, 2, 2, 1, 2, 0, 0, 0, 0, 0, 0])
y_pred = np.array([1, 1, 2, 1, 2, 1, 1, 2, 1, 1, 2, 0, 0, 2, 1, 2, 2, 2, 1, 1, 1, 2, 2, 1, 2, 0, 0, 0, 0, 0, 2])
# ================== CONFUSION MATRIX =======================
cm = confusion_matrix(y_true, y_pred)
plt.figure(figsize=(6, 5))
sns.heatmap(cm, annot=True, fmt="d", cmap="Blues",
xticklabels=["Bukan Maag", "Maag Ringan", "Maag Sedang"],
yticklabels=["Bukan Maag", "Maag Ringan", "Maag Sedang"])
plt.xlabel("Predicted Label")
plt.ylabel("True Label")
plt.title("Confusion Matrix - Diagnosa Maag")
plt.show()
# ================== KONVERSI BINER ==========================
y_true_binary = np.array([1 if x in [1, 2] else 0 for x in y_true])
y_pred_binary = np.array([1 if x in [1, 2] else 0 for x in y_pred])
cm_binary = confusion_matrix(y_true_binary, y_pred_binary)
TN, FP, FN, TP = cm_binary.ravel()
print("\nConfusion Matrix (Biner):\n", cm_binary)
print(f"True Positive (TP): {TP}")
print(f"False Positive (FP): {FP}")
print(f"True Negative (TN): {TN}")
print(f"False Negative (FN): {FN}")
accuracy_binary = accuracy_score(y_true_binary, y_pred_binary)
precision_binary = precision_score(y_true_binary, y_pred_binary)
recall_binary = recall_score(y_true_binary, y_pred_binary)
f1_binary = f1_score(y_true_binary, y_pred_binary)
print("\n=== Metrik Evaluasi ===")
print(f"Akurasi : {accuracy_binary:.2f}")
print(f"Presisi : {precision_binary:.2f}")
print(f"Recall : {recall_binary:.2f}")
print(f"F1-Score : {f1_binary:.2f}")