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refactor: add performance f1 score positive metric

This commit is contained in:
Mahen 2026-04-06 10:44:33 +07:00
parent 3dee52c56a
commit 817924bd8c
2 changed files with 30 additions and 23 deletions
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public/img/final_grafik_perbandingan_skripsi.png (Stored with Git LFS) Normal file
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src/utils/visualization.py
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@ -4,38 +4,41 @@ import numpy as np
# ==========================================
# 1. INPUT DATA HASIL EKSPERIMEN
# ==========================================
# Masukkan angka hasil run Anda di sini
scenarios = ['Skenario 1\n(Baseline)', 'Skenario 2\n(Tuned)', 'Skenario 3\n(Tuned + SMOTE + Chi-Square)']
# Data Metrik (Salin dari Classification Report Anda)
accuracy = [0.71, 0.73, 0.73] # Akurasi Global
macro_f1 = [0.62, 0.66, 0.67] # Keseimbangan Model
f1_negatif = [0.67, 0.69, 0.68] # Kemampuan Deteksi Komplain
f1_netral = [0.34, 0.42, 0.45] # Kemampuan Deteksi Ambigu
# Data Metrik
accuracy = [0.71, 0.73, 0.73]
macro_f1 = [0.62, 0.66, 0.67]
f1_negatif = [0.67, 0.69, 0.68]
f1_netral = [0.34, 0.42, 0.45]
f1_positif = [0.86, 0.86, 0.87]
# ==========================================
# 2. KONFIGURASI PLOT
# 2. KONFIGURASI PLOT (Disesuaikan untuk 5 Batang)
# ==========================================
x = np.arange(len(scenarios)) # Label lokasi
width = 0.2 # Lebar batang
width = 0.15 # Lebar batang diperkecil agar 5 batang muat berdampingan
fig, ax = plt.subplots(figsize=(12, 7)) # Ukuran gambar
fig, ax = plt.subplots(figsize=(14, 8)) # Ukuran sedikit diperlebar
# Membuat 4 batang untuk setiap skenario
rects1 = ax.bar(x - 1.5*width, accuracy, width, label='Accuracy', color='#d3d3d3', edgecolor='grey') # Abu-abu
rects2 = ax.bar(x - 0.5*width, macro_f1, width, label='Macro F1-Score',color='#3498db') # Biru
rects3 = ax.bar(x + 0.5*width, f1_negatif, width, label='F1 Negatif', color='#e74c3c') # Hijau
rects4 = ax.bar(x + 1.5*width, f1_netral, width, label='F1 Netral', color="#ffd468") # Merah (Highlight Peningkatan)
# Membuat 5 batang dengan offset yang simetris
# Posisi: -2, -1, 0, +1, +2 kali lebar batang
rects1 = ax.bar(x - 2*width, accuracy, width, label='Accuracy', color='#d3d3d3', edgecolor='grey')
rects2 = ax.bar(x - 1*width, macro_f1, width, label='Macro F1-Score', color='#3498db')
rects3 = ax.bar(x + 0*width, f1_negatif, width, label='F1 Negatif', color='#e74c3c')
rects4 = ax.bar(x + 1*width, f1_netral, width, label='F1 Netral', color="#ffd468")
rects5 = ax.bar(x + 2*width, f1_positif, width, label='F1 Positif', color="#2ecc71") # Hijau Emerald
# ==========================================
# 3. PERCANTIK TAMPILAN
# ==========================================
ax.set_ylabel('Skor (0.0 - 1.0)', fontsize=12)
ax.set_ylabel('Skor (0.0 - 1.0)', fontsize=12, fontweight='bold')
ax.set_title('Perbandingan Performa Model per Skenario', fontsize=14, pad=20, fontweight='bold')
ax.set_xticks(x)
ax.set_xticklabels(scenarios, fontsize=12)
ax.legend(loc='upper left', fontsize=11)
ax.set_ylim(0, 1.1) # Batas atas sumbu Y sedikit dilebihkan agar label masuk
ax.grid(axis='y', linestyle='--', alpha=0.5)
ax.set_xticklabels(scenarios, fontsize=11)
ax.legend(loc='upper center', bbox_to_anchor=(0.5, -0.1), ncol=5, fontsize=10) # Legend diletakkan di bawah agar rapi
ax.set_ylim(0, 1.1)
ax.grid(axis='y', linestyle='--', alpha=0.3)
# Fungsi otomatis memberi label angka di atas batang
def autolabel(rects):
@ -43,20 +46,21 @@ def autolabel(rects):
height = rect.get_height()
ax.annotate(f'{height:.2f}',
xy=(rect.get_x() + rect.get_width() / 2, height),
xytext=(0, 5), # 5 points vertical offset
xytext=(0, 5),
textcoords="offset points",
ha='center', va='bottom', fontsize=10, fontweight='bold')
ha='center', va='bottom', fontsize=9, fontweight='bold')
autolabel(rects1)
autolabel(rects2)
autolabel(rects3)
autolabel(rects4)
autolabel(rects5)
# ==========================================
# 4. SIMPAN HASIL
# ==========================================
plt.tight_layout()
filename = 'grafik_perbandingan_skripsi.png'
plt.savefig(filename, dpi=300) # dpi=300 agar gambar tajam saat dicetak
filename = 'final_grafik_perbandingan_skripsi.png'
plt.savefig(filename, dpi=300, bbox_inches='tight')
print(f"✅ Grafik berhasil disimpan sebagai: {filename}")
plt.show()