From 817924bd8c2cd607cb2dc12f9466c6cd85369950 Mon Sep 17 00:00:00 2001
From: Mahen <mahennekkers27@gmail.com>
Date: Mon, 6 Apr 2026 10:44:33 +0700
Subject: [PATCH] refactor: add performance f1 score positive metric

---
 .../img/final_grafik_perbandingan_skripsi.png |  3 ++
 src/utils/visualization.py                    | 50 ++++++++++---------
 2 files changed, 30 insertions(+), 23 deletions(-)
 create mode 100644 public/img/final_grafik_perbandingan_skripsi.png

diff --git a/public/img/final_grafik_perbandingan_skripsi.png b/public/img/final_grafik_perbandingan_skripsi.png
new file mode 100644
index 0000000..791466a
--- /dev/null
+++ b/public/img/final_grafik_perbandingan_skripsi.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3c947f231edbe687195948276137d2983ee1af783c370b52fb99bee7da6e4741
+size 164863
diff --git a/src/utils/visualization.py b/src/utils/visualization.py
index 4151594..d5b5b21 100644
--- a/src/utils/visualization.py
+++ b/src/utils/visualization.py
@@ -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()
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