bta_xgboost.py

# -*- coding: utf-8 -*-
"""XGBOOST.ipynb

Automatically generated by Colab.

Original file is located at
    https://colab.research.google.com/drive/1tDtwG0rHNfmKvSU-iQ32jGa1xB4HAqCn
"""

# -*- coding: utf-8 -*-
"""
BTA_Predict_FINAL.ipynb
================================================
BTA Thickness Prediction — Production-Ready Version
Strategy: Anchor-Based Wear Rate Model
          (didasarkan insight bahwa:
           - Ketebalan diukur manual → diskrit, bukan harian
           - Korelasi waktu vs ketebalan = -0.995
           - Korelasi suhu vs ketebalan = -0.16 (lemah)
           → Prediksi = Anchor terakhir + laju aus + suhu modifier)
================================================
"""

# ============================================================
# CELL 1: Install & Import
# ============================================================
# !pip install -q xgboost scikit-learn pandas numpy matplotlib seaborn

import pandas as pd
import numpy as np
from xgboost import XGBRegressor
from sklearn.linear_model import LinearRegression, Ridge
from sklearn.preprocessing import PolynomialFeatures
from sklearn.pipeline import Pipeline
from sklearn.metrics import mean_absolute_error, mean_squared_error
from datetime import datetime, timedelta
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import seaborn as sns
import warnings, io

warnings.filterwarnings('ignore')
print("✅ Libraries loaded.")


# ============================================================
# CELL 2: Upload & Load Data
# ============================================================
import os
import sys
import glob

# Cari semua berkas CSV di folder saat ini
csv_files = glob.glob('*.csv') + glob.glob('*.csv.csv')
# Hilangkan duplikat dan rapikan path
csv_files = list(set([os.path.basename(f) for f in csv_files]))

# Prioritaskan argumen baris perintah
if len(sys.argv) > 1:
    filename = sys.argv[1]
    if not os.path.exists(filename):
        print(f"❌ File '{filename}' yang diberikan melalui argumen tidak ditemukan!")
        sys.exit(1)
else:
    # Jika tidak ada argumen, cari CSV secara otomatis
    if not csv_files:
        raise FileNotFoundError("Tidak ditemukan file CSV di direktori saat ini.")
    elif len(csv_files) == 1:
        filename = csv_files[0]
    else:
        print("\n📂 Ditemukan beberapa file CSV di direktori saat ini:")
        for idx, f in enumerate(csv_files, 1):
            print(f"   [{idx}] {f}")
        
        # Cari default file jika ada
        default_file = 'data-temp-clean.csv'
        if default_file not in csv_files and 'data-temp-clean.csv.csv' in csv_files:
            default_file = 'data-temp-clean.csv.csv'
        
        default_idx = csv_files.index(default_file) + 1 if default_file in csv_files else 1
        
        try:
            choice = input(f"Pilih file untuk diproses (Tekan Enter untuk default [{csv_files[default_idx-1]}]): ").strip()
            if choice == "":
                filename = csv_files[default_idx-1]
            else:
                choice_idx = int(choice) - 1
                if 0 <= choice_idx < len(csv_files):
                    filename = csv_files[choice_idx]
                else:
                    print("❌ Pilihan tidak valid. Menggunakan file default.")
                    filename = csv_files[default_idx-1]
        except Exception:
            filename = csv_files[default_idx-1]

print(f"📂 Membaca file data: '{filename}'")
df = pd.read_csv(filename)

# Menentukan nama dasar berkas untuk keluaran dinamis
base_name = os.path.splitext(filename)[0]
if base_name.endswith('.csv'):
    base_name = os.path.splitext(base_name)[0]

# bersihin nama kolom dari spasi
df.columns = [str(c).strip() for c in df.columns]

# rename kolomnya biar gampang dipanggil
df = df.rename(columns={
    'Tanggal'           : 'tanggal',
    'Cone Depan (°C)'   : 'cone_depan',
    'Bodi Tengah (°C)'  : 'body_tengah',
    'Cone Belakang (°C)': 'cone_belakang',
    'Ketebalan BTA (mm)': 'ketebalan'
})

# pastiin tipe data udah angka
for col in ['cone_depan', 'body_tengah', 'cone_belakang', 'ketebalan']:
    df[col] = pd.to_numeric(df[col], errors='coerce')

df['tanggal'] = pd.to_datetime(df['tanggal'], dayfirst=False, errors='coerce')
df = df.dropna(subset=['cone_depan', 'ketebalan', 'tanggal']).sort_values('tanggal').reset_index(drop=True)

# referensi waktu dari hari pertama operasi
T0        = df['tanggal'].min()
df['hari_ke'] = (df['tanggal'] - T0).dt.days

print(f"✅ Data: {len(df)} baris | {df['tanggal'].min().date()} s/d {df['tanggal'].max().date()}")
print(f"   Ketebalan: min={df['ketebalan'].min():.0f}mm | max={df['ketebalan'].max():.0f}mm")
print(f"   Pengukuran terakhir: {df['ketebalan'].iloc[-1]:.0f}mm pada {df['tanggal'].iloc[-1].date()}")

# ============================================================
# CELL 3: Hitung Titik Pengukuran & Laju Aus
# ============================================================
#
# INSIGHT: Ketebalan BTA diukur manual secara berkala (tidak harian).
# Data harian hanya mengulang nilai pengukuran terakhir sampai ada
# pengukuran baru. Oleh karena itu, model yang benar adalah:
#   Ketebalan_hari_ini = Ketebalan_terakhir_diukur + (laju_aus × hari_berlalu)
#

# Identifikasi titik pengukuran aktual (ketika nilai berubah)
mask_change  = df['ketebalan'] != df['ketebalan'].shift(1)
df_ukur      = df[mask_change].copy().reset_index(drop=True)

# Hitung laju aus antar pengukuran
df_ukur['delta_tebal'] = df_ukur['ketebalan'].diff()          # negatif = menipis
df_ukur['delta_hari']  = df_ukur['hari_ke'].diff()
df_ukur['laju_aus']    = df_ukur['delta_tebal'] / df_ukur['delta_hari']  # mm/hari

# Suhu rata-rata selama periode antar pengukuran
for i in range(1, len(df_ukur)):
    h_start = df_ukur.loc[i-1, 'hari_ke']
    h_end   = df_ukur.loc[i,   'hari_ke']
    mask    = (df['hari_ke'] >= h_start) & (df['hari_ke'] < h_end)
    df_ukur.loc[i, 'suhu_avg_periode'] = df.loc[mask, 'cone_depan'].add(
        df.loc[mask, 'body_tengah']).add(df.loc[mask, 'cone_belakang']).div(3).mean()

df_ukur = df_ukur.dropna(subset=['laju_aus'])

# Laju aus statistik
laju_mean   = df_ukur['laju_aus'].mean()          # mm/hari (negatif)
laju_recent = df_ukur['laju_aus'].tail(5).mean()  # laju 5 periode terakhir

print(f"\n📊 Analisis Laju Aus:")
print(f"   Rata-rata historis   : {laju_mean:.4f} mm/hari")
print(f"   Rata-rata 5 terbaru  : {laju_recent:.4f} mm/hari")
print(f"   Pengukuran terakhir  : {df_ukur['ketebalan'].iloc[-1]:.0f}mm "
      f"pada hari ke-{df_ukur['hari_ke'].iloc[-1]}")
print(f"\n{df_ukur[['tanggal','ketebalan','delta_hari','laju_aus','suhu_avg_periode']].to_string(index=False)}")


# ============================================================
# CELL 4: Model XGBoost — Prediksi Laju Aus dari Suhu
# ============================================================
#
# Karena laju aus antar periode bervariasi, kita gunakan XGBoost
# untuk mempelajari: "Seberapa cepat BTA menipis berdasarkan suhu?"
# Lalu gunakan laju ini untuk proyeksi ke depan.
#

# Fitur: suhu rata-rata & karakteristik suhu selama satu periode
X_rate = df_ukur[['suhu_avg_periode']].fillna(df_ukur['suhu_avg_periode'].mean())
y_rate = df_ukur['laju_aus']   # target: laju aus (mm/hari)

model_rate = XGBRegressor(
    n_estimators    = 200,
    learning_rate   = 0.05,
    max_depth       = 3,
    subsample       = 0.8,
    random_state    = 42
)
model_rate.fit(X_rate, y_rate)

print(f"\n✅ Model laju aus dilatih pada {len(df_ukur)} titik pengukuran.")


# ============================================================
# CELL 5: Fungsi Prediksi Harian (PRODUCTION READY)
# ============================================================

# State terakhir yang diketahui
TEBAL_TERAKHIR  = float(df['ketebalan'].iloc[-1])           # mm
TANGGAL_UKUR    = df['tanggal'].iloc[-1]                    # tanggal pengukuran aktual terakhir
HARI_UKUR       = int(df['hari_ke'].iloc[-1])               # hari ke- dari pengukuran tsb

# Batas operasi
BATAS_KRITIS  = 115.0   # mm
BATAS_WARNING = 130.0   # mm
BATAS_SUHU    = 400.0   # °C
WARN_SUHU     = 375.0   # °C


def predict_bta_daily(t_depan: float, t_tengah: float, t_belakang: float,
                      tanggal_cek: str = None,
                      tebal_aktual: float = None):
    """
    Prediksi ketebalan BTA untuk monitoring harian produksi.

    Parameters
    ----------
    t_depan      : Suhu Cone Depan (°C) hari ini
    t_tengah     : Suhu Bodi Tengah (°C) hari ini
    t_belakang   : Suhu Cone Belakang (°C) hari ini
    tanggal_cek  : Tanggal pengecekan 'DD/MM/YYYY' (default: hari ini)
    tebal_aktual : (Opsional) Jika ada hasil pengukuran BTA hari ini,
                   masukkan di sini untuk update anchor secara otomatis
    """
    global TEBAL_TERAKHIR, TANGGAL_UKUR, HARI_UKUR

    # Update anchor jika ada pengukuran aktual baru
    if tebal_aktual is not None:
        TEBAL_TERAKHIR = float(tebal_aktual)
        TANGGAL_UKUR   = datetime.now() if tanggal_cek is None else datetime.strptime(tanggal_cek, '%d/%m/%Y')
        HARI_UKUR      = int((TANGGAL_UKUR - T0).days)
        print(f"🔄 Anchor diperbarui: {TEBAL_TERAKHIR}mm pada {TANGGAL_UKUR.date()}")

    # Tanggal hari ini
    tgl_cek = datetime.now() if tanggal_cek is None else datetime.strptime(tanggal_cek, '%d/%m/%Y')
    hari_sejak_ukur = (tgl_cek - (TANGGAL_UKUR if isinstance(TANGGAL_UKUR, datetime)
                                  else pd.Timestamp(TANGGAL_UKUR).to_pydatetime())).days

    # Suhu hari ini
    suhu_avg = (t_depan + t_tengah + t_belakang) / 3

    # Prediksi laju aus berdasarkan suhu hari ini
    laju_pred = model_rate.predict(pd.DataFrame([[suhu_avg]], columns=['suhu_avg_periode']))[0]

    # Koreksi: gunakan weighted average antara laju historis dan prediksi model
    # (karena data terbatas, beri bobot lebih ke historis)
    laju_efektif = 0.4 * laju_pred + 0.6 * laju_recent

    # Proyeksi ketebalan hari ini
    tebal_pred = TEBAL_TERAKHIR + (laju_efektif * hari_sejak_ukur)
    tebal_pred = max(tebal_pred, 100.0)   # floor fisik

    # Estimasi sisa hari ke batas kritis
    if tebal_pred > BATAS_KRITIS and laju_efektif < 0:
        sisa_tebal = tebal_pred - BATAS_KRITIS
        sisa_hari  = int(sisa_tebal / abs(laju_efektif))
        tgl_kritis = tgl_cek + timedelta(days=sisa_hari)
    else:
        sisa_hari  = 0
        tgl_kritis = tgl_cek

    # Status & alert
    if suhu_avg > BATAS_SUHU or tebal_pred < BATAS_KRITIS:
        status = "🔴 CRITICAL"
        aksi   = "SEGERA PERBAIKAN / SLAGGING — Koordinasi maintenance sekarang!"
        border = "!"*52
    elif suhu_avg > WARN_SUHU or tebal_pred < BATAS_WARNING:
        status = "🟡 WARNING"
        aksi   = "Siapkan jadwal maintenance. Monitor lebih sering."
        border = "="*52
    else:
        status = "🟢 AMAN"
        aksi   = "Operasi normal. Lanjutkan monitoring rutin."
        border = "-"*52

    print(f"\n{border}")
    print(f"   🏭 BTA DAILY MONITORING — {tgl_cek.strftime('%d %B %Y')}")
    print(f"{border}")
    print(f"  📡 Input Suhu      : Depan={t_depan}°C | Tengah={t_tengah}°C | Belakang={t_belakang}°C")
    print(f"  🌡️  Suhu Rata-rata  : {suhu_avg:.1f}°C")
    print(f"  📅 Anchor Terakhir : {TEBAL_TERAKHIR:.0f}mm ({(TANGGAL_UKUR if isinstance(TANGGAL_UKUR, datetime) else pd.Timestamp(TANGGAL_UKUR).to_pydatetime()).strftime('%d %b %Y')})")
    print(f"  ⏱️  Hari sejak ukur : {hari_sejak_ukur} hari")
    print(f"  📉 Laju Aus Est.   : {laju_efektif:.4f} mm/hari")
    print(f"{'='*52}")
    print(f"  🧱 PREDIKSI TEBAL  : {tebal_pred:.1f} mm")
    print(f"  📊 Status          : {status}")
    print(f"  ⚡ Aksi            : {aksi}")
    print(f"{'-'*52}")
    if tebal_pred > BATAS_KRITIS:
        print(f"  ⏳ Estimasi Sisa   : ± {sisa_hari} hari lagi")
        print(f"  🔧 Est. Kritis     : {tgl_kritis.strftime('%d %B %Y')}")
    else:
        print(f"  ⏳ Status          : Ketebalan sudah di/bawah batas kritis!")
    print(f"{border}\n")

    return {
        'tebal_prediksi' : round(tebal_pred, 1),
        'laju_aus'       : round(laju_efektif, 4),
        'sisa_hari'      : sisa_hari,
        'est_kritis'     : tgl_kritis.strftime('%d %B %Y'),
        'status'         : status
    }


# ============================================================
# CELL 6: Uji Coba Prediksi
# ============================================================
print("=" * 60)
print("🧪 UJI COBA PREDIKSI HARIAN")
print("=" * 60)

# Contoh 1: Suhu normal
_ = predict_bta_daily(t_depan=378, t_tengah=310, t_belakang=355)

# Contoh 2: Suhu tinggi (mendekati kritis)
_ = predict_bta_daily(t_depan=435, t_tengah=410, t_belakang=372)

# Contoh 3: Suhu rendah (kondisi baik)
_ = predict_bta_daily(t_depan=320, t_tengah=295, t_belakang=340)

# Contoh 4: Ada pengukuran BTA baru hari ini (misal diukur = 118mm)
# Ini akan update anchor sehingga prediksi ke depan lebih akurat
# _ = predict_bta_daily(t_depan=350, t_tengah=320, t_belakang=360, tebal_aktual=118)


# ============================================================
# CELL 7: Visualisasi Historis + Proyeksi
# ============================================================
sns.set_theme(style='whitegrid')
fig, axes = plt.subplots(2, 1, figsize=(16, 12))
fig.suptitle('BTA Thickness Monitoring — Rotary Furnace', fontsize=15, fontweight='bold')

# --- Panel 1: History + Proyeksi ---
ax1 = axes[0]

# Data aktual (stepwise — karena nilai hanya berubah saat diukur)
ax1.step(df['tanggal'], df['ketebalan'], where='post',
         color='royalblue', linewidth=2.5, label='Ketebalan Aktual (Pengukuran Manual)', zorder=3)

# Titik pengukuran aktual
ax1.scatter(df_ukur['tanggal'], df_ukur['ketebalan'],
            color='royalblue', s=80, zorder=5, label='Titik Pengukuran Aktual')

# Proyeksi 90 hari ke depan
tgl_terakhir = df['tanggal'].max()
proj_hari    = 90
proj_dates   = [tgl_terakhir + timedelta(days=i) for i in range(0, proj_hari+1)]

# Gunakan laju recent untuk proyeksi
suhu_asumsi  = df['cone_depan'].add(df['body_tengah']).add(df['cone_belakang']).div(3).tail(14).mean()
laju_proj    = model_rate.predict(pd.DataFrame([[suhu_asumsi]], columns=['suhu_avg_periode']))[0]
laju_proj    = 0.4 * laju_proj + 0.6 * laju_recent  # weighted

proj_tebal   = [max(TEBAL_TERAKHIR + laju_proj * i, 95) for i in range(0, proj_hari+1)]

ax1.plot(proj_dates, proj_tebal,
         color='darkorange', linewidth=2, linestyle='--',
         label=f'Proyeksi 90 Hari (laju={laju_proj:.4f} mm/hari)', zorder=4)

# Confidence band proyeksi
laju_hi = laju_proj * 0.7   # lebih lambat (optimis)
laju_lo = laju_proj * 1.3   # lebih cepat (pesimis)
proj_hi = [max(TEBAL_TERAKHIR + laju_hi * i, 95) for i in range(0, proj_hari+1)]
proj_lo = [max(TEBAL_TERAKHIR + laju_lo * i, 95) for i in range(0, proj_hari+1)]
ax1.fill_between(proj_dates, proj_lo, proj_hi, alpha=0.15, color='darkorange', label='Range Proyeksi (±30%)')

# Garis batas
ax1.axhline(y=BATAS_KRITIS,  color='red',    linestyle=':',  linewidth=2,   label=f'Batas Kritis ({BATAS_KRITIS}mm)')
ax1.axhline(y=BATAS_WARNING, color='orange', linestyle='--', linewidth=1.5, label=f'Batas Warning ({BATAS_WARNING}mm)')
ax1.axvline(x=tgl_terakhir,  color='gray',   linestyle=':',  linewidth=1,   label='Data Terakhir')

# Annotasi kapan kritis
for i, (tgl, tp) in enumerate(zip(proj_dates, proj_tebal)):
    if tp <= BATAS_KRITIS:
        ax1.annotate(f'Est. Kritis:\n{tgl.strftime("%d %b %Y")}',
                     xy=(tgl, BATAS_KRITIS), xytext=(-100, 30),
                     textcoords='offset points', color='red', fontsize=9, fontweight='bold',
                     arrowprops=dict(arrowstyle='->', color='red', lw=1.5))
        break

ax1.set_title('Historis Ketebalan BTA & Proyeksi ke Depan', fontsize=12)
ax1.set_ylabel('Ketebalan (mm)')
ax1.legend(loc='upper right', fontsize=8.5)
ax1.xaxis.set_major_formatter(mdates.DateFormatter('%b %Y'))
ax1.xaxis.set_major_locator(mdates.MonthLocator(interval=2))
plt.setp(ax1.xaxis.get_majorticklabels(), rotation=30)
ax1.set_ylim(90, 245)

# --- Panel 2: Suhu Monitoring ---
ax2 = axes[1]
suhu_avg_series = (df['cone_depan'] + df['body_tengah'] + df['cone_belakang']) / 3
ma7 = suhu_avg_series.rolling(7, min_periods=1).mean()

ax2.plot(df['tanggal'], suhu_avg_series, color='lightcoral', alpha=0.35, linewidth=1, label='Suhu Avg (Raw)')
ax2.plot(df['tanggal'], ma7, color='crimson', linewidth=2, label='Suhu Avg MA-7')
ax2.axhline(y=BATAS_SUHU, color='darkred', linestyle='--', linewidth=1.5, label=f'Batas Suhu ({BATAS_SUHU}°C)')
ax2.axhline(y=WARN_SUHU,  color='orange',  linestyle=':',  linewidth=1.2, label=f'Warning Suhu ({WARN_SUHU}°C)')
ax2.axvline(x=tgl_terakhir, color='gray',  linestyle=':',  linewidth=1)

ax2.set_title('Monitoring Suhu Harian', fontsize=12)
ax2.set_ylabel('Suhu (°C)')
ax2.set_xlabel('Tanggal')
ax2.legend(fontsize=8.5)
ax2.xaxis.set_major_formatter(mdates.DateFormatter('%b %Y'))
ax2.xaxis.set_major_locator(mdates.MonthLocator(interval=2))
plt.setp(ax2.xaxis.get_majorticklabels(), rotation=30)

plt.tight_layout()
output_monitoring = f'bta_monitoring_{base_name}.png'
plt.savefig(output_monitoring, dpi=150, bbox_inches='tight')
plt.show()
print(f"✅ Grafik disimpan: '{output_monitoring}'")


# ============================================================
# CELL 8: Simpan Model & State
# ============================================================
import pickle

output_model = f'model_rate_{base_name}.pkl'
with open(output_model, 'wb') as f:
    pickle.dump(model_rate, f)

# Simpan juga dalam format native JSON untuk kompatibilitas Hugging Face
output_model_json = f'model_rate_{base_name}.json'
model_rate.save_model(output_model_json)
model_rate.save_model('xgboost_bta.json')

state = {
    'T0'              : T0,
    'tebal_terakhir'  : TEBAL_TERAKHIR,
    'tanggal_ukur'    : TANGGAL_UKUR,
    'hari_ukur'       : HARI_UKUR,
    'laju_mean'       : laju_mean,
    'laju_recent'     : laju_recent,
    'batas_kritis'    : BATAS_KRITIS,
    'batas_warning'   : BATAS_WARNING,
}

output_state = f'bta_state_{base_name}.pkl'
with open(output_state, 'wb') as f:
    pickle.dump(state, f)

print("\n✅ Model & State disimpan:")
print(f"   📦 {output_model}  — Model laju aus XGBoost (Pickle)")
print(f"   📦 xgboost_bta.json     — Model laju aus XGBoost (JSON - HF compatible)")
print(f"   📦 {output_state}       — State pengukuran terakhir")
print("\n💡 TIP PENGGUNAAN HARIAN:")
print("   Setiap ada pengukuran BTA baru, gunakan parameter tebal_aktual=XXX")
print("   agar anchor diperbarui dan prediksi makin akurat.")
print("   Contoh: predict_bta_daily(350, 320, 360, tebal_aktual=118)")

"""##ACTUAL VS PREDICT"""

import pandas as pd

pred_values = []
pred_dates = []

# set patokan awal dari data pertama
current_anchor = df['ketebalan'].iloc[0]
anchor_date = df['tanggal'].iloc[0]

for index, row in df.iterrows():
    # update patokan kalau ada data ukur manual di hari itu
    if row['tanggal'] in df_ukur['tanggal'].values:
        current_anchor = row['ketebalan']
        anchor_date = row['tanggal']

    suhu_avg = (row['cone_depan'] + row['body_tengah'] + row['cone_belakang']) / 3

    # jalanin modelnya buat nebak laju aus
    laju_pred = model_rate.predict(pd.DataFrame([[suhu_avg]], columns=['suhu_avg_periode']))[0]

    # gabungin tebakan model sama rata-rata laju terbaru biar stabil
    laju_efektif = 0.4 * laju_pred + 0.6 * laju_recent

    hari_sejak = (row['tanggal'] - anchor_date).days
    tebal_pred = current_anchor + (laju_efektif * hari_sejak)

    pred_values.append(tebal_pred)
    pred_dates.append(row['tanggal'])

# jadiin format series sesuai yang diminta matplotlib kamu
historical_predictions = pd.Series(pred_values, index=pred_dates)

import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import pandas as pd

# Ensure necessary variables are available from previous cells.
# df, df_ukur, historical_predictions, BATAS_KRITIS, BATAS_WARNING

# Plotting the comparison
fig, ax = plt.subplots(figsize=(16, 8))

# Actual BTA thickness (stepwise)
ax.step(df['tanggal'], df['ketebalan'], where='post',
        color='royalblue', linewidth=2.5, label='Ketebalan Aktual (Pengukuran Manual)', zorder=3)
ax.scatter(df_ukur['tanggal'], df_ukur['ketebalan'],
           color='royalblue', s=80, zorder=5, label='Titik Pengukuran Aktual')

# Predicted BTA thickness (model-based) - using the pre-calculated historical_predictions
ax.plot(historical_predictions.index, historical_predictions.values,
        color='darkgreen', linestyle='-', linewidth=1.5, label='Prediksi Model Harian', zorder=2)

# Add thresholds
ax.axhline(y=BATAS_KRITIS,  color='red',    linestyle=':',  linewidth=2,   label=f'Batas Kritis ({BATAS_KRITIS}mm)')
ax.axhline(y=BATAS_WARNING, color='orange', linestyle='--', linewidth=1.5, label=f'Batas Warning ({BATAS_WARNING}mm)')

ax.set_title('Perbandingan Ketebalan BTA Aktual vs. Prediksi Model Historis', fontsize=15, fontweight='bold')
ax.set_ylabel('Ketebalan (mm)')
ax.set_xlabel('Tanggal')
ax.legend(loc='upper right', fontsize=10)
ax.xaxis.set_major_formatter(mdates.DateFormatter('%b %Y'))
ax.xaxis.set_major_locator(mdates.MonthLocator(interval=2))
plt.setp(ax.xaxis.get_majorticklabels(), rotation=30)
ax.set_ylim(90, 245) # Consistent y-axis with previous plots
plt.grid(True)
plt.tight_layout()
output_comparison = f'bta_comparison_{base_name}.png'
plt.savefig(output_comparison, dpi=150, bbox_inches='tight')
plt.show()

print(f"✅ Grafik perbandingan aktual vs. prediksi historis ditampilkan dan disimpan ke '{output_comparison}'.")

# Input suhu dari pengguna
t_depan_input    = float(input("Masukkan suhu Cone Depan (°C): "))
t_tengah_input   = float(input("Masukkan suhu Bodi Tengah (°C): "))
t_belakang_input = float(input("Masukkan suhu Cone Belakang (°C): "))

# Panggil fungsi prediksi dengan input suhu
result = predict_bta_daily(t_depan=t_depan_input,
                           t_tengah=t_tengah_input,
                           t_belakang=t_belakang_input)

print("\nRingkasan Prediksi:")
for key, value in result.items():
    print(f"- {key.replace('_', ' ').title()}: {value}")