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Azmikun1 2026-07-16 07:19:21 +07:00
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479
app1.py
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import requests
import streamlit as st
import joblib
import pandas as pd
import numpy as np
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.models import load_model
from datetime import date, timedelta
import yfinance as yf
from pathlib import Path
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
from matplotlib import rcParams
# Set font untuk mendukung karakter Indonesia
rcParams['font.family'] = 'DejaVu Sans'
# --- Konfigurasi Halaman ---
st.set_page_config(
page_title="Prediksi Harga Ethereum (GRU)",
page_icon="🪙"
)
# --- GLOBAL STYLES (UI only) ---
st.markdown("""
<style>
/* font & warna dasar */
html, body, [class*="css"] { font-family: "Inter", "DejaVu Sans", sans-serif; }
:root {
--card-bg: #ffffff;
--muted: #6b7280;
--ring: #e5e7eb;
}
/* gradient title */
.app-title {
text-align:center;
font-weight:800;
font-size: 32px;
background: linear-gradient(90deg, #6EE7F9 0%, #7C3AED 50%, #F59E0B 100%);
-webkit-background-clip: text;
-webkit-text-fill-color: transparent;
margin: 0.25rem 0 0.5rem 0;
}
/* subtitle */
.app-subtitle {
text-align:center;
color: var(--muted);
margin-bottom: 1.25rem;
}
/* card container */
.card {
background: var(--card-bg);
border: 1px solid var(--ring);
border-radius: 16px;
padding: 1rem 1.25rem;
box-shadow: 0 6px 20px rgba(0,0,0,0.05);
margin-bottom: 1rem;
}
/* section heading */
.h-section {
font-weight:700;
font-size: 20px;
margin: 0 0 .5rem 0;
}
/* table tweaks */
.dataframe tbody tr:hover { background-color: #fafafa; }
/* footer */
.footer {
text-align:center;
color: var(--muted);
font-size: 13px;
margin-top: 2rem;
}
</style>
""", unsafe_allow_html=True)
# --- Fungsi-fungsi Bantuan ---
@st.cache_data(ttl="1h")
def load_eth_data():
ticker = "ETH-USD"
df = None
# 1. COBA ONLINE
try:
df = yf.download(
ticker,
start="2024-01-01",
end=date.today() + timedelta(days=1),
progress=False,
auto_adjust=True,
multi_level_index=False
)
if df is not None and not df.empty:
# Bersihkan Index & Kolom
df = df.reset_index()
new_cols = []
for col in df.columns:
col_name = col[0] if isinstance(col, tuple) else str(col)
new_cols.append(col_name)
df.columns = new_cols
# Kolom pertama adalah Date
if 'Date' not in df.columns:
df = df.rename(columns={df.columns[0]: 'Date'})
# Hapus Timezone
df['Date'] = pd.to_datetime(df['Date']).dt.tz_localize(None)
# Simpan Backup (Timpa file lama agar fresh)
try:
df.to_csv("eth_backup.csv", index=False)
except:
pass
return df, "online"
except Exception as e:
print(f"Gagal Online: {e}")
# 2. COBA BACKUP (JIKA ONLINE GAGAL)
try:
df_backup = pd.read_csv("eth_backup.csv")
# Bersihkan kolom sampah jika ada
if "Unnamed: 0" in df_backup.columns:
df_backup = df_backup.drop(columns=["Unnamed: 0"])
# Pastikan kolom Date dikenali
if "Date" in df_backup.columns:
df_backup["Date"] = pd.to_datetime(df_backup["Date"])
elif df_backup.columns[0].lower() == "date":
df_backup = df_backup.rename(columns={df_backup.columns[0]: "Date"})
df_backup["Date"] = pd.to_datetime(df_backup["Date"])
return df_backup, "backup"
except FileNotFoundError:
# 3. GAGAL TOTAL
return None, "error"
def validate_scaler(scaler):
"""Validasi scaler agar konsisten dengan training."""
issues = []
warnings = []
if not isinstance(scaler, MinMaxScaler):
warnings.append(f"Scaler bukan MinMaxScaler (terdeteksi: {type(scaler).__name__}). Pastikan ini scaler training.")
required_attrs = ["n_features_in_", "data_min_", "data_max_", "scale_", "min_"]
for a in required_attrs:
if not hasattr(scaler, a):
issues.append(f"Scaler belum ter-fit atau tidak valid (atribut '{a}' tidak ada).")
if hasattr(scaler, "n_features_in_"):
if int(scaler.n_features_in_) != 1:
issues.append(f"Scaler mengharapkan {scaler.n_features_in_} fitur, tapi aplikasi hanya pakai 1 fitur ('Close').")
ok = (len(issues) == 0)
return ok, issues, warnings
def validate_model_input(model):
"""Pastikan input model bentuknya (None, time_step, 1)."""
issues = []
try:
shape = model.input_shape # biasanya (None, time_step, 1)
if not (isinstance(shape, (list, tuple)) and len(shape) == 3):
issues.append(f"input_shape tidak sesuai harapan: {shape} (harus 3 dimensi).")
else:
if shape[2] != 1:
issues.append(f"Jumlah fitur input model = {shape[2]}, tapi aplikasi membentuk fitur=1.")
if shape[1] is None or int(shape[1]) <= 1:
issues.append(f"time_step tidak valid: {shape[1]}.")
except Exception as e:
issues.append(f"Gagal membaca input_shape model: {e}")
ok = (len(issues) == 0)
return ok, issues
@st.cache_resource
def load_gru_assets():
"""Load model & scaler hasil training (wajib pakai scaler yang sama)."""
model_path = Path("gru_model.h5")
scaler_path = Path("scaler_gru.pkl")
if not model_path.exists() or not scaler_path.exists():
st.warning(
f"File tidak ditemukan.\n"
f"Model: {model_path.resolve().name} ada? {model_path.exists()}\n"
f"Scaler: {scaler_path.resolve().name} ada? {scaler_path.exists()}"
)
return None, None
try:
model = load_model(model_path, compile=False)
scaler = joblib.load(scaler_path)
ok_scaler, scaler_issues, scaler_warnings = validate_scaler(scaler)
for w in scaler_warnings:
st.warning("⚠️ " + w)
if not ok_scaler:
st.error("❌ Scaler tidak kompatibel:\n- " + "\n- ".join(scaler_issues))
return None, None
ok_model, model_issues = validate_model_input(model)
if not ok_model:
st.error("❌ Model input tidak kompatibel:\n- " + "\n- ".join(model_issues))
return None, None
return model, scaler
except Exception as e:
st.error(f"Gagal load aset: {e}")
return None, None
def predict_from_sequence_pure(model, initial_sequence_scaled, n_days):
seq = np.array(initial_sequence_scaled, dtype="float32").reshape(-1) # (time_step,)
preds = []
for _ in range(n_days):
x = seq.reshape(1, -1, 1) # (1, time_step, 1)
y = float(model.predict(x, verbose=0)[0, 0]) # output model (scaled)
preds.append(y)
seq = np.append(seq[1:], y) # autoregressive (murni)
return np.array(preds, dtype="float32").reshape(-1, 1)
def create_combined_chart(df, start_date, future_dates, future_predictions):
"""Membuat grafik gabungan dengan gaya yang sama seperti referensi."""
context_start_date = start_date - timedelta(days=609)
recent_df = df[(df["Date"] >= context_start_date) & (df["Date"] <= start_date)].copy()
window_size = 7
recent_df["Trend_Aktual"] = recent_df["Close"].rolling(window=window_size).mean()
predictions_flat = future_predictions.flatten()
fig, ax = plt.subplots(figsize=(14, 8))
ax.plot(
recent_df["Date"], recent_df["Trend_Aktual"],
color="#1f77b4", linewidth=2.5, label="Tren Harga Aktual", alpha=0.9
)
# Membuat tren prediksi menggunakan moving average 7 hari
pred_trend = (
pd.Series(predictions_flat)
.rolling(window=7, min_periods=1)
.mean()
)
ax.plot(
future_dates,
pred_trend,
color="#d62728",
linewidth=2.5,
label="Tren Harga Prediksi (GRU)",
alpha=0.9
)
ax.scatter(
recent_df["Date"], recent_df["Close"],
color="black", s=15, alpha=0.6, zorder=5, label="Data Harian Aktual"
)
# garis penghubung trend terakhir ke prediksi pertama
try:
last_trend_date = recent_df["Date"].iloc[-1]
last_trend_price = recent_df["Trend_Aktual"].dropna().iloc[-1]
first_prediction_date = future_dates[0]
first_prediction_price = predictions_flat[0]
ax.plot(
[last_trend_date, first_prediction_date],
[last_trend_price, first_prediction_price],
color="#d62728", linestyle="--", linewidth=2.0, alpha=0.7
)
except Exception:
pass
ax.set_xlabel("Waktu", fontsize=12, fontweight="bold")
ax.set_ylabel("Harga (USD)", fontsize=12, fontweight="bold")
ax.set_title("Analisis Tren Historis dan Prediksi Harga Ethereum", fontsize=16, fontweight="bold", pad=20)
ax.xaxis.set_major_formatter(mdates.DateFormatter("%b %d, %Y"))
plt.xticks(rotation=45)
ax.grid(True, alpha=0.3, linestyle="--")
handles, labels = ax.get_legend_handles_labels()
order = [2, 0, 1, 3]
if len(handles) == 4:
ax.legend([handles[idx] for idx in order], [labels[idx] for idx in order], loc="upper left", fontsize=11)
else:
ax.legend(loc="upper left", fontsize=11)
plt.tight_layout()
ax.set_facecolor("#f8f9fa")
fig.patch.set_facecolor("white")
return fig
# --- Tampilan Antarmuka Aplikasi ---
st.markdown("<div class='app-title'>Prediksi Harga Ethereum (GRU)</div>", unsafe_allow_html=True)
st.markdown("<div class='app-subtitle'>Data historis & prediksi ETH-USD berbasis GRU</div>", unsafe_allow_html=True)
df, data_source = load_eth_data()
model, scaler = load_gru_assets()
# LOGIC UI (TOAST/WARNING) DITARUH DI SINI (DILUAR CACHE)
if data_source == "backup":
st.toast("Koneksi Yahoo lambat. Menggunakan data backup lokal.", icon="⚠️")
elif data_source == "error":
st.error("❌ Gagal memuat data (Online gagal & Backup tidak ada).")
if df is not None and model is not None and scaler is not None:
# Menampilkan tabel data historis
st.markdown("<div class='card'>", unsafe_allow_html=True)
st.markdown("<div class='h-section'>📚 Data Historis Harga Ethereum</div>", unsafe_allow_html=True)
st.dataframe(df, height=300, use_container_width=True)
st.markdown("</div>", unsafe_allow_html=True)
# Menampilkan visualisasi line chart
st.markdown("<div class='card'>", unsafe_allow_html=True)
st.markdown("<div class='h-section'>📈 Visualisasi Harga Historis</div>", unsafe_allow_html=True)
st.line_chart(df.rename(columns={"Date": "index"}).set_index("index")["Close"])
st.markdown("</div>", unsafe_allow_html=True)
st.markdown("<div class='card'>", unsafe_allow_html=True)
st.markdown("<div class='h-section'>🎯 Mulai Prediksi Berdasarkan Tanggal</div>", unsafe_allow_html=True)
time_step = int(model.input_shape[1])
min_selectable_date = (df["Date"].min() + timedelta(days=time_step)).date()
max_selectable_date = df["Date"].max().date()
col1, col2 = st.columns(2)
with col1:
selected_date = st.date_input(
"Pilih tanggal mulai prediksi:",
value=max_selectable_date,
min_value=min_selectable_date,
max_value=max_selectable_date,
help=f"Pilih tanggal antara {min_selectable_date} dan {max_selectable_date}"
)
with col2:
prediction_days = st.slider(
"Pilih jumlah hari untuk prediksi (1-30 hari):",
min_value=1, max_value=30, value=15, step=1
)
if st.button("Buat Prediksi", key="predict_button"):
if prediction_days < 1:
st.warning("Jumlah hari prediksi minimal **1**. Silakan geser slidernya dulu. 🙂")
st.stop()
with st.spinner("Memproses dan menjalankan prediksi..."):
# scaling (mengikuti scaler training)
close_values = df[["Close"]].values.astype(float)
scaled_data = scaler.transform(close_values)
# Cocokkan berdasarkan tanggal (robust)
selected_date_dt = pd.to_datetime(selected_date)
mask = df["Date"].dt.date == selected_date_dt.date()
idxs = np.where(mask.to_numpy())[0]
if len(idxs) == 0:
st.error(f"Tanggal {selected_date_dt.strftime('%Y-%m-%d')} tidak ditemukan dalam dataset.")
st.stop()
start_index = int(idxs[0])
if start_index - time_step < 0:
st.error(
f"Tanggal terlalu awal untuk diprediksi. "
f"Butuh {time_step} hari data sebelumnya. Pilih tanggal setelah "
f"{(df['Date'].min() + timedelta(days=time_step)).strftime('%Y-%m-%d')}."
)
st.stop()
initial_sequence = scaled_data[start_index - time_step: start_index] # (time_step,1)
# ✅ Prediksi MURNI dari model
future_predictions_scaled = predict_from_sequence_pure(model, initial_sequence, prediction_days)
# ✅ Inverse transform MURNI dari scaler
future_predictions = scaler.inverse_transform(future_predictions_scaled)
# sanity-check
if not np.isfinite(future_predictions).all():
st.error("❌ Hasil prediksi mengandung NaN/Inf. Periksa kompatibilitas model/scaler.")
st.stop()
future_dates = [selected_date_dt + timedelta(days=i) for i in range(1, prediction_days + 1)]
st.markdown("<div class='card'>", unsafe_allow_html=True)
st.subheader("📊 Grafik Prediksi (Historis + Prediksi)")
fig = create_combined_chart(df, selected_date_dt, future_dates, future_predictions)
st.pyplot(fig)
st.markdown("</div>", unsafe_allow_html=True)
st.markdown("<div class='card'>", unsafe_allow_html=True)
#Test
st.subheader("📈 Grafik Detail Prediksi")
fig2, ax2 = plt.subplots(figsize=(12, 5))
ax2.plot(
future_dates,
future_predictions.flatten(),
marker='o',
linewidth=2.5
)
ax2.set_title(
"Grafik Detail Hasil Prediksi Harga Ethereum",
fontsize=14,
fontweight="bold"
)
ax2.set_xlabel("Tanggal")
ax2.set_ylabel("Harga Prediksi (USD)")
ax2.grid(True, alpha=0.3)
plt.xticks(rotation=45)
plt.tight_layout()
st.pyplot(fig2)
st.markdown("</div>", unsafe_allow_html=True)
st.markdown("<div class='card'>", unsafe_allow_html=True)
st.subheader("🧾 Tabel Detail Prediksi")
prediction_table_df = pd.DataFrame({
"Tanggal": [d.strftime("%Y-%m-%d") for d in future_dates],
"Harga Prediksi (USD)": [f"USD {price[0]:,.0f}" for price in future_predictions]
})
prediction_table_df.index = prediction_table_df.index + 1
st.dataframe(prediction_table_df, use_container_width=True)
st.markdown("</div>", unsafe_allow_html=True)
last_price = float(df.loc[start_index, "Close"])
max_prediction = float(np.max(future_predictions))
min_prediction = float(np.min(future_predictions))
st.info(f"""
📊 **Informasi Prediksi:**
- Harga terakhir pada tanggal {selected_date.strftime('%Y-%m-%d')}: USD {last_price:,.0f}
- Harga prediksi tertinggi: USD {max_prediction:,.0f}
- Harga prediksi terendah: USD {min_prediction:,.0f}
""")
st.markdown("</div>", unsafe_allow_html=True)
st.markdown("<div class='footer'>Dibuat oleh Muhammad Gilman Nadhif Azmi</div>", unsafe_allow_html=True)
elif model is None:
st.error("Gagal memuat model. Pastikan file 'gru_model.h5' ada di folder yang sama dengan aplikasi.")
elif scaler is None:
st.error("Gagal memuat scaler. Pastikan file 'scaler_gru.pkl' ada di folder yang sama dengan aplikasi.")
else:
st.error("Gagal memuat data. Coba cek koneksi internet atau sumber data yfinance.")

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apptest.py
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import requests
import streamlit as st
import joblib
import pandas as pd
import numpy as np
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.models import load_model
from datetime import date, timedelta
import yfinance as yf
from pathlib import Path
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
from matplotlib import rcParams
# Set font untuk mendukung karakter Indonesia
rcParams['font.family'] = 'DejaVu Sans'
# --- Konfigurasi Halaman ---
st.set_page_config(
page_title="Prediksi Harga Ethereum (GRU)",
page_icon="🪙"
)
# --- GLOBAL STYLES (UI only) ---
st.markdown("""
<style>
/* font & warna dasar */
html, body, [class*="css"] { font-family: "Inter", "DejaVu Sans", sans-serif; }
:root {
--card-bg: #ffffff;
--muted: #6b7280;
--ring: #e5e7eb;
}
/* gradient title */
.app-title {
text-align:center;
font-weight:800;
font-size: 32px;
background: linear-gradient(90deg, #6EE7F9 0%, #7C3AED 50%, #F59E0B 100%);
-webkit-background-clip: text;
-webkit-text-fill-color: transparent;
margin: 0.25rem 0 0.5rem 0;
}
/* subtitle */
.app-subtitle {
text-align:center;
color: var(--muted);
margin-bottom: 1.25rem;
}
/* card container */
.card {
background: var(--card-bg);
border: 1px solid var(--ring);
border-radius: 16px;
padding: 1rem 1.25rem;
box-shadow: 0 6px 20px rgba(0,0,0,0.05);
margin-bottom: 1rem;
}
/* section heading */
.h-section {
font-weight:700;
font-size: 20px;
margin: 0 0 .5rem 0;
}
/* table tweaks */
.dataframe tbody tr:hover { background-color: #fafafa; }
/* footer */
.footer {
text-align:center;
color: var(--muted);
font-size: 13px;
margin-top: 2rem;
}
</style>
""", unsafe_allow_html=True)
# --- Fungsi-fungsi Bantuan ---
@st.cache_data(ttl="1h")
def load_eth_data():
ticker = "ETH-USD"
df = None
# 1. COBA ONLINE
try:
df = yf.download(
ticker,
start="2024-01-01",
end=date.today() + timedelta(days=1),
progress=False,
auto_adjust=True,
multi_level_index=False
)
if df is not None and not df.empty:
# Bersihkan Index & Kolom
df = df.reset_index()
new_cols = []
for col in df.columns:
col_name = col[0] if isinstance(col, tuple) else str(col)
new_cols.append(col_name)
df.columns = new_cols
# Kolom pertama adalah Date
if 'Date' not in df.columns:
df = df.rename(columns={df.columns[0]: 'Date'})
# Hapus Timezone
df['Date'] = pd.to_datetime(df['Date']).dt.tz_localize(None)
# Simpan Backup (Timpa file lama agar fresh)
try:
df.to_csv("eth_backup.csv", index=False)
except:
pass
return df, "online"
except Exception as e:
print(f"Gagal Online: {e}")
# 2. COBA BACKUP (JIKA ONLINE GAGAL)
try:
df_backup = pd.read_csv("eth_backup.csv")
# Bersihkan kolom sampah jika ada
if "Unnamed: 0" in df_backup.columns:
df_backup = df_backup.drop(columns=["Unnamed: 0"])
# Pastikan kolom Date dikenali
if "Date" in df_backup.columns:
df_backup["Date"] = pd.to_datetime(df_backup["Date"])
elif df_backup.columns[0].lower() == "date":
df_backup = df_backup.rename(columns={df_backup.columns[0]: "Date"})
df_backup["Date"] = pd.to_datetime(df_backup["Date"])
return df_backup, "backup"
except FileNotFoundError:
# 3. GAGAL TOTAL
return None, "error"
def validate_scaler(scaler):
"""Validasi scaler agar konsisten dengan training."""
issues = []
warnings = []
if not isinstance(scaler, MinMaxScaler):
warnings.append(f"Scaler bukan MinMaxScaler (terdeteksi: {type(scaler).__name__}). Pastikan ini scaler training.")
required_attrs = ["n_features_in_", "data_min_", "data_max_", "scale_", "min_"]
for a in required_attrs:
if not hasattr(scaler, a):
issues.append(f"Scaler belum ter-fit atau tidak valid (atribut '{a}' tidak ada).")
if hasattr(scaler, "n_features_in_"):
if int(scaler.n_features_in_) != 1:
issues.append(f"Scaler mengharapkan {scaler.n_features_in_} fitur, tapi aplikasi hanya pakai 1 fitur ('Close').")
ok = (len(issues) == 0)
return ok, issues, warnings
def validate_model_input(model):
"""Pastikan input model bentuknya (None, time_step, 1)."""
issues = []
try:
shape = model.input_shape # biasanya (None, time_step, 1)
if not (isinstance(shape, (list, tuple)) and len(shape) == 3):
issues.append(f"input_shape tidak sesuai harapan: {shape} (harus 3 dimensi).")
else:
if shape[2] != 1:
issues.append(f"Jumlah fitur input model = {shape[2]}, tapi aplikasi membentuk fitur=1.")
if shape[1] is None or int(shape[1]) <= 1:
issues.append(f"time_step tidak valid: {shape[1]}.")
except Exception as e:
issues.append(f"Gagal membaca input_shape model: {e}")
ok = (len(issues) == 0)
return ok, issues
@st.cache_resource
def load_gru_assets():
"""Load model & scaler hasil training (wajib pakai scaler yang sama)."""
model_path = Path("gru_model.h5")
scaler_path = Path("scaler_gru.pkl")
if not model_path.exists() or not scaler_path.exists():
st.warning(
f"File tidak ditemukan.\n"
f"Model: {model_path.resolve().name} ada? {model_path.exists()}\n"
f"Scaler: {scaler_path.resolve().name} ada? {scaler_path.exists()}"
)
return None, None
try:
model = load_model(model_path, compile=False)
scaler = joblib.load(scaler_path)
ok_scaler, scaler_issues, scaler_warnings = validate_scaler(scaler)
for w in scaler_warnings:
st.warning("⚠️ " + w)
if not ok_scaler:
st.error("❌ Scaler tidak kompatibel:\n- " + "\n- ".join(scaler_issues))
return None, None
ok_model, model_issues = validate_model_input(model)
if not ok_model:
st.error("❌ Model input tidak kompatibel:\n- " + "\n- ".join(model_issues))
return None, None
return model, scaler
except Exception as e:
st.error(f"Gagal load aset: {e}")
return None, None
def predict_from_sequence_pure(model, initial_sequence_scaled, n_days):
seq = np.array(initial_sequence_scaled, dtype="float32").reshape(-1) # (time_step,)
preds = []
for _ in range(n_days):
x = seq.reshape(1, -1, 1) # (1, time_step, 1)
y = float(model.predict(x, verbose=0)[0, 0]) # output model (scaled)
preds.append(y)
seq = np.append(seq[1:], y) # autoregressive (murni)
return np.array(preds, dtype="float32").reshape(-1, 1)
def create_combined_chart(df, start_date, future_dates, future_predictions):
"""Membuat grafik gabungan dengan gaya yang sama seperti referensi."""
context_start_date = start_date - timedelta(days=609)
recent_df = df[(df["Date"] >= context_start_date) & (df["Date"] <= start_date)].copy()
window_size = 7
recent_df["Trend_Aktual"] = recent_df["Close"].rolling(window=window_size).mean()
predictions_flat = future_predictions.flatten()
fig, ax = plt.subplots(figsize=(14, 8))
ax.plot(
recent_df["Date"], recent_df["Trend_Aktual"],
color="#1f77b4", linewidth=2.5, label="Tren Harga Aktual", alpha=0.9
)
# Membuat tren prediksi menggunakan moving average 7 hari
pred_trend = (
pd.Series(predictions_flat)
.rolling(window=7, min_periods=1)
.mean()
)
ax.plot(
future_dates,
pred_trend,
color="#d62728",
linewidth=2.5,
label="Tren Harga Prediksi (GRU)",
alpha=0.9
)
ax.scatter(
recent_df["Date"], recent_df["Close"],
color="black", s=15, alpha=0.6, zorder=5, label="Data Harian Aktual"
)
# garis penghubung trend terakhir ke prediksi pertama
try:
last_trend_date = recent_df["Date"].iloc[-1]
last_trend_price = recent_df["Trend_Aktual"].dropna().iloc[-1]
first_prediction_date = future_dates[0]
first_prediction_price = predictions_flat[0]
ax.plot(
[last_trend_date, first_prediction_date],
[last_trend_price, first_prediction_price],
color="#d62728", linestyle="--", linewidth=2.0, alpha=0.7
)
except Exception:
pass
ax.set_xlabel("Waktu", fontsize=12, fontweight="bold")
ax.set_ylabel("Harga (USD)", fontsize=12, fontweight="bold")
ax.set_title("Analisis Tren Historis dan Prediksi Harga Ethereum", fontsize=16, fontweight="bold", pad=20)
ax.xaxis.set_major_formatter(mdates.DateFormatter("%b %d, %Y"))
plt.xticks(rotation=45)
ax.grid(True, alpha=0.3, linestyle="--")
handles, labels = ax.get_legend_handles_labels()
order = [2, 0, 1, 3]
if len(handles) == 4:
ax.legend([handles[idx] for idx in order], [labels[idx] for idx in order], loc="upper left", fontsize=11)
else:
ax.legend(loc="upper left", fontsize=11)
plt.tight_layout()
ax.set_facecolor("#f8f9fa")
fig.patch.set_facecolor("white")
return fig
# --- Tampilan Antarmuka Aplikasi ---
st.markdown("<div class='app-title'>Prediksi Harga Ethereum (GRU)</div>", unsafe_allow_html=True)
st.markdown("<div class='app-subtitle'>Data historis & prediksi ETH-USD berbasis GRU</div>", unsafe_allow_html=True)
df, data_source = load_eth_data()
model, scaler = load_gru_assets()
# LOGIC UI (TOAST/WARNING) DITARUH DI SINI (DILUAR CACHE)
if data_source == "backup":
st.toast("Koneksi Yahoo lambat. Menggunakan data backup lokal.", icon="⚠️")
elif data_source == "error":
st.error("❌ Gagal memuat data (Online gagal & Backup tidak ada).")
if df is not None and model is not None and scaler is not None:
# Menampilkan tabel data historis
st.markdown("<div class='card'>", unsafe_allow_html=True)
st.markdown("<div class='h-section'>📚 Data Historis Harga Ethereum</div>", unsafe_allow_html=True)
st.dataframe(df, height=300, use_container_width=True)
st.markdown("</div>", unsafe_allow_html=True)
# Menampilkan visualisasi line chart
st.markdown("<div class='card'>", unsafe_allow_html=True)
st.markdown("<div class='h-section'>📈 Visualisasi Harga Historis</div>", unsafe_allow_html=True)
st.line_chart(df.rename(columns={"Date": "index"}).set_index("index")["Close"])
st.markdown("</div>", unsafe_allow_html=True)
st.markdown("<div class='card'>", unsafe_allow_html=True)
st.markdown("<div class='h-section'>🎯 Mulai Prediksi Berdasarkan Tanggal</div>", unsafe_allow_html=True)
time_step = int(model.input_shape[1])
min_selectable_date = (df["Date"].min() + timedelta(days=time_step)).date()
max_selectable_date = df["Date"].max().date()
col1, col2 = st.columns(2)
with col1:
selected_date = st.date_input(
"Pilih tanggal mulai prediksi:",
value=max_selectable_date,
min_value=min_selectable_date,
max_value=max_selectable_date,
help=f"Pilih tanggal antara {min_selectable_date} dan {max_selectable_date}"
)
with col2:
prediction_days = st.slider(
"Pilih jumlah hari untuk prediksi (1-30 hari):",
min_value=1, max_value=30, value=15, step=1
)
if st.button("Buat Prediksi", key="predict_button"):
if prediction_days < 1:
st.warning("Jumlah hari prediksi minimal **1**. Silakan geser slidernya dulu. 🙂")
st.stop()
with st.spinner("Memproses dan menjalankan prediksi..."):
# scaling (mengikuti scaler training)
close_values = df[["Close"]].values.astype(float)
scaled_data = scaler.transform(close_values)
# Cocokkan berdasarkan tanggal (robust)
selected_date_dt = pd.to_datetime(selected_date)
mask = df["Date"].dt.date == selected_date_dt.date()
idxs = np.where(mask.to_numpy())[0]
if len(idxs) == 0:
st.error(f"Tanggal {selected_date_dt.strftime('%Y-%m-%d')} tidak ditemukan dalam dataset.")
st.stop()
start_index = int(idxs[0])
if start_index - time_step < 0:
st.error(
f"Tanggal terlalu awal untuk diprediksi. "
f"Butuh {time_step} hari data sebelumnya. Pilih tanggal setelah "
f"{(df['Date'].min() + timedelta(days=time_step)).strftime('%Y-%m-%d')}."
)
st.stop()
initial_sequence = scaled_data[start_index - time_step: start_index] # (time_step,1)
# ✅ Prediksi MURNI dari model
future_predictions_scaled = predict_from_sequence_pure(model, initial_sequence, prediction_days)
# ✅ Inverse transform MURNI dari scaler
future_predictions = scaler.inverse_transform(future_predictions_scaled)
# sanity-check
if not np.isfinite(future_predictions).all():
st.error("❌ Hasil prediksi mengandung NaN/Inf. Periksa kompatibilitas model/scaler.")
st.stop()
future_dates = [selected_date_dt + timedelta(days=i) for i in range(1, prediction_days + 1)]
st.markdown("<div class='card'>", unsafe_allow_html=True)
st.subheader("📊 Grafik Prediksi (Historis + Prediksi)")
fig = create_combined_chart(df, selected_date_dt, future_dates, future_predictions)
st.pyplot(fig)
st.markdown("</div>", unsafe_allow_html=True)
st.markdown("<div class='card'>", unsafe_allow_html=True)
#Test
st.subheader("📈 Grafik Detail Prediksi")
fig2, ax2 = plt.subplots(figsize=(12, 5))
ax2.plot(
future_dates,
future_predictions.flatten(),
marker='o',
linewidth=2.5
)
ax2.set_title(
"Grafik Detail Hasil Prediksi Harga Ethereum",
fontsize=14,
fontweight="bold"
)
ax2.set_xlabel("Tanggal")
ax2.set_ylabel("Harga Prediksi (USD)")
ax2.grid(True, alpha=0.3)
plt.xticks(rotation=45)
plt.tight_layout()
st.pyplot(fig2)
st.markdown("</div>", unsafe_allow_html=True)
st.markdown("<div class='card'>", unsafe_allow_html=True)
st.subheader("🧾 Tabel Detail Prediksi")
prediction_table_df = pd.DataFrame({
"Tanggal": [d.strftime("%Y-%m-%d") for d in future_dates],
"Harga Prediksi (USD)": [f"USD {price[0]:,.0f}" for price in future_predictions]
})
prediction_table_df.index = prediction_table_df.index + 1
st.dataframe(prediction_table_df, use_container_width=True)
st.markdown("</div>", unsafe_allow_html=True)
last_price = float(df.loc[start_index, "Close"])
max_prediction = float(np.max(future_predictions))
min_prediction = float(np.min(future_predictions))
st.info(f"""
📊 **Informasi Prediksi:**
- Harga terakhir pada tanggal {selected_date.strftime('%Y-%m-%d')}: USD {last_price:,.0f}
- Harga prediksi tertinggi: USD {max_prediction:,.0f}
- Harga prediksi terendah: USD {min_prediction:,.0f}
""")
st.markdown("</div>", unsafe_allow_html=True)
st.markdown("<div class='footer'>Dibuat oleh Muhammad Gilman Nadhif Azmi</div>", unsafe_allow_html=True)
elif model is None:
st.error("Gagal memuat model. Pastikan file 'gru_model.h5' ada di folder yang sama dengan aplikasi.")
elif scaler is None:
st.error("Gagal memuat scaler. Pastikan file 'scaler_gru.pkl' ada di folder yang sama dengan aplikasi.")
else:
st.error("Gagal memuat data. Coba cek koneksi internet atau sumber data yfinance.")

19
eth_backup.csv
View File

@ -908,4 +908,21 @@ Date,Close,High,Low,Open,Volume
2026-06-25,1564.816650390625,1656.646484375,1531.7803955078125,1619.8853759765625,15976182558
2026-06-26,1576.6160888671875,1590.2000732421875,1510.505859375,1564.6136474609375,15272225856
2026-06-27,1571.5882568359375,1607.865966796875,1561.972900390625,1576.5911865234375,6111411513
2026-06-28,1579.5999755859375,1582.509033203125,1561.6748046875,1571.6817626953125,6003073536
2026-06-28,1570.3612060546875,1585.2197265625,1548.761962890625,1571.564453125,6231562560
2026-06-29,1610.205810546875,1633.64501953125,1550.036376953125,1569.993408203125,11882501822
2026-06-30,1569.5838623046875,1611.3472900390625,1549.251953125,1610.163818359375,9639804637
2026-07-01,1608.960693359375,1642.454833984375,1551.1392822265625,1569.90966796875,11666600557
2026-07-02,1698.16943359375,1720.34326171875,1595.9393310546875,1609.0125732421875,13538465176
2026-07-03,1756.5186767578125,1772.543212890625,1693.038330078125,1698.41796875,10177742550
2026-07-04,1779.032958984375,1804.5887451171875,1743.564208984375,1756.57568359375,8083146790
2026-07-05,1783.0059814453125,1805.9140625,1749.1376953125,1779.042724609375,11323496681
2026-07-06,1797.57373046875,1829.513671875,1728.9652099609375,1783.002685546875,16997487730
2026-07-07,1768.511474609375,1810.329833984375,1756.712646484375,1797.562255859375,9901110376
2026-07-08,1742.6776123046875,1782.8583984375,1711.9000244140625,1768.3896484375,10110272581
2026-07-09,1744.484619140625,1760.206298828125,1721.298583984375,1742.664794921875,7763668656
2026-07-10,1795.6932373046875,1809.75830078125,1736.72314453125,1744.47314453125,9431692646
2026-07-11,1788.099609375,1827.83154296875,1786.295654296875,1795.8193359375,6901680020
2026-07-12,1805.7880859375,1824.98046875,1779.6370849609375,1787.568359375,6064480351
2026-07-13,1773.500732421875,1842.455078125,1749.3475341796875,1805.7579345703125,11436340419
2026-07-14,1889.4798583984375,1894.062255859375,1772.0177001953125,1773.471435546875,13835437084
2026-07-15,1932.93994140625,1938.8372802734375,1863.8087158203125,1889.969482421875,13269965824

1 Date Close High Low Open Volume
908 2026-06-25 1564.816650390625 1656.646484375 1531.7803955078125 1619.8853759765625 15976182558
909 2026-06-26 1576.6160888671875 1590.2000732421875 1510.505859375 1564.6136474609375 15272225856
910 2026-06-27 1571.5882568359375 1607.865966796875 1561.972900390625 1576.5911865234375 6111411513
911 2026-06-28 1579.5999755859375 1570.3612060546875 1582.509033203125 1585.2197265625 1561.6748046875 1548.761962890625 1571.6817626953125 1571.564453125 6003073536 6231562560
912 2026-06-29 1610.205810546875 1633.64501953125 1550.036376953125 1569.993408203125 11882501822
913 2026-06-30 1569.5838623046875 1611.3472900390625 1549.251953125 1610.163818359375 9639804637
914 2026-07-01 1608.960693359375 1642.454833984375 1551.1392822265625 1569.90966796875 11666600557
915 2026-07-02 1698.16943359375 1720.34326171875 1595.9393310546875 1609.0125732421875 13538465176
916 2026-07-03 1756.5186767578125 1772.543212890625 1693.038330078125 1698.41796875 10177742550
917 2026-07-04 1779.032958984375 1804.5887451171875 1743.564208984375 1756.57568359375 8083146790
918 2026-07-05 1783.0059814453125 1805.9140625 1749.1376953125 1779.042724609375 11323496681
919 2026-07-06 1797.57373046875 1829.513671875 1728.9652099609375 1783.002685546875 16997487730
920 2026-07-07 1768.511474609375 1810.329833984375 1756.712646484375 1797.562255859375 9901110376
921 2026-07-08 1742.6776123046875 1782.8583984375 1711.9000244140625 1768.3896484375 10110272581
922 2026-07-09 1744.484619140625 1760.206298828125 1721.298583984375 1742.664794921875 7763668656
923 2026-07-10 1795.6932373046875 1809.75830078125 1736.72314453125 1744.47314453125 9431692646
924 2026-07-11 1788.099609375 1827.83154296875 1786.295654296875 1795.8193359375 6901680020
925 2026-07-12 1805.7880859375 1824.98046875 1779.6370849609375 1787.568359375 6064480351
926 2026-07-13 1773.500732421875 1842.455078125 1749.3475341796875 1805.7579345703125 11436340419
927 2026-07-14 1889.4798583984375 1894.062255859375 1772.0177001953125 1773.471435546875 13835437084
928 2026-07-15 1932.93994140625 1938.8372802734375 1863.8087158203125 1889.969482421875 13269965824