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Azmikun1 2026-06-28 21:20:28 +07:00
parent 51a4b77e36
commit 38b54f89b9
3 changed files with 737 additions and 219 deletions
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292
app1.py
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@ -3,10 +3,10 @@ import streamlit as st
import joblib
import pandas as pd
import numpy as np
import os # Tambahan untuk manajemen file
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
@ -18,19 +18,9 @@ rcParams['font.family'] = 'DejaVu Sans'
# --- Konfigurasi Halaman ---
st.set_page_config(
page_title="Prediksi Harga Ethereum (GRU)",
page_icon="🪙",
layout="wide" # Opsional: agar tampilan lebih luas
page_icon="🪙"
)
# --- KONFIGURASI FILE ADMIN ---
DATA_FOLDER = 'dataset'
DATA_FILE = 'databackup_eth.csv'
DATA_PATH = os.path.join(DATA_FOLDER, DATA_FILE)
# Pastikan folder dataset ada
if not os.path.exists(DATA_FOLDER):
os.makedirs(DATA_FOLDER)
# --- GLOBAL STYLES (UI only) ---
st.markdown("""
<style>
@ -42,15 +32,6 @@ html, body, [class*="css"] { font-family: "Inter", "DejaVu Sans", sans-serif; }
--ring: #e5e7eb;
}
/* --- PERBAIKAN DISINI: MEMBATASI LEBAR KONTEN --- */
/* Ini memaksa konten tetap di tengah dengan lebar maksimal tertentu */
.block-container {
max-width: 1000px; /* Atur angka ini sesuai selera (misal 900px - 1200px) */
padding-top: 2rem;
padding-bottom: 2rem;
margin: auto; /* Posisi otomatis di tengah */
}
/* gradient title */
.app-title {
text-align:center;
@ -100,48 +81,73 @@ html, body, [class*="css"] { font-family: "Inter", "DejaVu Sans", sans-serif; }
""", unsafe_allow_html=True)
# --- Fungsi-fungsi Bantuan (JANGAN UBAH LOGIKA UTAMA) ---
# --- Fungsi-fungsi Bantuan ---
@st.cache_data(ttl="1h")
def load_eth_data():
"""
ticker = "ETH-USD"
df = None
"""
# Cek apakah file admin ada
if os.path.exists(DATA_PATH):
# 1. COBA ONLINE
try:
df = pd.read_csv(DATA_PATH)
# --- PEMBERSIHAN DATA AGAR SESUAI FORMAT MODEL ---
# 1. Hapus kolom index lama jika ada
if "Unnamed: 0" in df.columns:
df = df.drop(columns=["Unnamed: 0"])
df = yf.download(
ticker,
start="2024-01-01",
end=date.today() + timedelta(days=1),
progress=False,
auto_adjust=True,
multi_level_index=False
)
# 2. Standarisasi nama kolom Date
if "Date" not in df.columns:
# Coba cari kolom yang mirip 'date'
found = False
if df is not None and not df.empty:
# Bersihkan Index & Kolom
df = df.reset_index()
new_cols = []
for col in df.columns:
if col.lower() == "date":
df = df.rename(columns={col: "Date"})
found = True
break
if not found:
# Jika tidak ada header Date, asumsikan kolom pertama adalah Date
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'})
# 3. Konversi ke datetime
df["Date"] = pd.to_datetime(df["Date"])
# Hapus timezone jika ada agar kompatibel dengan matplotlib/numpy
if df["Date"].dt.tz is not None:
df["Date"] = df["Date"].dt.tz_localize(None)
# Hapus Timezone
df['Date'] = pd.to_datetime(df['Date']).dt.tz_localize(None)
return df, "admin_file"
# 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:
return None, f"error_read: {str(e)}"
else:
return None, "no_file"
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."""
@ -185,23 +191,44 @@ def validate_model_input(model):
@st.cache_resource
def load_gru_assets():
"""Load model & scaler hasil training."""
"""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 = []
@ -215,8 +242,8 @@ def predict_from_sequence_pure(model, initial_sequence_scaled, n_days):
def create_combined_chart(df, start_date, future_dates, future_predictions):
"""Membuat grafik gabungan."""
context_start_date = start_date - timedelta(days=60)
"""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
@ -226,24 +253,32 @@ def create_combined_chart(df, start_date, future_dates, future_predictions):
fig, ax = plt.subplots(figsize=(14, 8))
ax.plot(
recent_df["Date"], recent_df["Close"],
color="gray", linewidth=1.0, label="Harga Aktual (Harian)", alpha=0.5
)
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, predictions_flat,
color="#d62728", linewidth=2.5, label="Harga Prediksi (GRU)",
alpha=0.9, marker="o", markersize=4
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]
@ -280,35 +315,30 @@ def create_combined_chart(df, start_date, future_dates, future_predictions):
return fig
# --- HALAMAN: DASHBOARD PUBLIK (Landing Page) ---
def show_dashboard():
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)
# --- Tampilan Antarmuka Aplikasi ---
df, data_source = load_eth_data()
model, scaler = load_gru_assets()
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)
# LOGIC CHECK DATA
if data_source == "no_file":
st.warning("⚠️ Data historis belum tersedia. Silakan hubungi Admin untuk mengupload 'databackup_eth'.")
return # Stop eksekusi jika data tidak ada
elif "error" in data_source:
st.error(f"❌ Terjadi kesalahan membaca data: {data_source}")
return
df, data_source = load_eth_data()
model, scaler = load_gru_assets()
if df is not None and model is not None and scaler is not None:
# 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)
# --- PERUBAHAN DISINI: HAPUS .tail(100) ---
# Menggunakan height=400 agar tabel lebih tinggi dan bisa di-scroll
st.dataframe(df, height=400, use_container_width=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)
# Grafik ini sudah otomatis mengambil full data df
st.line_chart(df.rename(columns={"Date": "index"}).set_index("index")["Close"])
st.markdown("</div>", unsafe_allow_html=True)
@ -358,7 +388,8 @@ def show_dashboard():
if start_index - time_step < 0:
st.error(
f"Tanggal terlalu awal untuk diprediksi. "
f"Butuh {time_step} hari data sebelumnya."
f"Butuh {time_step} hari data sebelumnya. Pilih tanggal setelah "
f"{(df['Date'].min() + timedelta(days=time_step)).strftime('%Y-%m-%d')}."
)
st.stop()
@ -384,6 +415,39 @@ def show_dashboard():
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],
@ -398,66 +462,18 @@ def show_dashboard():
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}
""")
📊 **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:
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:
elif scaler is None:
st.error("Gagal memuat scaler. Pastikan file 'scaler_gru.pkl' ada di folder yang sama dengan aplikasi.")
# --- HALAMAN: ADMIN LOGIN & UPLOAD ---
def show_admin_page():
st.title("🔐 Admin Panel")
st.write("Area khusus admin untuk memperbarui dataset harga Ethereum.")
# Simple Password check
password = st.text_input("Masukkan Password Admin:", type="password")
if password == "admin123": # Ganti password sesuai keinginan
st.success("Akses Diterima.")
st.divider()
st.subheader("📂 Upload Dataset Baru")
st.info(f"File akan disimpan sebagai: `{DATA_FILE}` di dalam folder sistem.")
uploaded_file = st.file_uploader("Upload file CSV (Format: Date, Close, dll)", type=['csv'])
if uploaded_file is not None:
# Baca preview
try:
df_preview = pd.read_csv(uploaded_file)
st.write("Preview Data:", df_preview.head())
if st.button("💾 Simpan & Update Sistem"):
# Simpan file ke path yang ditentukan
df_preview.to_csv(DATA_PATH, index=False)
# Clear cache agar halaman public langsung berubah
st.cache_data.clear()
st.success(f"Berhasil! Data telah diperbarui. Pengguna publik sekarang melihat data baru.")
except Exception as e:
st.error(f"File error: {e}")
elif password:
st.error("Password Salah.")
# --- MAIN NAVIGATION (SIDEBAR) ---
# Ini adalah logika utama yang memisahkan tampilan Admin dan User Biasa
st.sidebar.title("Navigasi")
menu = st.sidebar.radio("Pilih Halaman:", ["Dashboard (Public)", "Admin Panel"])
if menu == "Dashboard (Public)":
show_dashboard()
elif menu == "Admin Panel":
show_admin_page()
else:
st.error("Gagal memuat data. Coba cek koneksi internet atau sumber data yfinance.")

479
apptest.py Normal file
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@ -0,0 +1,479 @@
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.")

25
eth_backup.csv
View File

@ -882,4 +882,27 @@ Date,Close,High,Low,Open,Volume
2026-05-30,2019.458251953125,2028.4417724609375,2000.165771484375,2011.909423828125,7478416074
2026-05-31,2004.341552734375,2034.1192626953125,1991.90673828125,2019.242431640625,9253505209
2026-06-01,2003.22119140625,2017.6077880859375,1956.1522216796875,2004.2425537109375,19458810001
2026-06-03,1848.1400146484375,1870.7220458984375,1848.860107421875,1857.32568359375,25659957248
2026-06-02,1857.71630859375,2003.970703125,1838.05859375,2003.26025390625,25205401403
2026-06-03,1811.7322998046875,1889.058837890625,1771.3145751953125,1857.6417236328125,24083350214
2026-06-04,1769.58056640625,1817.3465576171875,1717.802490234375,1811.7425537109375,27112623897
2026-06-05,1580.86083984375,1772.1077880859375,1540.1619873046875,1769.986083984375,39924177669
2026-06-06,1568.7677001953125,1599.9534912109375,1506.5057373046875,1580.88037109375,18426248218
2026-06-07,1686.396240234375,1714.970703125,1563.77587890625,1568.74951171875,15172606860
2026-06-08,1690.1485595703125,1713.4710693359375,1644.79150390625,1686.251220703125,17427269148
2026-06-09,1637.7066650390625,1695.52294921875,1613.834228515625,1690.21337890625,15196855528
2026-06-10,1620.1376953125,1665.7607421875,1603.8258056640625,1637.6348876953125,12655731412
2026-06-11,1672.2806396484375,1690.4488525390625,1620.08447265625,1620.1400146484375,12379882531
2026-06-12,1665.1278076171875,1689.4443359375,1650.628662109375,1671.8189697265625,9989137815
2026-06-13,1680.214599609375,1693.603271484375,1661.609130859375,1665.0487060546875,6101094488
2026-06-14,1724.61328125,1729.350830078125,1654.2105712890625,1680.146484375,7804982667
2026-06-15,1794.961181640625,1847.769775390625,1709.253662109375,1724.5716552734375,17921331594
2026-06-16,1790.398193359375,1837.2041015625,1758.130126953125,1794.93798828125,14698492893
2026-06-17,1747.885498046875,1807.2828369140625,1724.7178955078125,1790.396240234375,14185457929
2026-06-18,1709.533447265625,1760.880859375,1670.1038818359375,1747.8778076171875,12942260783
2026-06-19,1710.982421875,1717.1932373046875,1678.1962890625,1709.4986572265625,7855725190
2026-06-20,1739.3013916015625,1747.259521484375,1702.8017578125,1710.982177734375,7773536600
2026-06-21,1704.58056640625,1739.48583984375,1701.9364013671875,1739.341064453125,8498711726
2026-06-22,1726.5108642578125,1776.8321533203125,1704.5166015625,1704.6304931640625,13584368622
2026-06-23,1665.436767578125,1733.6123046875,1638.253173828125,1726.519775390625,10431438278
2026-06-24,1619.9249267578125,1687.4923095703125,1551.4842529296875,1665.309814453125,14059894232
2026-06-26,1556.2900390625,1568.429443359375,1556.8143310546875,1564.8594970703125,15403906048

1 Date Close High Low Open Volume
882 2026-05-30 2019.458251953125 2028.4417724609375 2000.165771484375 2011.909423828125 7478416074
883 2026-05-31 2004.341552734375 2034.1192626953125 1991.90673828125 2019.242431640625 9253505209
884 2026-06-01 2003.22119140625 2017.6077880859375 1956.1522216796875 2004.2425537109375 19458810001
885 2026-06-03 2026-06-02 1848.1400146484375 1857.71630859375 1870.7220458984375 2003.970703125 1848.860107421875 1838.05859375 1857.32568359375 2003.26025390625 25659957248 25205401403
886 2026-06-03 1811.7322998046875 1889.058837890625 1771.3145751953125 1857.6417236328125 24083350214
887 2026-06-04 1769.58056640625 1817.3465576171875 1717.802490234375 1811.7425537109375 27112623897
888 2026-06-05 1580.86083984375 1772.1077880859375 1540.1619873046875 1769.986083984375 39924177669
889 2026-06-06 1568.7677001953125 1599.9534912109375 1506.5057373046875 1580.88037109375 18426248218
890 2026-06-07 1686.396240234375 1714.970703125 1563.77587890625 1568.74951171875 15172606860
891 2026-06-08 1690.1485595703125 1713.4710693359375 1644.79150390625 1686.251220703125 17427269148
892 2026-06-09 1637.7066650390625 1695.52294921875 1613.834228515625 1690.21337890625 15196855528
893 2026-06-10 1620.1376953125 1665.7607421875 1603.8258056640625 1637.6348876953125 12655731412
894 2026-06-11 1672.2806396484375 1690.4488525390625 1620.08447265625 1620.1400146484375 12379882531
895 2026-06-12 1665.1278076171875 1689.4443359375 1650.628662109375 1671.8189697265625 9989137815
896 2026-06-13 1680.214599609375 1693.603271484375 1661.609130859375 1665.0487060546875 6101094488
897 2026-06-14 1724.61328125 1729.350830078125 1654.2105712890625 1680.146484375 7804982667
898 2026-06-15 1794.961181640625 1847.769775390625 1709.253662109375 1724.5716552734375 17921331594
899 2026-06-16 1790.398193359375 1837.2041015625 1758.130126953125 1794.93798828125 14698492893
900 2026-06-17 1747.885498046875 1807.2828369140625 1724.7178955078125 1790.396240234375 14185457929
901 2026-06-18 1709.533447265625 1760.880859375 1670.1038818359375 1747.8778076171875 12942260783
902 2026-06-19 1710.982421875 1717.1932373046875 1678.1962890625 1709.4986572265625 7855725190
903 2026-06-20 1739.3013916015625 1747.259521484375 1702.8017578125 1710.982177734375 7773536600
904 2026-06-21 1704.58056640625 1739.48583984375 1701.9364013671875 1739.341064453125 8498711726
905 2026-06-22 1726.5108642578125 1776.8321533203125 1704.5166015625 1704.6304931640625 13584368622
906 2026-06-23 1665.436767578125 1733.6123046875 1638.253173828125 1726.519775390625 10431438278
907 2026-06-24 1619.9249267578125 1687.4923095703125 1551.4842529296875 1665.309814453125 14059894232
908 2026-06-26 1556.2900390625 1568.429443359375 1556.8143310546875 1564.8594970703125 15403906048