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Azmikun1 2025-12-16 05:24:47 +07:00
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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():
"""
Versi 'Anti-Ribet'. Memaksa data menjadi format standar
supaya tidak lari ke backup terus.
"""
ticker = "ETH-USD"
df = None
# --- LANGKAH 1: DOWNLOAD ---
try:
# Kita pakai auto_adjust=True agar kolom lebih bersih
df = yf.download(
ticker,
start="2024-01-01",
end=date.today() + timedelta(days=1),
progress=False,
auto_adjust=True, # Penting: Mengambil harga Close yang sudah adjusted
multi_level_index=False # Penting: Mencegah kolom bertumpuk (fitur yfinance baru)
)
except Exception as e:
print(f"Error download: {e}")
# --- LANGKAH 2: PAKSA FORMAT AGAR SESUAI ---
# Cek apakah download berhasil (tidak kosong)
if df is not None and not df.empty:
# Kadang yfinance bandel tetap kasih MultiIndex, kita ratakan paksa:
if isinstance(df.columns, pd.MultiIndex):
df.columns = df.columns.get_level_values(0)
# Jika kolom namanya 'Adj Close', kita ubah jadi 'Close' biasa
if 'Adj Close' in df.columns:
df = df.rename(columns={'Adj Close': 'Close'})
# Cek apakah sekarang kolom 'Close' sudah ada?
if 'Close' in df.columns:
# Berhasil! Kita rapikan strukturnya
df = df.reset_index() # Keluarkan Date dari index menjadi kolom
# Pastikan nama kolom tanggal adalah 'Date'
if 'Date' not in df.columns and 'Datetime' in df.columns:
df = df.rename(columns={'Datetime': 'Date'})
# Bersihkan Timezone (UTC) agar jadi polosan
if 'Date' in df.columns:
df['Date'] = pd.to_datetime(df['Date']).dt.tz_localize(None)
# SIMPAN DATA BARU KE BACKUP (Supaya besok-besok file csv terupdate)
try:
df.to_csv("eth_backup.csv", index=False)
# st.success("✅ Data Yahoo Finance Berhasil Diupdate!") # Nyalakan jika ingin notifikasi
except:
pass
return df # KEMBALIKAN DATA ONLINE
# --- LANGKAH 3: JALAN BUNTU (BACKUP) ---
# Hanya jalan jika Langkah 2 gagal total
st.warning("⚠️ Mengambil data dari file backup lokal (eth_backup.csv).")
try:
df_backup = pd.read_csv("eth_backup.csv")
if "Date" in df_backup.columns:
df_backup["Date"] = pd.to_datetime(df_backup["Date"])
return df_backup
except:
st.error("❌ Fatal: Data Online Gagal & File Backup Tidak Ada.")
return None
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=60)
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["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
)
ax.plot(
future_dates, predictions_flat,
color="#d62728", linewidth=2.5, label="Harga Prediksi (GRU)",
alpha=0.9, marker="o", markersize=4
)
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 = load_eth_data()
model, scaler = load_gru_assets()
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)
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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Date,Close,High,Low,Open,Volume
2024-01-01,2352.327880859375,2352.327880859375,2267.01806640625,2282.870361328125,6906765990
2024-01-02,2355.83642578125,2431.21240234375,2348.892333984375,2352.593505859375,12910543630
2024-01-03,2210.761962890625,2385.11767578125,2113.92529296875,2355.9814453125,19332933581
2024-01-04,2269.0380859375,2294.608154296875,2204.86572265625,2210.529052734375,11044564896
2024-01-05,2268.647216796875,2276.7646484375,2209.537109375,2269.409423828125,10860953290
2024-01-06,2241.624755859375,2271.359375,2219.781982421875,2269.5400390625,5970741680
2024-01-07,2222.865966796875,2257.1279296875,2211.5625,2242.0126953125,6490053615
2024-01-08,2333.03271484375,2358.815673828125,2171.99365234375,2222.857666015625,13830287095
2024-01-09,2344.8271484375,2369.6416015625,2243.21923828125,2332.8681640625,14891130716
2024-01-10,2582.103515625,2626.976806640625,2341.943115234375,2344.923583984375,29042100476
2024-01-11,2619.619140625,2687.779052734375,2567.99365234375,2584.171630859375,22575246883
2024-01-12,2524.460205078125,2710.421875,2460.925537109375,2619.177001953125,23623839263
2024-01-13,2576.597900390625,2589.079833984375,2498.594482421875,2522.933837890625,12250316867
2024-01-14,2472.2412109375,2578.332275390625,2470.42431640625,2578.003662109375,9405587417
2024-01-15,2511.36376953125,2550.76904296875,2470.82080078125,2471.6669921875,9700630000
2024-01-16,2587.691162109375,2613.56689453125,2500.00390625,2510.627197265625,11063317095
2024-01-17,2528.369384765625,2592.737060546875,2508.432861328125,2587.044677734375,10441017520
2024-01-18,2467.018798828125,2546.263916015625,2426.135498046875,2528.59326171875,11900028080
2024-01-19,2489.49853515625,2501.30517578125,2414.7109375,2468.68896484375,11405278376
2024-01-20,2469.589111328125,2489.84765625,2456.095703125,2489.84765625,5297826161
2024-01-21,2453.9130859375,2479.760498046875,2452.377685546875,2469.798583984375,4578471955
2024-01-22,2310.826416015625,2463.447265625,2303.502685546875,2454.9873046875,13923771728
2024-01-23,2240.68603515625,2348.03125,2167.282470703125,2310.95166015625,16182147521
2024-01-24,2233.561767578125,2261.384521484375,2197.65673828125,2241.749755859375,10134722960
2024-01-25,2217.710205078125,2240.380859375,2173.6865234375,2233.969970703125,9302247037
2024-01-26,2267.19970703125,2280.3837890625,2196.14013671875,2217.44189453125,9975117607
2024-01-27,2267.885986328125,2282.54443359375,2252.38525390625,2267.3193359375,5144367230
2024-01-28,2257.208740234375,2306.898681640625,2242.683837890625,2268.19287109375,7296214994
2024-01-29,2317.064208984375,2320.02734375,2237.711669921875,2256.995361328125,8948195551
2024-01-30,2344.49365234375,2388.87060546875,2298.281982421875,2317.435791015625,10173440062
2024-01-31,2282.54443359375,2349.611328125,2264.443603515625,2343.558837890625,10807883277
2024-02-01,2303.82470703125,2309.842529296875,2243.57177734375,2282.17529296875,8895583113
2024-02-02,2308.0380859375,2323.053466796875,2282.230224609375,2303.7060546875,7186143091
2024-02-03,2296.038330078125,2327.346923828125,2293.5380859375,2307.980224609375,4647754021
2024-02-04,2289.546142578125,2309.01123046875,2272.304931640625,2296.116943359375,5438100035
2024-02-05,2298.888916015625,2334.6767578125,2270.06787109375,2289.20556640625,7277068110
2024-02-06,2372.201904296875,2389.826171875,2296.78857421875,2298.955078125,9520885493
2024-02-07,2423.7451171875,2442.63623046875,2353.7060546875,2372.2626953125,9660628536
2024-02-08,2419.906494140625,2459.556640625,2414.74951171875,2424.080078125,9941841732
2024-02-09,2487.515625,2522.724609375,2419.36279296875,2419.773681640625,13634203177
2024-02-10,2501.228271484375,2516.7197265625,2475.857666015625,2487.650390625,6474444159
2024-02-11,2507.570556640625,2537.67919921875,2495.21435546875,2501.1298828125,7347245813
2024-02-12,2658.115966796875,2663.8427734375,2473.81201171875,2507.578857421875,13022696866
2024-02-13,2642.185302734375,2686.455078125,2599.16943359375,2659.586181640625,18271237044
2024-02-14,2777.90234375,2786.8935546875,2621.025390625,2641.685302734375,21448973822
2024-02-15,2824.37890625,2865.845458984375,2764.010498046875,2777.601318359375,23734481937
2024-02-16,2803.69140625,2858.450439453125,2760.3310546875,2825.480712890625,17057114638
2024-02-17,2786.672607421875,2805.128662109375,2724.386962890625,2803.73583984375,17932379943
2024-02-18,2878.998046875,2892.843505859375,2767.9130859375,2786.709716796875,23355830478
2024-02-19,2943.57470703125,2983.37060546875,2860.263671875,2881.296875,15163110589
2024-02-20,3013.503662109375,3031.5244140625,2879.9033203125,2944.1064453125,20341598470
2024-02-21,2970.35546875,3017.1904296875,2875.41845703125,3015.6533203125,18897136867
2024-02-22,2971.00732421875,3030.666015625,2907.109375,2969.599853515625,18058908246
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2025-07-10,2954.84521484375,2995.152099609375,2757.2666015625,2770.7373046875,33929201261
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2025-07-12,2942.91162109375,2979.780029296875,2907.193603515625,2958.333740234375,16317198254
2025-07-13,2973.35888671875,3016.3935546875,2938.736572265625,2942.853515625,17361753131
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2025-07-16,3371.5068359375,3422.60302734375,3102.477783203125,3139.8896484375,46771354461
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2025-07-19,3595.273681640625,3608.275634765625,3512.97314453125,3548.926025390625,26031719746
2025-07-20,3759.471435546875,3819.398681640625,3582.349609375,3595.240478515625,44600451066
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2025-07-28,3787.42578125,3940.652099609375,3756.515869140625,3875.255859375,35831336149
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2025-07-30,3808.20166015625,3832.884033203125,3683.14453125,3793.58251953125,34392494235
2025-07-31,3696.707763671875,3877.47021484375,3685.004150390625,3808.24609375,32839839444
2025-08-01,3488.365966796875,3722.585693359375,3432.3798828125,3696.144287109375,46261558200
2025-08-02,3392.7412109375,3535.558837890625,3370.943115234375,3487.956787109375,30165065282
2025-08-03,3497.379150390625,3520.831298828125,3357.93896484375,3392.744384765625,19363593865
2025-08-04,3718.986083984375,3734.9775390625,3491.554931640625,3497.613037109375,30905749658
2025-08-05,3611.8994140625,3720.656494140625,3547.62109375,3719.81787109375,32778742296
2025-08-06,3683.920654296875,3698.124755859375,3567.095947265625,3612.038818359375,26924415763
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2025-08-09,4263.59912109375,4323.76123046875,4007.709228515625,4009.150634765625,45279715746
2025-08-10,4254.2177734375,4332.21044921875,4163.9404296875,4263.552734375,36173931367
2025-08-11,4226.966796875,4362.0927734375,4168.875,4254.2314453125,49989853803
2025-08-12,4590.9248046875,4634.05517578125,4222.3623046875,4227.20703125,57237189036
2025-08-13,4756.27587890625,4784.6689453125,4566.23876953125,4590.65771484375,62550014642
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2025-08-15,4439.98876953125,4667.72998046875,4375.54541015625,4549.07568359375,55445715824
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2025-08-21,4223.2099609375,4338.826171875,4205.77685546875,4334.5673828125,33618359942
2025-08-22,4831.3486328125,4884.22802734375,4209.47216796875,4223.73095703125,75965521535
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2025-08-24,4779.6474609375,4953.73291015625,4714.47265625,4776.70458984375,52405003873
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2025-08-26,4600.4267578125,4632.0732421875,4316.30078125,4372.88037109375,53829747706
2025-08-27,4503.39306640625,4659.9873046875,4489.2978515625,4600.51025390625,43509902322
2025-08-28,4507.177734375,4629.03125,4435.109375,4503.63134765625,36045274078
2025-08-29,4360.15283203125,4513.859375,4272.45947265625,4507.63134765625,46899991962
2025-08-30,4374.1533203125,4413.27490234375,4264.1953125,4360.0888671875,25883112278
2025-08-31,4390.01904296875,4497.1767578125,4373.5966796875,4374.8935546875,26683044984
2025-09-01,4314.47021484375,4490.3466796875,4221.24755859375,4389.6259765625,37530746508
2025-09-02,4325.36572265625,4414.93408203125,4260.45947265625,4314.39111328125,39884692334
2025-09-03,4450.38916015625,4489.19873046875,4286.2060546875,4324.6962890625,35260873497
2025-09-04,4298.744140625,4483.451171875,4268.5888671875,4450.2158203125,34919798552
2025-09-05,4306.98876953125,4484.361328125,4258.0498046875,4298.8369140625,44163736676
2025-09-06,4274.2421875,4327.43994140625,4244.7548828125,4306.97314453125,18108246446
2025-09-07,4305.34765625,4334.2744140625,4271.5341796875,4274.1669921875,17426783536
2025-09-08,4308.072265625,4381.27490234375,4279.97216796875,4305.38134765625,32277142378
2025-09-09,4309.04150390625,4381.2265625,4277.85302734375,4308.283203125,30703320925
2025-09-10,4349.14599609375,4450.41796875,4286.0625,4309.091796875,39521365146
2025-09-11,4461.2333984375,4471.69873046875,4339.716796875,4349.2001953125,35959212991
2025-09-12,4715.24609375,4734.27392578125,4453.28466796875,4461.4794921875,43839753626
2025-09-13,4668.1796875,4763.361328125,4609.12548828125,4714.70361328125,34843845977
2025-09-14,4609.59765625,4690.64208984375,4581.84716796875,4668.17138671875,28394160275
2025-09-15,4526.8203125,4670.5302734375,4469.861328125,4609.72314453125,40224062009
2025-09-16,4503.564453125,4537.599609375,4428.330078125,4526.078125,32761501436
2025-09-17,4592.7333984375,4617.23486328125,4429.64453125,4503.6357421875,44120899417
2025-09-18,4589.9189453125,4643.97265625,4556.27001953125,4592.44287109375,33497259158
2025-09-19,4470.9169921875,4620.79296875,4443.2646484375,4589.505859375,30352619653
2025-09-20,4482.265625,4509.81201171875,4459.1591796875,4470.978515625,18056494322
2025-09-21,4451.3291015625,4499.38916015625,4447.119140625,4482.583984375,19330306133
2025-09-22,4202.87744140625,4457.08251953125,4092.396728515625,4451.58056640625,58221334533
2025-09-23,4165.50390625,4227.7314453125,4120.8154296875,4203.021484375,32460075911
2025-09-24,4153.46923828125,4206.90185546875,4081.34765625,4165.41259765625,33538388785
2025-09-25,3868.333984375,4162.0625,3829.0107421875,4153.52099609375,68963092031
2025-09-26,4035.887939453125,4069.168701171875,3868.43359375,3868.69091796875,47873508328
2025-09-27,4018.658203125,4038.45166015625,3975.758056640625,4035.91455078125,20382555776
2025-09-28,4141.4765625,4143.00390625,3969.79296875,4018.65966796875,24631307054
2025-09-29,4217.341796875,4234.78271484375,4087.92724609375,4141.3564453125,38560429932
2025-09-30,4145.95751953125,4238.67138671875,4095.443603515625,4217.05517578125,37679153330
2025-10-01,4351.1123046875,4351.1123046875,4125.5419921875,4146.03369140625,46161664723
2025-10-02,4487.923828125,4517.6650390625,4336.5263671875,4352.24072265625,48074066058
2025-10-03,4514.87060546875,4591.44384765625,4431.47900390625,4486.9345703125,49603450230
2025-10-04,4489.197265625,4519.52685546875,4444.0126953125,4514.9091796875,21832270764
2025-10-05,4515.4228515625,4616.533203125,4472.138671875,4489.05322265625,44880806324
2025-10-06,4687.771484375,4736.208984375,4492.8701171875,4515.30078125,44992456374
2025-10-07,4451.15185546875,4755.22021484375,4443.5712890625,4687.7099609375,54879425294
2025-10-08,4527.64794921875,4556.2216796875,4417.76806640625,4451.10791015625,40456628235
2025-10-09,4369.1435546875,4531.716796875,4273.5595703125,4526.9609375,47903074596
2025-10-10,3843.0087890625,4395.57080078125,3460.222412109375,4369.0654296875,97736621123
2025-10-11,3750.611572265625,3882.241455078125,3652.7900390625,3840.96044921875,62475475938
2025-10-12,4164.427734375,4195.3974609375,3701.478271484375,3750.946044921875,61216174681
2025-10-13,4245.4677734375,4292.845703125,4061.224609375,4164.04931640625,50253782420
2025-10-14,4125.412109375,4265.10546875,3895.9736328125,4245.37255859375,67094148347
2025-10-15,3987.45947265625,4213.85595703125,3935.161376953125,4125.361328125,50462889453
2025-10-16,3894.75439453125,4079.648193359375,3829.654052734375,3987.14892578125,49147390731
2025-10-17,3832.558837890625,3950.56689453125,3678.620361328125,3894.377685546875,57404271888
2025-10-18,3890.34619140625,3927.24560546875,3822.266357421875,3833.009521484375,23815676385
2025-10-19,3984.649658203125,4029.35546875,3843.77294921875,3890.58349609375,32870655221
2025-10-20,3980.76025390625,4084.15966796875,3911.726318359375,3984.6962890625,40224612563
2025-10-21,3876.76416015625,4109.533203125,3843.227783203125,3980.740478515625,49960290350
2025-10-22,3808.122314453125,3890.63232421875,3718.0771484375,3877.363037109375,46173305673
2025-10-23,3856.0263671875,3930.974853515625,3801.241455078125,3808.0849609375,36361652714
2025-10-24,3934.5673828125,4002.561767578125,3849.724853515625,3856.033203125,34090708715
2025-10-25,3953.470947265625,3967.6455078125,3914.640625,3934.489990234375,15245229017
2025-10-26,4157.9892578125,4177.310546875,3923.192626953125,3953.437255859375,29120786315
2025-10-27,4120.1220703125,4250.669921875,4099.56982421875,4158.0107421875,40271333201
2025-10-28,3982.262451171875,4173.50537109375,3940.834228515625,4120.27490234375,39684155177
2025-10-29,3903.35302734375,4036.35302734375,3849.538818359375,3980.759033203125,35255876156
2025-10-30,3804.375244140625,3948.22265625,3681.909423828125,3903.322509765625,39981939289
2025-10-31,3847.080322265625,3900.580322265625,3797.669189453125,3805.082763671875,37800100395
2025-11-01,3874.18798828125,3906.7802734375,3832.634765625,3847.17138671875,17490279524
2025-11-02,3911.063232421875,3914.87548828125,3839.997314453125,3873.894287109375,20479611654
2025-11-03,3602.30810546875,3912.63330078125,3560.263671875,3911.747802734375,53091469560
2025-11-04,3292.574462890625,3652.687255859375,3063.0869140625,3602.040771484375,76827517773
2025-11-05,3425.171142578125,3479.8095703125,3170.513916015625,3292.148193359375,46955882330
2025-11-06,3312.2578125,3454.3359375,3245.28173828125,3425.361083984375,36731850433
2025-11-07,3435.301025390625,3471.804931640625,3195.896728515625,3312.029296875,42609994022
2025-11-08,3400.3759765625,3482.268310546875,3357.713134765625,3435.1474609375,23613870398
2025-11-09,3582.621337890625,3616.43701171875,3359.717529296875,3400.0986328125,28821543888
2025-11-10,3568.4599609375,3656.14892578125,3508.98583984375,3583.44482421875,35638045619
2025-11-11,3415.2822265625,3644.5283203125,3404.86181640625,3568.47265625,39734688531
2025-11-12,3413.09423828125,3586.013916015625,3373.711669921875,3415.7958984375,34898956623
2025-11-13,3232.757080078125,3561.86083984375,3156.03076171875,3412.994873046875,50283416669
2025-11-14,3103.78564453125,3252.660888671875,3071.970703125,3232.295166015625,47003809463
2025-11-15,3166.63134765625,3227.690185546875,3104.230224609375,3104.230224609375,19932940649
2025-11-16,3092.847412109375,3244.837646484375,3007.07275390625,3166.79345703125,30987886524
2025-11-17,3024.538818359375,3218.13037109375,2957.3095703125,3092.941650390625,41026793235
2025-11-18,3122.97900390625,3167.9326171875,2948.329345703125,3024.4140625,41581494270
2025-11-19,3023.07080078125,3122.64599609375,2871.22802734375,3122.64599609375,40229739844
2025-11-20,2831.777587890625,3057.78857421875,2790.597900390625,3022.885498046875,43917885207
2025-11-21,2765.69921875,2880.60302734375,2626.87158203125,2829.2646484375,51476486049
2025-11-22,2767.607421875,2795.4267578125,2704.502197265625,2766.090576171875,15408928941
2025-11-23,2801.676025390625,2856.446533203125,2767.472900390625,2767.594970703125,20358404499
2025-11-24,2952.71337890625,2982.822021484375,2763.3779296875,2801.204345703125,32491657389
2025-11-25,2957.936279296875,2978.704345703125,2857.8359375,2952.58447265625,23336017779
2025-11-26,3027.81201171875,3043.927001953125,2889.890625,2958.32958984375,21433162961
2025-11-27,3014.542236328125,3070.73193359375,2987.19775390625,3027.799072265625,16972780500
2025-11-28,3032.304443359375,3095.033203125,2995.76513671875,3014.543701171875,20659958964
2025-11-29,2991.685302734375,3051.962890625,2966.850830078125,3032.29541015625,12775481230
2025-11-30,2992.112548828125,3051.597900390625,2978.826171875,2991.375244140625,11530446514
2025-12-01,2800.18896484375,2996.8818359375,2720.4365234375,2991.923095703125,36679598313
2025-12-02,2997.939697265625,3032.76123046875,2784.390625,2800.22314453125,26593645111
2025-12-03,3191.57177734375,3212.559814453125,2988.14208984375,2997.801513671875,29949301036
2025-12-04,3134.31640625,3238.555419921875,3071.31005859375,3188.343505859375,27434991113
2025-12-05,3024.432861328125,3192.45703125,2989.83154296875,3134.357421875,28000268228
2025-12-06,3040.207763671875,3067.661376953125,3013.98193359375,3024.487548828125,10962819760
2025-12-07,3061.3017578125,3148.7734375,2930.645263671875,3040.17919921875,20383496376
2025-12-08,3125.197998046875,3177.86767578125,3043.703857421875,3061.013671875,25262165670
2025-12-09,3321.114990234375,3395.838623046875,3092.87646484375,3124.942626953125,32012553374
2025-12-10,3325.39013671875,3446.622802734375,3290.14697265625,3321.2041015625,30694387989
2025-12-11,3237.056884765625,3327.344482421875,3149.034423828125,3324.38671875,29066243707
2025-12-12,3084.172607421875,3265.37255859375,3050.267333984375,3237.025634765625,24391003174
2025-12-13,3116.6953125,3134.849365234375,3080.07861328125,3084.129638671875,9916869400
2025-12-14,3060.5947265625,3128.622802734375,3034.692626953125,3116.743896484375,15619543350
2025-12-15,2942.100341796875,3171.243896484375,2910.380126953125,3062.03076171875,29388769280
1 Date Close High Low Open Volume
2 2024-01-01 2352.327880859375 2352.327880859375 2267.01806640625 2282.870361328125 6906765990
3 2024-01-02 2355.83642578125 2431.21240234375 2348.892333984375 2352.593505859375 12910543630
4 2024-01-03 2210.761962890625 2385.11767578125 2113.92529296875 2355.9814453125 19332933581
5 2024-01-04 2269.0380859375 2294.608154296875 2204.86572265625 2210.529052734375 11044564896
6 2024-01-05 2268.647216796875 2276.7646484375 2209.537109375 2269.409423828125 10860953290
7 2024-01-06 2241.624755859375 2271.359375 2219.781982421875 2269.5400390625 5970741680
8 2024-01-07 2222.865966796875 2257.1279296875 2211.5625 2242.0126953125 6490053615
9 2024-01-08 2333.03271484375 2358.815673828125 2171.99365234375 2222.857666015625 13830287095
10 2024-01-09 2344.8271484375 2369.6416015625 2243.21923828125 2332.8681640625 14891130716
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gru_model.h5 Normal file

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requirements.txt Normal file
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streamlit
joblib
pandas
numpy
scikit-learn
tensorflow
yfinance>=0.2.40
matplotlib

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scaler_gru.pkl Normal file

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test_koneksi.py Normal file
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# File: test_koneksi.py
import yfinance as yf
import datetime
print("🚀 Memulai skrip pengujian koneksi yfinance...")
# Gunakan ticker yang umum dan tanggal yang baru
ticker_symbol = "ETH-USD"
start_date = "2024-01-01"
print(f"Mencoba mengunduh data untuk ticker: {ticker_symbol}")
print(f"Tanggal mulai: {start_date}")
print("-" * 30)
try:
# Kita akan mencoba mengunduh data dengan cara yang paling dasar
data = yf.download(
tickers=ticker_symbol,
start=start_date,
progress=True # Kita aktifkan progress bar untuk melihat aktivitas jaringan
)
print("-" * 30)
# Periksa hasil unduhan
if data.empty:
print("❌ HASIL: GAGAL. yfinance tidak menerima data apa pun.")
print("Ini mengindikasikan kemungkinan adanya masalah pada jaringan, firewall, atau DNS.")
else:
print(f"✅ HASIL: SUKSES! Berhasil menerima {len(data)} baris data.")
print("Berikut adalah 5 baris pertama data yang diterima:")
print(data.head())
except Exception as e:
print(f"🚨 TERJADI ERROR KRITIS SAAT EKSEKUSI 🚨")
print(f"Detail Error: {e}")
print("Error ini menunjukkan adanya masalah fundamental saat mencoba menghubungi server Yahoo Finance.")
print("\n🔬 Tes Selesai.")