Ml Di Tolet Umum Wwwfilemsarublogspotcomrar Full May 2026

| Step | Data Type | Privacy Mechanism | |------|-----------|-------------------| | Sensor Capture | Raw counts, flow, temperature | No PII | | Camera Capture | Low‑res grayscale frames | Edge‑level blur & skeletonization; no faces stored | | Transmission | Encrypted MQTT (TLS 1.3) | Mutual TLS authentication | | Storage | Time‑series in Cloud DB | Data retention policy (max 90 days for raw; aggregated for longer) | | Analytics | Model inputs only | Differential privacy for aggregate reporting | | User Feedback | Text via WhatsApp/Google Form | Consent‑based, GDPR‑compliant storage |

import pandas as pd
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense
from sklearn.preprocessing import MinMaxScaler
# -------------------------------------------------
# 1. Load historic door‑counter data (5‑min intervals)
# -------------------------------------------------
df = pd.read_csv('toilet_occupancy.csv', parse_dates=['timestamp'])
df.set_index('timestamp', inplace=True)
# -------------------------------------------------
# 2. Scale data to [0,1]
# -------------------------------------------------
scaler = MinMaxScaler()
scaled = scaler.fit_transform(df[['count']])
# -------------------------------------------------
# 3. Prepare supervised learning windows
# -------------------------------------------------
def create_dataset(series, look_back=12):
    X, y = [], []
    for i in range(len(series)-look_back):
        X.append(series[i:i+look_back])
        y.append(series[i+look_back])
    return tf.constant(X, dtype=tf.float32), tf.constant(y, dtype=tf.float32)
look_back = 12          # 12×5 min = 1 hour history
X, y = create_dataset(scaled, look_back)
# -------------------------------------------------
# 4. Build a simple LSTM model
# -------------------------------------------------
model = Sequential([
    LSTM(64, input_shape=(look_back, 1), return_sequences=False),
    Dense(1, activation='linear')
])
model.compile(optimizer='adam', loss='mae')
# -------------------------------------------------
# 5. Train (early stopping)
# -------------------------------------------------
es = tf.keras.callbacks.EarlyStopping(patience=5, restore_best_weights=True)
model.fit(X, y, epochs=50, batch_size=32, validation_split=0.2, callbacks=[es])
# -------------------------------------------------
# 6. Real‑time inference (example)
# -------------------------------------------------
def predict_next(current_window):
    """current_window: np.array shape (look_back, 1) already scaled"""
    pred_scaled = model.predict(tf.expand_dims(current_window, axis

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