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
The Indonesian Toilet Association provides official technical standards for public facility design and hygiene, including guidelines on paper dispenser requirements. Practical advice for reducing germ exposure in public restrooms, such as limiting surface contact, is available through health-focused platforms. Learn more about sanitation standards from the Indonesian Toilet Association AI responses may include mistakes. Learn more 6 Tips Terhindar dari Kuman di Toilet Umum - Halodoc
| 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 |
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