🏆 Proyek Akhir (Capstone)

Empat sistem IoT lengkap — masing-masing memakai seluruh 4 layer.

Smart farming: sensor tanah → LoRaWAN → platform → irigasi otomatis

📌 Proyek 1 — Smart Home: AC Otomatis

Layer dipakai: Sensor (DHT22) → MQTT → Platform (shadow) → Keputusan (threshold).

import json

# PLATFORM: tempat status AC disimpan
cloud = CloudPlatform("SmartHome")
cloud.register_device("ac-01")

# JARINGAN: sensor publish, controller subscribe
pub = MQTTClient("sensor-01"); pub.connect()
sub = MQTTClient("ac-controller"); sub.connect()

def on_message(client, userdata, msg):
    data = json.loads(msg["payload"])
    if data["suhu"] > 28:
        cloud.set_desired("ac-01", {"ac": "ON"})
        print("  ❄️ AC: perintah ON (suhu %.1f°C)" % data["suhu"])
    else:
        cloud.set_desired("ac-01", {"ac": "OFF"})

sub.subscribe("home/kamar/temp", on_message)

print("=== SMART HOME: AC OTOMATIS ===")
for i in range(6):
    suhu = read_sensor("dht22_temp")
    pub.publish("home/kamar/temp", {"suhu": suhu})
    ac = cloud.get_shadow("ac-01")["desired"].get("ac", "-")
    print("t=%3.0fs  suhu=%.1f°C  AC=%s" % (now(), suhu, ac))
    delay(10)

pub.disconnect(); sub.disconnect()

Output:

📌 Proyek 2 — Smart Farming: Irigasi Otomatis

Layer dipakai: Sensor (tanah + hujan) → Keputusan (irigasi hanya jika kering dan tidak hujan) → Time-series DB.

db = TimeSeriesDB()
print("=== SMART FARMING: IRIGASI OTOMATIS ===")
print("%-8s %-10s %-12s %s" % ("WAKTU", "TANAH", "HUJAN", "AKSI"))
print("-" * 52)

for i in range(10):
    tanah = read_sensor("soil")
    hujan = read_sensor("rain")

    # Keputusan: siram hanya jika TANAH KERING dan TIDAK HUJAN
    if tanah < 30 and hujan < 1:
        aksi = "🚿 IRIGASI NYALA"
    else:
        aksi = "istirahat"

    db.write("soil", tanah, tags={"bedeng": "A"})
    db.write("rain", hujan, tags={"bedeng": "A"})
    print("%-8s %-10s %-12s %s" % ("%.0fs" % now(), "%.0f%%" % tanah,
                                   "%.1f mm/h" % hujan, aksi))
    delay(10)

print()
print("Statistik tanah :", db.stats("soil"))
print("Statistik hujan :", db.stats("rain"))

Output:

📌 Proyek 3 — Fleet Tracking: Geofence

Layer dipakai: Sensor (GPS) → MQTT (topic per kendaraan) → Analitik (jarak Haversine + batas geofence).

import math

def haversine(lat1, lng1, lat2, lng2):
    """Jarak dua titik bumi (km)."""
    r = 6371.0
    dlat, dlng = math.radians(lat2 - lat1), math.radians(lng2 - lng1)
    a = (math.sin(dlat / 2) ** 2 +
         math.cos(math.radians(lat1)) * math.cos(math.radians(lat2)) *
         math.sin(dlng / 2) ** 2)
    return 2 * r * math.asin(math.sqrt(a))

GUDANG = (-7.95, 112.63)     # koordinat gudang (Malang)
JENJANG_KM = 50.0

pub = MQTTClient("telemetri"); pub.connect()
lokasi = {
    "truk-01": (-7.95, 112.63),   # jalan pelan
    "truk-02": (-7.96, 112.65),   # jalan pelan
    "truk-03": (-7.94, 112.62),   # jalan CEPAT (melawan arah)
}

print("=== FLEET TRACKING: GEOFENCE 50 KM ===")
for i in range(8):
    for vid in lokasi:
        lat, lng = lokasi[vid]
        if vid == "truk-03":
            lat += 0.06            # ~6.7 km per langkah
        else:
            lat += 0.001
        lng += 0.001
        lokasi[vid] = (lat, lng)

        dist = haversine(lat, lng, *GUDANG)
        status = "DI LUAR AREA 🚨" if dist > JENJANG_KM else "dalam area"
        pub.publish("fleet/%s/pos" % vid,
                    {"lat": round(lat, 5), "lng": round(lng, 5),
                     "km": round(dist, 2)})
        print("t=%3.0fs  %s  %5.1f km  %s" % (now(), vid, dist, status))
    delay(5)

pub.disconnect()

Output:

Perhatikan truk-03: pelan-pelan menjauh, lalu melebihi geofence 50 km di akhir — persis skenario truk nyasar / pencurian muatan.

📌 Proyek 4 — Monitoring Energi + Deteksi Anomali

Layer dipakai: Sensor (daya) → Time-series DB → Analitik (total energi, biaya, z-score).

db = TimeSeriesDB()
HARGA_KWH = 1441.0   # Rp/kWh (tarif rumah tangga)

print("=== MONITORING ENERGI: 1 jam, sampel tiap 5 menit ===")
for i in range(12):
    daya = read_sensor("power_w")
    if i == 9:
        daya = 2800   # simulasi: beban abnormal (kompresor nyala paksa)
    db.write("power_w", daya)
    delay(300)

# Energi: E = P × t  →  (W × s) / 3.600.000 = kWh
sampel = db.query("power_w")
dt = 300.0
energi_kwh = sum(p["v"] for p in sampel) * dt / 3.6e6
biaya = energi_kwh * HARGA_KWH

an = detect_anomalies([p["v"] for p in sampel], threshold=2.0)

print("Total energi     : %.2f kWh" % energi_kwh)
print("Estimasi biaya   : Rp %.0f" % biaya)
print("Anomali (indeks) :", an)
print("Daya saat anomali:", [sampel[i]["v"] for i in an], "W")
print()
print("→ Di dunia nyata: alarm + investigasi beban abnormal")
print("  (bocor, mesin rusak, atau pencurian listrik).")

Output:

🧠 Tantangan Lanjutan

Smart home: tambah histeresis — AC nyala jika > 28°, mati jika < 26° (bukan < 28°). Kenapa histeresis penting? (Petunjuk: flicker ON/OFF di sekitar threshold.)
Smart farming: simpan status irigasi di DB dan hitung total durasi irigasi. Tambah aturan: irigasi maksimal 30 menit per siklus.
Fleet: hitung kecepatan rata-rata tiap truk (km/jam) dari perubahan jarak. Tambah alert jika kecepatan > 80 km/jam.
Energi: hitung agregasi mean per 30 menit dan grafik-kan dengan ascii_chart. Di jam berapa beban puncak?
Bonus: gabungkan 2 proyek (mis. smart home + monitoring energi: AC ON menambah beban — korelasi keduanya).