IoT Beginner Tersedia Training Korporasi
#IoT #ESP32

Building Smart IoT Projects with ESP32 and TinyML

Learn to build intelligent, edge-computing devices from scratch. This project-based course takes a top-down, problem-driven approach to Internet of Things (IoT) and Artificial Intelligence. Instead of getting bogged down in heavy math, you will learn how to collect real-world data, train machine learning models using Edge Impulse, and deploy offline AI directly onto an ESP32 microcontroller. By the end of the course, you will have built a fully functional, smart environmental monitor that processes data locally and communicates efficiently with the cloud.

7 Weeks* | *Can be condensed to 4-day intensive bootcamp for corporate training | 20 Jam Sesi Langsung
Sertifikat Kelulusan
Pembelajaran Berbasis Proyek
Daftar Sekarang
2,000+
Alumni Terlatih
50+
Klien Korporasi
100+
Batch Training
7+
Tahun Pengalaman

Dipercaya oleh Perusahaan Terkemuka

Tentang Kursus Ini

Learn to build intelligent, edge-computing devices from scratch. This project-based course takes a top-down, problem-driven approach to Internet of Things (IoT) and Artificial Intelligence. Instead of getting bogged down in heavy math, you will learn how to collect real-world data, train machine learning models using Edge Impulse, and deploy offline AI directly onto an ESP32 microcontroller. By the end of the course, you will have built a fully functional, smart environmental monitor that processes data locally and communicates efficiently with the cloud.

A complete hardware kit, including the ESP32 DevKit and all necessary sensors, will be provided to every student for this course.

Silabus Kursus

01

Foundations of ESP32 and the IoT Ecosystem
  • Introduction to the ESP32 microcontroller architecture and pinouts.
  • Setting up the development environment (Arduino IDE / PlatformIO).
  • Mini Project: "Hello World" of Hardware – Blinking LEDs and reading basic digital/analog inputs.
02

Sensing the Physical World
  • Interfacing with environmental sensors (Temperature, Humidity, and Accelerometers).
  • Understanding data types, sampling rates, and noise filtering.
  • Mini Project: Building a basic environmental logger that outputs data to the Serial Monitor.
03

The TinyML Paradigm and Edge Impulse
  • What is Edge AI? Understanding the shift from Cloud ML to Edge ML.
  • Introduction to Edge Impulse Studio and the machine learning pipeline.
  • Mini Project: Creating an Edge Impulse project and understanding the classification workflow.
04

Data Harvesting and Pre-processing
  • The importance of high-quality datasets for TinyML.
  • Connecting the ESP32 (or a smartphone) to Edge Impulse to collect raw sensor data.
  • Mini Project: Recording and labeling a custom dataset (e.g., motion gestures or audio samples).
05

Training and Validating the AI Model
  • Extracting features (e.g., Spectral Analysis for motion or MFCC for audio) without writing complex code.
  • Training lightweight Neural Networks designed for microcontrollers.
  • Mini Project: Training the classification model in the cloud and testing its accuracy against unseen validation data.
06

Edge Deployment and C++ Integration
  • Exporting the trained model as an optimized C++ library.
  • Integrating the AI library into the ESP32 codebase.
  • Mini Project: "Offline Intelligence" – Running real-time inference on the ESP32 to detect patterns without an internet connection.
07

IoT Telemetry and Cloud Dashboards
  • Connecting the smart device to the internet (WiFi or Cellular).
  • Sending inference results (insights), rather than raw data, to a backend system to save bandwidth.
  • Mini Project: Pushing AI-triggered alerts to a cloud database or IoT dashboard.
08

Power Management and Real-World Assembly
  • Transitioning from breadboards to standalone devices.
  • Introduction to ESP32 Deep Sleep modes to conserve battery life.
  • Course Wrap-up: Final review of the architecture and preparation for the Capstone.

Proyek Akhir

EcoSentinel: Smart Acoustic & Environmental Monitor

The Capstone Project challenges students to build an end-to-end, intelligent monitoring system designed for real-world application, such as agricultural monitoring, predictive maintenance, or home security. Students will interface an I2S microphone with the ESP32, collect specific audio samples (e.g., machine anomalies, glass breaking, or pest noises), and train a custom TinyML model. The final device will actively listen to its environment offline. When an anomaly is detected with high confidence, the ESP32 will wake up its network module and transmit a lightweight alert payload to a cloud dashboard.

Offline Audio Classification: The device processes and classifies continuous audio streams locally on the ESP32 using a custom-trained TinyML model, ensuring privacy and zero latency.
Bandwidth-Efficient Telemetry: Instead of streaming heavy raw audio to the cloud, the system only transmits small JSON payloads (e.g., {"event": "anomaly_detected", "confidence": 0.92}) when a specific condition is met.
Event-Driven Cloud Alerts: Seamless integration with a backend service to log events over time or trigger real-time notifications (like a Telegram bot alert or a dashboard update).
Hardware Integration: Practical experience wiring and synchronizing digital sensors (I2S microphone) with the ESP32 processing loop.
Training Korporasi

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Diskon hingga 30%

Diskon Tim

Dapatkan harga khusus untuk pendaftaran grup. Semakin banyak peserta, semakin besar diskonnya.

Solusi Kustom

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Jadwal Fleksibel

Jadwal Fleksibel

Pilih waktu training yang sesuai dengan tim Anda: hari kerja, akhir pekan, atau sesi khusus di kantor Anda.

Sertifikat Profesional

Sertifikat Resmi

Semua peserta menerima sertifikat profesional setelah menyelesaikan training.

Dukungan 30 Hari

Dukungan Pasca-Training

Dapatkan akses konsultasi gratis selama 30 hari setelah training untuk memastikan implementasi yang sukses.

Proyek Nyata

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FAQ

Pertanyaan yang Sering Diajukan

Temukan jawaban untuk pertanyaan umum tentang program training kami

Ya, kami menyediakan opsi training online (remote), offline (di kantor Anda, khusus korporasi), atau hybrid berdasarkan kebutuhan tim Anda. Semua format mendapatkan materi dan sertifikat yang sama.

Untuk training korporasi, minimum adalah 3 peserta. Namun, kami juga menerima pendaftaran individu.

Tentu saja. Kami menawarkan layanan kurikulum kustom di mana materi dapat disesuaikan dengan tumpukan teknologi, proyek aktif, dan kebutuhan spesifik tim Anda.

Ya, kami menawarkan diskon grup khusus: 10% untuk 5-9 peserta, 15% untuk 10-14 peserta, dan 20-30% untuk 15+ peserta dari perusahaan yang sama.

Durasi training bervariasi tergantung materi. Untuk training korporasi, jadwal dapat disesuaikan dengan kebutuhan tim Anda - hari kerja, akhir pekan, atau jadwal kustom.

Ya, semua peserta yang menyelesaikan training akan mendapatkan sertifikat resmi dari Rumah Coding. Sertifikat digital dapat diverifikasi secara online.

Tentu. Kami menyediakan dukungan konsultasi gratis selama 30 hari setelah training untuk membantu implementasi. Peserta juga mendapatkan akses ke komunitas eksklusif dan materi rekaman training.

Masih punya pertanyaan?

Hubungi Tim Kami
Investasi
Rp 8,000,000
Per peserta
Daftar Sekarang
Durasi: 7 Weeks* *Can be condensed to 4-day intensive bootcamp for corporate training
Sertifikat Kelulusan

Untuk Perusahaan?

Dapatkan penawaran khusus untuk training tim Anda

  • Diskon hingga 30%
  • Kurikulum kustom
  • Jadwal fleksibel
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