Building Smart Applications for Everyday Life, from Zero — A rice cooker that knows when the rice is done, a map that finds a way around a traffic jam, a phone that puts the scam messages in one folder and the real ones in another: each of them looks at a situation, applies some knowledge to what it sees, and decides. That is all an intelligent system is.

Overview

Chapter 1 looks at the intelligent systems we meet on an ordinary morning, explains what makes a system intelligent and how the family of methods fits together, and sets up the workbench — VS Code, JDK 25 and Python 3.14 on Windows, macOS and Linux. It ends with a first intelligent program: three rules that say whether to take an umbrella or a raincoat, and why.

Chapters 2 to 6 are about rules. Chapter 2 helps Bu Sari, who sells cilok and siomay in front of a primary school, decide how many portions to make for tomorrow, with IF–THEN rules kept in a file, and checks the rules against 112 days of her notebook. Chapter 3 builds a repair helper for a water pump and a rice cooker with forward chaining, reasoning from what the owner sees to the likely fault and explaining why it asks each question and how it reached each conclusion. Chapter 4 turns the direction round: a first-aid guide for babies and young children uses backward chaining to ask a worried parent only the questions that matter, the dangerous signs first, and always says when to go to the puskesmas or the hospital. Chapter 5 lets a durian grower answer “probably” and “not sure” and weighs the uncertain evidence with confidence factors. Chapter 6 puts all of it together in a full expert system for the front desk of a small clinic: a knowledge base in SQLite, an engine that stops to ask, explanations, safe editing checked by stored test cases, and a web interface.

Chapters 7 and 8 build systems that learn from the person who uses them. Chapter 7 sorts the complaints of a small laundry with Ripple Down Rules, which grow a knowledge base one correction at a time without ever changing an earlier right answer. Chapter 8 routes the messages of a home catering business, where one message often carries several jobs at once, with Multiple-Classification Ripple Down Rules, and measures the router with precision, recall and F1.

Chapters 9 and 10 are about matters of degree. Chapter 9 gives a neighborhood shop fair prices for near-expiry and fast-selling goods with fuzzy logic, and keeps its fairness promises as firm guardrails. Chapter 10 builds a watering controller for a chili bed as a fuzzy inference system in both the Mamdani and the Sugeno style, and tries it on a simulated garden against a plain timer.

Chapters 11 to 13 search for good answers among millions of possibilities. Chapter 11 plans the lunch-box round of an ojek or becak driver whose customers want their food within set times, with a genetic algorithm. Chapter 12 writes a fair weekly shift schedule for a small clinic with a genetic algorithm that never breaks a hard rule and grants as many wishes as it can. Chapter 13 makes a laundry’s slow database reports fast again by choosing the right indexes with genetic simulated annealing, measuring every choice on a real SQLite database.

Chapter 14 joins the methods into a smart shop assistant: rules, fuzzy logic and a genetic algorithm do the reasoning, a local language model understands the question and words the answer, and the program checks that every number in the answer came from the facts. Chapter 15 takes intelligent systems into the real world: packaging and deployment, testing, monitoring, and the privacy and ethics that Indonesia’s Personal Data Protection law (UU PDP) now asks of any system that makes decisions about people.

Every chapter follows the path a real project takes: the problem and who it helps, the idea in plain words, the application built step by step, running it, making it better, using it for real in your own shop, farm, clinic or house, common mistakes, a summary, exercises with complete solutions, and references. Where an application gives advice about health, it says plainly when to see a doctor, a midwife or the puskesmas.

Every program in this book was run before its output was printed. The programs are collected, chapter by chapter, in the accompanying Code folder, from Chapter01 to Chapter15, each with a java and a python folder, the data it needs, and a README that explains how to run it in a terminal and in VS Code on Windows, macOS and Linux.

Inside the Book

1. Intelligent Systems Around Us, and Your Workbench
2. Street-Vendor Restock Advisor: IF–THEN Rules
3. Rice-Cooker and Water-Pump Repair Helper: Forward Chaining
4. Baby-and-Child First-Aid Guide: Backward Chaining with Safe Referral
5. Durian and Fruit-Tree Disease Advisor: Confidence Factors
6. Building a Full Expert System: Knowledge Base in SQLite, Explanation, Web Interface
7. A Complaint Classifier That Learns from Corrections: Ripple Down Rules
8. A Message Router with Many Answers at Once: Multiple-Classification Ripple Down Rules
9. A Fair Pricing and Discount Advisor for a Small Shop: Fuzzy Logic
10. A Smart Plant-Watering Controller: Fuzzy Inference with Mamdani and Sugeno
11. A Delivery Route for an Ojek or Becak Driver: the Genetic Algorithm
12. A Weekly Shift Scheduler for a Shop or Clinic: a Genetic Algorithm with Constraints
13. Faster Database Reports: Genetic Simulated Annealing on Relational Databases
14. A Smart Shop Assistant: Rules, Fuzzy Logic, a Genetic Algorithm and a Local LLM Together
15. Taking Intelligent Systems to the Real World: Deployment, Testing, Monitoring, Privacy and Ethics