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All Journal AnoaTIK: Jurnal Teknologi Informasi dan Komputer
Gunawan Gunawan
Universitas Halu Oleo
Computer Science & IT

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SISTEM CERDAS IRIGASI MENGGUNAKAN METODE FUZZY LOGIC PADA TANAMAN TOMAT Juliarni Pogasang; Gunawan Gunawan; La Surimi
AnoaTIK: Jurnal Teknologi Informasi dan Komputer Vol 2 No 2 (2024): Desember 2024
Publisher : Program Studi Ilmu Komputer FMIPA-UHO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33772/anoatik.v2i2.69

Abstract

This study designs an IoT-based intelligent irrigation system using Fuzzy Logic to manage tomato plant watering. The system monitors soil moisture and temperature in real-time, automating irrigation to optimize water usage based on plant needs. Using the Fuzzy Sugeno method, it combines moisture and temperature data to determine watering duration. Key components include sensors, a microcontroller, an IoT module integrated with the Blynk app, and an automatic water pump. Testing shows the system functions effectively, detecting environmental changes with a 0.47% deviation from manual methods and achieving a 99.50% automation success rate.
SISTEM PAKAR DIAGNOSA PENYAKIT KULIT KUCING DENGAN METODE FORWARD CHAINING Juang Julian; Herdi Budiman; Gunawan Gunawan; Ferdinand Murni Hamundu; Andi Tenriawaru; Putu Nara Kusuma Prasanjaya
AnoaTIK: Jurnal Teknologi Informasi dan Komputer Vol 3 No 1 (2025): Juni 2025
Publisher : Program Studi Ilmu Komputer FMIPA-UHO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33772/anoatik.v3i1.73

Abstract

In this study, an expert system has been developed for diagnosing skin diseases in cats using the forward chaining method. This system is designed to assist cat owners and veterinary health experts in identifying common skin diseases in cats based on observed symptoms. Forward chaining is used as the inference technique, where the system starts from the initial information or symptoms provided by the user and then progresses through relevant rules to reach a conclusion or diagnosis. The system utilizes a knowledge base consisting of various rules and facts about feline skin diseases. Testing results from 15 test data show that the system has an accuracy rate of 93% and an error rate of 7%. This indicates that the system has a high level of accuracy in diagnosing various types of skin diseases in cats and provides appropriate treatment solutions based on the generated diagnosis.
Co-Authors Andi Tenriawaru Budiman, Herdi Juang Julian Juliarni Pogasang La Surimi Putu Nara Kusuma Prasanjaya