Implementasi Sistem Monitoring Alat Pengering Biji Kopi Berbasis IoT (Internet of Things)

Ika Noer Syamsiana; Moh Ari Wahyudi; Tresna Umar Syamsuri; Nihayatun Nafisah; Arwin Datumaya Wahyudi Sumari

doi:10.33795/elposys.v11i1.4964

Authors

Ika Noer Syamsiana Politeknik Negeri Malang
Moh Ari Wahyudi Politeknik Negeri Malang
Tresna Umar Syamsuri Politeknik Negeri Malang
Nihayatun Nafisah Politeknik Negeri Malang
Arwin Datumaya Wahyudi Sumari Institut Teknologi Dirgantara Adisutjipto

DOI:

https://doi.org/10.33795/elposys.v11i1.4964

Keywords:

Coffee Bean Dryer, Coffee Bean Drying Methods, Energy Consumption, IoT, Temperature

Abstract

Indonesia holds the third position in global coffee bean supply, with a 3.3% increase in demand in 2021 according to ICO data. In line with ICO Resolution 407, coffee bean quality must be high, indicated by a 12.5% moisture content through drying processes. Mechanical drying is faster than traditional methods, thus becoming the focus of this research. This study implements an IoT-based monitoring system for coffee bean drying equipment. The aim is to create and implement a monitoring system for temperature (0°C - 70°C) and electrical energy consumption (kWh) in coffee bean drying, displayed on the ThingSpeak dashboard. Test results show that the temperature monitoring system using DHT22 sensors at the inlet and outlet of the rotary dryer tube has an average error of 2.1%. The energy monitoring system using PZEM-004T has a 0% energy measurement error. These results indicate a reliable monitoring system that enables remote monitoring via the internet. Furthermore, the study tests manual (on/off) and fuzzy logic control methods to achieve 12% moisture content in coffee beans. The fuzzy logic control method demonstrates better temperature stability and 11.2% cost savings in operational expenses. This research benefits coffee farmers by facilitating efficient monitoring of the bean drying process, and recommends the use of the fuzzy logic control method for stable temperature and lower operational costs.

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