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All Journal Sistemasi: Jurnal Sistem Informasi ELECTRA : Electrical Engineering Articles
Arif, Machnun
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering

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Desain Prototype Alat Kontrol Serta Deteksi Suhu dan Kelembaban Kandang Ayam Broiler Dengan Metode Fuzzy Berbasis IoT Abidin, Zainal; Nadhif, Mohammad; Arif, Machnun
ELECTRA : Electrical Engineering Articles Vol. 3 No. 1 (2022)
Publisher : UNIVERSITAS PGRI MADIUN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25273/electra.v3i01.13976

Abstract

In this study, the authors designed the control of temperature and humidity detection in chicken coops using the IoT-based Fuzzy method. Control prototype design and detection of temperature and humidity of broiler cages using IoT-based fuzzy methods, and can be monitored remotely with a smartphone using several components, namely NodeMCU ESP32 as a data processor and controller between the Dht-11 temperature sensor and the output in the form of an LCD to determine the temperature of the cage. directly and also in the form of Smartphone communication data via the internet network with Blynk software. the DHT-11 sensor is in charge of reading the temperature and humidity of the chicken coop, the LCD serves to provide a direct display of the results of the temperature and humidity monitoring data parameters, the blynk application itself is tasked with displaying sensor parameters remotely on a smartphone which in the blynk software can monitor or get notification if the temperature state is crossing a predefined threshold. All components require 24V voltage generated by the 220V SMPS power supply to 24V DC.  The use of fuzzy rules in temperature and humidity control can regulate the output performance of lights and fans that work according to the specified rules.
Using Regression Model Analysis for Forecasting the Likelihood of Particular Symptoms of COVID-19 Pangestu, Agung; Sumirat, Ucu; Al-Hakim, Rosyid Ridlo; Yusro, Muhammad; Ekawati, Risma; Alrahman, Mahmmoud H. A.; Arif, Machnun; Muchsin, Achmad; Wahyudiana, Nadhilla H
Sistemasi: Jurnal Sistem Informasi Vol 13, No 1 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i1.3463

Abstract

A certainty factor (CF) rule-based technique is frequently used by traditional expert systems (TES) in the medical industry to compute several symptoms and identify the inference solutions. The primary concern for this TES was predicting the likelihood of a particular ailment in the circumstances of new patients. Based on symptoms connected to clinical indicators in patients' diagnosis, CF is estimated. This TES probably won't be able to forecast unknown things, like the possibility of a particular ailment. Therefore, supervised learning techniques like linear regression can address this issue. We attempted to analyze the current COVID-19 TES by modeling the regression equation to forecast the chance of a particular disease that is COVID-like based on the CF value and the confidence level of the symptoms. To examine the most effective regression model to address the issue, we employed multi-linear regression (MLR) and multi-polynomial regression (MPR). The findings demonstrate that the MLR and MPR models are the most accurate regression models for estimating the chance of a disease associated with COVID-like symptoms. Our work built a basis for the creation of expert systems by concentrating more on MLES (machine learning expert systems) analytical techniques than TES.
Co-Authors Achmad Muchsin Agung Pangestu Al-Hakim, Rosyid Ridlo Alrahman, Mahmmoud H. A. Muhammad Yusro Nadhif, Mohammad Risma Ekawati, Risma Ucu Sumirat Wahyudiana, Nadhilla H Zainal Abidin