Decision Tree Regression with AdaBoost Ensemble Learning for Water Temperature Forecasting in Aquaponic Ecosystem

Taufiqurrahman, Akbar; Putrada, Aji Gautama; Dawani, Febri

doi:10.1109/icidm51048.2020.9339669

Cited by 22 publications

(12 citation statements)

References 23 publications

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“…Selain itu, metode ini memiliki waktu pelatihan yang cukup singkat [11]. Pada penelitian terdahulu, konsep forecasting telah diterapkan pada penelitian mengenai sebuah sistem yang dapat meramalkan kondisi suhu air dapat menjadi solusi untuk masalah perubahan suhu air yang ekstrim, hasilnya dapat disimpulkan bahwa model decision tree forecasting dengan nilai mean squared error (MSE) sebesar 0,01211 dan nilai R-Squared 0,80920 [12]. Hal ini menunjukkan algoritma forecasting mampu meramalkan kondisi suhu air pada aquaponic.…”

Section: Pendahuluanunclassified

Model Decision Tree Forecasting Berbasis DHT22 pada Smart Hydroponic Microgreen

Hadi,

Yasi,

Prasetyo

2024

J. Telecomm. Electr. Control Eng.

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Sensor DHT22 diharuskan aktif selama 4 hari penuh, jika dilihat dari waktu rata-rata budidaya microgreen kacang hijau menggunakan smart hydroponic. Penggunaan sensor DHT22 dalam jangka waktu tertentu dapat mempengaruhi kestabilan jangka panjang dalam hal pembacaan suhu. Jika pembacaan DHT22 mulai tidak akurat, smart system tidak mampu bekerja maksimal. Peneliti mengusulkan sebuah sistem untuk menggantikan peran sensor tanpa mengurangi atau mengganggu kinerja dari smart system untuk mengurangi ketergantungan komponen elektronika seperti sensor suhu. Seiring kemajuan teknologi, terdapat salah satu model machine learning yang dapat diterapkan untuk menggantikan peran sensor yaitu prediksi suhu melalui forecasting. Algoritma forecasting yang digunakan adalah decision tree. Algoritma ini dipilih karena mampu memprediksi data hanya dengan satu jenis input data berupa waktu dengan proses pelatihan yang cukup cepat. Data latih dihasilkan dari perekaman data selama 4 hari pada smart hydroponic microgreen kacang hijau. Model akan dibuat menggunakan grid search cross validation dan feature scaling. Hasil penelitian menunjukkan channel 80 layak dipilih menjadi model prediksi. Prediksi suhu model decision tree forecasting menghasilkan nilai R-squared sebesar 0,870955 dan mean square error (MSE) sebesar 0,074171. Kedua nilai tersebut menunjukkan bahwa model cukup kuat dalam memprediksi suhu dan layak untuk diterapkan dalam memantau suhu smart hydroponic microgreen kacang hijau.

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Section: Pendahuluanunclassified

Model Decision Tree Forecasting Berbasis DHT22 pada Smart Hydroponic Microgreen

Hadi,

Yasi,

Prasetyo

2024

J. Telecomm. Electr. Control Eng.

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“…Decision Tree is a decision-making model that resembles a tree, where the tree is formed from the result of training [182]. Several methods are available to construct a decision tree, including iterative dichotomiser 3 (ID3) and classification and regression tree (CART) [183].…”

Section: ) Supervised Learningmentioning

confidence: 99%

Machine Learning Methods in Smart Lighting Toward Achieving User Comfort: A Survey

et al. 2022

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Smart lighting has become a universal smart product solution, with global revenues of up to US$5.9 billion by 2021. Six main factors drive the technology: light-emitting diode (LED) lighting, sensors, control, analytics, and intelligence. The Internet of things (IoT) concept with the end device, platform, and application layer plays an essential role in optimizing the advantages of LED lighting in the emergence of smart lighting. The ultimate aim of smart lighting research is to introduce low energy efficiency and high user comfort, where the latter is still in the infancy stage. This paper presents a systematic literature review (SLR) from a bird's eye view covering full-length research topics on smart lighting, including issues, implementation targets, technological solutions, and prospects. In addition to that, this paper also provides a detailed and extensive overview of emerging machine learning techniques as a key solution to overcome complex problems in smart lighting. A comprehensive review of improving user comfort is also presented, such as the methodology and taxonomy of activity recognition as a promising solution and user comfort metrics, including light utilization ratio, unmet comfort ratio, light to comfort ratio, power reduction rate, flickering perception, Kruithof's comfort curve, correlated color temperature, and relative mean square error. Finally, we discuss in-depth open issues and future challenges in increasing user comfort in smart lighting using activity recognition.

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“…where a i is the Lagrange multiplier, y i is the y value of x i , and b is the intercept. e DT is a classification model which is essentially a binary tree, where each branch in the tree is an ordinary ifelse decision [43]. However, the if-else decision comes from a training process through several stages [44].…”

Section: Theoremmentioning

confidence: 99%

CIMA: A Novel Classification‐Integrated Moving Average Model for Smart Lighting Intelligent Control Based on Human Presence

et al. 2022

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Smart lighting systems utilize advanced data, control, and communication technologies and allow users to control lights in new ways. However, achieving user comfort, which should be the focus of smart lighting research, is challenging. One cause is the passive infrared (PIR) sensor that inaccurately detects human presence to control artificial lighting. We propose a novel classification-integrated moving average (CIMA) model method to solve the problem. The moving average (MA) increases the Pearson correlation (PC) coefficient of motion sensor features to human presence. The classification model is for a smart lighting intelligent control based on these features. Several classification models are proposed and compared, namely, k-nearest neighbor (KNN), support vector machine (SVM), decision tree (DT), näive Bayes (NB), and ensemble voting (EV). We build an Internet of things (IoT) system to collect movement data. It consists of a PIR sensor, a NodeMCU microcontroller, a Raspberry Pi-based platform, a relay, and LED lighting. With a sampling rate of 10 seconds and a collection period of 7 days, the system achieved 56852 data records. In the PC test, movement data from the PIR sensor has a correlation coefficient of 0.36 to attendance, while the MA correlation to attendance can reach 0.56. In an exhaustive search of an optimum classification model, KNN has the best and the most robust performance, with an accuracy of 99.8%. It is more accurate than direct light control decisions based on motion sensors, which are 67.6%. Our proposed method can increase the correlation value of movement features on attendance. At the same time, an accurate and robust KNN classification model is applicable for human presence-based smart lighting control.

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Decision Tree Regression with AdaBoost Ensemble Learning for Water Temperature Forecasting in Aquaponic Ecosystem

Cited by 22 publications

References 23 publications

Model Decision Tree Forecasting Berbasis DHT22 pada Smart Hydroponic Microgreen

Model Decision Tree Forecasting Berbasis DHT22 pada Smart Hydroponic Microgreen

Machine Learning Methods in Smart Lighting Toward Achieving User Comfort: A Survey

CIMA: A Novel Classification‐Integrated Moving Average Model for Smart Lighting Intelligent Control Based on Human Presence

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