Prediction of LED Arrangement Illumination on Street Light Armature Using Random Forest Regression Method

2022 International Conference on Electrical

Engineering, Computer and Information Technology (ICEECIT)

Sponsorship:

ICEECIT

PROCEEDINGS

2022

22 - 23 , 2022

^{th th}

November

Universitas Jember https://iceecit.unej.ac.id/

2022 International Conference on Electrical Engineering, Computer and Information Technology (ICEECIT) | 978-1-6654-9352-9/22/$31.00 ©2022 IEEE | DOI: 10.1109/ICEECIT55908.2022.10030497

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Table of Content Pages

Optimization of Random Forest with Genetic Algorithm for Determination of

Assessment 1-6

Feature Selection Application to Classify Medicinal Plant Leaves using LVQ 7-13 Attractive Learning Media for Introduction to Popular Fruits Using Computer Vision 14-19 Chronic Kidney Disease Severity Identification Using Template Matching Feature

Selection Statistics Based 20-24

Hotel Review Sentiment Analysis Using Indonesian Language Based Machine

Learning 25-28

Comparison of Classification Algorithm to Determine Potential Fishing Area Based

on Coral Reefs 29-33

A New Approach Puch and Kick Identification in Martial Sport Pencak Silat using

Fuzzy Logic 34-38

Optimization Silhouette Coefficient on Genetic Algorithm for Clustering 39-43 Comparison of Classification Methods Using Feature Selection for Smartphone

Sentiment Analysis 44-48

Time-Series Analysis of Cryptocurrency Price: Bitcoin as a Case Study 49-53 Detection Of Road Damage Using Faster Regional-Convolutional Neural Network

Method 54-59

Lane Departure Warning based on Road Marking Detection using Mask R-CNN 60-64 Implementation of the K-means clustering for the Public Health Center data 65-69 Implementation Goalkeeper Robot's Movement Response With Odometry Use

PID Method 70-75

A Robust Visual-IMU-Wheel Odometry Using PID Controller for Autonomous

Soccer Robots 76-81

Fuzzy Logic Control-Based Interleaved Boost Converter for Proton Exchange

Membrane Fuel Cell System Applications 82-87

PID Control Design and Kinematic Modelling of 3-DoF Robot Manipulator 88-94 Vessel Monitoring Application Using Automatic Identification System Data 95-98 Design a Smartphone App of an Internet Of Things-Based Monitoring System

Potato Plants on Agricultural Land 99-103

2022 International Conference on Electrical Engineering, Computer and Information Technology (ICEECIT) | 978-1-6654-9352-9/22/$31.00 ©2022 IEEE | DOI: 10.1109/ICEECIT55908.2022.10030543

International Conference on Electrical Engineering, Computer and Information Technology (ICEECIT 2022)

Table of Content Pages

Temperature Characteristics of l0nm N- and P-Channel Si-FinFET structure 104-107 Prediction of LED Arrangement Illumination on Street Light Armature Using

Random Forest Regression Method 108-112

Interfacing PCI 1710 with Real Time Windows Target for Induction Motor Speed

Control Based on Vector Control designed by Variable Flux Reference 113-117 Daily Power Plant Load Prediction using Grammatical Evolution 118-122 Design of a Dynamometer as a Measuring Tool for Power Efficiency and

Performance Characteristics of a Brushless DC Motors 123-130 Early Warning System Design for Flood Disasters Using the IoT-Based Fuzzy Logic

Control Method 131-135

Inverter Design With Selective Harmonic Elimination Pulse Width Modulation (SHE

PWM) Method For Agricultural Irrigation Pump 136-140

Prototype Control of DC-DC Boost Converter in Solar Cell Energy Management

System Based on Fuzzy Logic Method 141-148

Global Horizontal Irradiance Forecasting Using Hybrid method: Convolutional

Neural Network-Long Short Term Memory 149-152

Implementation of Monitoring Device for Fault Location, Isolation, and Service

Restoration (FLISR) in Jakarta Area Distribution Network 153-158 Analysis Of Protection System Based On-Line Current Differential Using Nominal

Phi Method On 150 Kv Transmission Line Ngimbang-Babat 1 159-166 Prediction and Gaussian Distribution Analysis of Power Consuming in University

Building Using BiLSTM Deep Learning 167-173

Impact of DFIG Inertia on Short Circuit Fault Current Components 174-178 Analysis Of 500 Ohm Neutral Grounding Resistance (NGR) Grounding System On A

60 MVA Power Transformer Of One Phase To Ground Faults At The Banyuwangi 150 kV Substation

179-186

K-means algorithm to identify the optimal production period of a photovoltaic

array installed at any point in a very large area 187-192

Non-Intrusive Load Monitoring Based on Advanced-Boosting Algorithm 193-198 Dynamic voltage stability study of a stand-alone photovoltaic microgrid supplying

an electric mill in Benin rural zone 199-204

205-208 VLC System Serving 8 Users

OFDM Application in MIMO NOMA

Analysis of DCO

International Conference on Electrical Engineering,

Table of Content Pages

- - - 209-214

Implementation of a 3D Indoor Positioning System Using MLP -

based Distance Estimation for IoT Applications

215-221 Prototype of Building Monitoring System Using Vibration Sensor Based on Wireless

Sensor Network

222-225 LoRa SNR Evaluation as Portable Sensor

226-231 Parameters Quality Performance of Signal Fluctuation Based on Data Grouping

232-240 Design Of Wireless Sensor Network (WSN) System Using Point To Point And

Waiting Protocol Methods For Solar Panel Monitoring

241-246 Fuzzy Logic Controlled Sepic Converter System For 36 W Vertical Wind Turbine

Performance Improvement

247-252 Regenerative Braking Using Fuzzy Logic Control on BLDC Motor Driven Electric

Vehicles

253-257 Reliability Index Evaluation of Distribution System Using Section Technique Method

at Penyulang Watu Ulo PT. PLN UP3 Jember ULP Ambulu

258-261 Emission Impact between Coal

-

Fired and Renewable Energy Power Plant Development: Evidence from Indonesia

262-267

Fault Prediction in Photovoltaic Systems with Extreme Learning Machine Modeling 268-273 274-279 Improving The Stability of Sulselrabar System with Dual Input Power System

Stabilizer Based On Imperialist Competitive Algorithm

Monitoring and Controlling Overfeeding Ammonia in Smart Lobster Ponds based

on Internet of Things Technology

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2022 International Conference on Electrical Engineering, Computer an d Information Technology (ICEECIT) | 978-1-6654-9352-9/22/$31.00 ©2022 IEEE 2022 International Conference on Electrical Engineering, Computer and Information Technology (ICEECIT) | 978-1-6654-9352-9/22/$31.00 ©2022 IEEE | DOI: 10.1109/ICEECIT55908.2022.10030091

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AUTHOR INDEX

A Sumarudin Abdelhafid Tobbal Abdelmoughni Toubal Abdul Madjid

Abdullah Syah

Achmad Danil Endrico Adi Suheryadi

Adinda Ayu Triasasti Adri Senen

Agus Setiawan Ahmad Firyal Adila Ahmad R. H. Tahier Aji Wahyu Prasetyo Akmalul Fata Ali Rizal Chaidir Amevi Acakpovi Andi Junaidi Andi Kurniawan Andi Tejawati Andrita Ceriana Eska Anindita Septiarini Arionmaro Simaremare

Arizal Mujibtamala Nanda Imron Assaidah Assaidah

Atthariq Atthariq Aulia Istiqomah Avip Zain Haq Azam Zamhuri Fuadi Bagas Satya Dian Nugraha Bambang Kaloko

Bambang Sujanarko Billel Bengherbia Burhan Kholis Chao-Rong Chen

Cossi Télesphore Nounangnonhou Cyriaque Bosco Mitokpe

Dedy Kurnia Setiawan Dedy Wahyu Herdiyanto Devita Ayu Larasati Dewandy Des Ramadhana Dezetty Monika

Dhani N.I Syamputra Djoko Saryono

2022 International Conference on Electrical Engineering, Computer and Information Technology (ICEECIT) | 978-1-6654-9352-9/22/$31.00 ©2022 IEEE | DOI: 10.1109/ICEECIT55908.2022.10030115

International Conference on Electrical Engineering, Computer and Information Technology (ICEECIT 2022)

Dodi Setiabudi

Eko Mulyanto Yuniarno Eko Noerhayati

Era Purwanto Erika Fiqrilinia Faaris Mujaahid Fitri Anik

François Xavier Nicolas Fifatin Gamma Aditya Rahardi Ghiyalti Novilia

Gigih Prabowo Ginas Alvianingsih Guido Kalandro Hamdani Hamdani Hamza Hentabli Hari Putranto Harun Ismail Haryanto Haryanto

Hasanur Mohammad Firdausi Hasna Satya Dini

Herri Akhmad Bukhori Ian Jack Permana Ifan Prihandi

Ilham Ramadani Arianto Imam Robandi

Immawan Wicaksono Indra Ardhanayudha Aditya Indra Ferdiansyah

Indra Ranggadara Inung Wijayanto Irawan Dwi Wahyono Jangkung Raharjo Joseph Cahya Putra Karisma Putra Katherin Indriawati Khoirudin Asfani

Kossoko Babatoundé Audace Didavi Lalitya Nindita Sahenda

Lamidi Taohidi Alamou Langlang Gumilar Lilik Anifah

Lisa Yihaa Roodhiyah

Luthfansyah Mohammad

Macaire Agbomahena

International Conference on Electrical Engineering,

Mahmoud Elsisi Mamat Rokhmat Masna Wati Melinda Melinda Moch Syaiful Komar

Mochammad Alif Naufal Yafi Mochammad Fahrizal Firdaus Moh. Iskandar Zulkanain Moh. Rozaq Febri Sudibyo Moh. Zainul Zainul Falah

Mohamad Agung Prawira Negara Mohamad Rodhi Faiz

Mohamed el amine Bougheriza Mohd Murtadha Mohamad

Mohd Nihra Haruzuan Bin Mohamad Said

Mohd Shafie Rosli Mounir Bouhedda Muh Asnoer Laagu Muh Jamil

Muhammad Irhamsyah Muhammad Makarimal Akhlak Muhammad Muhammad Muhammad Ridho Rosa Muhammad Rizani Rusli Muhammad Ruswandi Djalal Muhammad Sarman Noorlah Muhammad Syariffuddien Zuhrie Nien-Che Yang

Noor Dayana Abd Halim Novianti Puspitasari Nur Hafnita

Nur Holis

Nur Widi Priambodo Nurbiha A Shukor Nurul Zainal Fanani Pawenary Pawenary Qasem Abu Al-Haija Ramzi Adriman Rb Moch Gozali Reza Fuad Rachmadi Richard Agbokpanzo Rio Irawan

Rizdky Oktaviari Pratama Putra

International Conference on Electrical Engineering, Computer and Information Technology (ICEECIT 2022)

Rizky Nuzul Kurnianda Rudy Rachman Ruri Wahyuono S Sunarti

Satriananda Satriananda Satrio Yudho

Satryo Utomo Soraya Ananta P Soraya Mustika

Stieven Netanel Rumokoy Suhendra Suhendra Sujito Sujito

Sumardi Sumardi Suprihadi Prasetiono Suyanto Suyanto Syahrial Syahrial Syam Widiyanto Taufik F. Abidin

Timotius Heries Noventino Triwahju Hardianto Wahyu Muldayani Wawan Gunawan Widjonarko Widjonarko Willy Permana Putra Yasir Hashim

Yuli Mauliza Yunidar Yunidar Yunita Sartika Sari Zainal Arifin

Zeyad Albatati Action

Zilvanhisna Emka Fitri

Zul Arsil Mustofa

International Conference on Electrical Engineering,

PREFACE TO THE CONFERENCE PROCEEDINGS

On behalf of the conference committee, first of all, I would like to thank God Almighty for holding this activity. I also extend a warm welcome to all involved in our International Conference on Electrical, Computer Technology, and Information Engineering (ICEECIT).

As the General Chair of this conference, I am honored and would like to thank those who have played a part in this important event.

First of all, I would like to thank and appreciate the leadership of the University of Jember, who has been willing to provide funds and other facilities so that this event can be held. Second, I express my gratitude and appreciation to the IEEE Indonesia Section and the Power Energy System IEEE Indonesia Section Chapter for sponsoring this activity. Third, my thanks to the reviewers that have examined the papers so that only the ones that meet the quality can be published in conferences and proceedings. Fourth, thanks to the authors, moderators, and participants for collaborating in improving science and technology. Fifth, I express my highest gratitude and appreciation to my colleagues from the organizing committee and my students for their tireless efforts in managing this conference.

I realize that organizing this first conference still has several obstacles, although my team and I have tried our best to manage it as best we can.

Finally, I hope that our conferences and proceedings that have been published can significantly contribute to the welfare of humanity.

Prof. Dr. Ir. Bmbang Sujanarko, MM, IPM ICEECIT General Chair

2022 International Conference on Electrical Engineering, Computer and Information Technology (ICEECIT) | 978-1-6654-9352-9/22/$31.00 ©2022 IEEE | DOI: 10.1109/ICEECIT55908.2022.10030096

978-1-6654-9352-9/22/$31.00 ©2022 IEEE 108

Prediction of LED Arrangement Illumination on Street Light Armature Using Random Forest

Regression Method

I 81 Widjonarko

Electrical Engineering Jember University

Jember, Indonesia widjonarko.teknik@unej.ac.id

Satryo Budi Utomo

Electrical Engineering Jember University

Jember, Indonesia satryo.teknik@unej .ac.id

Bambang Sri Kaloko

Electrical Engineering Jember University

Jember, Indonesia kaloko.teknik@unej .ac.id

Mochammad Fahrizal Firdaus

Electrical Engineering Jember University

Jember, Indonesia frizal500@gmail.com Abstract-LED's have a lifetime of up to 30 thousand

hours. In addition, it has a level of effectiveness and efficiency of 80%-90%. The current problem is to determine the performance of the LED array arrangement using conventional methods so that the LED array arrangement must be made frrst and operated for testing. In this study, the LED array arrangement test determines each test sample's performance. mumination prediction computations were performed on each sample using different test parameters.

The prediction of illumination has a different predictive value in each test sample. The first and sixth samples have prediction accuracy below 95%. However, samples 2,3,4 and 5 have prediction accuracy above 95%. The interests of the current and temperature variables are 50% and 48%, while the voltage is only 3%. The value of R2, which is not by the research hypothesis, which is greater than 98%, is caused by the ability of the dependent variable, namely limited illumination. In addition, because the independent variables are less varied, they do not provide information on the dependent variable during the training data process. The low R2 value can also influence by the ability of the prediction test parameters formed.

Keywords- LED, Prediction, Random Forest Regression.

I. IN1RODUCTION

Conventional lamps such as sodium lamps emit only 30% of the light they need. So it is very inefficient to use;

besides being expensive for the electricity bill, this lamp does not emit light as a whole [1]. Different from LED lamps which have advantages and disadvantages over conventional lamps. Relatively long lifetime with a durability of up to 30 thousand hours. LEDs also have an energy use efficiency of up to 80 to 90 %, much better than other types of lamps. In addition, LEDs also do not distort the surrounding color, so they are safer to use for public street lighting [2]. In fact, 70% to 85% of the power delivered to the LED circuit is converted to heat. So this causes a high temperature at each junction of the LED array.

And will cause a decrease in lamp performance such as illumination and even cause damage to the LED. In addition, the lighting of the resulting LED array will decrease faster than the lifetime of the LED itself. So, in this case, th^ermal management on the ^armature is needed [3].

Research on the characteristics of temperature and current on the LED array conducted by Widjonarko showed the

978-1-6654-9352-9/22/$31.00 <\:12022 IEEE 108

average LED array current increases by 21.12% and causes an increase in temperature in the circuit by 91.3 7% [ 4]. This causes a decrease in the illumination of the LED array by 4.82%. The voltage on the LED array circuit also decreased by about 0.52% [5]. This study aims to determine the performance of the LED array arrangement without making hardware first and only using input variables in the form of input temperature, current, and voltage using the random forest regression method. The random forest regression method is a suitable method to make a prediction; Primajaya and Sari's (2018) research that predicts rainfall using the random forest regression method produces prediction accuracy and accuracy of 99.45% [6].

II. METHOD A. Random Forest Regression

Fig. ^1. Random Forest Regression Algorithm

2022 International Conference on Electrical Engineering, Computer and Information Technology (ICEECIT) | 978-1-6654-9352-9/22/$31.00 ©2022 IEEE | DOI: 10.1109/ICEECIT55908.2022.10030620

Authorized licensed use limited to: Universitas Indonesia. Downloaded on February 08,2023 at 02:29:09 UTC from IEEE Xplore. Restrictions apply.

108 The research uses random forest regression because this method has the advantages of high accuracy, high efficiency, and high performance in dealing with many variables and has a faster computation time

than

^other

methods [7]. Prediction of the illumination of the LED array on the P JU

arma

ture, there are steps in data processing using the random forest regression method as follows:

• Before starting the first step, namely preparing (preprocessing) the data used as training data.

• Next, determine Ntree, the number of trees tested from variable p

aram

eter data.

• Then from the data train, it goes through the bootstrap stage. The data has been divided into train and test at this stage.

• OOB data will be included in the calculation, and errors from predictions to be processed are reprocessed into the bootstrap process.

• The stage of determining the formation of trees in a random forest on the results of bootstrap.

• To determine the value of the final prediction results using the prediction results from each tree will be aggregated. The following is the average mathematical equation for all tree predictions in a random forest:

Keterangan

Ntree

� =

^-1-

"' �

Ntree b_

⁽¹⁾

�

: Final prediction result

Ntree : Total number of trees in random forest

�

:The result of the prediction tree to n B. Data Logger

re=�==-::::--r�-�-�-:---:---�

I t).;s(l"tl()ft;

I ....

..,'$1-a'r �_,, ...

I

...

^L(OA·•'9

.(-\'CC("•W•t ,.. (-G,.o•tiJ

...

^f·)

I i> \ I-nc:�

Fig. 2 Ele-;;tri-;;�"D";�"L;;gg�;"---

Arduino nano for the control center of the test system.

In addition to monitoring the system's temperature, current, and voltage when testing [8]. A data logger method is needed during testing to simplify the process of processing and storing data. So in the system's electrical circuit, there is PLX.dax which can monitor when testing the LED array arrangement. The data read by the sensor will be recorded into Microsoft excel in real time using PLX.dax.

C. Test Design

The research was conducted using the LED module array design with an array configuration of 6x3. In addition,

108

the arrangement of each sample will have a varying array distance of 6 variations. For a total of LED chips, all samples have the same number of 18 LED chips arranged in a series-parallel. The purpose of this LED array variation is to produce the best distance so that the LED perfo

rman

^{ce is}

also excellent

TABLE I

L

^{ED ARRAY} SPECIFICATIONS S

amp

^le Dimensions Jarak C

h

ⁱ

p

^LED

L(mm) W(mm) H(mm) V(mm)

I 147 55 20 20

2 198 55 30 20

3 248 55 40 20

4 144 75 20 30

5 196 75 30 30

6 244 75 40 30

L : Length ; W: Wtdth; H: Honzontal ; V : Vertikal

Fig. 3 Array LED PCB design (H :20 ; V : 20)

I I I I I I

. . . .

Ill

Iii • • • • •

Fig. 5 lllumination and T

emp

^e

rature

^Test

In the general street lighting

arma

ture used for the lantern or housing of the LED array had designed to provide an air gap for air circulation generated on the LED array. Temperature and illumination tests are conducted on each sample to determine its characteristics.

D. Block Diagram

In general, street lighting

arma

tures for lanterns or housings of LED arrays; are to provide air gaps for air circulation in the LED arrays.

Fig. 6 Block Diagram

108 III. RESULTS AND DISCUSSION

The research begins with testing the electrical components used in the research testing process. That is to produce accurate data from the sensor readings used. The sensors used to process the research data logger are the INA219 sensor and the DS 18B20 temperature sensor.

Sensor calibration is essential because it will affect the LED module array illumination data. For the INA219 sensor calibration process, the value reading compares between the Arduino IDE serial monitor and the multimeter measuring instrument [9]. However, it is different from the calibration of the DS18B20 temperature sensor; namely, the reading compares with a digital thermometer which is already accurate according to SNI. The illumination prediction data will be processed using the random forest regression method using the open-source Kaggle platform.

A. INA219 Calibration

After calibration, the test produces a comparison value between the current reading on the INA219 sensor, and the resulting value is an average error of 0.42% or an MAE (Mean Absolute Error) value of 0.417 rnA. From the value of the regression results obtained the appropriate current reading value and has a relatively small error value.

No I 2 3 4 5 6 7 8 9 10 11 12 13 14 15

TABLE II. CURRENT CALffiRATION OF INA219 Reference

Voltage

(V)

I

2 3 4 6 5 7 8 9 10 11 12 13 14 15

1

²⁰⁰

� ]

150

INA219 Multimeter Percent Sensor Value(mA) error

Value(mA) (%)

9.49 9.57 0.84%

18.68 18.56 0.64%

27.03 27.03 0.00%

36 35.92 0.22%

45.26 45.22 0.09%

55.32 55.14 0.33%

64.65 64.46 0.29%

74.09 73.93 0.22%

84.3 83.91 0.46%

95.074 94.71 0.38%

104.49 104.14 0.33%

114.16 113.72 0.39%

123.46 122.89 0.46%

133.02 132.41 0.46%

142.91 142.23 0.48%

Eror(%) 0.42%

• Sensor INA219 ^• Multimeter Fig. 7 Comparison of reading the current value ofiNA219 with

multimeter calibration results.

MAE (rnA) 0.080 0.120 0.000 0.080 0.040 0.180 0.190 0.160 0.390 0.364 0.350 0.440 0.570 0.610 0.680 0.417

In testing the INA219 sensor voltage, the readings from the calibration INA219 sensor so that the percent error value is 1.09% and the mean absolute error (MAE) is 0.090 volts.

Based on the linear regression results, the INA219 sensor has a reading corresponding to a relatively small error.

108

TABLE ill VOLTAGE CALffiRATION INA219 SENSOR No

1 2 3 4 5 6 7 9 8 10 11 12 13 14 15 16 17 18

Reference Sensor voltage voltage value

(V)

^INA219

(V)

1.00 0.80

2.00 1.85

3.00 2.81

4.00 3.70

5.00 4.63

6.00 5.66

7.00 6.67

8.00 7.57

9.00 8.50

10.00 9.29

11.00 11.06 12.00 11.70 13.00 12.83 14.00 13.89 15.00 14.84 16.00 15.87 17.00 16.87 18.00 17.90

Eror(%)

20.00

� 15.00

ru, 10.00

.... ro

g 5.00

0.00 · •• Il l

1 3

5

⁷

Multimeter Error Voltage (%)

0.79

(V)

1.88%

1.84 0.81%

2.77 1.39%

3.64 1.57%

4.59 0.86%

5.56 1.77%

6.60 1.05%

7.49 1.06%

8.45 0.59%

9.20 0.97%

10.92 1.27%

11.59 0.94%

12.70 1.01%

13.73 1.15%

14.72 0.81%

15.75 0.76%

16.73 0.83%

17.75 0.84%

1.09%

11111111

9 11 13

15

¹⁷

Test to-

• Sensor INA219 ^• Multimeter Fig. 8 Comparison of reading the voltage value of INA219 with

multimeter calibration results.

B. Calibration DS18B20 Sensor

MAE

(V)

0.015 O.oi5 0.039 0.058 0.040 0.100 0.070 0.080 0.050 0.090 0.140 0.110 0.130 0.160 0.120 0.120 0.140 0.150 0.090

The temperature sensor test uses a smoothing technique for a more stable sensor reading. Retrieval of valid data is taken with ten iterations and then determines the average of the data. The average value of the ten iterations uses as the temperature reading value on the DS18B20 sensor [10].

That aims to get a stable temperature value and has a relatively small error when testing with the LED module array load.

TABLE N DS18B20 SENSOR CALffiRATION RESULTS Sensor reading Nilai

No value DS 19B20 thermometer Error(%) MAE

t_<'C) (OC)

I

76.08 76 0.11% 0.08

2 70.68 71 0.45% 0.32

3 70.19 70.4 0.30% 0.21

4 68.72 68.8 0.12% 0.08

5 67 67.3 0.45% 0.3

6 65.77 66 0.35% 0.23

7 64.54 64.7 0.25% 0.16

8 63.31 63.7 0.62% 0.39

9 62.09 62.5 0.66% 0.41

10 61.1 61.5 0.65% 0.4

Error Average 0.39% 0.258

The percent error value is smaller when compared to the DS 18B20 temperature sensor before calibration. The percent error value after calibration is 0.39%, with a mean absolute error of 0.258°C.

Authorized licensed use limited to: Universitas Indonesia. Downloaded on February 08,2023 at 02:29:09 UTC from IEEE Xplore. Restrictions apply.

108

�80 u

� 60

�

⁴⁰

�

20

�

⁰

f-< ^{8 9}

I

Sample -

• Sensor DS18B20 (1) ^• Termometer

Fig. 9 DS18B20 Sensor calibration

C. Illumination prediction using random forest regression

• Test sample 1

10

In test sample 1, the p^arameter n_estimator is equal to 100, n_sample split is equal to 6, min_sample_leaf is equal to 4, and random state is valued at 40 and has a low predictive accuracy with R2 of 0.821 or 82.1% with an accuracy of forecasting model (MSE) of 0.0032 and the level of accuracy (MAE) of 0.020. The low R2 value can also influence by the ability of the prediction test parameters formed.

�

^72.78

a 72.76

o= 72.74

I

0

I

:1

^72.72

s 72.7

2 Data-3 4 5 6 7 8

• Illumination Measurement (Lux) ^• Prediction Results Fig. 10 Comparison of illumination measurement and prediction of first

test sample

• Test sample 2

At the R2 value of the second sample test with the prediction test parameters n_estimator equal to 500, n_sample split equal to 2, min_sample_leaf equal to 1, and random state worth 40 have the best value in the test sample test of 0.993 or 99.3%. This parameter has a perfect level of accuracy with an MAE value of 0.0008 and has good predictive accuracy with an MSE value of 0.0004.

� 76.11

�

^76.11

,:: 76.11

i ' l

0 76.11

-�

1;! ^76.10 76.10 s 76.10

2 3 4 5 6 7 8

• Illumination Measurement

Figure 11 Comparison of illumination measurement and prediction of second test sample

• Test Sample 3

The prediction of the third sample with the highest R2 value of 99.6% has the correct illumination prediction accuracy. So by using these parameters, it can be used as a reference for testing the LED array module illumination prediction.

• Test sample 4

In the 4 test sample, the value of R2 with the test parameter n _estimator is equal to 50, n _sample split is equal to 10, min_sample_leaf is equal to 4, and the random state

108

is worth 40 has a small R2 value (not close to value= 1) so that the level of accuracy with the model parameter is the prediction result. The R2 value is 0.901 or 90.1 %. That is indicated by the MSE and MAE values, 0.0054 and 0.027.

� 78.00

�

,:: 76.00

J

".j:l 0 .. 74.00

J

� 72.00

5 6 7 8

• Illumination Measurement ^• Prediction Results Fig. 12 Comparison of illumination measurement and prediction of

third test sample

� 77.52 a

�

11.s1

.g

^77.50

.s

_77.49

]

� 77.48

2 ^{3 4}

� � ,

^{5 6 7}

• Illumination Measurement ^• Prediction Results 8 Fig. 13 Comparison of illumination measurement and prediction of

fourth test sample

• Test sample 5

The fifth sample prediction test with the highest R2 value of 99.3% has the correct illumination prediction accuracy. So by using these parameters can be used as a reference for testing the LED array module illumination prediction, which has almost the same data variation. With only 50 trees, the results of prediction accuracy and good accuracy indicate that the fifth test sample data has less variation in input data, so in testing, predictions to avoid the bootstrap process, the data is not good.

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The 6th sample prediction test with the highest R2 value of 95.1% has the correct illumination prediction accuracy.

In the sixth sample, there is a lack of varied data on the input voltage value because when testing, it uses a constant voltage, so the test parameter value is enough to use 50 trees with split bootstrap data 6.

The varying distance between LED chips causes the effect of the difference in the illumination value of each sample. So that the heat generated by each LED chip will increase the current due to the more excellent resistance on the LED chip. This phenomenon results in power dissipation and a decrease in efficiency, so the illumination value in the LED module array will also decrease. This

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analysis by studying the degradation system using the SPD (Spectral Power Distribution) method proves that the degradation of the LED array module arrangement is caused by the current and temperature values. So that the quality of the lighting strength (illumination) gets worse under conditions of high temperature and high current values [11]. The temperature and current values of the LEDs are essential to qualify and optimize the LED array design. LED brightness depends on both current and temperature. LED connection temperature depends on heat sources such as injection current and ambient temperature [12]. This experiment uses the random forest regression method to determine the illumination prediction on the LED armature arrangement of street lights. The random forest regression method has many advantages, such as high accuracy, high efficiency, and high performance in handling significant variables [13]. In designing the parameters of the random forest regression method, several steps are carried out: preprocessing data, tree formation (bootstrap, stopping criteria, variable separation), calculating predictions, and the average prediction of each tree formed. The following is an explanation of the design of the random forest regression method in carrying out the computational process.

D. Variable Importances

In this study, the current and temperature variables had high weights, namely 50% and 48%, respectively. That is because the two variables affect the performance of the LED array arrangement and data processing with random furest regression. Meanwhile, the value of the voltage variable, which acts as the control variable in the study, has a lower weight than the current and temperature weights, which is 3%.

IV. CONCLUSION

The conclusion of the research is the result of testing and data processing. Following are the conclusions of the research. The performance of random forest regression in the LED array illumination prediction research is influenced by several factors. So that each test samples 1 and 6 has predictive data whose accuracy level is below the research target, which is <98.5%. These factors are varied data values for research variables, some trees, minimum split, and a minimum and random state built on random forest regression. However, in the test sample that has a lot of varied data, test samples 5, 4, 3, and 2 have a predictive value with R2 above 98.5%. So that in the test sample, the accuracy and precision of the prediction value is very high compared to the sample tests 1 and 6. In this case, it can be concluded that in determining the variation of the data, it is very important to make predictions using the random forest regression method. In the random forest regression method, a variable's weight importance determines the LED array

108

illumination prediction result. Each weight that the variable has a different value. In this study sequentially, the current and temperature variables have high consequences, namely 50% and 48%. That is because the two variables affect the performance of the LED array arrangement and data processing with random forest regression. Meanwhile, the value of the voltage variable, which acts as the control variable in the study, has a lower weight than the current and temperature weights, which is 3%.

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