International Conference of Science and Information Technology in Smart Administration 2022

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International Conference Of Science and Information Technology In Smart

Administration

Innovation and Transformation for Technopreneurship and Digital Era in Global Community.

Bali - Indonesia, 10 November 2022

I GLOBAL

TECHNOPRENEUR

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Technical Program Committee

Mohammed Aal-Nouman Al-Nahrain University Iraq

Mohd Helmy Abd Wahab Universiti Tun Hussein Onn Malaysia Malaysia Zahereel Ishwar Abdul Khalib University of Malaysia Perlis Malaysia Sukarya Ade Indonesian Researcher and Scientist Institute Indonesia

Baba Alhaji Nigerian Defence Academy Niger

Mustafa Ali Mustansiriyah University, Baghdad Iraq

Rakan Antar Northern Technical University Iraq

Eko Aribowo Ahmad Dahlan University Indonesia

Andria Arisal Indonesian Institute of Sciences Indonesia

Azizul Azizan Universiti Teknologi Malaysia (UTM) Malaysia

Alessandro Carrega UNIGE Italy

Tai-Chen Chen MAXEDA Technology Taiwan

Wichian Chutimaskul King Mongkut's University of Technology Thonburi Thailand

Akhmad Dahlan Universitas Amikom Yogyakarta Indonesia

Noriko Etani Kyoto University Japan

Edi Faizal Universitas Teknologi Digital Indonesia Indonesia

Dhomas Hatta Fudholi Universitas Islam Indonesia Indonesia

Zoohan Gani Victoria University Australia

Alireza Ghasempour University of Applied Science and Technology USA Javier Gozalvez Universidad Miguel Hernandez de Elche Spain

Gunawan Gunawan Politeknik Negeri Medan Indonesia

Ibnu Hadi Purwanto Universitas AMIKOM Yogyakarta Indonesia

Hamdani Hamdani Universitas Mulawarman Indonesia

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Seng Hansun Universitas Multimedia Nusantara Indonesia

Richki Hardi Universitas Mulia Indonesia

Lucia Nugraheni Harnaningrum STMIK AKAKOM Yogyakarta Indonesia

Purwono Hendradi Universitas Muhammadiyah Magelang Indonesia

Leonel Hernandez ITSA University Colombia

Roberto Carlos Herrera Lara National Polytechnic School Ecuador

Nurulisma Ismail Universiti Malaysia Perlis Malaysia

Anggun Isnawati Institut Teknologi Telkom Purwokerto Indonesia

Ramkumar Jaganathan Dr NGP Arts and Science College India

Shaidah Jusoh Xiamen University Malaysia Malaysia

Dimitrios Kallergis University of West Attica Greece

Nitika Kapoor Chandigarh University India

Rikie Kartadie Universitas Teknologi Digital Indonesia Indonesia

Sandy Kosasi STMIK Pontianak Indonesia

Sumit Kushwaha Chandigarh University India

Armin Lawi Hasanuddin University Indonesia

Ziping Liu Southeast Missouri State University USA

Pavel Loskot ZJU-UIUC Institute China

Mahdin Mahboob Stony Brook University USA

Sukrisno Mardiyanto Institut Teknologi Bandung Indonesia

Prita Dewi Mariyam Universitas Indonesia Indonesia

Ratheesh Kumar Meleppat University of California Davis USA

Othman Mohd Universiti Teknikal Malaysia Melaka Malaysia

Philip Moore Lanzhou University China

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Hu Ng Multimedia University Malaysia

Nitish Ojha Sharda University, Greater Noida, UP India

Edy Prayitno STMIK AKAKOM Yogyakarta Indonesia

Tri Priyambodo Universitas Gadjah Mada Indonesia

Agfianto Putra Universitas Gadjah Mada Indonesia

Yuansong Qiao Athlone Institute of Technology Ireland

Melky Radja Universitas Flores Indonesia

Ali Rafiei University of Technology Sydney Australia

Suwanto Raharjo Institut Sains & Teknologi AKPRIND Yogyakarta Indonesia

Sarni Rahim Universiti Teknikal Malaysia Melaka Malaysia

Rianto Rianto Universitas Teknologi Yogyakarta Indonesia

Bagus Rintyarna Universitas Muhammadiyah Jember Indonesia

Amer Sallam Taiz University Yemen

Leo Santoso Petra Christian University Indonesia

Vaibhav Saundarmal Marathwada Institute of Technology, Aurangabad India

Enny Sela Universitas Teknologi Yogyakarta Indonesia

Amel Serrat USTO MB Algeria

Bayu Setiaji Universitas AMIKOM Yogyakarta Indonesia

Iwan Setyawan Satya Wacana Christian University Indonesia

Nadheer Shalash Al-Mamoon University College Iraq

Aditi Sharma Parul University, Vadodara India

Karthik Sivarama Krishnan Rochester Institute of Technology USA China Sonagiri Institute of Aeronautical Engineering India Sritrusta Sukaridhoto Politeknik Elektronika Negeri Surabaya Indonesia

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Chakib Taybi Mohammed First University Morocco

Ivanna Timotius Satya Wacana Christian University Indonesia

Leonardo Tomassetti Ferreira Neto University of Sao Paulo Brazil Evi Triandini Institut Teknologi dan Bisnis STIKOM Bali Indonesia

Mochammad Wahyudi Universitas Gadjah Mada Indonesia

Oyas Wahyunggoro UGM Indonesia

Ratna Wardani Yogyakarta State University Indonesia

Ferry Wahyu Wibowo Universitas Amikom Yogyakarta Indonesia

Wihayati Wihayati Satya Wacana Christian University Indonesia Thaweesak Yingthawornsuk King Mongkut's University of Technology Thonburi Thailand

Uky Yudatama Universitas Muhammadiyah Magelang Indonesia

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A C D E F G I J M P S W

A

A C D E F G I J M P S W A Markova-Chain Approach to Model Vehicles Traffic Behavior

Academic Smart Chatbot to Support Emerging Artificial Intelligence Conversation

Algorithms for minimizing disjunctions of complex conjunctions based on first-order neighborhood information for solving systems of Boolean equations

Automatic Sound Alarm Classification Using Deep Learning For the Deaf and Hard of Hearing

C

A C D E F G I J M P S W Collision Avoidance System with Model Predictive Control for Mobile Robot Navigation

Comparison of K-means and K-medoids in Tourist Attraction Clustering based on Visitor Characteristics

Comparison of K-Nearest Neighbor and Logistic Regression Algorithms on Sentiment Analysis of Covid-19 Vaccination on Twitter with Vader And Textblob Labeling

Customer Segmentation of Cellular Telecommunication Company Using K-Means Algorithm (Case Study of PT Indosat) Customer Segmentation Using Fuzzy C-Means Algorithm in Telco Industry

D

Decision Support System Terms of SME's Credit Lending Based on Machine Learning Approach Design and Analysis of Automated Machine Learning (AutoML) in PowerBI Application Using PyCaret

E

Effective methods for solving systems of nonlinear equations of the algebra of logic based on disjunctions of complex conjunctions

Embedded Flu Detection System based Cough Sound using MFCC and kNN Algorithm Evaluation of Information Security Management Using the KAMI Index Framework

Explainable Artificial Intelligence (XAI) on Hoax Detection Using Decision Tree C4.5 Method for Indonesian News Platform

F

A C D E F G I J M P S W Face Completion with Multi-Output Estimators

Frameless Coded Slotted ALOHA Scheme with Frequency Domain-Extended on NLOS Visible Light Communication Channel

G

Generated Tabular Data with Multi-GANs for Arrhythmia Classification Based on DNN Models

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I

Identification of Pests on Black Orchid Plants Using Naïve Bayes Method Based on Leaf Image Texture Implementation of Machine Learning in Predicting Length of Punishment at Bandung Court

iOS Digital Evidence Comparison of Instant Messaging Apps IT Triple-A Strategy to Improve SMEs' Supply Chain

J

A C D E F G I J M P S W Job Assignment Problem on Online Transportation Order Using Hungarian Method

M

A C D E F G I J M P S W Microcontroller Based Automatic Fuel Consumption Gauge

Minimalistic Vector Extension For Image Detection On Edge Nodes Using ‘VGG16' Mobile Application for Personalized Culinary Tourism Based on User Preference Modeling the Behavior of the Linear Wireless Sensor Networks

ModifiedECS (mECS) Algoritm for Madurese - Indonesian Rule Based Machine Translation

P

Performance Analysis of Artificial Neural Network - Chimpanzee Leader Election Optimization on Classification Case Performance Evaluation of LoRaWAN in Pulse Status Monitoring with Clustering of Wireless Sensor Network

Photoplethysmography (PPG) Signal-Based Chronological Age Estimation Algorithm

PLS-SEM-ANN- based technique for understanding behavioral intention to use a learning management system Potential Target of 5G Digital Business Telecommunication Company "PT. MNO Indonesia"

Process Mining Analysis and Implementation on Customer Complaints Dataset

S

Selection System of Madura Tourism Object as The Best Recommendation Based on Comparative Analysis of K-NN and Naive Bayes Methods

Sentiment Analysis of The Public Perspective Electric Cars In Indonesia Using Support Vector Machine Algorithm Sentiment classification twitter of LRT, MRT and Transjakarta transportation using Support Vector Machine Simple Fuzzy Decision Support Model for Evaluating the Cryptocurrency's Performance

Software Agent and Data Marker for Data Synchronization in Integrated Systems

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W

Webots Simulator for Lyapunov-based Cooperative Omnidirectional Mobile Robots Evaluation

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Author Session Start

page Title

A Abdullah,

Muhammad Haziq Lim

5.2 194 Academic Smart Chatbot to Support Emerging Artificial Intelligence Conversation

Adinata, Harni 2.5 89 Design and Analysis of Automated Machine Learning (AutoML) in PowerBI Application Using PyCaret

Agung, Hendi 1.5 24 Webots Simulator for Lyapunov-based Cooperative Omnidirectional Mobile Robots Evaluation

Agustini, Fajar 3.7 143 Sentiment classification twitter of LRT, MRT and Transjakarta transportation using Support Vector Machine

Akbar, Sabriansyah

5.8 215 Embedded Flu Detection System based Cough Sound using MFCC and kNN Algorithm

Al-Faaruuq, Muhammad

2.4 83 iOS Digital Evidence Comparison of Instant Messaging Apps

Alameka, Faza 4.5 167 Identification of Pests on Black Orchid Plants Using Naïve Bayes Method Based on Leaf Image Texture

Alfianti, Zulia 3.7 143 Sentiment classification twitter of LRT, MRT and Transjakarta transportation using Support Vector Machine

Alhari, Muhammad

4.3 155 Sentiment Analysis of The Public Perspective Electric Cars In Indonesia Using Support Vector Machine Algorithm

Alhchimy, Zainab

3.3 123 Modeling the Behavior of the Linear Wireless Sensor Networks

Anamisa, Devie

4.4 161 Comparison of K-means and K-medoids in Tourist Attraction Clustering based on Visitor Characteristics

5.1 188 Selection System of Madura Tourism Object as The Best Recommendation Based on Comparative Analysis of K-NN and Naive Bayes Methods Andreswari,

Rachmadita

1.6 30 Process Mining Analysis and Implementation on Customer Complaints Dataset 1.7 35 Customer Segmentation Using Fuzzy C-Means Algorithm in Telco Industry 1.8 39 Comparison of K-Nearest Neighbor and Logistic Regression Algorithms on

Sentiment Analysis of Covid-19 Vaccination on Twitter with Vader And Textblob Labeling

1.9 45 Customer Segmentation of Cellular Telecommunication Company Using K- Means Algorithm (Case Study of PT Indosat)

Apriono, Catur 5.5 200 Potential Target of 5G Digital Business Telecommunication Company "PT. MNO Indonesia"

Arif, Ahmad 3.6 139 Microcontroller Based Automatic Fuel Consumption Gauge Aritonang,

Mendarissan

2.5 89 Design and Analysis of Automated Machine Learning (AutoML) in PowerBI Application Using PyCaret

Asyrafi, Hilman

3.4 128 Photoplethysmography (PPG) Signal-Based Chronological Age Estimation Algorithm

Aulianita, Rizki 3.7 143 Sentiment classification twitter of LRT, MRT and Transjakarta transportation using Support Vector Machine

Aziz Muslim, 5.6 204 Collision Avoidance System with Model Predictive Control for Mobile Robot

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Muhammad Navigation

B Bustamam,

Alhadi

2.2 69 Generated Tabular Data with Multi-GANs for Arrhythmia Classification Based on DNN Models

C Che Pee,

Naim

Chowanda, Andry

2.1 63 Explainable Artificial Intelligence (XAI) on Hoax Detection Using Decision Tree C4.5 Method for Indonesian News Platform

D Dahlan,

Akhmad

5.9 220 Performance Analysis of Artificial Neural Network - Chimpanzee Leader Election Optimization on Classification Case

Dani Prasetyo Adi, Puput

2.8 105 Performance Evaluation of LoRaWAN in Pulse Status Monitoring with Clustering of Wireless Sensor Network

Darmawan, Ibnu

4.7 178 IT Triple-A Strategy to Improve SMEs' Supply Chain

Dexius, Jawangi

E

Edi, Misna 3.6 139 Microcontroller Based Automatic Fuel Consumption Gauge F

Faridah, Faridah

Fazrin, Fadhilah

1.8 39 Comparison of K-Nearest Neighbor and Logistic Regression Algorithms on Sentiment Analysis of Covid-19 Vaccination on Twitter with Vader And Textblob Labeling

Fonseca, Nicholas

3.1 111 Face Completion with Multi-Output Estimators

G Gowda,

Sanjith

1.2 6 Minimalistic Vector Extension For Image Detection On Edge Nodes Using

‘VGG16'

H Hamami,

Faqih

1.7 35 Customer Segmentation Using Fuzzy C-Means Algorithm in Telco Industry 1.9 45 Customer Segmentation of Cellular Telecommunication Company Using K-

Means Algorithm (Case Study of PT Indosat) Hambali,

Akhmad

5.7 210 Frameless Coded Slotted ALOHA Scheme with Frequency Domain-Extended on NLOS Visible Light Communication Channel

Handojo, Andreas

4.8 183 Job Assignment Problem on Online Transportation Order Using Hungarian Method

Haq, Ahmad 4.4 161 Comparison of K-means and K-medoids in Tourist Attraction Clustering based on Visitor Characteristics

5.1 188 Selection System of Madura Tourism Object as The Best Recommendation Based on Comparative Analysis of K-NN and Naive Bayes Methods

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Hardi, Richki 5.2 194 Academic Smart Chatbot to Support Emerging Artificial Intelligence Conversation

Hasson, Saad 3.2 117 A Markova-Chain Approach to Model Vehicles Traffic Behavior 3.3 123 Modeling the Behavior of the Linear Wireless Sensor Networks

Hazboun, Fadi 2.3 75 Decision Support System Terms of SME's Credit Lending Based on Machine Learning Approach

Herawati, Sri 1.11 57 Mobile Application for Personalized Culinary Tourism Based on User Preference I

Ifada, Noor 1.10 51 ModifiedECS (mECS) Algoritm for Madurese - Indonesian Rule Based Machine Translation

Imamah, Imamah

1.11 57 Mobile Application for Personalized Culinary Tourism Based on User Preference

Imanuel, Jason

Indrawati, Veronica

1.5 24 Webots Simulator for Lyapunov-based Cooperative Omnidirectional Mobile Robots Evaluation

Irfiyanda, Chairani

1.7 35 Customer Segmentation Using Fuzzy C-Means Algorithm in Telco Industry

Ismayanti, Rika

4.5 167 Identification of Pests on Black Orchid Plants Using Naïve Bayes Method Based on Leaf Image Texture

J Jauhari,

Achmad

1.11 57 Mobile Application for Personalized Culinary Tourism Based on User Preference 4.4 161 Comparison of K-means and K-medoids in Tourist Attraction Clustering based

on Visitor Characteristics

5.1 188 Selection System of Madura Tourism Object as The Best Recommendation Based on Comparative Analysis of K-NN and Naive Bayes Methods

Jayadi, Aldi 5.8 215 Embedded Flu Detection System based Cough Sound using MFCC and kNN Algorithm

K Kabulov,

Anvar

2.6 95 Effective methods for solving systems of nonlinear equations of the algebra of logic based on disjunctions of complex conjunctions

2.7 100 Algorithms for minimizing disjunctions of complex conjunctions based on first- order neighborhood information for solving systems of Boolean equations Kintanswari,

Lusia

Kristiyanti, Dinar

3.7 143 Sentiment classification twitter of LRT, MRT and Transjakarta transportation using Support Vector Machine

Kusuma, Hendra

3.5 133 Automatic Sound Alarm Classification Using Deep Learning For the Deaf and Hard of Hearing

L Linanzha,

Aldila

4.1 149 Software Agent and Data Marker for Data Synchronization in Integrated Systems

Lubis, Muharman

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Lucky, Henry 2.1 63 Explainable Artificial Intelligence (XAI) on Hoax Detection Using Decision Tree C4.5 Method for Indonesian News Platform

Lussak, Assed 4.7 178 IT Triple-A Strategy to Improve SMEs' Supply Chain M

Maksum, Hasan

3.6 139 Microcontroller Based Automatic Fuel Consumption Gauge

Manurung, Jonson

Mubarak, Amirsyah Rayhan

5.5 200 Potential Target of 5G Digital Business Telecommunication Company "PT. MNO Indonesia"

Mufarroha, Fifin Ayu

5.1 188 Selection System of Madura Tourism Object as The Best Recommendation Based on Comparative Analysis of K-NN and Naive Bayes Methods Mukhlisin,

Rouf

1.11 57 Mobile Application for Personalized Culinary Tourism Based on User Preference

Mulyanasari, Ade

5.7 210 Frameless Coded Slotted ALOHA Scheme with Frequency Domain-Extended on NLOS Visible Light Communication Channel

N Nata Atmadja,

Hendrico

2.2 69 Generated Tabular Data with Multi-GANs for Arrhythmia Classification Based on DNN Models

Nugroho, Devis

P

Pamukti, Brian 5.7 210 Frameless Coded Slotted ALOHA Scheme with Frequency Domain-Extended on NLOS Visible Light Communication Channel

Pitogo, Vicente

1.4 18 PLS-SEM-ANN- based technique for understanding behavioral intention to use a learning management system

Prasetio, Barlian

Prastiti, Novi 1.11 57 Mobile Application for Personalized Culinary Tourism Based on User Preference Pratiwi,

Oktariani

1.8 39 Comparison of K-Nearest Neighbor and Logistic Regression Algorithms on Sentiment Analysis of Covid-19 Vaccination on Twitter with Vader And Textblob Labeling

Priambodo, Dimas

2.4 83 iOS Digital Evidence Comparison of Instant Messaging Apps

Pribadi, Agung 5.2 194 Academic Smart Chatbot to Support Emerging Artificial Intelligence Conversation

Pujawan, 4.8 183 Job Assignment Problem on Online Transportation Order Using Hungarian

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Nyoman Method Pulungan,

Reza

4.1 149 Software Agent and Data Marker for Data Synchronization in Integrated Systems

Purnama, Alief 4.4 161 Comparison of K-means and K-medoids in Tourist Attraction Clustering based on Visitor Characteristics

5.1 188 Selection System of Madura Tourism Object as The Best Recommendation Based on Comparative Analysis of K-NN and Naive Bayes Methods Purwanto,

Wawan

Putri, Dwi 3.7 143 Sentiment classification twitter of LRT, MRT and Transjakarta transportation using Support Vector Machine

R Rachmahwati,

Desfiana

1.9 45 Customer Segmentation of Cellular Telecommunication Company Using K- Means Algorithm (Case Study of PT Indosat)

Rachman, Fika

1.10 51 ModifiedECS (mECS) Algoritm for Madurese - Indonesian Rule Based Machine Translation

1.11 57 Mobile Application for Personalized Culinary Tourism Based on User Preference Rahabillah,

Mohammad

5.1 188 Selection System of Madura Tourism Object as The Best Recommendation Based on Comparative Analysis of K-NN and Naive Bayes Methods Ramadani,

Gita

1.10 51 ModifiedECS (mECS) Algoritm for Madurese - Indonesian Rule Based Machine Translation

Rusdi, Jack 5.2 194 Academic Smart Chatbot to Support Emerging Artificial Intelligence Conversation

Rusli, Mochammad

5.6 204 Collision Avoidance System with Model Predictive Control for Mobile Robot Navigation

S

Santosa, Budi 4.8 183 Job Assignment Problem on Online Transportation Order Using Hungarian Method

Saputra, Hendra

Sardjono, Tri Arief

Sari, Ilmiyati 1.3 12 Implementation of Machine Learning in Predicting Length of Punishment at Bandung Court

Saymanov, Islambek

2.6 95 Effective methods for solving systems of nonlinear equations of the algebra of logic based on disjunctions of complex conjunctions

2.7 100 Algorithms for minimizing disjunctions of complex conjunctions based on first- order neighborhood information for solving systems of Boolean equations Sihombing,

Denny Jean Cross

Singgih, Moses

4.8 183 Job Assignment Problem on Online Transportation Order Using Hungarian Method

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Sood, Kanika 3.1 111 Face Completion with Multi-Output Estimators Sudianto,

Achmad

5.6 204 Collision Avoidance System with Model Predictive Control for Mobile Robot Navigation

Sunarno, Sunarno

Syafti, Yolana 3.6 139 Microcontroller Based Automatic Fuel Consumption Gauge Syauqy,

Dahnial

T

Turki, Ahmed 3.2 117 A Markova-Chain Approach to Model Vehicles Traffic Behavior U

Utama, Ditdit 1.1 1 Simple Fuzzy Decision Support Model for Evaluating the Cryptocurrency's Performance

Utami, Lilyani 3.7 143 Sentiment classification twitter of LRT, MRT and Transjakarta transportation using Support Vector Machine

V Vincent,

Vincent

W

Wahyuni, Sri 1.10 51 ModifiedECS (mECS) Algoritm for Madurese - Indonesian Rule Based Machine Translation

Wanti Wulan Sari, Nariza

4.5 167 Identification of Pests on Black Orchid Plants Using Naïve Bayes Method Based on Leaf Image Texture

Waruwu, Memory

Wibowo, Ferry Wahyu

Widasari, Edita

Wihayati, Wihayati

Wijaya, Rony 3.4 128 Photoplethysmography (PPG) Signal-Based Chronological Age Estimation Algorithm

Wulansari, Tina

4.6 173 Evaluation of Information Security Management Using the KAMI Index Framework

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Sentiment Classification Twitter of LRT, MRT, and Transjakarta Transportation using Support Vector

Machine

1^st Dinar Ajeng Kristiyanti Information System Universitas Multimedia Nusantara

Tangerang, Indonesia [email protected]

4^th Lilyani Asri Utami Information System Universitas Nusa Mandiri

Jakarta, Indonesia [email protected]

2^nd Rizki Aulianita Information System Universitas Nusa Mandiri

5^th Fajar Agustini Accounting Information System of

Kampus Kabupaten Karawang Universitas Bina Sarana Informatika

3^rd Dwi Andini Putri Information Technology Universitas Bina Sarana Informatika

Jakarta, Indonesia [email protected] 6^th Zulia Imami Alfianti Information System of Kampus

Kabupaten Karawang Universitas Bina Sarana Informatika

Reducing the use of private transport and increasing the use of public transportation are two important tasks to tackle urban transport problems. Along with the trend of the fastest mobilization using public transport over the last decade by the government. So understanding the loyalty of public transport passengers is important, because customer loyalty is seen as the main determinant of the company's long-term financial performance and is considered the main source of competitive advantage [1].

MRT, LRT, and Transjakarta public transportation services are one of the most talked about topics on Twitter.

Many people rely on public transportation because public transportation is a fairly effective option, when living in an area that is hit by traffic jams every day. The public's response to the services provided by the MRT, LRT and Transjakarta transportation service providers varies, such as positive and negative responses. To find out the reviews, users can see which public transportation providers are popular, comfortable and well-received. Policies aimed at increasing the use of public transport should improve its image, but at the same time, the public transport system needs to become more market-oriented and competitive. This requires improving the quality of service, which can only be achieved with a clear understanding of travel behavior and consumer needs and expectations. Therefore, measuring the

level of public transportation services is an important issue in this study to find out the opinions of public transportation facilities on the MRT and Transjakarta LRT so that it can provide alternative instructions for public transportation management in the process of evaluating improvements and making decisions on the existence of public transportation [2].

The development of information and communication technology makes it easier for humans to connect with each other and share information, one of which is through social networks. Social networking media is a common medium for someone to submit ideas and thoughts on a particular problem or share things that are considered interesting either through digital text or multimedia. One of the most widely used social networks in Indonesia is Twitter [3]. Sentiment analysis can be done to see the opinions that passengers wrote on Twitter. Sentiment analysis in observation is done to examine the opinions, judgments, attitudes, and feelings of people mainly based on what they write. Sentiment analysis is applied because it relates to perception and reality as well as the choices to be made. As in making decisions, it is not uncommon for someone to seek the opinion of others [4]. In order to process and analyze text data coming from social networks like Twitter, we need a tool capable of collecting unstructured text data and extracting relevant information.

In this study, we focus on these two tools, namely sentiment analysis and topic modeling. Topic modeling consists of algorithms that aim to characterize texts, based on the topics discussed [5]. Sentiment or opinion mining analysis is the computational study of people's opinions, behaviors, and emotions towards entities. The magnitude of the influence and benefits of sentiment analysis causes research and application-based sentiment analysis to develop rapidly. Even in America there are about 20-30 companies that focus on sentiment analysis services [6]. The time span covered in this study includes data from 2022. To get tweets for that year, code written in Python using Twitter's Official API constraints is used. The code can search for tweets on the LRT MRT and Transjakarta Twitter pages and automatically saves available tweets in the Twitter Search browser.

Based on these problems, the researcher intends to conduct a sentiment analysis regarding the LRT, MRT, and Abstract— The use of public transportation facilities such

as MRT, LRT, and Transjakarta by the people of the capital city is an alternative in reducing congestion. However, the services provided by MRT, LRT and Transjakarta transportation service providers vary, such as positive and negative responses. The effectiveness of public transportation facilities can be seen through public opinion. This study aims to classify positive and negative tweet sentiments sourced from Twitter data using the Support Vector Machine (SVM) algorithm. The results of this study indicate that the Support Vector Machine method is able to classify positive and negative sentiment text with an accuracy result of 91.89% with 79.2%

positive sentiment and 20.8% negative sentiment.

Keywords— Public Transportation, Sentiment Analysis, Sentiment Classification, Support Vector Machine, Twitter.

I. INTRODUCTION

2022 International Conference of Science and Information Technology in Smart Administration (ICSINTESA)

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Transjakarta based on public opinion using the SVM method. There are many researchers who have conducted research on sentiment analysis regarding transportation and chose the SVM method to analyze it. Recently, a research result on the Jakarta Rapid Bus Transportation Service showed that the majority of Jakarta residents had a bad impression of bus rapid transit, so that many customers were disappointed with the service [7]. The Naive Bayes and SVM algorithms were compared in developing predictive models to classify consumer questions regarding common motorcycle problems, the results showed that the SVM model with the uni-trigram model performed better than the Naive Bayes algorithm [8]. This study aims to classify positive and negative tweet sentiments using the Support Vector Machine algorithm to be able to see public opinion on LRT, MRT and Transjakarta as public transportation.

.

A. Sentiment Analysis

Process of analyzing text using machine learning to identify the polarity of the text. One of the dominant text sources for conducting sentiment analysis is Twitter. As a social network, users can use Twitter without restrictions to get opinions on a topic [9]. Twitter users generate 12 GB of data every day. They express opinions on various public topics and voice their complaints to businesses and government agencies [10].

So that this information is useful as a determinant in making business decisions [11]. The process of sentiment analysis includes the extraction of people's opinions, emotions, attitudes, and feelings about a topic or situation from a large amount of unstructured data [12]. This process requires two phases, namely the data collection preparation stage and the sentiment analysis stage [13].

B. LRT, MRT and Transjakarta Bus

On October 1, 2018, the Government inaugurated a public transportation system integration program in Jakarta called the Jak Lingko program. This program is attempted to reduce the level of air pollution and congestion in DKI Jakarta. The Jak Lingko integration system includes the integration of routes, payments, and infrastructure for all types of public transportation managed by the government, such as Mass Rapid Transit (MRT), Light Rail Transit (LRT), and Transjakarta Bus (TJ) [14].

In May 2022, there were 1,561,680 people using the Jakarta MRT service with an average of 50,377 people using the Jakarta MRT per day with 7,047 train trips.

This number shows public confidence in the Jakarta MRT service during this pandemic, which has increased by around 413,688 people from the previous month [15].

The LRT has been built and operates in East Jakarta, but is considered expensive because it costs twice that of a Transjakarta bus and three times that of a commuter line [16]. The LRT management held various promotional activities to attract visitors to use this mode of transportation. The activities take place at the station or carriage, such as fashion shows for the launch of a local

fashion brand, cultural week activities, playing music, lion dance performances, and other entertainment [17].

The number of Transjakarta passengers has gradually increased again after the Covid-19 case in Jakarta declined. The number of passengers now reaches an average of 650 thousand people per day. The existence of an integrated transportation service program affects the increase in the number of Transjakarta customers [18].

The DKI Jakarta Transportation Agency revealed that the switch over (SO-5) service system at Manggarai Station boosted the number of passengers for three mass transportations in Jakarta, namely Mass Rapid Transit (MRT), Light Rail Transit (LRT), and Transjakarta Bus (TJ). Since the enactment of SO-5 on 28 May 2022-27 June 2022, the number of passengers using Transjakarta for the Manggarai-UI route has increased to 57,127 people from the previous 36,513 people. The number of MRT Jakarta passengers also increased in the period from May 28, 2022 to June 27, 2022, reaching 1.88 million compared to before SO-5, which was 1.45 million people. The number of passengers on the Jakarta LRT also increased to 60,173 people from the previous 50,876 people [19].

C. Support Vector Machine

Support Vector Machine (SVM) is basically a supervised learning methodology combined with learning algorithms that can be used to perform classification and regression analysis in an appropriate way. In addition, it can be used to design the optimal dividing hyperplane along with the largest margin width between the two classes for the sample dataset T [20]. In SVM, extreme points help create the hyperplane. Thus, the algorithm is called the Support Vector Machine and the extreme point is called the support vector. SVM is popular and widely used by researchers because of its ability to handle large amounts of data, small number of hyperparameters, theoretical guarantees, and excellent performance in practice [21]. [21]. In SVM, extreme points help create the hyperplane. The hyper plane search process is done by first looking for support vector from the two classes that are owned [22]. Support Vector Machine is one of the best methods that can be used in the problem of classification, the concept of SVM stems from the problem of classification two class so that requires training set positive and negative [23] Thus, the algorithm is called the Support Vector Machine and the extreme point is called the support vector. SVM is popular and widely used by researchers because of its ability to handle large amounts of data, small number of hyperparameters, theoretical guarantees, and excellent performance in practice [24].

he objective of this algorithm is to classify data points by using hyperplane or separator function between classes [25].The advantage of the SVM method is its generalizability, which is the ability to classify other data that is not included in the data used in machine learning [26].

II. RELATED WORK

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The following is a diagram of the research methodology that we carried out:

Fig. 1 Research Framework

In Figure 1 above, Diagrams used in the research phase:

1. Data Collecting

The data used is taken from the Twitter dataset, with the Twitter API crawling technique using the help of rapid miners. The data is 1869, 1479 positive and 390 negative, then the data from the Twitter API is pre- processed using Python.

Fig. 2. Dataset Twitter

2. Pre- Processing

In this pre-processing, namely converting raw data into data that is ready to be processed. There are several stages of pre-processing used:

a. Case folding

Technique for converting all text to lowercase all at once to remove punctuation.

b. Tokenizing

Used to separate sentences into words or strings. The things that are done in this process are: remove number, remove punctuation, remove white space leading and trailing, remove multiple whitespace into single white space, remove duplicate text.

c. Normalization

The tweeter uses a lot of language that is not standard or unknown and also a lot of text that includes the name of the account on the tweeter, or there is less text that needs to be cleaned or normalized. Change non-standard words into standard forms.

d. Stemming

The process of changing a sentence or word into its basic form, without having an affix. The stemming used is in English so that the existing tweets need to be changed first into English and then converted into basic words.

3. Word Weighting with Term Frequency-Inverse Document Frequency (TF-IDF)

After pre-processing, the next stage is weighting. The weighting used is TF-IDF. Serves to calculate the frequency of a word in a text document and its relevance to text mining.

4. Splitting Data

The next step is to carry out training data and data testing. In the training data here, the intention is to train the algorithm to find the right model. Data testing is carried out for testing the performance of the model being carried

5. Testing Data with K-Fold Cross Validation

The performance of the model is validated using 10 K- Fold Cross Validation. K-Fold validation provides the most accurate results to be applied to different training data in the same dataset. Using k-fold cross validation can estimate prediction error in modeling

6. Learning Method

Algorithms that are proven to be used for text classification, namely Support Vector Machine. We use Support Vector Machine because the algorithm is known to be reliable in text classification.

7. Model Evaluation

Evaluation of Machine Learning on the Support Vector Machine Model results in accuracy, precision and recall.

The resulting accuracy means that the model that is carried produces the right accuracy which is then displayed through the confusion matrix.

We will describe the results and discussion in more detail below:

1. Data Collecting

Sentiment analysis research on Twitter data in Python.

In this study, 1869 tweets from Twitter were collected using the Twitter API on RapidMiner, with 1479 positive tweet sentiments and 390 negative tweet sentiments. Sentiment of using LRT, MRT and Transjakarta transportation. The search words used are Indonesian tweet settings and then we translate to english using Python. Then do the data processing in Python. there are 10 testing data of tweet data with III. RESEARCH METHODOLOGY

IV. RESULT AND DISCUSSION

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search keywords: #lrt #mrt #mrtjakarta #ubahjakarta

#infojkt #wajahbarujakarta and #enjoyjakarta which will go through the preprocessing stage.

2. Pre- processing

The data that has been collected is then cleaned before being processed. We use several pre-processing methods, namely:

a. Case Folding

The function of case folding to remove unnecessary punctuation marks and remove numbers from the data taken and convert all text to lowercase.

Fig.3 Case Folding b. Tokenizing

At this stage is to divide the sentences in a document into tokens or words.

Fig. 4 Tokenizing c. Normalization

The normalization process in this study is described as follows:

Fig. 5 Normalization d. Stemming

In this process is to change a word into a root word.

Fig. 6 Stemming

Then label each tweet which is categorized as positive and negative tweets after pre processing:

Fig 7. Labeling

3. Word Weighting with Term Frequency – Inverse Document Frequency (TF-IDF)

This process serves to give weight to each word. This weighting is very important to represent data from text to numeric. The weighting in the text is to calculate the weight of each word that appears in a document. In this study we give weights to the test data.

Fig 8. TF-IDF 4. Splitting Data

In this study, we split the data by dividing the data into training data and testing data, the result of 1443 training data and 426 testing data.

5. K-Fold Cross Validation

K Fold Cross Validation which intends to test cross validation by conducting random data and then dividing it into 10 K-Fold data groups.

TABLE 1.Testing Data with K-Fold Cross Validation Precision Recall F1-

Score

Support

K=1 Negative 0.91 0.97 0.94 212

Positive 0.97 0.90 0.93 214

Accuracy 0.94 ⁴²⁶

Macro avg 0.94 0.94 0.94 426

Weighted avg 0.94 0.94 0.94 426

K=2 Negative 0.89 0.93 0.91 198

Positive 0.94 0.90 0.92 220

Accuracy 0.92 426

Macro avg 0.91 0.92 0.92 426

Weighted avg 0.92 0.92 0.92 426

K=3 Negative 0.91 0.92 0.92 220

Positive 0.92 0.90 0.91 206

Accuracy 0.91 426

Macro avg 0.91 0.91 0.91 426

Weighted avg 0.91 0.91 0.91 426

K=4 Negative 0.89 0.97 0.93 215

Positive 0.97 0.88 0.92 211

Accuracy 0.92 426

Macro avg 0.93 0.92 0.92 426

Weighted avg 0.93 0.92 0.92 426

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From the initial 1869 data, the data was divided into two parts, namely training data of 1443 and testing data of 426 data, then K Fold was used as Cross Validation which contained 10 k. Table 1 above is an explanation that we use 10 K Fold Cross Validation. The system has a measurable performance through precision and recall, which will be discussed further in the point model evaluation. The greatest accuracy at the value of k = 1 with an accuracy of 0.94.

6. Learning Method.

Support Vector Machine is a classification algorithm based on the principle of linear classifier which is able to solve problems with faster computation time for large data. The Support Vector Machine method is used in this study to get the best accuracy results. To use Support Vector Machine in Python you need the pandas library.

The library used in this study is numpy. Vector Machine support is invoked in Python using Sklearn.

Fig.9 Part of Modeling Support Vector Machine 7. Model Evaluation

The confusion matrix serves to measure the classification performance based on the interpretation of data that is classified as true or false so that the actual value with the value of the predicted output of the model can be compared. The result of the confusion matrix is an evaluation matrix, namely accuracy, precision, recall,

F-1 score. The following is a sample confusion matrix from the results of this study:

Fig. 10 Confusion Matrix

In Figure 10. The Accuracy value generated from the confusion matrix above can be calculated as follows:

Accuracy= TP + TN ...(1) TP + FP + FN + TN

= 206 + 193

206 + 6 + 21 + 193 = 399/426

= 0.9366 (0.94)

The following are the average accuracy, precision, recall, F1-score:

average Accuracy : 0.9189 average Precision : 0.9214 average recall: 0.9188 average f1-score: 0.9187

This study also displays a wordcloud which is an image that shows a list of words used in the MRT, LRT and Transjakarta Twitter opinions, generally the more words used, the larger the size of the word in the image.

Fig. 11 The Wordcloud Sentiment Classification Twitter of LRT, MRT and Transjakarta Transportation

In this research, sentiment analysis of LRT, MRT and Transjakarta uses the SVM method. Support Vector Machine (SVM) is a text classification method that is reliable and capable of producing high accuracy. This is evidenced in the analysis of Twitter sentiment research by Precision Recall F1-

Score

Support

K=5 Negative 0.80 0.96 0.92 224

Positive 0.95 0.86 0.90 202

Accuracy 0.91 426

Macro avg 0.92 0.91 0.91 426

Weighted avg 0.92 0.91 0.91 426

K=6 Negative 0.89 0.95 0.92 213

Positive 0.94 0.80 0.91 213

Accuracy 0.92 426

Macro avg 0.92 0.92 0.92 426

Weighted avg 0.92 0.92 0.92 426

K=7 Negative 0.86 0.90 0.92 202

Positive 0.97 0.86 0.91 223

Accuracy 0.92 425

Macro avg 0.92 0.92 0.92 425

Weighted avg 0.92 0.92 0.92 425

K=8 Negative 0.89 0.94 0.92 208

Positive 0.94 0.89 0.92 217

Accuracy 0.92 425

Macro avg 0.92 0.92 0.92 425

Weighted avg 0.92 0.92 0.92 425

K=9 Negative 0.90 0.96 0.92 216

Positive 0.96 0.86 0.91 209

Accuracy 0.91 425

Macro avg 0.92 0.91 0.91 425

Weighted avg 0.92 0.91 0.91 425

K=10 Negative 0.90 0.96 0.93 220

Positive 0.96 0.89 0.92 205

Accuracy 0.93 425

Macro avg 0.93 0.93 0.93 425

Weighted avg 0.93 0.93 0.93 425

V. CONCLUSION

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producing an accuracy of 91.89%, 92.14% precision and 91.88% recall with positive sentiments of 1481 data and negative sentiments of 389 data.

ACKNOWLEDGMENT

We would like to thank Universitas Multimedia Nusantara for the assistance and support in getting the research done.

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