Analisis Akun Twitter Berpengaruh terkait Covid-19 menggunakan Social Network Analysis

Kartino, Aprillian; Anam, M. Khairul; Rahmaddeni,; Junadhi,

doi:10.29207/resti.v5i4.3160

Cited by 11 publications

(14 citation statements)

References 10 publications

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“…Dalam penelitian ini memerlukan beberapa informasi yang bersifat objektif yang digunakan sebagai acuan penelitian [14]. Dari informasi tersebut diharapkan penelitian ini dapat menghasilkan penelitian yang berkualitas.…”

Section: Metode Penelitianunclassified

Prototype Smart Time Scheduler Lampu Lalu Lintas Menggunakan Algoritma Haar Cascade

Nirwanda¹,

Yenni²,

Anam³

et al. 2023

JTI

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The traffic light functions as a traffic controller at crossroads and also as smoothness at intersections to avoid congestion. The current application of conventional traffic lights still has weaknesses in determining the duration of traffic lights which are not adjusted to the number of vehicle volumes which often change every time. Especially when there is a dense flow of vehicles at intersections, especially at certain times. Based on observations of traffic density at the intersection that connects Soekarno -Hatta road and Arifin Achmad road in Pekanbaru City. There is often a high density of vehicles from the direction of Jalan Soekarno-Hatta (Morning Market) to Jalan Jenderal Sudirman during working hours so that the duration of the traffic lights cannot reduce the amount of queue density in the light traffic. The implementation of the "Smart Time Scheduler" is a solution for adjusting the duration of traffic lights based on the level of traffic density, by building a tool to detect and calculate the number of vehicle queues at the Traffic Light and then enter the calculation results into 3 categories, namely Normal, Medium and Dense Density. The system uses Haar Cascade with the open cv library on the Raspberry pi, the results of the system testing that has been carried out can count the number of vehicles and instruct Arduino to set the duration of the traffic lights where in the category of normal vehicle queues, the green traffic light gets a waiting time of 10 seconds in countdown, Moderate category for 15 seconds, and Solid Category, for 20 seconds, while the calculation error value uses the Error Percentage equation formula, namely comparing the results of vehicle calculations by the system with Manual Calculation Results multiplied by 100% Then the System Calculation Error is 16% and the accuracy level of the tool is 100% -16% = 84%, the size of the error value is influenced by the quality of the light intensity, and the distance of the camera's detection of the object when performing the detection.

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Section: Metode Penelitianunclassified

Prototype Smart Time Scheduler Lampu Lalu Lintas Menggunakan Algoritma Haar Cascade

Nirwanda¹,

Yenni²,

Anam³

et al. 2023

JTI

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“…Penelitian ini dilakukan dengan melakukan kombinasi pendekatan metode Social Network Analysis (SNA) dan metode eksperimen. Dalam penelitian ini, untuk mengetahui aktor berpengaruh menggunakan metode degree centrality [12] dan analisis aktor popular menggunakan metode follower rank [13]. Sosial Network Analysis (SNA) adalah metode analisis dalam pemetaan jaringan sosial media untuk menemukan node yang paling berpengaruh dalam jaringan informasi terkait tagar "#LuhutPenghisapDarahRakyat" [14] [15].…”

Section: Metodologi Penelitianunclassified

“…Menurut persamaan (2) diatas, F1 adalah jumlah pengikut atau followers dari node. F3 adalah aktor yang terhubung ke node lain atau sentralitas derajat [13]. Nilai yang lebih tinggi berarti lebih banyak pengikut atau popular.…”

Section: B Follower Rankunclassified

“…e. Follower Rank Merupakan proses ini untuk mendapatkan 10 aktor/node popular berdasarkan nilai degree centrality tertinggi, sesuai dengan persamaan (2) F1 adalah jumlah pengikut atau followers dari node. F3 adalah aktor yang terhubung ke node lain atau sentralitas derajat [13]. f. Profil 10 Aktor Utama Merupakan proses analisa profil dari 10 aktor dengan nilai degree centrality tertinggi, dimana setiap akun dari 10 aktor/node ini analisa mulai kapan akun dibuat berdasarkan tanggal aktif dan analisa apakah akun tersebut masih aktif, tidak aktif ataupun disuspended oleh Twitter berdasarkan keterangan.…”

Section: Degree Centralityunclassified

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Analisis Node Dengan Metode Degree Centrality Dan Follower Rank Pada Tagar Twitter

Talapessy

Hendry²

2022

Jur. Ris. Kom.

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Twitter is one of the most popular social media platform as a means of information and interaction with other people. Since its launch in 2006, Twitter has been classified as a microblogging site. Data in the form of node analysis (actors) on Twitter can be analyzed using a social network analysis method approach. This study was designed to analyze data on tweet containing the hastag “#LuhutPenghisapDarahRakyat”. This analysis uses the degree centrality method to identify influential nodes (actors) and the follower rank method to find popular nodes (actors). The data were 2803 nodes and 8030 edges data was taken from November 6,2021 to November 7,2021. The results show that the @arthan38836243 account is the actor with the higshest degree centrality value, which is 265 and the @teriwinarno account is the actor with the highest popularity value or follower rank, which is 0.988081224. This shows that influential actors are not necessarily the same as popular actor. This studt that among the 10 main actors with the highest degree centrality value, one of the actors was a buzzer account on Twitter

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“…The second stage was looking for the relationship between one entity unit and other entity units with the help of graph theory [25][26] [27]. The SNA method was chosen because this study required a technique.…”

Section: Introductionmentioning

confidence: 99%

Framework for Analyzing Netizen Opinions on BPJS Using Sentiment Analysis and Social Network Analysis (SNA)

Anam¹,

Mahendra

Agustin³

et al. 2022

intensif

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The Social Security Administrative Body is a legal entity established to administer social security programs. News about BPJS policies is often found online and social media that has received responses from netizens as a form of public opinion on the policy. One of them is the opinion of netizens on social media Twitter. Ideas can be positive, neutral, or negative. These opinions are processed using the Support Vector Machine (SVM) method, in some SVM studies still getting unsatisfactory results, with rates below 60%. For this reason, it is necessary to have feature selection or a combination with the other methods to obtain higher accuracy. To see the actors who influence the opinion of netizens on the topic of BPJS, the Social Network Analysis (SNA) method is used. Based on the SVM Method's test results, the best accuracy results are obtained in combining the SVM Method with Adaboost, with an accuracy rate of 92%. Compared to the pure SVM method by 91%, the Combination of SVM Particle Swarm Optimization (PSO) by 87% and SVM using Feature Selection Genetic Algorithm (GA) by 86%.

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Analisis Akun Twitter Berpengaruh terkait Covid-19 menggunakan Social Network Analysis

Cited by 11 publications

References 10 publications

Prototype Smart Time Scheduler Lampu Lalu Lintas Menggunakan Algoritma Haar Cascade

Prototype Smart Time Scheduler Lampu Lalu Lintas Menggunakan Algoritma Haar Cascade

Analisis Node Dengan Metode Degree Centrality Dan Follower Rank Pada Tagar Twitter

Framework for Analyzing Netizen Opinions on BPJS Using Sentiment Analysis and Social Network Analysis (SNA)

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