Analysis of Marketplace Conversation Trends on Twitter Platform Using K-Means

Nasron, Ulil Amri; Habibi, Muhammad

doi:10.28989/compiler.v9i1.579

Cited by 4 publications

(5 citation statements)

References 7 publications

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“…K-means Clustering is a partitioning strategy that splits data into k non-overlapping subsets (or clusters) that lack internal structure [14], [15]. K-means clustering involves the following steps:…”

Section: K-means Clusteringmentioning

confidence: 99%

A Comparative Analysis between K-Means and Agglomerative Clustering Techniques in Maritime Skill Certification

Setyawan,

Purwatiningsih

2024

Compiler

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The maritime industry must constantly adjust seafarer training to meet evolving operational demands and ensure compliance with new regulations. This study addresses the challenge of assessing the relevance of Certificate of Proficiency (COP) services by categorizing them to determine which qualifications are essential for marine professionals. The goal is to identify obsolete or misaligned training programs that need updates or enhancements to better serve industry needs. To this end, the study employed two clustering algorithms, K-Means and Agglomerative Clustering, on data from 2021 to 2023. K-Means was chosen for its efficiency in processing large datasets and creating clear, non-overlapping groups. Agglomerative Clustering was selected for its ability to offer a detailed, hierarchical view of data, which helps in understanding the complex structure of certification demands more comprehensively. The analysis identified three main clusters; notably, Cluster 2 indicated a high demand for critical certifications, while Cluster 1, containing the majority of certifications, received little interest, suggesting they may be less relevant. This insight encourages training providers to consider refining their offerings. Although comprehensive, the study's three-year timeframe suggests extending this period in future research for a more detailed trend analysis and forecasting in maritime training adaptations.

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Section: K-means Clusteringmentioning

confidence: 99%

A Comparative Analysis between K-Means and Agglomerative Clustering Techniques in Maritime Skill Certification

Setyawan,

Purwatiningsih

2024

Compiler

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“…banyak digunaka untuk penelitian, seperti pengelompokan followers influencer berdasarkan like dan komen [15], analisis trend percakapan terkait marketplace [16]. Analisis sentimen merupakan salah satu implementasi dari klasifikasi teks.…”

Section: Pendahuluanunclassified

Tweet Analysis of Mental Illness Using K-Means Clustering and Support Vector Machine

Kusumaningtyas,

Habibi,

Dwijayanti

et al. 2023

Telematika

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Purpose: Social media, particularly Twitter, provides a venue for individuals to share their thoughts. The public's perception of mental illnesses is often debated on Twitter. So yet, no evaluation of community tweets connected to data on mental health conditions has been performed. The purpose of this study is to examine tweets linked to mental illnesses in Indonesia in order to identify the themes of conversation and the polarity trends of these tweets.Design/methodology/approach: To address this issue, the K-Means Clustering algorithm is utilized to aggregate tweet data that is used to find themes of conversation. The emotion polarity value of each cluster result was then determined using the Support Vector Machine (SVM) approach.Findings/results: This study generated five topic clusters based on tweets about mental illness. While sentiment analysis revealed that all clusters had more negative sentiment classes than positive. Cluster 4 and Cluster 5 had the highest number of negative sentiment values. These clusters emphasize the necessity of consulting with psychiatrists and psychologists if people have mental health disorders, as well as financing for mental health disorder treatment through BPJS Kesehatan services.Originality/value/state of the art: The analysis was done in two stages: data grouping to find themes of conversation using K-Means clustering and SVM to look for positive and negative polarity values associated to twitter data about mental illness.

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“…Preprocessing Data adalah teknik data mining awal yang mengubah data mentah atau data yang biasa disebut data mentah, yang dikumpulkan dari berbagai sumber menjadi informasi yang lebih bersih yang dapat digunakan untuk diproses lebih lanjut [13], [14]. Preprocessing data dilakukan dengan menormalisasikan dan mengubah data yang belum sesuai dengan format.…”

Section: Preprocessing Dataunclassified

Autoregressive Integrated Moving Average (ARIMA) Models For Forecasting Sales Of Jeans Products

Permata,

Habibi

2023

Telematika

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Purpose: To be able to compete with other companies, it is necessary to estimate and forecast jeans products that will be ordered according to consumer demand every month, so that there is no excess inventory and product shortage. If there is a shortage of goods, the consumer will be disappointed with the seller, and vice versa if the goods are overstocked, the quality will continue to decline to the detriment of the seller and the buyer, resulting in a shortage of materials.Methodology: To overcome the problem of selling jeans products, the ARIMA method is suitable to overcome the problem of forecasting the stock of jeans sales. ARIMA model is a model that completely ignores the independent variables in making forecasts. ARIMA uses past and present values of the dependent variable to produce accurate short-term forecasting.Results: The built forecasting has a MAPE accuracy rate of 17.05% so it can be said that predicting has good results according to the criteria. Forecasting results in the following year show that sales tend to increase from the previous year.Originality: This research was conducted using sales data of jeans products at company XYZ and using the ARIMA method which previous researchers have never done.

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Analysis of Marketplace Conversation Trends on Twitter Platform Using K-Means

Cited by 4 publications

References 7 publications

A Comparative Analysis between K-Means and Agglomerative Clustering Techniques in Maritime Skill Certification

A Comparative Analysis between K-Means and Agglomerative Clustering Techniques in Maritime Skill Certification

Tweet Analysis of Mental Illness Using K-Means Clustering and Support Vector Machine

Autoregressive Integrated Moving Average (ARIMA) Models For Forecasting Sales Of Jeans Products

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