Pemanfaatan Big Data Media Sosial Dalam Menganalisa Kemenangan Pilkada

Wulandari, Dewa Ayu Putri; Sudarma, Made; Paramaita, Nyoman

doi:10.24843/mite.2019.v18i01.p15

Cited by 3 publications

(4 citation statements)

References 2 publications

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“…Terakhir, diasumsikan bahwa penggunaan Big Data dalam pembelajaran jarak jauh di daerah 3T akan memberikan kontribusi yang signifikan dalam pengembangan inovasi pendidikan. Dengan menganalisis data yang diperoleh, akan mungkin untuk mengidentifikasi kebaharuan dan keunggulan inovasi pembelajaran yang dapat diterapkan di daerah 3T (Wulandari, 2019). Dalam konteks penelitian ini, inovasi dapat mencakup penggunaan teknologi yang tepat, pengembangan platform pembelajaran yang sesuai, dan penggunaan metode pengajaran yang efektif dalam mengatasi tantangan khusus yang dihadapi oleh daerah 3T.…”

Section: Pendahuluanunclassified

Pemanfaatan Big Data dalam Pembelajaran Jarak Jauh (PJJ) selama Pandemi pada Daerah 3T (Terdepan, Terpencil dan Tertinggal)

Widiasanti,

Zahra,

Sholikha

et al. 2023

Cetta

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The COVID-19 pandemic has prompted the adoption of distance education worldwide, including in 3T regions facing challenges of limited infrastructure and accessibility. The utilization of Big Data holds great potential in enhancing the quality and accessibility of education in these 3T regions. This study aims to analyze the utilization of Big Data in distance education during the pandemic in 3T regions, identify its benefits in personalized learning, monitoring the learning process, and predicting student needs, and provide recommendations for further research. The research approach employed data analysis from a survey involving 500 schools in 3T regions. Data was collected through questionnaires that encompassed the aspects of Big Data usage in distance education and its impact on educational quality in 3T regions. Data analysis reveals that the utilization of Big Data in distance education in 3T regions has significantly contributed to student data collection and analysis, monitoring the learning process, and predicting student needs. These findings are consistent with previous research that highlights the benefits of Big Data in personalized learning and educational performance management. The utilization of Big Data in distance education in 3T regions during the pandemic holds immense potential in improving the quality and accessibility of education. Adequate training and education should be provided to teachers and school staff regarding the utilization of Big Data.

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Section: Pendahuluanunclassified

Pemanfaatan Big Data dalam Pembelajaran Jarak Jauh (PJJ) selama Pandemi pada Daerah 3T (Terdepan, Terpencil dan Tertinggal)

Widiasanti,

Zahra,

Sholikha

et al. 2023

Cetta

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“…(3) F1score menjelaskan mengenai perhitungan yang bertujuan untuk menghitung mean ataun rata-rata dari Precision dan Recall [7,8].…”

Section: Pengujian Dan Validasiunclassified

“…Confusion Matrix adalah sebuah tabel yang dimana digunakan untuk menghitung performa model klasifikasi data uji[7,8]. Confusion Matrix dapat membantu analisis data dan membantu memetakan masing-masing kelas yang memiliki Precision dan Recall rendah.…”

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Analisis Sentimen Pola Pikir Masyarakat Indonesia Terkait Virus Covid-19 Dalam Media Sosial Twitter Menggunakan Metode Rule Based Leksikon

Riskiyanti,

Darma Putra,

Wiranatha

2022

jitter

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The Covid-19 virus has managed to wreak havoc in various sectors around the world. Not only does it affect a person's physical condition, but also affects the psychological condition of the people's mindset. On the other hand, the Covid-19 pandemic has also had an impact on the high use of social media among the Indonesian people. Individual opinions on various matters are expressed on the web and can be collected into big data for processing. Twitter API is an application created by Twitter to make it easier for other developers to access Twitter web information such as the large amount of data used in this research. Rule-Based Lexicon is a classification method that utilizes rules to distinguish one class from another with positive, negative, and neutral sentiment word classes. This study discusses the results of the sentiment analysis of the mindset of the Indonesian people during the Covid-19 pandemic using the Rule-Based Lexicon classification method with data sourced from Twitter social media as much as 4,068,464 tweet data which obtained an average accuracy value of 81%, a precision value of 93%, 95% recall value, and 83% f1 score. The results of the highest overall mindset sentiment were in July 2021 with 10,011 (23.53%) tweet data classified as positive sentiment on the topic of PPKM in Bangka Belitung Province, 8,629 (19.42%) classified as neutral sentiment on PPKM topic in Gorontalo Province, and 4,216 (8.61%) classified as negative sentiment in Bengkulu Province which leads to a toxic positivity conformity mindset.

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“…Efforts to streamline the voting process have led to developing technologies such as evoting applications, leveraging tools such as Optical Character Recognition for swift and accurate data collection [27]. Furthermore, integrating big data from social media platforms has been explored to analyze election outcomes and public engagement [28]. These technological advances aim to optimize the electoral process and ensure transparency and efficiency.…”

Section: Introductionmentioning

confidence: 99%

Optimizing the 2024 Governor Election Quick Count with Extreme Gradient Boosting (XGBoost) to Increase Voting Prediction Accuracy

Suacana,

Suhariyanto,

Nuru

2024

ijsecs

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This research aims to increase the accuracy of vote predictions in the Quick Count process in the 2024 Governor Election using the XGBoost algorithm. Quick Count is a fast method for obtaining estimates of election results based on some of the data that has been calculated. The XGBoost algorithm was chosen because it has proven effective in various applications, including predictive modeling. This research analyzes the implementation of the XGBoost algorithm in modeling vote predictions for Quick Count, especially in the context of the 2024 gubernatorial election. By using various evaluation metrics such as accuracy, precision, recall, and F1-score, this research provides a comprehensive understanding of the performance of the XGBoost model. The research results show that the XGBoost algorithm achieves high accuracy, precision, recall, and F1 score, demonstrating its ability to classify sounds accurately. The practical implications of this research are significant in improving the integrity of the democratic process by providing more reliable and transparent election results. Additionally, this research paves the way for developing more sophisticated Quick Count methods by leveraging insights from previous research on machine learning techniques and data security.

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Pemanfaatan Big Data Media Sosial Dalam Menganalisa Kemenangan Pilkada

Cited by 3 publications

References 2 publications

Pemanfaatan Big Data dalam Pembelajaran Jarak Jauh (PJJ) selama Pandemi pada Daerah 3T (Terdepan, Terpencil dan Tertinggal)

Pemanfaatan Big Data dalam Pembelajaran Jarak Jauh (PJJ) selama Pandemi pada Daerah 3T (Terdepan, Terpencil dan Tertinggal)

Analisis Sentimen Pola Pikir Masyarakat Indonesia Terkait Virus Covid-19 Dalam Media Sosial Twitter Menggunakan Metode Rule Based Leksikon

Optimizing the 2024 Governor Election Quick Count with Extreme Gradient Boosting (XGBoost) to Increase Voting Prediction Accuracy

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