Implementasi keamanan informasi file dokumen shipping menggunakan algoritma AES Shipping

Cited by 3 publications

(4 citation statements)

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“…In line with research findings [8], TF IDF and TF RF comparisons also show a similar trend. The TF RF method outperforms TF IDF by a 4% margin in terms of accuracy.…”

Section: Discussionsupporting

confidence: 89%

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The Effect of Feature Weighting on Sentiment Analysis TikTok Application Using The RNN Classification

Aufa,

Prasetiyowati,

Sibaroni

2023

bits

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Social media is a medium used by people to express their opinions. In its development, social media has become a necessity in social life. One of the most popular social media applications since 2020 is TikTok. Short videos with an average duration of 60 seconds can entertain the community so that they don't feel isolated. There are 17 million TikTok application reviews in the Google Play store in Indonesia from various user ages. The rapid development of information and technology has led to the pros and cons of this application. Freedom of expression without specific restrictions on content publication negatively impacts the user's mentality. Based on this, sentiment analysis is very important to reveal trends in opinions about applications that are useful for the community in increasing awareness of whether the application is good before use. Proper feature weighting is required to improve the sentiment analysis results' accuracy. More optimal results can be obtained by determining the appropriate weight for different feature weighting. This study compares the TF IDF, TF RF, and Word2Vec feature weighting methods with the RNN classifier on the TikTok app review. The experiment shows that TF RF is superior to TF IDF, with successive feature weighting accuracy with TF RF of 87,6%, TF IDF of 86%, and Word2Vec of 80%. The contribution of this research lies in its exploration of different feature weighting methods to enhance sentiment analysis accuracy and provide valuable insights for decision-making processes.

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“…In line with research findings [8], TF IDF and TF RF comparisons also show a similar trend. The TF RF method outperforms TF IDF by a 4% margin in terms of accuracy.…”

Section: Discussionsupporting

confidence: 89%

“…Therefore, the role of sentiment analysis is needed. Sentiment analysis is a branch of Text Mining used to analyze text opinions [8].…”

Section: Introductionmentioning

confidence: 99%

The Effect of Feature Weighting on Sentiment Analysis TikTok Application Using The RNN Classification

Aufa,

Prasetiyowati,

Sibaroni

2023

bits

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“…Articles that relate one article to another, namely the article entitled Nusantara as the capital of Indonesia, has a quote from the article entitled case study (Adiningrat & Raynadhi, 2022;Dharmendra et al, 2022), while the article entitled case study has several quotations from the article entitled juridical analysis (Khair, 2022;Putra et al, 2023;Sembiring, 2023;Sondakh et al, 2023), Nusantara national capital (Aripiyanto et al, 2022;Pribadi et al, 2023), land acquisition disputes (Adiningrat & Raynadhi, 2022), land (Firnaherera & Lazuardi, 2022;Nurahmani & Sihombing, 2022), and development (Fadillah, 2022;Firnaherera & Lazuardi, 2022). Because only a few articles are related in the network visualization, most others have no direct connection or relationship between one article and another (Figure 2 and Figure 3).…”

Section: Resultsmentioning

confidence: 99%

Exploring Research Trends in the Indonesian Archipelago’s New National Capital

Hamjen,

Said

2023

JICP

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The Indonesian government announced the relocation of the national capital from Jakarta to East Kalimantan province in 2019. The term ‘Capital of the Archipelago’ or Ibu Kota Nusantara (IKN) was approved as the name of the new capital of the Republic of Indonesia. This study conducted visualization and bibliometric analysis using VOSviewer 1.6.20 from 100 scientific articles with the keyword “Ibu Kota Nusantara” collected from Google Scholar. The frequency of article titles relevant to IKN in 2022 was greater than in 2023. In the comparison of titles with the term IKN in 2022, there were 22 articles; in 2023, there were 15 articles, so the difference was 22/15 or 1.467. Following the 12 images of IKN, there were five images related to some scientific articles that have been published, including those related to innovation and technology where IKN was imaged as a smart city, sustainable capital, environmentally friendly, green city, and greening. However, seven images need attention for the following research topics: renewable, legal, cultural, user-friendly road, unique, city of change, and solutions for equality. This study helps provide insight as a basis for further research on various innovative themes related to IKN.

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“…Penelitian yang dilakukan Dharmendra menunjukkan hasil sebaliknya, dimana penelitian ini terkait dengan analisis sentimen Bahasa Indonesia pada opini alumni PT dengan model SVM dan Maximum Entropy dengan algoritma stemming Sastrawi. Hasilnya menunjukkan bahwa SVM dengan algoritma stemming Sastrawi menghasilkan akurasi sebesar 71.21% [11]. Pada Penelitian yang dilakukan Haris terkait analis sentimen terhadap kebijakan pemerintah.…”

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Analisis Sentimen: Pengaruh Jam Kerja Terhadap Kesehatan Mental Generasi Z

Muhammad Daffa Al Fahreza,

Ardytha Luthfiarta,

Muhammad Rafid

et al. 2024

J. Appl. Comput. Sci. Technol.

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Mental health is a significant concern in society today, particularly for Generation Z, who are vulnerable to experiencing mental health problems that can disrupt daily productivity. The influence of working hours also contributes to the mental health of this generation. To assess public opinion on this issue, sentiment analysis is needed on social media, especially twitter. This research uses the Gaussian Naïve Bayes algorithm and Support Vector Machine with various stemming algorithms such as Nazief-Adriani, Arifin Setiono, and Sastrawi. The sentiment analysis method is used to assess positive, negative, and neutral sentiment in related tweets. The research results show that the Sastrawi stemming algorithm on the Gaussian Naïve Bayes model achieves 84% precision, 84% recall, and 84% f1-score, with 84% accuracy. Meanwhile, Support Vector Machine achieved 91% precision, 90% recall, 90% f1-score, and 91% accuracy. The Nazief-Adriani stemming algorithm on the Gaussian Naïve Bayes model has 80% precision, 80% recall, and 80% f1-score, with 80% accuracy. Meanwhile, on the Support Vector Machine, precision is 87%, recall is 85%, f1-score is 86%, and accuracy is 85%. Arifin Setiono's stemming algorithm on the Gaussian Naïve Bayes model achieved 81% precision, 81% recall, 81% f1-score, with 82% accuracy, while on Support Vector Machine, 88% precision, 86% recall, 86% f1-score, with 86% accuracy. Public opinion was recorded as 33% positive, 9% neutral, and 58% negative. This research aims to increase public awareness of the importance of mental health, especially regarding the influence of working hours, to create a healthy work environment for Generation Z and society in general, as well as improving the quality of mental health.

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Implementasi keamanan informasi file dokumen shipping menggunakan algoritma AES Shipping

Cited by 3 publications

References 9 publications

The Effect of Feature Weighting on Sentiment Analysis TikTok Application Using The RNN Classification

The Effect of Feature Weighting on Sentiment Analysis TikTok Application Using The RNN Classification

Exploring Research Trends in the Indonesian Archipelago’s New National Capital

Analisis Sentimen: Pengaruh Jam Kerja Terhadap Kesehatan Mental Generasi Z

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