There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Manajemen Sumber Daya Manusia

As of January 2021, with 2,066,175 deaths, 95,612,831 confirmed cases have been reported globally. Indonesia's COVID-19 Task Force report shows that there are currently 27,203 deaths, with reported cases exceeding 951,651, among the highest in Asia. The President of the Republic of Indonesia created a national team to speed up the production of vaccines for COVID-19. It stipulates that the government will arrange the provision, delivery, and vaccination of COVID-19 vaccines. The vaccination scheme would then become the pros and cons of Indonesian society. This research assesses the opinion of the Indonesian people through a social network analysis of the COVID-19 vaccine in January 2021. We used sentiment analysis using Naive Bayes Algorithm by crawling Twitter data with 'Vaccine COVID-19' as keywords. We perform the data crawling process manually using the access token received from the Twitter API using the Rapid miner tools to extract the requested information and data. Data crawling continued with the Drone Emprit Academic Streaming Public Twitter Tool because of limited manual crawling resulting in more than 6000 tweets related to selected keywords on January 15-22, 2021. The result of sentiment measurement with over 3.4 thousand negative tweets (56%), over 2.4 thousand positive tweets (39%), and the remaining 301 tweets (1%) was neutral during the period of the week.

https://iopscience.iop.org/article/10.1088/1757-899X/1088/1/012045/pdf

Sentiment analysis of COVID-19 vaccine in Indonesia using Naïve Bayes Algorithm

The WHO announced that more than 52 million people tested positive for Covid-19, and 1.2 million died in the second week of November 2020. Meanwhile, Indonesia recorded 463 thousand individuals with 15,148 deaths that were confirmed positive. Strategy against pandemics by incorporating socialization. However, learning that was initially bold as a technique became controversial due to the briefness of the adaptation process. a wide continuum of social reactions has resulted in the sudden transition from face-to-face learning to bold learning on a large scale. This research focuses on public opinion on online learning during the Indonesian COVID-19 pandemic in early November 2020. The analysis was carried out on Twitter by mining document-based text that was interpreted using the Naive Bayes algorithm. The results show that online learning has a positive sentiment of 30 percent, a negative sentiment of 69 percent, and a neutral 1 percent over the period. Due to community dissatisfaction about online learning, a significant amount of negative sentiment is created. Some tweets indicate disappointment with the words' stress 'and' lazy 'in the conversation being high-frequency words.

/pdf/analisis-sentimen-pembelajaran-daring-pada-twitter-di-masa-4gid7ejeht.pdf

Analisis Sentimen Pembelajaran Daring Pada Twitter Di Masa Pandemi COVID-19 Menggunakan Metode Naïve Bayes

Kemanan website akses jaringan internet  merupakan hal yang perlu diperhatikan sangat serius. Serangan yang diakibatkan oleh sql injection mampu membobol sistem pertahanan website yang kita miliki jika tidak dilengkapai dengan pengamanan yang kuat.  Mengingat website ini dapat diakses secara luas, maka dinilai perlu memperhatikan keamanan website. Terdapat beberapa cara yang dapat digunakan untuk melakukan pengujian terhadap kemanan website, salah satunya adalah dengan melakukan SQL Injection. SQL injection adalah kerentanan yang terjadi ketika penyerang memiliki kemampuan untuk mempengaruhi Structured Query Language (SQL) query yang melewati suatu aplikasi ke database back-end. Dengan diadakannya penelitian ini, diharapkan dapat diperoleh kelemahan dari website. Kelemahan tersebut akan dianalisa sehingga memperoleh solusi kedepan guna pengembangan website yang lebih aman.

/pdf/implementasi-sistem-keamanan-sql-injection-dalam-berbasis-28zutyzu9w.pdf

Implementasi Sistem Keamanan SQL Injection Dalam berbasis web

Implementation of Apriori Algorithm for Analysis of Consumer Purchase Patterns

https://iopscience.iop.org/article/10.1088/1757-899X/1156/1/012011/pdf

Online learning sentiment analysis during the covid-19 Indonesia pandemic using twitter data

Traveloka is currently the most popular startup in Indonesia with share traffic reaching 78.49% using smartphone and monthly visits whichreached 28.92 million based on a report in similarweb.com in May 2019. Traveloka, based on record, has been downloaded 10 million times since 2014with rating reaches 4.4 out of 5 stars. As of May 2019, there were 386,646 reviews from users in the PlayStore, ranging from positive and negativereviews. However, it is necessary to analyze with certain methods to summarize the review. Every review given will get a conclusion after collected, andsentiment analysis will provide user experiences from the Traveloka application within certain period. This research was conducted using the NaïveBayes Classifier method based on a review from the playstore to determine service quality. The purpose of this study is to find out the perceptions ofusers based on the measurement of service quality so that the results can be an evaluation for Traveloka in improving services. Studies show that duringthis period public opinion produced negative sentiments with Vmap value of 0.31020 greater than positive sentiment with a value of 0.16132.

Sentiment Analysis Of Traveloka App Using Naive Bayes Classifier Method

E-Government development has become government commitment as established in the Inpres 3/2003 concerning the National E-Government Development Policy and Strategy. However, this government policy has not been fully implemented in local government, resulting in the e-Government role as the pledge of the government that cannot run well. Resistance to change is one of the inhibiting factors in eGovernment development. Law No. 6 of 2014 about Villages states that the implementation of village governance by utilizing Information Technology can be done through e-Government Villages. An in-depth study of models suitable for e-Government villages is needed. The process of identifying the Village e-Government needs is carried out with a Critical Success Factor (CSF) analysis. The results of the analysis produce four stages of the model which are divided into dimensions of technological complexity and organization with the level of data and information integration. Whereas the results of portfolio futures from the development of this model using McFarlan's Analyst Grid so that strategic modules, high potential, support, and operational key can be elaborated.

/pdf/e-government-village-model-11sbbjz83u.pdf

Ronal Watrianthos

Papers

Analisis Sentimen Pembelajaran Daring Pada Twitter Di Masa Pandemi COVID-19 Menggunakan Metode Naïve Bayes

Implementation of Apriori Algorithm for Analysis of Consumer Purchase Patterns

Online learning sentiment analysis during the covid-19 Indonesia pandemic using twitter data

Sentiment Analysis Of Traveloka App Using Naive Bayes Classifier Method

e-Government Village Model