Kebutuhan transformasi bank digital Indonesia di era revolusi industri 4.0

The occurrence of the Covid-19 pandemic forced humans to adapt to technology, not least in terms of finance, namely transactions in banking. This study aims to analyze the transformation of the banking industry into a digital bank in providing services to customers. This research is a type of qualitative research using secondary data from reliable literacy. The results of this study indicate that the transformation into a digital bank provides many advantages for banks to be able to continue to survive in running their business. Where digital banks are the choice of customers in transacting in the financial sector because it is easy, fast, and efficient. Transforming into a digital bank can also increase more customers and fee base income for banks because they can collaborate with fintech and e-commerce and can be efficient in managing bank assets which in turn can increase bank profits exponentially. However, in transforming into a digital bank, banks must have a strong foundation, especially in terms of infrastructure and security of customer data that is vulnerable to hacking attacks (cyber crime). So that in the Industrial Revolution 4.0 era technology is the main key in winning the competition and remaining sustainable in running a business, for that the banking sector in Indonesia is required to continue to innovate in providing the best service to its customers

Prediksi Sentimen Investor Pasar Modal Di Jejaring Sosial Menggunakan Text Mining

The decline in optimism for capital market investors is one of the financial impacts on the business world that arose from the SARS-COVID19 pandemic. This event was reflected in a decrease in trading volume followed by a sharp drop in the JCI on the Indonesia Stock Exchange starting March 2020. Thus, a slowdown in the economic recovery resulting from the pandemic is reflected in investor sentiment in the capital market. On the one hand, the rapid development of the internet in Indonesia has triggered the investor's activities in the information searching prior buy and sell securities, mostly use online platforms, which contribute to influencing investor preferences and sentiment. This study conducted a qualitative examination of the features/terms of stock investment in the capital market and collected them in a compact dictionary (lexicon). Therefore, lexicon-based investor opinion extraction was extracted from Twitter, followed by the text sentiment analysis, and forming a classification model based on Naive Bayes and Decision Tree. This research output shows that the polarity of capital market investor sentiment is optimistic with the sentiment features that often appear, namely "cuan", "bearish," "serok", "copet", "untung", "cut loss", and "nyangkut." Meanwhile, the Decision Tree classification model provides better performance.Keywords                        :  investor, lexicon, social network, stock exchange, text miningCorrespondence to        : [email protected] Penurunan optimisme investor pasar modal adalah salah satu dampak keuangan pada dunia usaha yang timbul akibat pandemi SARS-COVID19. Hal ini tercermin dari turunnya volume perdagangan yang diikuti penurunan tajam IHSG di Bursa Efek Indonesia mulai Maret 2020. Sehingga kekhawatiran atas perlambatan pemulihan ekonomi sebagai dampak pandemi, tercermin dari sentimen investor di pasar modal. Di satu sisi, perkembangan internet di Indonesia yang pesat, memicu kecenderungan aktivitas investor dalam pencarian informasi sebelum membeli dan menjual surat berharga  secara online, turut berkontribusi dalam mempengaruhi preferensi dan sentimen investor. Penelitian ini menggali ekspektasi investor yang tercermin pada sentimen investasi, dimana pasar modal sebagai salah satu barometer penting perekonomian suatu negara. Kajian ini mengeksplorasi fitur/terms investasi saham yang kerap muncul di pasar modal dan mengumpulkannya dalam kamus leksikon. Kemudian, dilakukan ekstraksi opini investor berbasis leksikon yang digali dari jejaring sosial Twitter, dilanjutkan dengan tahap text mining yaitu menganalisis sentimen, dan membentuk model klasifikasi berbasis Naive Bayes dan Decision Tree. Keluaran penelitian ini  menunjukkan bahwa polaritas sentimen investor pasar modal adalah positif dengan fitur sentimen yang sering muncul yaitu “cuan”, “bearish”, “serok”, “copet”, “untung”, dan “cut loss”. Sedangkan model klasifikasi Decision Tree memberikan performansi akurasi yang kebih baik.Kata Kunci                  : Analisis sentimen; Investor; Leksikon; Text mining; Twitte

Using association rules mining to analyze human rights violations in Indonesia

Human rights are a set of basic rights inherent in humanity. Understanding of human rights is an important part of individual status as human beings who possess dignity and values of mutual respect for each other. Moreover, comprehending human rights violations also significant ly enrich es our knowledge regarding diversity of violation actions occur ring in everyday life including abuse and ignorance of basic human rights. This paper discusses how to detect violation patterns by using association rules mining. These techniques provide powerful tools to identify patterns which occur in a database. Finding human rights vi olation patterns is one of the challenges in this work. The paper provides an overview of our human rights violations database and describes how data preparation is provided. Moreover, it discusses how data mining could provide solutions for finding frequ ent patterns human rights violations in Indonesia, and how it could uncover new knowledge about types of violations. --Conference: 'International multi-conference on computer, electrical, electronics and mechanical engineering' (IMCEEME'12). Held: 12-13 December, 2012, Baltam Island, Indonesi

The Role of Teacher-Librarian in Building Lifelong Learning for Students

Teacher Librarian is usually a teacher who also acts or is responsible as a librarian in the school library. Teachers who have a minimum education in the field of library science Diploma two (D-II) at an accredited university or complete a minimum Diploma two (D-II) education which is then continued with training and learning in the library field. However, in Indonesia there is often a phenomenon of teacher-librarians who do not have scientific provision in the library field so that in some cases in real life, the management of the library system becomes less than optimal and cannot be utilized by users properly. In contrast to other countries, it is known that many schools in Europe use teacher-librarians as school library staff. This is so that teacher-librarians can collaborate with teachers to improve the quality of learning as well as the literacy skills of their students. School libraries should also have interesting and effective programs because this can affect students' learning achievement. Lifelong Learning is learning that is carried out throughout life without coercion on one's part to improve their abilities and knowledge so that they can create good competitiveness for their environment. Students who act as lifelong learners need proper guidance, namely with the help of Teacher-Librarians and also teaching teachers during the learning process. This article uses a literature review method that critically reviews ideas or findings in academically oriented literature

Mining Indonesian cyber bullying patters in social networks

Conference held: 20-23 January 2014, Auckland University of Technology, Auckland, New Zealan