Data Mining - Concepts and Techniques.

Understanding the data and reaching accurate conclusions are of paramount importance in the present era of big data. Machine learning and probability theory methods have been widely used for this purpose in various fields. One critically important yet less explored aspect is capturing and analyzing uncertainties in the data and model. Proper quantification of uncertainty helps to provide valuable information to obtain accurate diagnosis. This paper reviewed related studies conducted in the last 30 years (from 1991 to 2020) in handling uncertainties in medical data using probability theory and machine learning techniques. Medical data is more prone to uncertainty due to the presence of noise in the data. So, it is very important to have clean medical data without any noise to get accurate diagnosis. The sources of noise in the medical data need to be known to address this issue. Based on the medical data obtained by the physician, diagnosis of disease, and treatment plan are prescribed. Hence, the uncertainty is growing in healthcare and there is limited knowledge to address these problems. Our findings indicate that there are few challenges to be addressed in handling the uncertainty in medical raw data and new models. In this work, we have summarized various methods employed to overcome this problem. Nowadays, various novel deep learning techniques have been proposed to deal with such uncertainties and improve the performance in decision making.

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Handling of uncertainty in medical data using machine learning and probability theory techniques: a review of 30 years (1991–2020)

Understanding data and reaching valid conclusions are of paramount importance in the present era of big data. Machine learning and probability theory methods have widespread application for this purpose in different fields. One critically important yet less explored aspect is how data and model uncertainties are captured and analyzed. Proper quantification of uncertainty provides valuable information for optimal decision making. This paper reviewed related studies conducted in the last 30 years (from 1991 to 2020) in handling uncertainties in medical data using probability theory and machine learning techniques. Medical data is more prone to uncertainty due to the presence of noise in the data. So, it is very important to have clean medical data without any noise to get accurate diagnosis. The sources of noise in the medical data need to be known to address this issue. Based on the medical data obtained by the physician, diagnosis of disease, and treatment plan are prescribed. Hence, the uncertainty is growing in healthcare and there is limited knowledge to address these problems. We have little knowledge about the optimal treatment methods as there are many sources of uncertainty in medical science. Our findings indicate that there are few challenges to be addressed in handling the uncertainty in medical raw data and new models. In this work, we have summarized various methods employed to overcome this problem. Nowadays, application of novel deep learning techniques to deal such uncertainties have significantly increased.

Handling of uncertainty in medical data using machine learning and probability theory techniques: A review of 30 years (1991-2020)

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.

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

The library at SMP N 5 Bandarlampung is a junior high school library that has library management staff and the number of book collections according to library standards, and has adequate library facilities and infrastructure. Students and students are required to become members of the school library to support the learning process. However, the process of borrowing and returning books currently carried out is still conventional, that is, all library administration data collection in borrowing and returning books is still written in the book, and when looking for the required data, you must open the book page. Application of library administration information systems using this user experience design, users can better understand and easily use applications that are made to maximize the performance of library staff, school principals, and students in managing library administration. The design of the library administration information system using the user experience design at SMP Negeri 5 Bandarlampung using the user centered design method has 5 stages, namely Empethized, Define Problem Statements, Indentation, and Prototype in accordance with the needs of users in the library administration information system based on the user experience design undertaken. The results of testing the criteria for the DeLone and McLean Model criteria for the success of the information system were 84.31% with the criteria being Very

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Penerapan Sistem Informasi Administrasi Perpustakaan Menggunakan Model Desain User Experience

Avana Indonesia is a social commerce startup headquartered in Malaysia. Wanting to expand their business and enter the Indonesian market, they still don't have the best marketing strategy in place, so a service sales deal is not enough. That's why we need a marketing strategy that focuses on customers with customer relationship management, one of which is customer segmentation. Customer segmentation can be done by implementing a data mining process which is carried out using the K-Means clustering algorithm based on the RFM (Recency, Frequency, Monetary) model. The number of clusters in the clustering process is determined using the elbow method. Cluster analysis based on customer value using the recency clustering method reveals active, warm, cold, and inactive customers. Then the two from recency frequency (customer value) segmentation produce common, ultra-high, low, and high clusters.

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Analisis Segmentasi Recency dan Customer Value Pada AVANA Indonesia Dengan Algoritma K-Means dan Model RFM (Recency, Frequency and Monetary)

Selection of new admissions is the usual routine universities every year, this activity is done as a way to get students who have quality standards in accordance with the acceptance criteria determined by the university. However, at the National University in particular on FTKI, admission is actually doing is good enough but the results of the selection has not been implemented to the maximum, making it less optimal data processing activities such selection. Because the need for Decision Support System to be the result of the value of some of the criteria for the standardization of prospective students. In making the decision support system, TOPSIS method is used to determine students based on proper criteria by comparing the level of the quality of each prospective student using TOPSIS calculation will produce a ranking to help become FTKI UNAS selection consideration. The criteria used in this TOPSIS SPK, among others: CBT Computer, Mathematics and Language CBT, CBT Sciences, Interview, Department of the Home School, Home School Accreditation and Average - Average Value UN. The system in this research alternative filter 75 of the total 275 alternative data. The system accuracy up to 82% by making comparisons and calculations manually. CBT Computer, Mathematics and Language CBT, CBT Sciences, Interview, Department of the Home School, Home School Accreditation and Average - Average Value UN. The system in this research alternative filter 75 of the total 275 alternative data. The system accuracy up to 82% by making comparisons and calculations manually. CBT Computer, Mathematics and Language CBT, CBT Sciences, Interview, Department of the Home School, Home School Accreditation and Average - Average Value UN. The system in this research alternative filter 75 of the total 275 alternative data. The system accuracy up to 82% by making comparisons and calculations manually.

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Topsis Method for Selection of New Student Admissions at the Faculty of Communication and Information Technology at the National University of Jakarta: Topsis Method for Selection of New Student Admissions at the Faculty of Communication and Information Technology at the National University of Jakarta

Ira Diana Sholihati

Papers

Analisis Segmentasi Recency dan Customer Value Pada AVANA Indonesia Dengan Algoritma K-Means dan Model RFM (Recency, Frequency and Monetary)