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

In the year 2018, 18.9% of the population in Indonesia mentioned that the main reason for their use of the Internet is social media. One of the social media with an active user of 6.43 million users is Twitter. Based on the surge of information published via Twitter, it is possible that such information may contain the user's opinions on an object, such objects may be events around the community such as a product or service. This makes the company use Twitter as a medium to disseminate information. An example is an Internet Service Provider (ISP) such as Indihome. Through Twitter, users can discuss each other's complaints or satisfaction with Indihome's services. It takes a method of sentiment analysis to understand whether the textual data includes negative opinions or positive opinions. Thus, the authors use the Support Vector Machine (SVM) method in sentiment analysis on the opinions of the Indihome service user on Twitter, with the aim of obtaining a sentiment classification model using SVM, and to know how much accuracy the SVM method generates, which is applied to sentiment analysis, and to see how satisfied the Indihome service users are based on Twitter. After testing with SVM method The result is accuracy 87%, precision 86%, recall 95%, error rate 13%, and F1-score 90%

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Analisis Sentimen Terhadap Layanan Indihome Berdasarkan Twitter Dengan Metode Klasifikasi Support Vector Machine (SVM)

Human-Centered Design Method is a method of approach in the development and design of a system that focuses on the user according to aspects of the needs and habits of the user. Difficulty in accessing information on the website becomes a problem faced by the user and in terms of visual website can not be responsive when accessed via mobile. The initial stage carried out in this method is observation which aims to find and to better understand the problems faced by users to conduct testing to find out whether the solutions provided can be understood and easily used by the user. Website testing is done by giving tasks to the user to interact on the website prototype, as the final result of success in the aspect of ease and comfort of the user using the website. After testing the user directly the test results are obtained, ie the user already feels quite understanding and easy when using the website that was created. The responsive mobile feature created also makes users feel helped when using a website on a smartphone.

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Perancangan User Interface Design dan User Experience Mobile Responsive Pada Website Perusahaan

Seleksi Paskibraka dilakukan pada setiap tahun dengan tujuan mencari putra putri terbaik yang nantinya akan ditugaskan pada upacara peringatan kemerdekaan Indonesia untuk mengibarkan duplikat bendera pusaka. Pada beberapa wilayah, seleksi Paskibraka masih menggunakan perhitungan dengan excel atau perhitungan manual lainnya dalam menentukan keputusan. Perhitungan manual ini tentunya membutuhkan waktu yang lebih lama dan hasil yang dapat kurang akurat. Oleh karena itu, dibuatlah aplikasi sistem pendukung keputusan dengan menggunakan metode Weighted Product(WP) dan Simple Additive Weighting (SAW) yang dapat memberikan nilai akhir penilaian seleksi sesuai dengan kriteria dan bobot yang telah ditetapkan. Hasil dari kedua metode ini nantinya akan dibandingkan agar didapatkan hasil yang terbaik. Hasil dari aplikasi ini berupa perangkingan peserta berdasarkan kecamatannya. Kata Kunci: Sistem Pendukung Keputusan; Paskibraka; Seleksi; Simple Additive Weighting; Weighted Product.

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Perbandingan Metode Weighted Product (WP) dan Simple Additive Weighting (SAW) terhadap Sistem Pendukung Keputusan Rekomendasi Calon Paskibraka

The Covid -19 virus spread in the world, especially in Indonesia, very fast. This epidemic is of concern around the world because it has a quite bad impact in various sectors. With existing technological advances, the Expert System can assist medical personnel in detecting the Covid -19 Virus. The purpose of the author in conducting the study was to detect the Covid-19 virus as easily as possible with symptom data obtained from patients who had consultations. The Naive Bayes method is a method that uses probability and statistics that can predict a person's chance of being exposed to Covid-19 in the future based on symptoms experienced in the previous period packed with a web-based program. For comparison, the author uses the Certainty Factor Method. Certainty Factor is a method that aims to determine the certainty value which is based on the previous calculation of CF value by manual calculation. The Naive Bayes method can group the symptoms obtained from the official WHO website which has been given an indicator of the percentage of someone exposed to the Covid-19 Virus based on the symptom data experienced to determine a person exposed to the Covid-19 Virus. While the Certainty Factor method gets the confidence of someone exposed to the symptoms of the Covid-19 virus by using the calculation indicator on the CF value that has been consulted by the user, which can provide a percentage level of confidence that is 86%. Keywords: Expert System, Covid-19, Naive Bayes, Certainty Factor.

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Implementasi Sistem Pakar untuk Mendeteksi Virus Covid-19 dengan Perbandingan Metode Naïve Bayes dan Certainty Factor

Pengenalan objek menggunakan Augmented Reality sudah menjadi trend di dunia media promosi kepada anak-anak usia dini hingga masyarakat umum. Objek yang digunakan berupa hewan, tumbuhan, huruf, angka dan lain lain. Penelitian ini menggunakan objek berupa hewan purbakala yang sudah punah sejak jutaan tahun yang lalu. Tujuan penelitian ini yaitu berfokus pada pengenalan hewan-hewan purbakala utnuk anak-anak bahwa terdapat hewan reptil yang berpostur raksasa telah hidup di zaman dahulu. Meskipun reptil ini telah punah, mereka akan menggunakan Augmented Reality pada penelitian ini sebagai media informasi yang menarik. Model ADDIE dikembangkan pada penelitian ini yang disusun oleh Natural Feature Tracking (NFT) menggunakan Algoritma FAST Corner Detection ke arah tingkat keberhasilan yang tinggi. Hasil pengujian pada beberapa versi android berupa objek gambar memiliki tingkat keakuratan yang tinggi melalui perhitungan FAST Corner Detection dan pengujian metode NFT. Semakin tinggi rating objek yang ditunjukkan pada vuforia, maka semakin tinggi ketelitian dalam mendeteksi objek pada marker.

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Ira Diana Sholihati

Papers

Analisis Sentimen Terhadap Layanan Indihome Berdasarkan Twitter Dengan Metode Klasifikasi Support Vector Machine (SVM)

Perancangan User Interface Design dan User Experience Mobile Responsive Pada Website Perusahaan

Perbandingan Metode Weighted Product (WP) dan Simple Additive Weighting (SAW) terhadap Sistem Pendukung Keputusan Rekomendasi Calon Paskibraka

Implementasi Sistem Pakar untuk Mendeteksi Virus Covid-19 dengan Perbandingan Metode Naïve Bayes dan Certainty Factor

Model Addie Pada Augmented Reality Hewan Purba Bersayap Menggunakan Algoritma Fast Corner Detection Dan NFT