Klasifikasi Tingkat Kepuasan Mahasiswa Terhadap Pembelajaran Secara Daring Menggunakan Algoritma Naïve Bayes

Since the spread of Covid-19 in Indonesia, in early March 2020, the activities of Educational Institutions have not been disrupted. As conventional learning. Learning at Singaperbangsa University began with regulation from the Ministry of Education and Culture of the Republic of Indonesia, from learning that boldly affects concentration, influences concentration, such as signals, learning atmosphere, and teaching methods, so that factors affect the level of student satisfaction in learning. This study aims to determine the level of student satisfaction with learning who dares to use the Bayes naive algorithm using RapidMiner tools with results obtained with an accuracy rate of 76.92%, class precision of 100.00%, class recall 57.14%, and an AUC value of 0.881 or close to, so the resulting model is good. In other words, the results obtained using the Naïve Bayes algorithm can be used as material for making decisions about the level of online learning satisfaction.

A Note on the Computation of the Modular Inverse for Cryptography

In literature, there are a number of cryptographic algorithms (RSA, ElGamal, NTRU, etc.) that require multiple computations of modulo multiplicative inverses. In this paper, we describe the modulo operation and we recollect the main approaches to computing the modulus. Then, given a and n positive integers, we present the sequence (zj)j≥0, where zj=zj−1+aβj−n, a<n and GCD(a,n)=1. Regarding the above sequence, we show that it is bounded and admits a simple explicit, periodic solution. The main result is that the inverse of a modulo n is given by a−1=⌊im⌋+1 with m=n/a. The computational cost of such an index i is O(a), which is less than O(nlnn) of the Euler’s phi function. Furthermore, we suggest an algorithm for the computation of a−1 using plain multiplications instead of modular multiplications. The latter, still, has complexity O(a) versus complexity O(n) (naive algorithm) or complexity O(lnn) (extended Euclidean algorithm). Therefore, the above procedure is more convenient when a<<n (e.g., a<lnn).

The application of deep learning for the classification of correct and incorrect SNP genotypes from whole-genome DNA sequencing pipelines

A downside of next-generation sequencing technology is the high technical error rate. We built a tool, which uses array-based genotype information to classify next-generation sequencing–based SNPs into the correct and the incorrect calls. The deep learning algorithms were implemented via Keras. Several algorithms were tested: (i) the basic, naïve algorithm, (ii) the naïve algorithm modified by pre-imposing different weights on incorrect and correct SNP class in calculating the loss metric and (iii)–(v) the naïve algorithm modified by random re-sampling (with replacement) of the incorrect SNPs to match 30%/60%/100% of the number of correct SNPs. The training data set was composed of data from three bulls and consisted of 2,227,995 correct (97.94%) and 46,920 incorrect SNPs, while the validation data set consisted of data from one bull with 749,506 correct (98.05%) and 14,908 incorrect SNPs. The results showed that for a rare event classification problem, like incorrect SNP detection in NGS data, the most parsimonious naïve model and a model with the weighting of SNP classes provided the best results for the classification of the validation data set. Both classified 19% of truly incorrect SNPs as incorrect and 99% of truly correct SNPs as correct and resulted in the F1 score of 0.21 — the highest among the compared algorithms. We conclude the basic models were less adapted to the specificity of a training data set and thus resulted in better classification of the independent, validation data set, than the other tested models.

Analytical Study of Some Selected Classification Algorithms and Crime Prediction

To prevent the crime these days police exercises particularly in the case of investigation, emphasis on Artificial Intelligence, data mining and Machine learning aspect. To prevent future crimes it is necessary to understand the crime behavior from the earlier crime records. The more numbers of recorded crime data approaches the research to analysis the data for prediction and prevention of crime. Most of the researcher use to cluster the data for further classification to predict the definitive of crime. To prevent the crime these tools are xeric applicable to predict the most sensitize zone in the city. Thesis concentrated on the methods to predict the crime and on his hidden arrays in the existing past records. The objectives of the thesis is to predict the certain possibility of crime by applying data ruining approach through WEKA are applied to confirm criminality y proclamation. three algorithms are referred from different groups of methods: SMO Zero R and J 48 decision trees. Over 10000 records from Indian police department are collected to predict the frequency of crime in overall and its behavior in which Naive algorithm shows the reliable prediction represent against crime frequency. This paper compares the three diverse order classification to be specific, SMO, Zero R and J 48 Decision Tree for anticipating Crime Category' for various states in India. The outcomes from the examination demonstrated that, Decision Tree calculation out performed calculation and sieved 41.44% and 73.33%% Accuracy in anticipating Crime Category for various conditions of India. order classification to be specific, SMO, Zero R and J 48 Decision Tree for anticipating Crime Category' for various states in India. The outcomes from the examination demonstrated that, Decision Tree calculation out performed calculation and sieved 41.44% and 73.33%% Accuracy in anticipating Crime Category for various conditions of India.

Computing the Galois group of a polynomial over a p-adic field

We present a family of algorithms for computing the Galois group of a polynomial defined over a p-adic field. Apart from the “naive” algorithm, these are the first general algorithms for this task. As an application, we compute the Galois groups of all totally ramified extensions of [Formula: see text] of degrees 18, 20 and 22, tables of which are available online.

METODE PENALARAN BERBASIS KASUS (CASE BASE REASONING) DALAM PENENTUAN KELAYAKAN SEKOLAH PERAWATAN

Nurse is a job that has many roles in everyday life. Obtained from the ability to become a nurse. In 1980, there was an explosion in the number of registrants at the Ljubljana nursing school, Slovenia. The data is then collected in a data collected data that is a nursery dataset. There are several things related to health conditions, family status, financial conditions and other things that are considered feasible or not to enter the nursing school. A system that can be used for this problem needs to be created. In this study, a system will be made by applying the method of punishment based on cases and classifying domains to increase computational time. Each new case will be calculated the similarity value to the old case using the Bayes naive algorithm. The system built will produce a decision about whether or not the applicant is suitable in nursing school. Of the 100 data tested, 96 data were obtained that produced true values. With a computing time between 0.253 seconds - 0.607 seconds.

METODE SAMPLE BOOTSTRAPPING UNTUK MENINGKATKAN PERFORMA ALGORITMA NAIVE BAYES PADA CITRA TUNGGAL PAP SMEAR

ABSTRACT Research on cell classification of single pap smear images is an interesting thing to discuss, where the value of consent is very important to determine whether the cells are normal or not. An example of this study is to determine whether using the bootstraping sample method can improve the performance of the Bayes naive algorithm to classify single pap smear images that are on the herlev dataset. Approval values will be given for two classes and seven classes. The method used consists of several stages, namely preprocessing, knowledge rules, evaluation, and performance reports. The results of this study prove that the bootstrap sample method can increase the accuracy of seven classes to 85.24% and 93.24% for accuracy values with two classes.Keywords: Sample Bootstrapping; Naive Bayes; Pap Smear. ABSTRAK Penelitian mengenai klasifikasi sel citra tunggal pap smear menjadi hal yang menarik untuk dibahas, dimana nilai akurasi tersebut sangat penting untuk menetukan apakah sel-sel tersebut normal atau tidak. Penelitian ini bertujuan untuk menentukan apakah penggunaan metode sample bootstrapping dapat meningkatkan kinerja algoritma naive bayes untuk mengklasifikasikan citra tunggal pap smear yang ada pada dataset herlev. Nilai akurasi akan diperiksa untuk dua kelas dan tujuh kelas. Metode yang digunakan terdiri dari beberapa tahapan yaitu preprocessing, knowledge rule, evaluation, dan performance report. Hasil penelitian ini menunjukkan bahwa metode sample bootstrapping dapat meningkatkan nilai akurasi tujuh kelas menjadi 85,24% dan 93,24% untuk nilai akurasi dengan dua kelas.

BACK-NAVIGATION STRING MATCHING ALGORITHM (BSMA)

This research took a comparative analysis on string matching algorithms. The study focused on developing an efficient algorithm (Back-navigation String matching algorithm) which will be used for large documents. The algorithm whilst compared to the existing system introduced a pattern of search which was done backwardly, from the last character to the first. The existing system considered more number of shifts which was slow and has a bad character shift. The research aimed at developing an efficient string matching for large documents sorting. The research methodology adopted for this project research is the verification and validation methodology which perform its test in a reverse manner so the software developer can at each stage review its step. The proposed system was executed and produced a result which compared with the existing system was termed efficiency. The system introduces a faster means of searching which starts form the last character to the first. The developed system which is the efficient string matching algorithm was analyzed and displayed a faster means of searching documents and is termed efficient because of its computing speed of 2 milliseconds while the naïve algorithm which ran with the computing speed of 154 milliseconds for a total number of 5000 characters.