Reviewerly/exHarmony huggingface dataset:
Personalized Recommendation System of E-learning Resources Based on Bayesian Classification Algorithm

In order to meet learners' personalized learning needs, realize learners' personalized development, and solve the problem of learners' information Trek and overload, a development scheme of e-learning resources personalized recommendation system based on Bayesian algorithm is proposed. This paper studies the personalized Association recommendation model integrating association rule mining and Bayesian network, and improves the association rule mining algorithm by combining historical record pruning and Bayesian network verification. In the process of association rule mining, combined with user history, the frequent itemsets in association rules are filtered, and the itemsets below the given threshold are pruned. The pruned item set is input into the Bayesian verification network for personalized verification, and the verification results are sorted and recommended according to the ranking, so as to give priority to the readers who really like the books. The recommendation model solves the problem of weak personalization in the existing recommendation system to a certain extent. Experiments show that Bayesian network can improve the personalization of association recommendation.
Analysis Platform of Rail Transit Vehicle Signal System Based on Data Mining

According to the increasing demand of interactive information of rail transit on-board signal equipment, the author designed a rail transit on-board monitoring and maintenance system based on data mining to set association rules for operation data acquisition and propose a correlation rules algorithm to obtain more reliable understanding and operation quality evaluation of train operation information.From a lot of logs, quickly find key issues, applied in the train test and repair field.The simulation experiment results show that after analyzing the simulation data and the curve, the system extraction results have certain error in the manual calculation results, and the error value is between 0.5 and 0.6, but the overall meets the actual work needs, and optimize the invalid data to reduce the error.The reliable operation and maintainability of the system are verified.
Enhancing Requirements Change Request Categorization and Prioritization in Agile Software Development Using Analytic Hierarchy Process (AHP)

Software development now relies heavily on agile methods, which call for the efficient administration and prioritization of change requests. In order to improve requirement prioritization using the Analytic Hierarchy Process (AHP) in Agile methods, this study article presents a new framework for classifying software requirements into Small Change Requests (SCRs) and Large Change Requests (LCRs). The paper examines the difficulties associated with requirement prioritization and categorization in Agile settings and offers a methodical system for dividing change requests into categories based on complexity, impact, and timeline. In order to provide a thorough grasp of the project scope and objectives, the framework considers both functional and non-functional needs. A case study containing several Agile software development projects is used to evaluate the performance of the suggested categorization and prioritization model. According to the findings, the combination of SCR and LCR categorization with AHP enables more effective teamwork and greater matching of development goals with partner objectives. The research also shows that the suggested framework's integration into the Agile development process results in a more efficient decision-making process, less time wasted on talks, and improved resource distribution. The model aids in risk mitigation by allowing a methodical and quantifiable approach to requirement prioritization. These risks are related to quick changes in project scope and changing client requirements. By presenting a fresh framework for requirement categorization and prioritization, this study adds to the current discussion on successful requirement management in Agile methods. Agile software development projects become more effective and adaptable overall thanks to the incorporation of AHP, which guarantees a more methodical and objective prioritization process. This study has the potential to greatly improve the administration of shifting needs and user expectations in Agile settings by offering a structured method to classify and rank change requests.
Robust Video Watermarking Algorithm based on DCT-SVD approach and Encryption

Sharing of digital media content over the internet is increasing everyday .Digital watermarking is a technique used to protect the intellectual property rights of multimedia content owners. In this paper, we propose a robust video watermarking scheme that utilizes Discrete Cosine Transform (DCT) and Singular Value Decomposition (SVD) for embedding a watermark into video frames. The proposed method uses encryption to make the watermark more robust against malicious attacks. The encryption key is used to modify the watermark before it is embedded in the video frames. The modified watermark is then embedded in the DCT and SVD coefficients of the video frames. The experimental results show that the proposed method provides better robustness against various attacks such as compression, noise addition, and filtering, while maintaining good perceptual quality of the watermarked video. The proposed method also shows better resistance against geometric attacks such as cropping, rotation, and scaling. Overall, the proposed method provides an effective solution for protecting the intellectual property rights of multimedia content owners in video distribution and transmission scenarios.
Architecture and Framework for Group Profiling System in Smart Homes

Smart homes are becoming a progressive reality in our society. Automation and customization are at the center of the functionality of smart homes. User profiles record the user preferences of the inhabitants. User profiles are the heart of smart home systems. Real-world smart homes have multiple residents in them. Most smart homes treat the gathering of users in the same area just as a collection of users, but in real-world scenarios, such a group has its own identity. The proposed system tackles this problem by introducing the notion of Group Profiling. This paper presents the significance of profiles and group profiles in a smart home to achieve better customization and automation.
Pseudo Random Binary Sequences Obtained Using Novel Chaos Based Key Stream Generator and their Auto-correlation Properties

In this paper, psuedo random binary sequences are generated from the “Chaos Based Key Stream Generator- using novel Permutation technique with two dimensional patterns and substitution technique with Z4 mapping” and investigation of auto correlation property for the generated seuwnces is presented. Initially a chaotic function, considering Logistic map is used to generate a Pseudo Random Numbers (PRNs). Then these numbers are converted into binary sequences using binary mapping. These sequences are further modified by novel permutation techniques defined using 2-Dimensional patterns, and substitution technique defined over Z4 transformation in order to improve their statistical properties. The resulting sequences are investigated for auto correlation properties using Normalized Hamming Auto Correlation function. The purpose of this work is to assessing the quality of sequences of uniformly distributed pseudorandom numbers from the proposed generator. It is found that, generated sequences exhibit good auto-correlation property which is suitable for key sequence or secret key for cryptographic applications.
Development of Mathematics E-Module Using Kodular and Flipbook for Junior High School Students: Differences

To develop media such as e-modules, a comprehensive understanding of the applications used to create them is necessary. It is essential to comprehend the features of using e-module applications to ensure their effectiveness in the development of superior e-modules in the future. The objectives of the present study were showing that mobile learning by e-module using different applications, i.e. Kodular and Flipbook, are valid, practical, effective and finding differences between the characteristics of e-module development to get a comprehensive idea for future development for researchers who want to develop e-module using both applications. The present study is a research and development study. The selected development model was ADDIE i.e. Analyze, Design, Development, Implementation and Evaluation. The Subject e-module learning media users were junior high school students. The validity of e-module media by Kodular is 92.55%, which is very good. The validity of e-module media by Flipbook is 90.35%, which is very good. The test result on the respondents who used e-module by Kodular is 83.45% feasible to use and the result of e-module by Flipbook is 86.39% also feasible to use. The Kodular e-module had an average student learning outcome score of 78.28, while the Flipbook e-module had an average score of 70.67. The two e-modules from the application were examined, and five differences were identified in the characteristics of e-modules from Kodular and Flipbook, in order to support the continuous development of e-modules.
Students' Perceptions of Kahoot as an Online Quiz Tool in Intensive English Class

Online learning media is one of the tools which can support the teaching and learning process. Kahoot, Quizizz, Padlet, and learning Apps are examples of online learning media. Hence in this study, the researchers wanted to find out about the students' perceptions of online quiz applications or better known as Kahoot. This study employed a descriptive quantitative approach because the researchers described students' perceptions of Kahoot as an online quiz for teaching and learning English in class. Thirty-nine English Department, Lambung Mangkurat University students were selected as the samples in this study from the 2019 Intensive English Class. The instruments used in this study consisted of questionnaires with five answers: strongly agree, agree, neutral, disagree, and strongly disagree. The questionnaire was made and spread online by using Google Forms. The data was analyzed quantitatively using the implementation of a Likert scale. The findings show that the students' perceptions of Kahoot as an online quiz tool are positive. It can be seen from the score of instruments distributed by the researchers. The score gets 2.192 from 2.730 as the maximum score. The maximum score was gotten from (Q1: number of students, K: maximum score for each question and Q: number of questions) = 39 x 5 = 195 x 14 = 2.730, which means that Kahoot is a good online quiz application for teaching English in Intensive English Class based on students' perceptions. Most of the students agree that the process of teaching and learning via Kahoot as an online quiz tool is interesting, motivating, and fun. They also agree that they feel a positive effect on using its application and try to repeat to play Kahoot.
IMPLEMENTATION OF SUPPORT VECTOR MACHINE ALGORITHM WITH HYPER-TUNING RANDOMIZED SEARCH IN STROKE PREDICTION

Stroke is a severe health problem and can significantly impact a person's quality of life. Therefore, it is crucial to predict stroke early so that preventive measures can be taken before it is too late. This study demonstrates the importance of hyper tuning and hyperparameters in a stroke prediction model. Literature studies show that many studies on stroke prediction need to explain this, even though this is very important for developing the performance of stroke prediction models. In this study, we use the Support Vector Machine (SVM) algorithm to predict stroke and evaluate the algorithm's performance without hyper tuning and with hyper tuning Randomized Search CV. We also divide the data into training and test data by 75% and 25%. The results of this study indicate that hyper-tuning can improve the accuracy of the stroke prediction algorithm. The algorithm's accuracy is 77% without hyper-tuning, whereas, with hyper-tuning, the accuracy increases to 96%. Hypertuning with the Randomized Search CV method can improve the performance of the stroke prediction algorithm and is very important to do in developing predictive models.
IMPLEMENTATION OF DESIGN THINKING APPROACH METHODS IN CURVING SELL MAINTENANCE WEBSITES (CASE STUDY: KAI DAOP 5 PURWOKERTO)

PT KAI DAOP 5 Purwokerto is one of Indonesia's railroad operating units under the auspices of PT Kereta Api Indonesia (Persero). PT KAI DAOP 5 Purwokerto has several divisions, one of which is the rail and bridge unit. The rail line and bridge units at KAI DAOP 5 Purwokerto are still manually recording plan data and inspecting tracks. So, a system is needed to record plan data and review railroad tracks. This is done to make it easier for inspection officers to store data and reduce the risk of losing data from inspecting railroad tracks. Methods of data collection using literature study methods, interviews, and discussions with prospective website users and the team. This research aims to assist users in recording planning data and to inspect rail lines and bridges. Designing a website using the Design Thinking approach can make it easier to make User Interface designs that suit user needs. Applying the Design Thinking approach, a prototype design was found, which was used as a reference in implementing the Sell Maintenance website. The results of tests conducted on one of the employees on the user interface design were 8 accepted, 2 rejected, and 3 revisions. Applying the Design Thinking approach, a prototype design was found, which was used as a reference in implementing the Sell Maintenance website. The results of tests conducted on one of the employees on the user interface design were 8 accepted, 2 rejected, and 3 revisions. Applying the Design Thinking approach, a prototype design was found, which was used as a reference in implementing the Sell Maintenance website. The results of tests conducted on one of the employees on the user interface design were 8 accepted, 2 rejected, and 3 revisions.
OPTIMIZATION OF LUNG CANCER CLASSIFICATION METHOD USING EDA-BASED MACHINE LEARNING

Lung cancer is one of the three deadliest diseases in the world and has rapidly developed. Based on this, researchers conducted research to predict the factors that influence lung cancer. One method to identify this is using data mining methods and classification techniques. Researchers used several popular algorithms in classification to make comparisons of the most accurate algorithms for lung cancer classification. The algorithms used include K-Nearest Neighbor, Random Forest Classifier, Logistic Regression, Linear SVM, Naïve Bayes, Decision Tree, Random Forest, Gradient Boosting, Kernel SVM, and MLPClassifier. The researcher used this algorithm because, in the research that the researcher found on the Kaggle platform, he examined the comparison of the algorithm using the breast cancer dataset. In previous studies, their researchers used SVM, which obtained an accuracy of 96.47%, Neural Networks of 97.06%, and Naïve Bayes with an accuracy of 91.18% to study breast cancer. The difference from previous research is that this study uses several existing algorithms in Machine Learning such as K-Nearest Neighbor, Random Forest Classifier, Logistic Regression, Linear SVM, Naïve Bayes, Decision Tree, Random Forest, Gradient Boosting, Kernel SVM, and MLPClassifier. In addition, this research was conducted to see whether the results of the accuracy of the algorithm that the researchers carried out using the lung cancer dataset had different results. The results of this study found that the more accurate algorithms were Random Forest and Gradient Boosting with an accuracy value of 100%, whereas in previous studies, it was the same. Still, Gradient Boosting had a higher accuracy value than Random Forest. Then, based on the data used in this study, the most influencing factors in predicting a diagnosis of lung cancer are obesity and coughing up blood. The results of this study found that the more accurate algorithms were Random Forest and Gradient Boosting with an accuracy value of 100%, whereas in previous studies, it was the same. Still, Gradient Boosting had a higher accuracy value than Random Forest. Then, based on the data used in this study, the most influencing factors in predicting a diagnosis of lung cancer are obesity and coughing up blood. The results of this study found that the more accurate algorithms were Random Forest and Gradient Boosting with an accuracy value of 100%, whereas in previous studies, it was the same. Still, Gradient Boosting had a higher accuracy value than Random Forest. Then, based on the data used in this study, the most influencing factors in predicting a diagnosis of lung cancer are obesity and coughing up blood.
FORENSIC NETWORK ANALYSIS AND IMPLEMENTATION OF SECURITY ATTACKS ON VIRTUAL PRIVATE SERVERS

ABSTRACT-PT Kodinglab Integrasi Indonesia's Virtual Private Server (VPS) product requires good quality standards, including security. The challenge that arises is still frequent disruptions to the protection of PT Kodinglab's VPS customers, where it is difficult to identify the source of the attack. Network forensics in the form of dead forensics and live forensics using the NIST method with the stages of collection, examination, Analysis, and reporting are used to find the source of the attack. Data for dead forensics comes from snort tools, and data for live forensics comes from capture Wireshark. The collection stage involves collecting attack data from snort logs and wireshark for life forensics. While the examination dataset stages are further analyzed and mapped. Advanced check on the server via syslog snort. From the attack testing carried out to obtain information in the form of the attacker's IP address, destination IP address, date of the attack, server time, and type of attack from testing the TCP Flooding and UDP Flooding attacks, all attacks on the customer's VPS can be identified. The information obtained regarding the attacker is in the form of the date and time the attack occurred, the attacker's IP address and the victim's IP address, and the protocol used. Kata kunci : Network Forensic, Dead Forensic, Live Forensic, Virtual Private Server, DDos, TCP Flooding, UDP Flooding.
EARLY DETECTION OF BREAST CANCER USING THE K-NEAREST NEIGHBOUR (K-NN) ALGORITHM

ABSTRACT- Cancer is one of the Non-Communicable Disease groups whose growth and development are high-speed. One type of cancer is breast cancer (carcinoma mammae). Breast cancer is the leading cause of death for women. The first breast cancer cells can grow into tumors as large as 1 cm, spanning 8-12 years. The prevalence rate of breast cancer in Indonesia is 50 per 100,000 female population. The method used in this study uses the K-Nearest Neighbor (K-NN) algorithm by comparing k values, namely 3, 5, and 7. The dataset used was obtained from the UCI Machine Learning Repository with the Number of datasets after preprocessing, namely 653 data with a class consisting of benign tumors (benign) and malignant tumors (malignant). The variables used in this study take into account the variables of clump thickness, cell size uniformity, cell shape uniformity, marginal adhesion, single epithelial cell size, cell nucleus size, chromatin, normal cell nucleus, and mitosis. The results of the most influential classification for training and testing are using k = 3 with an accuracy of training and testing at a proportion of 70:30 of 83.8074% and 75%; the ratio of 80:20 is 84.6743% and 74.8092%; the percentage of 90:10 is 84.0136% and 84.6154%. Using the value of k = 3, the resulting gap between training and testing is similar.
Empathy for The Graph

<italic>Empathy for the Graph</italic> draws attention to the energy graph as a type of future-visualizing device that involves <italic>intellectual calculation</italic> associated with interpreting the scale of coordinates and time frames that re-enact the event inscribed; and <italic>sensual intuition</italic> associated with the rising or falling lines and rhythm of direction. On display in the energy graph is a rationalist confidence in abstract indicators to generate forms of validation that correspond to the real. But <italic>the visual abstractness</italic> of the graph suggests that this confidence is misplaced, producing a kind of emotional excess that, during the 2000s, enables the possibility among North American and European populations for developing empathy over a distanced sensual world. Empathy is a quality of identification with the emotional life of another and in the context of the energy graph a process of feeling into aesthetic production. Hence, the graph is concerned with accomplishments as an abstract indicator but also with what the visual irradiates beyond its facts.
ANN modeling of lead adsorption from water using biowaste material as adsorbent

This research employed an Artificial Neural Network (ANN) approach to model the adsorption of lead from water using biowaste material as an adsorbent. To develop the ANN model, 39 experimental data point values for training and 15 experimental data point values for testing were used. The model involved the use of a tansigmoid transfer function for the input, purelin for the output, and a hidden layer consisting of 19 neurons to minimize the square error. The highest percentage of lead removal was achieved with optimal adsorption parameters (i.e., pH, concentration of lead, and biosorbent dosage). The experimental data closely matched the predicted values obtained from the ANN model with a R2 (regression coefficient) of 0.988. To determine the maximum percentage of lead removal and the optimal adsorption parameters, a pattern-search approach using a genetic algorithm was employed. In this study, adsorbent powder was prepared using biowaste materials, such as Borasus flabellifer coir.
Virtual Reality in Ancient Village Cultural Heritage Protection: Zhen Shan Village Case Study

Virtual Reality (VR) is a transformative technology that simulates a 3D environment, offering users an immersive experience [1]. Since its 20th-century inception, VR's potential in cultural heritage protection has grown [2]. This paper examines VR's role in preserving "Zhen Shan Village", an ancient Buyi village in Huaxi District, Guiyang City. We discuss VR's processes, benefits, and challenges in heritage protection. The article outlines VR's history, its applications in heritage both locally and globally, and its specific impact on Zhen Shan Village. We conclude by analyzing VR's pros and cons in heritage protection and suggest future improvements. This research aims to guide similar heritage projects and champion ancient villages through VR.