Reviewerly/exHarmony huggingface dataset:
Cyberbullying Detection on Social Networks using LSTM Model

Cyberbullying is a big problem in today’s society. The side effect of increasing social media, cyberbullying has emerged as a serious problem afflicting children, adolescents and young adults. Social networks provide a rich environment for bullies to uses these networks as vulnerable to attacks against victims. The development cyberbullying victims increases recently due to online harassment such as sharing private chats, rumours and sexual remarks take suitable actions to detect and prevent it. Several techniques are used to detect bullying messages in social media and its helps to construct secure social media surroundings. In previous Machine learning models they use various classification algorithms such as SVM, Navie Bayes, decision tree (DT) gives only an accuracy of 67.63%, 61.58%, 69.07% respectively. This research paper proposes Long Short Term Memory model (LSTM), a deep learning approach, is utilized for detecting and preventing cyberbulling actions. Extracting features using Long Short Term Memory, training the model and analyzing the model gives a more accurate result. The final evaluation of the proposed approach shows that LSTM achieves an accuracy of 75.12 % which is much better than previous models.
Fragile Watermark Based on Singular Value Decomposition

Electronic files are vulnerable to tampering and corruption. Undetected these changes can alter the meaning and value of critical evidence. In this paper, a new method is suggested to detect any small change in the document based on the singular value decomposition (SVD), this suggestion works on any type of image (color or gray), Color image converted to three channels (red, green, and blue), each channel will be transferred to three matrices by using the SVD transformation. The difference between the first and third matrix for each transformed color channel is determined. This value is the secret key used to detect tampering with the image. Logo can be added to the image and give the same results. Many performance tests were used to prove the efficiency of this suggestion, and it gives very promised results.
Implementation of Agile Scrum Methodology in P4AI Application Development

The profiling for Aptitude Inventory (P4AI) application is an application developed to help students carry out self-assessments so they can understand the interests of the appropriate majors and careers. The application was developed using the agile scrum methodology, which is a software engineering method that applies the principles of the AGILE approach so that it relies on the strength of team collaboration, incremental product, and iterative processes to realize the product with the best final result. The Scrum. The purpose of this study was to evaluate whether the scrum methodology applies to the requirements of application development. The results of the research are several outputs from the Agile Scrum Methodology process which is implemented in the development of the artificial intelligence based P4AI Application aptitude test website.
An approach to unveil the masked faces: Occluded Face-Recognition System

Face recognition is one of the most widely used biometric identification systems due to its practicality and ease of use. The COVID-19 outbreak has recently expanded rapidly over the world, posing a major threat to people’s health and economic well-being. Using masks in public places is an efficient approach to prevent the spread of infections. However, due to the absence of facial features detail, a masked face recognition system is a difficult task. We offer a technique to identify the masked faces in this project.Masked face recognition is a subset of occluded face recognition that requires prior knowledge of the obscured portion of the targeted face. Occluded face recognition is a current research topic that has captured the interest of the computer vision community. Occluded face recognition systems have previously been focused on detecting and recognizing an individual’s face in the wild when the occluded part of the face is in a random form and position. Meanwhile, the nose, mouth, and cheeks of a masked face are frequently hidden. The eyes, brows, and forehead may be the only remaining clear areas. As a result, a masked face recognition system could effectively concentrate on analyzing traits that can be derived from the subject’s uncovered portions, such as the eyes, brows, and forehead.
Artificial Intelligence Framework for COVID Protocol Detection

Artificial intelligence is now penetrating into all the domains. Any domain can incorporate artificial intelligence to automate their process. In the outbreak of COVID pandemic, artificial intelligence has been very useful in many ways. artificial intelligence helps in automating process where it’s not always possible for people to do and to reduce the wastage of human resource. Here we proposed a frame work to automate the detection of covid protocol violation in public places. Our work detecting people with & without masks and detects social distancing with a single model. The best performing model from the standard convolution neural network architectures namely VGG16 and MobileNetV2 are used in the present work, from the experiments it’s found that MobileNetV2 outperformed VGG16. The developed system can easily be integrated/implemented on various embedded devices with limited computational capacity by using the MobileNetV2 architecture. Compared to other previous works, our work outstands by having good accuracy and compatible to use in real life application because of its requirement of less computational complexity.
Augmented Reality Application for Book Detection with Google Colaboratory and SnapML Lens Studio

Artificial intelligence is one of the technological breakthroughs in science, especially in the field of computers. Object detection and recognition presents a new challenge on how to make machines detect objects automatically. To detect an object, the previous system will take the classifier of an object and evaluate it at various locations and various scales in the image. Smartphones currently play an important role in everyday life, but the existence of smartphones can be one of the causes of the user's lack of interest in reading books. With this Augmented Reality-based object detector, it is hoped that users can experiment on smartphones with the Snapchat application installed to detect objects in books that are around them and the number of objects will be counted automatically, it is also expected to attract users' desire to read books. Image recognition identification is taken through the phone's camera which is recognized as a lens. Lens AR in the Lens Studio application is supported by the SnapML machine learning algorithm, this machine learning function is to display book information and the number of books detected. This research was conducted using Cloud Computing such as Google Collaboratory which uses the python programming language, the book object dataset is selected from the COCO class, the model is exported to Open Neural Network Exchange (.ONNX) and then imported into the Lens Studio application. Publish lenses in the Snapchat app. Finally, a model test was carried out to detect objects and the number of objects in books that had been made using a Smartphone. Open the Snapchat application, scan the Snapcode generated by Lens Studio, AR lenses can be used in real time. AR Book Detection has been tested by other users on Snapchat with 18,845 Lens Views, 18,737 Lens Plays and 16 Lens Shares.
Security Risk Assessment &amp; Mitigation Framework for Cloud-based IT Systems

More often than not, the need for security is realized only after the system has been broken. Adding security afterwards is often difficult and much costlier and sometimes, nearly impossible. Hence, Security needs to be embedded into an IT system in order to develop secure IT systems which is possible only when a Security Engineering Framework is well defined for the entire lifecycle of an IT system. In this Paper, we have designed and developed an implementable Framework for Security Engineering of Cloud-based IT Systems. Cloud-based IT Systems are vulnerable to a plethora of cyber-attacks because of their accessibility and their services exposed in a multi-tenant environment. The Cloud-based IT Systems Security Risk Assessment & Mitigation Framework (CISSRAMF) is agnostic to Cloud Service Providers (CSPs) as well as to the type of Services provided, i.e., Infrastructure as a Service (IaaS), Platform as a Service (PaaS) and Software as a Service (SaaS). CISSRAMF encompasses the entire lifecycle of any Cloud-based IT System right from the conception phase till decommissioning of the system. CISSRAMF utilizes the Standards for Information Security and Cybersecurity like ISO/IEC 27001:2013, ISO/IEC 27002:2013, NIST SP 800-53 Rev. 5 and OWASP Application Security Verification Standard (ASVS) Project.
Sentiment Analysis of the Use of Telecommunication Providers on Twitter Social Media using Convolutional Neural Network

Telecommunication technology continues to develop starting from 1G, 2G, 3G, 4G, and currently entering the 5G era. The Global System for Mobile Communications (GSM) based telecommunication industry in Indonesia consists of three big names: Telkomsel, XL, and Indosat. During the Covid-19 pandemic, activities carried out outside the home should be done online. People hope that the internet network can work properly. However, the reality is not as expected, because many networks are experiencing slow internet problems and many complaints are delivered through social media. Therefore, this research aims to find the insight opinions that have been conveyed to the telecommunications operator in social media. This research used the Convolutional Neural Network (CNN) algorithm to classify text sentiment (negative or positive) about telecommunication providers. The experiment with text data from Twitter is conducted after preprocessing and weighting of the Word2Vec process. The confusion matrix experiment shows that the CNN algorithm’s performance reaches an average accuracy value of around 86.22%. The experiment was carried out by dividing the training data and testing the data 5 times in 10 times. The study results indicated that disruption of cellular telecommunications operators provided many sentiments, especially negative sentiment at the beginning of the COVID-19 pandemic.
Sentiment Analysis of State Capital Relocation of Indonesia using Convolutional Neural Network

The current government policy led by President Joko Widodo regarding relocating the capital city from Daerah Khusus Ibukota (Capital Special Region) of Jakarta to East Kalimantan has drawn a variety of comments, ranging from praise, criticism, suggestions, innuendo to hate speech. This is supported by many Indonesian political figures who have Twitter accounts to provide support or opinions on this policy. This study aims to analyze the sentiments about this issue. There is very varied sentiment from this issue, either positive or negative responses. This research used Convolutional Neural Network (CNN) algorithm as a part of the Deep Learning method to classify sentiments towards government policy on moving capital city of Indonesia with data obtained from Twitter. The process begins with text pre-processing containing case folding, tokenizing, stop-words removing, stemming, and changing the emoticon to word. Then, the word embedding process used Word2Vec. The result of experiment of CNN algorithm with 1,515 tweets in the Indonesian language and 15 times of experiment shows that the average accuracy is 66.68% with the highest accuracy is 70.3%. The experiment used five training and testing data splitting variations, with three epochs, among others: 10 epochs, 30 epochs, and 100 epochs.
Augmented Reality-Based Learning Media for Simple Distillation Practical Apparatus

In the simple distillation practicum, some of the equipment used was the equipment that was not always used during practicum. Augmented reality has become one of the technologies that could help in introducing simple distillation equipment. This study aims to describe the display of learning media, analyze the validation test results, and analyze the results of feasibility using the Multimedia Development Life Cycle (MDLC) method. The study results obtained that the display of AR media consisted of the main page, consisting of an information menu containing guide information and profile, logout, and log In. The scan AR page consisted of a camera menu to access the marker and assembling menu for assembling simple distillation equipment and a video of assembling simple distillation equipment The validation results showed an R-count value of around 0.90 and a feasibility test of 94%, which means that the learning media could be stated to be used properly.
User Experience Measurement of ERebana Application using User Experience Questionnaire

This research develops a tambourine musical instrument in digital (electronic) form, combining elements of the original tambourine traditional art with information technology as an effort to preserve the traditional tambourine art from children, teenagers, to adults. Of course with a proper functional needs analysis from a tambourine art expert and supported by a non-functional needs analysis from a multimedia expert. The collaboration will produce a software in the form of an application with the name E-Rebana. This study was to measure the experience of using the E-Rebana application, especially for students in the Informatics Engineering Study Program at Wahid Hasyim University. This measurement is intended to predict and compare the capabilities of technological elements in the form of user experience of the application through analytical studies with the application of the User Experience Questionnaire (UEQ) method. The target respondents are students, especially in the Informatics Engineering study program at Wahid Hasyim University. Most results of the user experience measurement are positive. In the grouped result, E-Rebana application has positive scores in Attractiveness, Pragmatic Quality and Hedonic Quality. On all six scales of UEQ result (Attractiveness, Perspicuity, Efficiency, Dependability, Stimulation and Novelty) E-Rebana application has a positive benchmark. The benchmark results are ranging from above average to excellent. E-Rebana application also has a positive result and sufficient consistency in all of six scales.
Engineering Web resource summaries using Pointwise mutual information (PMI)-based for web document summarization

Internet is an ocean of information which is growing everyday. It is impossible for any human being to read the whole information. It is very difficult to decide which document is relevant. If someone gets summary of the document that is the easiest way to decide. Therefore, there is a need to create automatic text summarization systems which generate a precise summary. In this paper, a study of summaries generated using PMI based web document summarization technique, which creates summary on ranking of sentences on the basis of Total PointWise Mutual Information of sentences. The data is gathered basically from documents of engineering domain, for text summarization. The results show that the method outperforms the other techniques by exhibiting the best results for the closest mean score and generating a good quality summary of sentences of different lengths. It also depicts that the proposed approach has minimum error compared to other machine learning approaches.
Assessing Students’ Information Security Awareness through the Knowledge, Attitude, and Behavior Model

Student awareness of information security (IS) ensures the security of students in the current technological era, but compliance with IS has not been fully carried out by students. This measurement of Information Security Awareness (ISA) is important to improve student understanding and long-term compliance that secures all aspects of devices, students, organizations, and schools. Using the Knowledge, Attitude, and Behavior model, this study aims to measure students’ information security awareness. An online survey was conducted to evaluate student’s information security awareness in 4 focus areas (passwords, internet safety, email safety, and digital payment) through the use of Likert scales. The overall awareness level of students was measured to be 73.79%, which is considered an average level of ISA. The awareness level of the password, internet safety, and email safety areas were average (69.19%, 71.31%, and 77.83% respectively, while the awareness level for the digital payment area was good at 80.35%. The Pearson’s correlation coefficient (R) for the knowledge and behavior dimensions was 0.9497, indicating a strong positive correlation, while the coefficient for the attitude and behavior dimensions was 0.5428, indicating a moderate positive correlation. Thus, it can be recommended that awareness programs dedicated to raising ISA for students can focus on strengthening knowledge about IS so that students can exhibit good ISA behavior.
Mobile Application for Augmented Shopping: Virtual Shoe Try-on and Virtual Equipment Placement

The shopping experience has recently witnessed drastic changes with the ubiquity of online shopping, especially with the introduction of shopping through smartphones. The accessibility of these devices and their expanding functionalities have made them more popular in providing a full shopping experience. An obvious issue with common homeshopping applications is that customers cannot try the clothing items themselves. Also, when purchasing furniture or equipment that takes a significant amount of space, it is impossible to know if the item suits the room until it is already bought and delivered. Augmented reality can help customers to test whether the product matches what they are looking for. In this paper, we propose an augmented reality solution to enhance the online shopping experience. The proposed tool views the 3D equipment model using AR and sees if it fits in the identified space through the camera in the room. Also, users can try shoes through the cameras of their phones. Finally, a shoe size recommendation system is developed using a machine learning algorithm that detects the foot and returns the recommended shoe size. The application was evaluated on 10 users. The results show the effectiveness and efficiency of the developed solution. A set of four tasks are utilized to evaluate the solution in terms of the System Usability Scale (SUS) and task completion time. The research outcome can be used to help in developing a better user experience for a wider range of AR shopping applications.
Implementation of Face Detection to Count the Number of Mall Visitors

In the current digital era, various data can be generated with the capabilities of a computer system, making it easier and faster to make decisions. Mall is a place for shopping as well as entertainment that presents many well-known brands. In marketing the room to tenants, data is needed that can attract tenants to cooperate with the mall. The method used in this research is the Viola-Jones method which is used to detect objects because this method has an efficient algorithm, so it does not take long to detect objects. There are four main contributions to the Viola-Jones theory, including the Haar Feature, Integral image, Adaptive Boosting or AdaBoost, and the Combination Classifier of Cascade. The purpose of this study is to analyze and evaluate the number of visitors who come as a reference for determining prices for tenants. The results of this study found that the Viola-Jones method can effectively calculate the number of visitors with a face detection approach and make it easier for mall management to get mall visitor data.
New Narrative of Role Human-Computer Interaction in System Development

Interaction between humans and computer is an interesting topic that studied in Human-Computer Interaction (HCI). HCI focuses on how humans can feel comfortable interacting with computers. HCI will help designers, analysts, and users to identify system requirements from text, fonts, layout, graphics, and colors. At the same time, HCI is usually connected with usability. Usability alone will confirm whether the system is efficient, secure, utility, easy to learn, remember, use, and evaluate. However, in its development, HCI is only limited to interface design and is used only at the design stage. Therefore, this study aims to learn and understand more about the importance of HCI and its usefulness in developing systems that will lead to user satisfaction. The main result of this research is to find out the importance of HCI implementation in system development. Moreover, this research also mentioned four types of HCI models or approaches that can be used in developing a system.
Can Sentiment Mining of Novice and Expert User Survey Feedback Enhance the Digital User Experience?

In recent years, sentiment mining has been used within politics and social media to understand the emotions and opinions that people have about society and different political groups to help bring change. However, in the user centered design process, a main component that helps bring change is the use of user feedback and there has been little research in terms of adopting sentiment mining to assist with this and the user experience of an application. This research paper implements the use of sentiment mining on user survey feedback from Jupyter Notebook and has identified different components of the user experience that caters for novice and expert users independently. Recommendations from this research will help Jupyter Notebook create a user experience that caters to novice and expert users and their workflow needs. This initial analysis has identified that sentiment mining provides important and subtle enhancements for user experience that other tools and techniques do not provide. Therefore, this is a worthwhile method of user experience assessment to foster to enhance a digital user experience.
Comparative Performance of Convolutional Neural Networks Architecture for Face Biometric Authentication System

Biometric authentication plays a vital role nowadays compared to password or token-based authentication. There are a lot of methods for biometric authentication algorithms that have been proposed but it can be said that the Deep Learning method give much more reliable and secure compared to other methods specifically Convolutional Neural Networks (CNN) for face recognition. Therefore, this paper will review the performance of top CNN architectures which are LeNet, AlexNet, VGGNet, GoogleNet, and ResNet by using the proposed face dataset of 7 celebrity classes where each class has 35 images that have been collected from Google Images. Data augmentation has been performed to increase the size of the dataset before it was fed into the CNN model. The experiment shows that AlexNet shows promising results compared to the other architectures on the proposed dataset.