Reviewerly/exHarmony huggingface dataset:
Analysis of Apilogy.id Email Domain Security Status Using DMARC (Domain-Based Message Authentication, Reporting, and Conformance)

The rapid development of technology not only provides benefits that facilitate all human activities. However, it also encourages more security loopholes due to a person's lack of awareness in maintaining data security. Phishing emails are one of the fastest growing security crimes and harm both businesses and individuals. This research aims to implement DMARC (Domain-based Message Authentication, Reporting, and Conformance) as an email authentication method that can monitor the security status of all types of messages that enter Apilogy.id email domains. This research uses email phishing and spoofing as penetration testing techniques to analyze how good that email security method is. DMARC is implemented in three types of scenarios (none, quarantine, and reject) which manage to get different delivery rates and the success rate of DMARC work performance reaches 90% in monitoring and managing all email messages that go to the Apilogy.id email domain. In the situation without policy enforcement, the DMARC system effectively observed a 100.0% external delivery rate during the phishing email delivery test. In the scenario where a quarantine policy was implemented, the DMARC system successfully monitored the external sending of phishing emails at a rate of 88.5%. Similarly, in the case of the reject policy scenario, DMARC effectively oversaw incoming phishing emails with an external delivery rate of 97.7%.
Making Sense of Developing Artificial Intelligence-Based System in Software Development Life Cycle Manner and Addressing Risk Factors

When dealing with real business problems at a company, developing an artificial intelligence system no longer only relies on the success of compiling a program. The program should meet the efficacy requirements of the business. In particular, focusing on a machine learning model to reach 100% accuracy will encounter a complex and hardly used end-to-end application; thus, the project does not satisfy a functional minimum viable product (MVP). An intricate project also considers good risk management, facilitating software development performances like user interface design, interpretability, robustness, and development costs. In addition, we found that the presence of literatures apparently discusses a different sense of meaning related to this topic, i.e. the use of artificial intelligence to enhance the performance of software development process. For this reason, this paper tries to make sense the terms of developing an artificial intelligence-based system in software development life cycle and risk factors that could lead to project's success with the spirit of sense-making of Dervin's theory. To this end, compared to other models, our proposed model presents the foundation of AI-SDLC in the waterfall model concerning the risk factors. Experimental evidence supporting our analysis is also discussed.
Usability Evaluation on Universitas Mercu Buana Jakarta Library Website Using SUS

Usability is the quality assessment of whether a product is suitable for use or must be repaired. The purpose of the research was to determine the level of satisfaction of users of the Mercu Buana University Jakarta library website while using the website. The Library of Mercu Buana University Jakarta has a website that students can access to find the information they need. At present, the website has rarely been used, and usability problems are still encountered. Evaluation of usability on a website is critical so that users of the website can be comfortable when using the website, and the results of the review can be used as a basis for further development. The method used is a quantitative method and uses a questionnaire based on the System Usability Scale. The advantages of this method are that respondents easily understand it, and it does not require many respondents. Still, it is very accurate, and this test can be used to check whether the application has beneficial value. The research data that has been collected produces a level of user satisfaction on the website of 71.56, according to SUS calculations, with the conclusion that the website can be used by users with a few obstacles at points that must be repaired immediately. Recommendations for improvements are given based on input from each respondent who has used the website.
How's Metaverse Applied to Support Business - Systematic Literature Review

The application of the metaverse as a technology to support life is a hot topic these days. This systematic literature review analyzes how the metaverse is used to support business, especially in marketing based on previous research. From the Scopus database, we obtained 11 articles that we reviewed to find how the metaverse applied in marketing to support a business. In this research, we group the types of businesses and the application of the metaverse and marketing strategies to support their business. We draw the conclusion that each type of business has a tendency to use a certain type of metaverse. In each type of metaverse, they use certain marketing strategies to maximize their business. In conclusion, this research suggests a thematic concept of marketing strategies based on metaverse type, that hopefully can be tested and discussed in future research as a guide for implementing marketing strategies through the metaverse.
Critical Understanding Performance of Huffman and Lempel Zip to Pattern Audio Data 16-bit

This paper provides different perspectives on the Huffman and Lempel Ziv algorithms performance in terms of lossless compression. This performance will be tested using 16-bit audio data, which in related research is still not as much analysis as in this paper. Huffman and Lampel Zip are still included in entropy coding with a linear estimation combining system. Each method has advantages and disadvantages depending on the type of data being compressed, and until now no one has claimed which algorithm is the most effective and efficient in compressing data. As a result, this study attempts to assess size of the file, proportion, velocity, compression factor, and waveform properties. In the final results, it is known that Huffman is fast in compression with a ratio of 18.0051%, while Lempel Zip is 12.3554%. From the results of the final analysis, it was found that there were many differences in performance perceptions from related previous studies.
Chatbot for University Students Major Determination with Rasa Framework

Education is one of the important factors in a nation. In Indonesia, compulsory education is implemented for 12 years, namely Elementary School (SD), Junior High School (SMP) and Senior High School (SMA). After passing Senior High School, students who continue their education will be faced with the choice to take a major that suits their abilities. There are still many Senior High School students who have problems choosing the right major. The making of this chatbot aims to assist prospective students in determining majors according to their personality. The majors' questionnaire is one way to assist students in recommending what majors they should have according to their personality. Chatbot is an application of Natural Language Processing that is used to interact between users and computers. The chatbot in this system serves to help answer user questions related to any university and what majors are in it. This makes it easier for users to see what options are available.
An Integrated Technique for Detecting Seven-Segment Digits on Medical Devices

Biometric information is generated by many medical devices. It can be time-consuming to manually collect biometric data from medical equipment. A seven-segment digit is typically displayed on medical devices like blood pressure monitors, glucometers, digital scales, etc. For large datasets of biometric data, seven-segment digits from medical devices are automatically analyzed using computer image processing. This research aims to identify seven-segment digit screens on medical equipment. The seven-segment screen detection using deep learning is the first step of the intentional method. The seven segment digit positions are then located using a variety of image processing techniques and parameters. Utilizing 200 seven-segment digit images, experimental results were reported with an accuracy (F-measure) of 94%.
Offline Interactive Map from Hybrid App Development: A Case from Geologic Map App

This work represents a fundamental developing strategy for the self-creation of digital-format geologic maps in the form of mobile apps. The digital geologic maps are apparently more convenient to work with than traditional paper-format geologic maps as the maps are user-interactive, self-directed, and can be easily updated. The digital geologic map mobile apps have recently become significant interactive presentation tools for geology students, geoscientists, and even the public. However, lack of technical methods and software development of those apps have yet revealed. In this study, we provide brief guidelines for database and software development methods to build a geologic map mobile app. The software used in this work is open source. The database file including geological information, spatial relationship and distribution, and a base map of Thailand, was prepared through a geographic information system or GIS. The hybrid app development process, which includes two open-source frameworks, namely Ionic and Angular, is key to produce a mobile app representing geologic maps via mobile devices. Additionally, basic programming language skills such as HTML, CSS, and JavaScript seem to be required for software coding. Following these steps, we produced a mobile app, named DooHin, for displaying a digital geologic map of Thailand with a scale of 1:50000 that is available via the app stores. This app is apparently practical and useful in fieldwork as it encourages a user-interactive experience, general geological information, and a user-positioning system.
Trajectory-Based Convergence Acceleration of Evolutionary Algorithms

Evolutionary algorithms are heuristic, nature-inspired search methods based on the concept of evolution and survival of the fittest. While they have proven to be effective across a variety of problems they are often inefficient as they do not use information generated during the search and could therefore require extensive computer resources to converge. To address this issue this paper proposes a new method for evolutionary convergence acceleration which is inspired by the method of successive-over-relation for the solution of linear equations sets. The main concept is to determine the direction in which the population centroid has shifted between successive generations, which suggests a favourable direction towards an optimum. The population of solutions is then propagated along that direction to accelerate its convergence. The proposed algorithm is flexible and can be applied to a variety of evolutionary algorithms. An extensive performance analysis based on representative test functions shows the effectiveness of the proposed algorithm.
Fine-Tuning Deep Learning Model for Detecting Indonesian Traffic Accident Tweet

Twitter is one of Indonesia's most used social media, with total visits reaching 192 million visits per month, making it the fifth most-visited website in Indonesia. Twitter users exchange information with each other daily. One of them is a traffic accident event. With over 100,000 cases every year from 2017–2021, it is not usual to see a tweet that contains traffic accident information. By implementing text classification, traffic accidents can be detected through tweets and used to send alert messages to emergency responders to respond as quickly as possible. Previous research used a machine learning approach to solve this text classification problem. The study resulted in the best accuracy of 85% from the SVM model, which leaves room for improvement using state-of-the-art deep learning models. This research proposes a transfer learning method using two BERT-based and two RoBERTa-based pre-trained models, namely IndoBERT, IndoBERTweet, Indonesian RoBERTa Base Wiki, and Indonesian RoBERTa Base OSCAR to create the best model to classify tweets that contain traffic accident information. We used two kinds of fine-tuning architecture: the vanilla version fine-tuning architecture and the modified version, which combines the pre-trained model with CNN. This study used 1,002 manually labeled tweets, where 360 were labeled as traffic accident tweets. The experiment found that transfer learning from a pre-trained model Indonesian RoBERTa OSCAR provided the best result with an f1-score of 88.73 % and an accuracy of 92%, which means a 7% accuracy improvement over the previous research's best model.
Analysis of Advanced 2D Convolution in Image Processing by Using AVX and OpenMP

In the field of digital image processing, the image data are required to operate for some enhancement operations such as image filtering, image restoration, image transformation, and so on. The memory allocation of image data is presented in an array and executed by two-dimensional matrix-matrix multiplication. The convolution operation is an essential operation among many filtering algorithms (e.g. Gaussian filter). In this paper, the characteristics of different convolution methods have been examined, and the important role of convolution and advanced parallel version of 2D convolution to improve the performance and instructions per clock cycle on a multi-core architecture by using AVX and OpenMP. Generally, convolution is performed by sliding the kernel over the image and moving the left side direction of the kernel through all data of the input matrix until all values of the image have been calculated. This paper intends to utilize and analyze how 1D, 2D, and 3D convolutions work in image processing and the parallel versions of 1D and 2D convolutions using padding on CPUs are proposed.
Guidance System for Visually Impaired Persons Using Deep Learning and Optical Flow

Visually impaired persons find it difficult to know about their surroundings while walking on a road. Walking sticks used by them can only give them information about the obstacles in the stick's proximity. Moreover, it is mostly effective in static or very slow-paced environments. Hence, this paper introduces a method to guide them in a busy street. To create such a system it is very important to know about the approaching object and its direction of approach. To achieve this objective we created a method in which the image frame received from the video is divided into three parts i.e. center, left, and right to know the direction of approach of the approaching object. Object detection is done using YOLOv3. Lucas Kanade's optical flow estimation method is used for the optical flow estimation and Depth-net is used for depth estimation. Using the depth information, object motion trajectory, and object category information, the model provides necessary information/warning to the person. This model has been tested in the real world to show its effectiveness.
Visual Glitches Classification for Video Game Using Deep Learning-Based Techniques

Glitches, unintended and anomalous behaviors in video games, often degrade player experiences. Our objective is to develop a glitch classification model that can accurately identify and categorize different types of visual glitches. The proposed model incorporates Neural Architecture Search (NAS) to identify the optimal architecture and hyperparameters for the classification of these anomalies within in-game visual. We construct a unique, comprehensive dataset composed of numerous instances of common visual glitches along with non-glitched game footage. The proposed deep learning model is then trained and evaluated on this dataset, and our results demonstrate a notable enhancement in glitch detection accuracy 88% compared to conventional. Experimental results demonstrate the potential of the visual glitches classification model.
Cognitive Performance Evaluation in Early Stage of Dementia: A Hybrid EEG/Eye Movement Analysis

The number of people around the world with a risk of severe disease from being dementia is increasing significantly. Especially, the elderly, those with preexisting conditions are particularly at risk from the major factor to be changed to an Alzheimer's disease. The condition greatly affects the cognitive functions of the body which will eventually lead to deterioration and deformation of the body to perform regular activities. Traditional approaches have been carried out for identifying the causal factors underlying variations in terms of medical technologies, e.g., the non-invasive imaging methods and noninvasive digital biomarkers using digital signal processing. In this paper, therefore, a noninvasive hybrid approach using electroencephalography (EEG) neurofeedback and eye tracking method has been proposed to identify significant features of an early stage of dementia which later has 80% chance pro-gression of symptoms to Alzheimer's disease. The presence of modern imaging technologies which are expensive and not readily available in terms of temporal resolution response, thus, our proposed noninvasive hybrid EEG/eye tracking analysis proved to be an effective method to classify the control group from cognitively impaired group (or an early stage of dementia) using generalized linear models (GLM) classifier which obtained an average classification accuracy of 89.95%.
Mental Health Sentiment Analysis on Social Media TikTok with the Naïve Bayes Algorithm

Mental health disorders can affect a person's emotions and behavior and can impact their health and daily activities. The most serious consequence of poor mental health is death by suicide. The problem in this research is about mental health which can be analyzed using the field of natural language processing with the science of sentiment analysis on the developing TikTok platform. Content contained on the TikTok platform will have comments made by fellow users, then these comments are collected for sentiment analysis. This study utilizes a classification algorithm, namely naïve Bayes. The dataset obtained is preprocessed by text first, then the process from TF-IDF will be used to be able to see the appearance of words in the data. The data used in this study is 6300 data. The results of this study are accuracy as measured by a metric evaluation which produces 80.95%. The sentiment in this study focuses on positive and negative sentiments. With visualization, words that often appear are tired, sick, tired, and hurt in negative comments. Meanwhile, the words that often appear in positive comments are able, passionate, strong, and happy.
Indonesian Hate Speech and Abusive Tweets Classification with Deep Learning Pre-trained Language Models

Hate speech and abusive language on social media can spread massively and escalate into conflict between two or more individuals or parties. Previous studies about multi-label classification to identify whether the tweet contains hate speech, abusive language, or not have been conducted with machine learning techniques. They applied machine learning with a combination of feature extraction and achieved a good result in recognizing the tweet with hate speech content. In this research, we apply two approaches: first, we use vanilla pre-trained models of IndoBERT, IndoBERTweet, and Indonesian RoBERTa; second, we combine the three previous models with CNN. Our experiment shows that our proposed method has better results compared to previous research with the best performance achieved by IndoBERTweet+CNN with 93.9% Accuracy.
Improving the Performance of K-Means Clustering to Reduce Repetitive Work by Addressing the Gap in Findings

The k-means clustering algorithm calculates the distances between data points within each cluster and determines new centroids for each cluster until the data points remain unchanged within their respective groups. This process affects the iteration of the k-means algorithm, leading to an increase in computation time. Consequently, the overall execution time of the algorithm is prolonged. This research presents a process that utilizes the farthest first and canopy algorithms to reduce the number of iterations in the k-means clustering. This improvement focuses on enhancing the centroid initialization method for each cluster. The research study utilized ten datasets from the UCI machine learning repository to test the effectiveness of improving the k-means clustering algorithm. It compares the number of iterations and the sum of squares error as performance metrics. The results showed that the farthest first and canopy algorithms significantly reduce the number of iterations in the k-means clustering when compared to random centroid initialization. These research findings confirm the hypothesis that the method of centroid initialization for each cluster impacts the iteration process of k-means clustering.
SMOTE on Numeric Breast Cancer Dataset to Overcome Imbalance Class

The Coimbra breast cancer dataset contains a relatively small proportion of the imbalance dataset. However, it can pose challenges when developing machine learning models because the model may be biased for the majority class and have difficulty precisely predicting the minority class. This research tries to mitigate the imbalance class using SMOTE. After implementing SMOTE, we use the Random Forest algorithm to build a machine-learning model based on 10-fold cross-validation and evaluate the model using accuracy, precision, and recall. The result shows that the model accuracy increases from 76.72% to 80.47%, the precision score rises from 76.60% to 80.00%, and the recall score from 69.23% to 81.25%. Significant implications exist for enhancing the diagnosis and treatment of breast cancer by providing more accurate prognoses of patient outcomes based on their clinical and demographic characteristics.
Improving Indonesian Informal to Formal Style Transfer via Pre-Training Unlabelled Augmented Data

Neural Machine Translation is already used for Indonesian informal to formal style transfer. It works by translating the input language to the target language. In Indonesian informal-to-formal style transfer task, informal sentence work as an input language, and formal sentence is the target the model needs to translate to. Currently, the STIF parallel dataset is the only manually labelled informal-to-formal dataset. We need sufficient data to achieve a good model for style transfer performance. In contrast, the current Indonesian informal to formal dataset is insufficient. We adopted the pre-train augmentation architecture introduced by work done in GEC tasks to elevate the Low-Resource data. We create the augmented dataset with a simpler word replacement approach. We benchmark several transformer-based pre-trained model architectures, including BART, GPT2, and BERT Encoder Decoder. We train the augmented dataset to all models as a pre-trained model and fine-tune it with the STIF dataset. We perform the sacreBLEU benchmarking techniques to find which approach with better style transfer quality. The result is the BART model that was pre-trained with an augmented dataset and fine-tuned with the STIF dataset with a score of sacreBLEU 53,19.