Reviewerly/exHarmony huggingface dataset:
Conceptual Framework Integration of Cultural Elements in Role-Playing Game Character Design via Analysis of the Malaysian Games Industry

As Malaysia's gaming industry continues to flourish, game developers are taking advantage of the country's diverse cultural background to create unique and original character designs. Incorporating local cultural elements into character designs has helped create more interesting and engaging games and allowed Malaysia to showcase its rich cultural heritage to a global audience. However, creating a sense of national identity in Malaysia, which incorporates the customs, art, and culture of its diverse peoples, has been a challenging task due to the country's multi-ethnic nature and the lack of data that evolved from the early stage because of most of the company are mostly start-ups. The study intends to use qualitative methods, involving interviews with game designers and companies and observation through successful game creators who create cultural character designs. Then the data will be analyzed and synthesized to formulate relevant recommendations for integrating culture into the visual appearance of game characters. This paper proposes a conceptual framework for the importance of visual design and culture in the creation of game characters. It emphasizes the need for designers to focus on designing characters and styles that capture the attention of players and reflect the values of the culture in which they are created. This method would serve as the foundation for describing the intrinsic components of the Malaysian gaming landscape, with an emphasis on identifying the underlying strengths and deficiencies.
THE PRIVATE/PUBLIC LAW DIVIDE IN DIGITAL LAW

As the digitization process of last decades affects the way of life, the need for digital law regulations is felt more. Thus, courts and scholars feel the need to categorize the digital law either under public law domain or under private law domain, so that the principles that govern the sub-discipline of digital law becomes clear. This study discusses the criteria that were employed since Romans for distinguishing public law from private law and analyses digital law, with its subcategories as to whether it falls under public law or private law. The study finds that public-private law divide is not perfectly severe, to classify every relation or dispute of law under one of the two domains and that digital law is not a field of law that can be categorized technically under either of these domains, but it can be related to the public sphere rather than the private sphere.
Sentiment Analysis of the Indonesia Presidential Election 2024 with Ensemble Learning for Reducing Disinformation on Social Media

Disinformation on social media is a frequent problem, especially in the political context related to the 2024 Presidential Election. The purpose of this sentiment analysis research is to reduce disinformation through the application of the Ensemble Learning method using 4 classifiers namely SVM, Random Forest, Gradient Boosting, and Stacked Ensemble, by utilizing TF-IDF and Cross-Validation. Data used in modeling is 11,611 data. The results of the research show that SVM, Random Forest, Gradient Boosting Classifier, and Stacked Ensemble are able to classify disinformation with significant accuracy. SVM achieved 86.44% accuracy, Random Forest achieved 87.65% accuracy, and Gradient Boosting Classifier achieved 88.20% accuracy. Stacked Ensemble achieves the same accuracy as GBC. Ensemble Learning through Stacked Ensemble has proven to provide promising results in efforts to reduce disinformation on social media. The results of this research make an important contribution in developing the use of sentiment analysis to address the problem of disinformation in social media
A Study on the Implementation of YOLOv4 Algorithm with Hyperparameter Tuning for Car Detection in Unmanned Aerial Vehicle Images

Unmanned Aerial Vehicles (UAVs) for surveillance and monitoring have become more prevalent due to their versatility and mobility. These vehicles capture high-resolution images that provide a broad field of view in real-time. Today, enhancing object detection accuracy on images captured by unmanned aerial vehicles (UAVs) has become a significant challenge. Through extensive research, it has been established that the correct setup of hyperparameters is imperative to achieving the highest accuracy in machine learning. Our study introduces a technique that utilizes hyperparameter tuning to implement the YOLOv4 algorithm, enabling the detection of cars in unmanned aerial vehicle images. In general, all scenarios of this study have different accuracy results, which have implications for their detectability. Thus, scenario 3 of YOLOv4 hyperparameter tuning is the best model accuracy. Our approach utilizes the PSU Aerial Car Images Dataset from previous studies. During this research, accuracy values were obtained through testing at the model validation stage rather than at the testing stage. In this study, we achieved a validation performance of the detection model by using a validation dataset proportion of 20%. Based on our research, it has been revealed that the YOLOv4 algorithm is a highly efficient car detection system when it comes to unmanned aerial vehicle images. Through rigorous testing of multiple hyperparameter tuning scenarios, we achieved an exceptional accuracy of 99.02% in the optimal model scenario, which utilized YOLOv4. Similarly, in replicating a research paper's hyperparameter tuning methods on YOLOv3, the highest accuracy of 98.40% was attained in scenario 2.
Performance Comparison Analysis of Virtual Router Redundancy Protocol (VRRP) with Gateway Load Balancing Protocol (GLBP) on a DMVPN Network

Dynamic Multipoint Virtual Private Network (DMVPN) is a combination of mGRE, NHRP and IPsec Encryption technologies that can maintain network security in companies, especially aspects of confidentiality, data integrity and authentication. However, companies also require a consistently available network to ensure uninterrupted business processes, so First Hop Redundancy Protocol (FHRP) can be used as a solution for the availability aspect of the network. In previous research, the implementation of the DMVPN network used one of the FHRP protocols, namely HSRP, so this research was conducted to test the DMVPN network using other FHRP protocols, namely VRRP and GLBP using a star topology with a variation of Dual HUB to test network redundancy using evaluation metrics of network convergence time and Quality of Service (QoS) in the form of throughput, delay and packet loss. The results of QoS testing on sending TCP packets using GLBP on the DMVPN network are better based on throughput, delay, and packet loss. This is due to the load-balancing feature in GLBP. However, in UDP packet transmission, it was found that the use of VRRP on the DMVPN network was better than using GLBP based on the results of throughput, delay, packet loss and network convergence time. This happens because the load-balancing feature on GLBP does not work on UDP packet transmission and network convergence time when the primary router has a problem, VRRP is faster with 3.58 seconds compared to GLBP which takes 9.56 seconds.
A Comparative Study for Language Recognition using Learning-based Approaches

Language recognition is helpful for determining the natural language in a given document or part of text. Language recognition has attracted more attention in recent times due to its wide-ranging applications, including speech translation, multilingual speech recognition and more. Indeed, language recognition should be effective to ensure practical implementation. Therefore, learning-based approaches are introduced to enhance the effectiveness of language recognition. In this paper, a total of six learning-based approaches have been implemented for solving the language recognition problem. Experiments and evaluations are conducted to study the effectiveness of these learning-based approaches on identifying 5 different languages which are English, German, Czech, French, and Swedish. The experimental results show that the 1D-CNN model achieves the highest accuracy score of 65.99%.
Scam Calls Detection Using Machine Learning Approaches

In recent years, scam calls have become increasingly prevalent resulting in financial loss, identity theft, and other fraudulent activities. This research proposes a machine learning-based approach for scam call classification and detection using natural language processing (NLP) along with deep learning techniques. The model uses the dataset of scam and non-scam calls to train and understand the context of the caller and determine if the conversation is a scam or not. NLP techniques are leveraged, such as preprocessing text, converting audio samples to texts with Google API, and word embeddings, to build an accurate and reliable classifier. The highest results obtained is Long Short-Term Memory (LSTM) algorithm with an accuracy of 85.61% in detecting scam calls.
Comparative Analysis of CNN and LSTM Performance for Hate Speech Detection on Twitter

The internet's current development has become one factor that gives social media users opportunities to leave comments and posts containing hate speech. Detecting hate speech on social media, particularly on Twitter, has recently become a widely researched topic. Research that has been conducted usually applies a standard machine learning approach. The deep learning approach has become popular because it provides better and more effective results. However, it's still rare to be applied to detect hate speech in Indonesian language texts. This research shows the results of a performance comparison from the deep learning approach using CNN, LSTM, and CNN+LSTM architecture models for detecting hate speech in tweets using the Indonesian language. The dataset used is divided into a general dataset which is the entire dataset and a specific topic dataset that deals with the topic of government, which was taken from the general dataset. The research shows better results when the CNN architecture model is implemented on Indonesian language tweet data compared to the results obtained from the LSTM architecture model and the combination of CNN+LSTM with accuracy and F1-score reaching 81%. Furthermore, the implementation of deep learning models in detecting hate speech performs better than previous research using the same dataset but applying machine learning models with feature extraction. This research also shows that specific data discussing a particular topic significantly impact the model's performance. Thus the version of the model becomes better when applied to data with a general topic and a more extensive vocabulary.
The Development of Indonesian Paraphrase Datasets for Automatic Paraphrase Generation System

Paraphrasing can be defined as the expression of a text using different diction but referring to the same meaning. Systems capable of automatically generating paraphrases play a crucial role in Natural Language Processing (NLP). In previous research, the generated paraphrase dataset was extracted using machine translation, assuming that text pairs inherently possess semantic similarity. Consequently, the applied filter focused solely on differences in diction variety. As a result, the produced dataset tended to be unsatisfactory in terms of lexical diversity and semantic similarity. Therefore, this study aimed to generate a paraphrase dataset by utilizing a task other than machine translation, specifically Abstractive Summarization on the Liputan6 dataset. Human-generated summaries within the Liputan6 dataset were paired with system-generated summary texts. Subsequently, text pairs were filtered based on the average of semantic similarity using BERTScore and lexical diversity using inverseSacreBLEU. The resulting dataset was then evaluated as training data for a paraphrase generator and manually assessed by humans. The employed filtration process proved successful in enhancing lexical diversity compared to previous research, as demonstrated by the increase in inverseSacreBLEU scores from 57.42 to 72.76. While the resulting dataset from Liputan6 (146,030 data) was nearly 40 times smaller than in previous research (5,753,296 data), it exhibited higher semantic similarity and lexical diversity scores with an improvement of 1-2 points. This indicates the superior quality of the dataset produced in comparison to prior studies.
Inflation Rate Prediction by Involving Interest Rate Using Vector Autoregression Model

In 2022, the majority of countries experienced a significant increase in inflation, and it is predicted that the phenomenon of high inflation rates will continue to hit the world in 2023. A high inflation rate encourages a country to raise its interest rate, which on the other hand has a risk of limiting economic growth and increasing the possibility of a country’s recession. With so many uncertainties in macroeconomics in the future, this could result in excessive public concern. So, a time series model that can predict fluctuations in Indonesia’s inflation rate is very needed. Therefore, this study proposes forecasting Indonesia’s inflation using the Vector Autoregression (VAR) method by involving an interest rate dataset based on a strong Granger relationship from interest rates to inflation. The VAR model is used because the Akaike Information Criteria (AIC) value obtained is the smallest among other Vector Autoregression Moving Average (VARMA) models, indicating it to be the most optimal model in terms of both goodness of fit and model complexity. The prediction results will be evaluated using the Mean Absolute Percentage Error (MAPE). According to research, the proposed predictions from the VAR model show that the VAR(5) method can be said to be the optimal method to predict Indonesia’s inflation rate, with a MAPE value of 10.2%.
A Comparative Study of Learning-based Approaches for Chinese Character Recognition

This paper presents a comprehensive comparison study of various learning-based approaches for Chinese Character Recognition (CCR). The study examines eight types of models that belong to the machine learning model and deep learning model categories. These models include Bagging k-Nearest Neighbor, Random Forest, Support Vector Machine (SVM), Bagging Decision Tree, Xception, LeNet, Multi-Layer Perceptron (MLP), and Visual Geometry Group 16 (VGG16). To conduct the study, a dataset of handwritten Chinese characters is collected. The dataset consists of 5,000 samples distributed across 10 classes of Chinese characters. From the experiment results, we conclude that the best-performing algorithm for the classification model is VGG16, which achieved the highest accuracy score among the eight learning-based models tested in the study. Specifically, VGG16 scored a remarkable accuracy of 99.20%, outperforming the other seven learning-based models. These findings demonstrate the potential of deep learning models, such as VGG16, to improve Chinese character recognition algorithms and enhance their accuracy and performance.
Analysis and Implementation of Digital Signature Algorithm in PDF Document

A digital signature is a cryptographic tool used to sign and verify messages to provide authentication, integrity, and non-repudiation of an electronic document. Digital signatures have been widely used in commercial systems, especially since the Corona Virus Disease 2019 (COVID-19) pandemic forced many people to do work from home, so they had to send documents between people digitally. Many digital signature algorithms have been proposed before, however, the most suitable algorithm for Portable Document Format (PDF) documents from the point of view of security and digital signature processing time are unknown. Apart from this, the available digital signature service systems are relatively expensive. To overcome this, this study proposes an analysis of digital signature algorithms in terms of security, memory usage, and processing time on PDF documents. This study also proposes the development of prototype web service digital signatures that use the best digital signature algorithms that have been analyzed as proof of concept as a result of the analysis made by this study. The methods used in this study are study of the literature on digital signature algorithms, Analysis of digital signature algorithms, Development of prototypes, Performance testing, and analysis. The outcome of this research is that ECDSA is an algorithm that can sign PDF documents with good processing time and memory usage. This research succeeded in creating a digital signature web using the ECDSA algorithm which can perform digital signatures on PDF documents and verify digital signatures on PDF documents.
A Survey on Privacy Preservation Methods in Future Vehicular Networks

As increasing vehicular technology is becoming more popular due to global climate change and the promotion of renewable energy sources, electric vehicles (EVs) with an array of improved features are becoming increasingly prevalent in the realm of automobile technology. As EVs require frequent charging sessions, it is integral to consider the potential privacy and security risks associated with the extensive data collected when charging or discharging from smart grids, smart buildings, or other EVs. This sensitive data includes users’ locations, driver’s licenses, and more. Therefore, preserving their privacy is a major concern. This paper investigates the existing privacy-preserving solutions developed in the literature to address these issues, including blockchain technology which is essential in protecting user data and thwarting privacy attacks around the charging station-to-vehicle (CS2V) and vehicle-to-vehicle (V2V). Additionally, privacy-preserving context-based online dispatching systems (PCOEDS) are presented as an effective strategy to authenticate users’ information and protect their privacy in the context of vehicle-to-building (V2B) charging/discharging. Finally, a thorough comparison of the existing works is presented along with future research directions that will serve to further enhance the security levels of future EV transportation systems.
Blockchain-based Mutual Authentication Model for Customer Services

The Identity Management (IDM) system faces a challenge in its current state due to the Public Key Infrastructure (PKI) based identity verification process that involves stakeholders sharing excessive information with a trusted central party. This centralization of IDM is problematic, intricate, and increases the risk of identity theft and privacy infringement. Thus, it is imperative to prioritize the privacy of stakeholders to foster trust and establish a positive reputation with service providers. To address this issue, this study recommends a decentralized approach by implementing a blockchain-based mutual authentication model. This model integrates QR codes to conceal Personally Identifiable Information (PII) and facilitate mutual authentication. Additionally, the blockchain serves as an immutable reference point for validation which helps proposed model to achieve 100% accuracy in average 11s of authentication time taken, stable and reliable.
Combination of Local Outlier Factor and Winsorization for Clustering Outlier in Medical Records

In medical records, the doctors need to stay updated about the condition of their patients, but it would make a problem for the medical records. The medical records would be inconsistent due to changes in information regarding the result of examinations and leads to data that contains noises and outliers. But in medical records, the data sometimes can be mistaken as noise and it cannot be deleted as it is, because the medical records might contain one or more important information. In this research, the medical records are taken from one of the Regional Public Hospital in North Sumatera with limited work of ethics, and efforts will be made to solve the outlier problem in medical records by clustering the outliers into normal cluster by combining the Local Outlier Factor and Winsorization technique. The dataset consists 2 medical record data which contains data on outpatients and inpatients with various types of diagnoses in the last 3 years, and the variable that will be used are Gender, Date of Birth, and the Diagnosis, but it will be reduced into 2 variables, which is Diagnosis and Age (Years Old). In the end, the results are evaluated using a Confusion Matrix to see the percentage of performance for the proposed combination between the Local Outlier Factor method and the Winsorization technique, and the results are: Precision 96.8%, Recall 99.8%, Accuracy 96.9%, F1 Score 98.3%. The results have achieved the best percentage between the K-values.
NFC Integrated IoT based Attendance with Data Analytics

This paper introduces a smart attendance system that integrates NFC and IoT technologies to enhance the monitoring and analysis of attendance data in classrooms. The study aims to examine the effectiveness of this system in improving attendance tracking and reducing teacher workload. Two research questions are addressed: 1) Does the integration of NFC and IoT technologies enhance attendance monitoring and analysis? 2) Does the proposed system significantly improve attendance tracking efficiency and reduce teacher workload? The study proposes two hypotheses: Hypothesis 1 suggests that the integration of NFC and IoT technologies significantly enhances attendance monitoring and analysis, while Hypothesis 2 states that the proposed system significantly improves attendance tracking efficiency and reduces teacher workload. The proposed smart attendance system's conceptual contribution lies in its integration of IoT and NFC technologies, offering an efficient and reliable attendance tracking solution with data analytics capabilities. It contributes to the IoT field by showcasing the effectiveness of IoT devices and data analytics in an educational context. Moreover, it enhances the education field by enabling better monitoring and analysis of students' attendance and performance. The methodology involves a novel approach that combines IoT and NFC technologies for attendance tracking. The system's performance will be evaluated through experiments to determine its effectiveness in enhancing attendance tracking and reducing teacher workload. This empirical evidence will provide insights into the potential of the proposed system to optimize educational processes and outcomes.
Credit Card Fraud Detection using TabNet

The adopting of cashless payment methods, such as credit card payments and online transactions, has significantly enhanced the payment experience and added convenience to our daily lives. However, with the increase in cashless payment usage, financial fraud has become more sophisticated, posing a significant challenge to the security of these payment systems. In response, machine learning-based approaches have gained popularity in fraud detection. In this research paper, we propose the application of a deep tabular learning model, TabNet, for classifying transactions into fraudulent or non-fraudulent categories. TabNet utilizes a sequential attention mechanism to learn from tabular data. It comprises a series of decision steps where each step selects relevant features and updates the internal representation of the data. This mechanism enables the model to effectively capture complex, non-linear relationships between features, making it highly effective for fraud detection. The utilization of TabNet in fraud detection can contribute to strengthening the security of the payment system and reduce the chance of financial fraud. To evaluate the efficacy of our proposed approach, we conducted experiments on three widely used credit card fraud datasets, including the MLG-ULB dataset, the IEEE-CIS Fraud dataset, and the 10M dataset. Our experiments revealed that TabNet outperforms the state-of-the-art approaches across all three datasets, demonstrating its robustness and effectiveness in detecting fraudulent transactions.