Reviewerly/exHarmony huggingface dataset:
Public Awareness of Phishing in Indonesia: System Literature Review

In an era where technology is advancing at an unprecedented rate, the internet has become an indispensable tool for everyday life, offering both convenience and efficiency. While the internet has simplified tasks ranging from communication to information retrieval, it also poses significant challenges in the realm of cyber security. Users are not just potential victims but can also become unwitting perpetrators of cybercrimes. This highlights the urgent need for cyber security awareness among the general populace. Focusing on Indonesia, a nation where cyber security education is still in its infancy, this study aims to assess the level of public awareness regarding the specific threat of phishing. Employing the Systematic Literature Review (SLR) methodology, this research analyzes a comprehensive range of literature encompassing subjects such as phishing, data leakage, broader cyber security threats, and the current state of public awareness about phishing. The objective is to provide a detailed overview of existing knowledge and identify areas where educational efforts should be concentrated. The results indicate a considerable gap in the understanding of phishing among Indonesians. Many studies reviewed did not offer a clear definition or explanation of phishing, creating an environment where individuals are more susceptible to these cyber-attacks. This research underscores the urgent need for targeted educational initiatives aimed at increasing public awareness and understanding of phishing and other cyber threats.
Performance Analysis of Hyperparameter Darknet-Based Model on 3D Object Detection Autonomous Driving

Autonomous driving is an artificial intelligence development technology equipped with a sensor camera called LiDAR (Light Detection and Ranging). LiDAR is the data that can retrieve the objects from the point cloud in 3D(3 Dimensions). Since the LiDAR data is used to detect the object in the autonomous driving, the method that can compute fast and accurately is needed. In this paper, investigating the complex You Only Look Once (YOLO) version 3 Tiny and complex YOLO version 4 Tiny which are Darknet-based Tiny Model are performed to detect the object based on LiDAR data for autonomous driving application. Further, the tiny models are used in order to reduce the complexity and computation time. Moreover, the method of changing the momentum value is carried out to improve object detection performance in autonomous driving. Input data that used in the form of velodyne obtained from KITTI (Karlsruhe Institute of Technology and Toyota Technological Institute) Benchmark. Output result on this research is in the form of a model that already has a bounding box in each object with the results of its performance accuracy. The analysis carried out in this research focuses on the value of momentum 0.1, 0.5, 0.9 and 1.0. The best performing model is found in Complex-YOLOv4-Tiny with 0.1 momentum which produces mAP value of 75.3%.
Aspect Based Sentiment Analysis Using Recurrent Neural Networks (RNN) on Social Media Twitter

One of the social media platforms that has seen the most success in Indonesia is Twitter. Because of the enormous number of users and the intensity with which it is utilized, Twitter may also be used to perform sentiment analysis to search for information relating to a product or topic. On Twitter, movie reviews are consistently one of the most popular topics of conversation. Because everyone’s thoughts on movie reviews might refer to a variety of elements, aspect-based sentiment analysis is a method that can be used to discover the opinions that Twitter users have regarding movie reviews based on those characteristics. As a result, this method can produce more accurate and useful results. The research developed an aspect-based sentiment analysis system by using datasets of movie reviews written in Indonesian. These datasets consisted of three aspects: the plot, the acting, and the director. The classification model makes use of the Reccurent Neural Networks (RNN) approach, which extracts features using TF-IDF and expands on features using FastText. Unbalanced data is handled with SMOTE. The research found that plot elements had an accuracy of 77.24 percent and an F1 Score of 77.19 percent, while acting aspects had an accuracy of 96.59 percent and an F1 Score of 96.59 percent, and director aspects had a 97.75 percent accuracy and an F1 Score of 97.74 percent.
Face-recognition based Attendance Authentication System

Attendance in a classroom lecture can be monitored as a means of tracking student participation. However, the current method of recording attendance using paper-based systems and QR code scanners has proven to be unstable, inefficient, and time-consuming, especially in large classrooms. Differentiating between absentees and proxy attendees becomes challenging with the traditional approach. To address these issues, a web-based attendance system with facial recognition capabilities has been developed. This system integrates an existing web camera with a facial recognition system, enabling automatic and efficient attendance tracking with minimal setup requirements. Through this web application, lecturers have the option to reject student attendance if they violate rules and regulations, and they can also generate attendance reports. Similarly, students can view their attendance percentage for each registered course. Additionally, this research proposes the integration of smart contract-based blockchain technology into the system. In conclusion, this system aims to assist lecturers in monitoring student attendance by utilizing facial recognition technology with an accuracy rate of 98%, while the proposed smart contract ensures secure and efficient management of attendance records.
Comparison of Bandung Social Media-based Sentiment Classifier using Multinomial Logistic Regression and Gradient Boosting Models

Social media has become a tool for citizens to express their opinions and comments. In this study, we followed a sentiment analysis approach to classify Instagram comments. Our methodology involved constructing an Indonesian-based social media data dictionary, collecting social media data, performing data pre-processing, training, and classification, and implementing the model on social media. We categorized the comments into several categories, namely, health, education, administration, economy, infrastructure, citizenship, social, and environment. We built an Indonesian-based social media data dictionary taken from a news portal, comprising 2,205 records. Additionally, we scraped 800 Instagram comments. Subsequently, we pre-processed and trained the dataset using two models: multinomial logistic regression and gradient boosting. Our experimental results show that the Gradient Boosting model demonstrated higher accuracy than the Multinomial Logistic Regression, both on the training and testing dataset: 98.07% and 73.11%, respectively. The results indicate that Gradient Boosting is more reliable than the Multinomial Logistic Regression for our small dataset. However, we identified that our model misclassified certain comments into different categories. This result needs for further analysis to improve the accuracy and precision of the model’s capabilities.
Indonesian-Kailinese Machine Translation

The Kailinese language, spoken in Indonesia’s Central Sulawesi Province, faces challenges due to limited everyday usage. According to an article published on the brin.go.id website, some dialects of Kailinese have only four family speakers. To address these concerns, this research introduces a dedicated machine translation model and dataset for Kailinese. Our translation system utilizes the IndoBART-V2 model, enabling seamless translations between Indonesian and Kailinese. We conducted two testing scenarios: one with a diverse dataset of reviews and random topics, and another focusing on review-type datasets. By employing default parameters and preprocessing techniques using the “Colloquial Indonesian Lexicon Dictionary,” our translation model achieves impressive SacreBLEU scores compared to not using preprocessing or changing default parameters. For scenario 1 (Indonesian to Kailinese translation), the model achieves a score of 19.8, while for scenario 2 (Kailinese to Indonesian translation), the score is 23.0. In scenario 2, where both training and testing data consist of review-type sentences, the model achieves scores of 18.4 and 22.7 for Indonesian to Kailinese and Kailinese to Indonesian translations, respectively. These results demonstrate the effectiveness and accuracy of the developed model. Furthermore, our analysis reveals that sentence composition significantly influences the model’s performance, with no notable difference between scenario 1 and scenario 2. This emphasizes the importance of considering sentence types in the translation model.
Noisy Image Filtering Methods for an Improved Sundanese Script Handwriting Classification

The Sundanese script is a cultural heritage of the Sundanese people from the land of Sunda, West Java. The Sundanese people used the Sundanese hand when they first recognized writing. Image is visual information, but not all images are of good quality. Sometimes there is noise that can damage the information in it. Therefore, this research improves image quality on Sundanese handwritten image data using the Arithmetic Mean Filter and Gaussian Filter methods to be classified using the Support Vector Machine method. The Arithmetic Mean Filter and Gaussian Filter methods effectively improve image quality. We can also see this from data classification results before and after improving image quality. Noise data on Gaussian noise, salt and pepper noise, and spackle noise before being improved gets an accuracy value of 19%, 54%, and 52%. After being improved using the Gaussian Filter method, the accuracy value increases by 52%, 56%, and 70%. Meanwhile, using the Arithmetic Mean Filter method, the accuracy value increases by 59%, 65%, and 74%. Based on the overall test results, we can conclude that the Arithmetic Mean Filter method outperforms and yields better results than the Gaussian Filter method in enhancing image quality for the classification of Sundanese script handwriting.
Automatic True/False Question Generation using Template-based Framework

Indonesia is a developing country that is experiencing developments in the field of science and technology. To support this development, it is also necessary to improve Indonesian students’ way of thinking. In this paper, we propose a model of generating true/false questions automatically using a template-based framework so that true/false questions can be used as a method of evaluate the way of thinking of Indonesian students. We evaluate the model with qualitative and quantitative testing methods. We use 3 criteria on qualitative testing: fluency, relevance, and answerability. The results of human evaluation for model quality experiments show that the majority (>%) assess the model output to be in the range between ‘okay’ and ‘good’ for fluency, relevance, and answerability. On the other hand, quantitative evaluation results show that the model we made was able to generate up to 3 questions per input sentence, but the number of questions generated by the methods in the template-based framework model in this paper differs significantly. Specifically, the synonym/antonym substitution method in the model generates an excessive number of questions, while negation modification method generates an insufficient number of questions. We analyzed that there is still ample room for improvement especially in terms of the fluency of the generated questions.
Analysis of CNN Optimizer for Classifying Letter Images

Given the advancements in technology, numerous experts are focused on creating resource-efficient detection systems for devices while maintaining high accuracy in recognizing specific objects, such as characters. This study involves the development of a system designed to accurately classify images of alphabet letters that are written in children’s handwriting. The main objective is to create a robust and efficient system that can effectively recognize and classify the letters in children’s handwriting, the system’s classification capabilities will contribute to enhancing handwriting recognition technologies which can have various applications in educational tools, automated document processing, and other relevant domains. Convolutional Neural Network (CNN) algorithm is used as a method of making the system, where CNN can recognize letter images without using additional feature extraction algorithms. The research demonstrates that Convolutional Neural Network (CNN) achieve a high level of accuracy in handwriting classification. The results indicate that the CNN model accurately recognizes handwriting, with an impressive accuracy rate of 96% obtained from the training phase. Based on the analysis carried out, the best architectural results is obtained using the proposed scheme for 80%: 20% train validation data and learning rate of 0.001 with the accuracy result 99% obtained from the testing phase.
Naive Bayes Classification Model for Precondition-Postcondition in Software Requirements

The quality of a test case primarily depends on the software requirements. However, manually identifying crucial elements like preconditions and post-conditions within software requirements can be time-consuming and labor-intensive. This challenge has prompted a research study to propose a novel approach for test case generation using text classification. The proposed approach involves categorizing software requirements into two labels: ”none” and ”both.” These labels indicate the presence or absence of preconditions and post-conditions in software requirements. To achieve this, the research employs the Naive Bayes algorithm, a widely used probabilistic classification algorithm in text classification tasks. The algorithm leverages two libraries, namely Scikit-learn and Natural Language Toolkit (NLTK). The Scikit-learn model proves quite effective through experimentation, achieving an impressive accuracy score of 0.86. This result demonstrates the feasibility of reducing the effort and time required for classifying test case components based on software requirements. By automating this process, the proposed approach offers a promising route for enhancing the efficiency and effectiveness of test case generation in software testing. By leveraging text classification and machine learning techniques, the proposed approach not only streamlines the identification of essential components in software requirements but also opens up possibilities for further automation and optimization of the testing process.
Sentiment Analysis of 2024 Presidential Candidates in Indonesia: Statistical Descriptive and Logistic Regression Approach

Sentiment analysis is a technique for analyzing public sentiment or a particular segment of sentiment toward an object. One of the implementations of sentiment analysis is toward potential presidential and vice-presidential candidates in the upcoming 2024 Indonesian Presidential Election (Pilpres). This upcoming Presidential Election is a topic that is frequently discussed in Indonesian politics, by voters, various political parties, and people’s community daily conversation anywhere. Many prospective candidates are rumored to be supported by various political parties to become their presidential and vice-presidential candidates, without any concern about their reputation and sentiment among targeted voters. However, the sentiment towards these candidates was not always clear, whether it leans towards positive or negative. To address this issue, this sentiment analysis uses Logistic Regression and Descriptive Statistics to do classification and analysis of the sentiment of tweets regarding each of the prospected candidates. The result of the sentiment analysis was a polarity score of tweets for these prospective candidates. The Logistic Regression method has an overall average accuracy score of 79% on all five prospected 2024 presidential candidate datasets. The highest accuracy is obtained at 93% on the Prabowo Subianto dataset and the lowest at 70% on the Anies Baswedan and Muhaimin Iskandar datasets. This analysis also provides valuable insights into the public sentiment toward potential candidates for the 2024 Presidential Election in Indonesia.
Penerapan Program Literasi Numerasi Pada Pemecahan Masalah Matematika Kelas V Studi Kasus Di MIN 2 Mojokerto

Numeracy literacy can help improve counting with examples of problems that exist in everyday life. Thus, it can be seen that the existence of analytic knowledge has an important meaning in learning mathematics in order to reason and help students solve problems. The purpose of this study is to describe and analyze the factors that support and hinder the implementation of the digital literacy program to solve the problems of grade 5 students in the MIN case study. 2 Mojokerto. This research method uses a qualitative approach to data collection, using observation, interview, and note-taking techniques. Thus, it can be concluded that the implementation of this digit literacy program has been done well, which can help improve the math problem-solving ability of 5th-grade students through 3 steps, which is the familiarization phase with the activity. Read aloud 15 minutes before studying. beginning, the development phase of literacy activities, where students have the opportunity to present the results of group discussions to develop student skills, and the stage where teachers learn using interesting concepts and strategies so that students can complete material taught through arithmetic knowledge.
Penerapan Metode PAIKEM Pada Pembelajaran Bahasa Jawa di Madrasah Ibtidaiyah Kelas Rendah

This study will discuss the application of the PAIKEM method to learning Javanese in lower grades. This type of research is qualitative research using a qualitative descriptive approach. Research data were obtained through observation and interviews conducted directly at MI NU Manafi'ul Ulum 01 Gebog Kudus. The results of research conducted at MI NU Manafi'ul Ulum 01 Gebog show that the application of the PAIKEM method to learning Javanese in low grades has a positive impact on students. In the application of the PAIKEM method at MI NU Manafi'ul Ulum 01 Gebog Kudus, Javanese language learning is carried out through three stages, namely the first stage of planning, in the planning process for the PAIKEM method it is explained that the teacher plans and implements teaching and learning activities that motivate students to play an active role in their education. In the second stage, namely implementation, the PAIKEM method is carried out by implementing learning by stimulating active, innovative, creative, effective and fun learning. Through the use of this method, learning Javanese is very effective, to be applied to lower grades in MI/SD because applying this method can make students more active, not bored, and excited during the learning process.
Implementasi Model Pembelajaran Numbered Head Together (NHT) Pada Materi Magnet Kelas VI MI Nurul Ulum Sidomukti

The purpose of this research is to describe how an implementation of the Numbered Head Together (NHT) learning model in the magnetic material procedure for students in class VI MI. Nurul Ulum Sidomukti. Research being carried out at this time uses the library method where the method originates from journals, books, or others. The subjects used in the study were students of class VI MI. Nurul Ulum Sidomukti, totaling 25 students. The analysis used in this research is using content analysis where the analysis is carried out by an in-depth study that is related to the content of written and printed news in the media. The results of this study revealed that the Numbered Head Together learning model in class VI MI magnetic materials. Nurul Ulum Sidomukti has been running quite well and has experienced an increase in understanding of learning material. This activity is carried out because there is an implementation in learning, namely there are four stages in the Numbered Head Together learning model including numbering, asking a question, thinking in groups, and sharing answers.
KNCD: Knowledge-Enhanced Network for Cyberbullying Detection

In the past few years, the growth of online social networks has led to an increasingly serious problem of cyberbullying, making its detection a pressing issue. In this study, we propose the Knowledge Enhancement Network for Cyberbullying Detection (KNCD), an approach that utilizes the Large Language Model (LLM) to extract global information from the labeled data to enhance the knowledge of individual samples. Specifically, KNCD extracts essential information from representative samples with the LLM. After text encoding, the K-Nearest Neighbor (KNN) approach is applied to align this extracted knowledge with the samples. By combining the global knowledge with the samples, the semantic information of the samples is expanded. Experimental results on tweet data show that our KNCD model achieves promising performance in cyberbullying detection.
Enhanced Implicit Collaborative Knowledge Graph for Recommendation

Knowledge-aware Recommendation (KGR) with Graph Neural Network (GNN) aggregates information on the knowledge graph to capture high-order knowledge representations for the recommendation task, which becomes the mainstream approaches in the field of KGR. While these studies have achieved great success, we emphasize that strict supervision signals of training can have negative impacts on the model. Specifically, the supervision signals force the model to align constantly with sparse positive interaction data, which can result in the loss of valuable knowledge semantics. Moreover, it may also weaken model's ability to explore the potential interests for user. To address the aforementioned issues, we propose a new model named Enhanced Implicit Collaborative Knowledge Graph for Recommendation (EICKR), which uses a multi-task learning schema to extract user interests. This model calculates implicit relations among users, items, and entities to structure rich implicit features across different views. Subsequently, it leverages the knowledge graph and users' historical interaction data to generate an implicit collaborative knowledge graph, bridging the semantic difference between the knowledge graph and interaction data. Furthermore, a graph-enhanced completion task based on implicit relations is introduced to explore unobserved yet valuable interaction features among users, items, and entities, which can be complementary to the recommendation task. We conduct extensive experiments on benchmark datasets, which demonstrates the effectiveness of our approach and its components.
Research on Navigation Light Sheets Cleaning Target Detection Technology Based on YOLO-v5s

Carrying out the cleaning and maintenance of airport navigation light sheets is crucial for ensuring safe aircraft takeoff and landing, and the detection of navigation light sheets is the fundamental step in this process. In this study, we utilized the YOLO-v5s system to establish a target detection model for navigation lights and conducted experiments involving image annotation, parameter selection, model construction, model training, and prediction. The research findings demonstrate that the YOLO-v5s target detection model has reasonable parameter selection and achieves accurate training results. The recognition accuracy of the navigation light target detection model is 95.8%, with a mAP@0.5 value of 95% and a Recall value of 94%, indicating that the model's predictions meet the requirements for precision and real-time performance in practical applications. Moreover, the model exhibits high intelligent recognition accuracy in different conditions, such as normal and low light conditions, demonstrating excellent generalization ability and robustness, making it suitable for complex engineering scenarios in practice.