Reviewerly/exHarmony huggingface dataset:
Usability Testing Analysis of a Company Website in Indonesia

Usability testing is a method of evaluating the user experience of a product or service by testing it with real users. Usability testing is an essential step in the product development process, as it helps to ensure that the product is easy to use and meets the needs of its intended users. Websites are an important communication and interaction channel for many businesses, and it is important to ensure that they are easy to use and provide a good user experience. It provides various information technology facilities that can support companies in improving and optimizing their business processes. However, some website still has deficiencies in terms of website interactivity and a responsive display when the website changes to a User Interface display on several gadget devices. Therefore, the main objective of this testing process is to ensure that each scenario in the application has run as expected and meets the standard requirements written in three main aspects: System Aspect, User Aspect, and Interaction Aspect. This study uses the UAT (User Acceptance Testing) method to determine the responses of respondents (users) to the system that has been built. Based on the data from the questionnaire results, three aspects are assessed: the system aspect, which gets an average score of 3.243, and the user aspect, which is around 3.186. and interaction aspects with an average value of 3.22. In short, the research that has been carried out using usability testing analysis in our case study has generated positive ratings that can satisfy users when accessing the website, although some respondents still give pretty bad ratings.
An Investigation on Speech to Sign Language Translator for Hearing Impaired (SSLT) using Machine Learning Techniques

The language of the hearing impaired people is a visual language. It has to transmit sound patterns in the form of signs. A person’s thoughts are expressed by hand signals and facial expressions. Deaf people normally get struggle by making conversation with normal people; The languages will be different for the various group of deaf people all over the world as well. Our project is to guide the hearing deaf or speech defected persons made communicating with normal persons. It automatically translates the speech in English into Indian sign language. It is the sign-language translating system. For the communication between the normal person and the impaired persons, it could be used as a translator for their natural way of speaking. It’s helpful for people who didn't understand sign language, the sign language gestures are emojis. Our proposed model brings the accuracy improvement by 10% compare to existing models of CNN based classification and SVM_HMM models. FPV and PPV improved by 23.52 % and 37.34 %.
A Time-Window Approach to Recommending Emerging and On-the-rise Items

The recommendation system (RS) filters large sales transaction data, to promote item Y as an alternative or pair to item X that the application user is looking for. Items that are no longer in season usually decrease in transactions, even to zero. Constantly recommending these items is irrelevant, while other items that are more relevant and profitable are not recommended, such as the emerging and on-the-rise items. The proposed solution to this problem is the time window approach, which is a block of data of a certain size and recorded at a certain time stamp. Item combinations (itemsets) are mined from each window with an association rule approach, and changes in the number of transactions containing these items are evaluated from window to window. From the number of transactions that contain the itemsets, the system distinguishes status items into four types: risky, normal, emerging, and on-the-rise. Items that are currently suffering from zero sales risk the same fate in the next window, so they are called risky items. The system will notify admins to review this item, and look for other emerging or on-the-rise items to promote along with item X. Experiments were carried out on two datasets, and it was found that 42.7% and 59.4% items from each dataset are risky.
Mobile Augmented Reality for Japanese Vocabulary and Hiragana Letters Learning with Mnemonic Method

Learning a foreign language is a must in the current era of globalization. This also includes learning Japanese for the Indonesian people, considering that Japan is a developed country, both in terms of education, technology, and strong culture. Learning Japanese, can be started at an early age by introducing vocabulary to objects around them. This study develops an educational media using Augmented Reality and a mnemonic approach to teach Japanese vocabulary and hiragana letters. The mnemonic approaches employed in this application includes visual imagery techniques that display 3D objects utilizing Augmented Reality, organizational approaches for classifying Japanese vocabulary, and rhyme and jingle techniques for memorizing words through songs. According to the study's findings, this application can aid children in learning, remembering, and comprehending Japanese vocabulary. The rise in child respondents' average pre- and posttest scores, which ranged from 11 to 80 out of a possible 100 points, indicates significant effect of the use of applications on improving children's learning outcomes. These findings are supported by the opinions of respondents who strongly agree that this application can be an educational media for learning Japanese vocabulary for children.
Overhead Line Insulator Type Classification Using YOLOv3 Architectures

Overhead line insulators are among key elements in any power transmission and distribution systems. They are used for line insulation and fastening. Insulators support the electrical safety and isolation integrity of the overhead lines as well as substation equipment. Thorough inspection of insulators is extremely important as a premature failure potentially causes power outage leading to significant expenses to electricity providers. Over the past years, visual inspection of the insulators via unmanned aerial vehicle (UAV) imaging became quite popular since this method enables early identification of insulator faults in a shorter time period with least safety threats to personnel. In this regard, solutions based on artificial intelligence (AI) are highly beneficial in reducing costs and increasing efficiency in overhead power line inspections. In this work, a state-of-the-art object detection algorithm, namely, You Only Look Once (YOLO), is trained for insulator detection and type classification. In particular, YOLOv3 model and its variants, namely, YOLOv3-SPP and YOLOv3-tiny, are trained using 1832 images of different post- and suspension-type insulators. The results demonstrate that YOLOv3 and its variants can successfully detect and identify the insulator type despite external challenges such as crowded image, low brightness, and partially imaged test object. In particular, YOLOv3 showed a slightly better predictive performance than its variants. In applications where computational efficiency is desired, YOLOv3-tiny is an attractive alternative due to its lighter architecture with ~7 times fewer parameters than YOLOv3. Effective implementation of robust insulator localization and type identification technique are essential steps in development of an automated insulator condition diagnostic tool which is the primary motivation behind this study.
Conditional Random Field for Crime News Information Extraction with Enhancement of SMOTE

Crime Information Extraction is a task to extract some entities from crime incident. Getting information from digital crime news is a task that can help people to know what happen from crime-related information. This task is one of the Natural Language Processing tasks that help people to analyze crime information news documents. The purpose of this task is converting the unstructured data from crime news article to structured data text form. This task can be done with rule based or machine learning algorithm. In this research, we used Conditional Random Field and SMOTE that enhancement with some addition rules. Because the unbalanced data from CRF labeling so we need to handle the lack of data from some of categorize. We conduct training to extract 5W1H from 3 digital crime news which consists of 1500 news. We extract news into 13 classes and build a model based on the training data. The test is carried out with real data which is crawled automatically and produces an accuracy of 77.52% for the SMOTE model and 75.61% for the NONSMOTE model. The SMOTE development was successfully used to better extract crime information for data that was not in the training data.
Hybrid Frequency Domain based Feature Extraction methods for Human Face Recognition

With the rising demands of public safety systems, face recognition has gained extra notice for the researchers in recent times. Real-world face recognition systems require cautious balancing of two important concerns: execution Time, recognition rate. The most important methods for feature extraction and classification are Dimension Reduction-Discrete Cosine Transform (DR-DCT), Dimension Reduction-Discrete Fourier Transform (DR-DFT) and Dimension Reduction-Difference of Gaussian (DoG) along with the Extreme Learning Machine (ELM) classifiers. The feature vector of the proposed algorithm is reduced by means of subspace method Principal Component Analysis (PCA). The execution time of the proposed DR-DFT, DR-DoG and DR-DCT algorithms along with ELM classifier is less when compared to the traditional methods such as Hough transform, Radon Transform, Discrete Fourier Transform (DFT) and Discrete Cosine Transform (DCT) for ORL face database. Similarly, Hybrid methods are proposed by combining DR-DCT &DR-DFT (Hybrid Method 1), DR-DCT & DR-DoG (Hybrid Method 2) and DR-DFT & DR-DoG (Hybrid Method3) along with ELM classifier. Hybrid Method 1 attains 98.50% recognition rate with the feaure size of 60×1 for ORL dataset. It achieves optimal execution time of 0.020 sec.
Implementation of Discrete Cosine Transform and Permutation-Substitution Scheme Based on Henon Chaotic Map for Images

The use of images from the internet that are not obtained from their creators is a copyright infringement. Illegal access to confidential images can be detrimental to the owner of the image. Things like this can be prevented by securing digital images through steganography and encryption processes. The methods that can be used to perform steganography and secure encryption of images are the Discrete Cosine Transform (DCT) method and A New Permutation-Substitution Scheme Based on Henon Chaotic Map for Image Encryption. Encryption uses the Henon Map algorithm to generate a key and the encryption process is carried out by randomizing each pixel in the image then XOR is performed to encrypt the color of the image. The DCT method is a lossy compression scheme where <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{N}\times\mathrm{N}$</tex> blocks are transformed from the spatial domain to the DCT domain. The results of this study can hide and secure the image using a Discrete Cosine Transform and A New Permutation-Substitution Scheme Based on Henon Chaotic Map. The best combination of key-value <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{X}_{0}=0.017$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{Y}_{0}=0.061$</tex> . The result stego image is resistant to 0.1% noise attacks but is not resistant to 1% noise attacks and changes in contrast because the value of pixel changes too much so it can affect the message image that hides in the cover image.
User Experience Evaluation of IT Support Mobile Application Using System Usability Scale (SUS) and Retrospective Think Aloud (RTA)

The development of today's digital world has been running more rapidly at the time of the presence of covid-19 pandemics, in which all aspects of life are very dependent on digitalization that time, and companies keep doing the work of enhancing innovation in their businesses, which is the developing of IT such as mobile application. PT XYZ is one of the companies engaged in printing and digital services. PT XYZ uses the mobile application IT Support to assist technicians in completing reports of printer damage experienced by consumers. During the order services process, users oftenly feel that menu on mobile application IT Support not effective and efficient yet on helping them to process order service, the elements in the mobile application are inconsistent so it complicated the user. To improve and finding the usability problem specifically needed the process user experience evaluation mobile application with system usability scale (SUS) and retrospective think aloud (RTA). Total sample used in the research was 52 respondents from active user IT Support using purposive sampling technique. The SUS score result is 50.77 in grade D. The less satisfied, because the SUS score is still smaller than 68. Retrospective Think Aloud (RTA) results with 11 respondents showed problems with usability and needed recommendations for interface improvements. The result of user experience evaluation is user interface recommendation to enhance the technician's performance to be more optimal. (Abstract)
Multibranch Convolutional Neural Network For Gender And Age Identification Using Multiclass Classification And FaceNet Model

The human face provides a wealth of information regarding gender, age, ethnicity and emotions. Gender and age are considered as important biometrics and attributes for the identification process. However, the identification of gender and age is influenced by many dynamic factors that can change over time such as aging, hairstyles and expressions. The identification process have a problem in accuracy and the loss, several face recognition methodologies have been tried to overcome these dynamic factor problems, one of them is multibranch convolutional neural networks. The previous studies used these methods to deal with overfitting and backpropagation, but other supporting methods are still needed to increase the accuracy. The goal of this work is to optimize accuracy and mean absolute error (MAE), multiclass classification it's used to grouping data for age and facenet is used to solve problems related to face verification and overfitting, multibranch convolutional neural network (CNN) can be used to optimize backpropagation and reduce the error rate by adjusting weight based on the difference in output and the desired target.
The Comparison of Sentiment Analysis Algorithm for Fake Review Detection of The Leading Online Stores in Indonesia

Online store users continue to grow and develop throughout the year so that the number of reviews from customers is also increasing. Customers can write reviews about the products and services and then post them on social media, whether it's a good or bad review. The reviews written are also very diverse such as product specification review, product advantages, product disadvantages and so on. Sometimes it is difficult for readers to determine which reviews are real and correct because there are so many written reviews. Reviews really play a very important role for potential customers to decide whether to buy the product or service or not. This study used several algorithms of sentiment analysis to detect fake reviews of online stores on Twitter. The algorithms were used to calculate the accuracy value of the review. The algorithms used were support vector machine, Naive Bayes, and logistic regression. The accuracy values of the three algorithms were compared and the highest is selected. The review text that was processed to produce fake review detection comes from the 3 leading online stores in Indonesia namely Tokopedia, Shopee, and Bukalapak. The results of calculating the accuracy value for real data using support vector machine is 70.58%, using naïve bayes is 69.89%, and using logistic regression is 70.49%. Comparison of these three results determines that the most appropriate algorithm for detecting fake reviews of online stores is the support vector machine algorithm.
Design and Simulation of Object Detection Based Autonomous Trash Collector Bot

Nowadays, due to the increased population, usage of public places becomes very high. However, garbage is left uncleaned in most public areas. There is no constant survey of the area at a given time so that the waste gets accumulated. The most crowded places like malls and parks remains unclean. This affects not only the environment but also the mindset of the people visiting the places, so the proposed idea provides a great solution to these problems through the following steps. It continuously monitors a given area and detects the presence of garbage in that given area. The live image is then calibrated to detect the distance and to operate the arm to pick up the trash. The picked trash is then thrown in the collector attached at the back of the bot and released when it is full, on the established disposal area. This will help to clean the environment in a drastic way and also in attracting the people to visit the area. The bot will help in reducing the burden of human beings and also provides 24/7 maintenance to the predefined area. For a much larger area using this bot will be the most commercially feasible, the bot will not only be most effective for the above scenario but it will be most commercially effective as well as a viable option as well. This is the main objective for making a bot that does the above function. In this paper, a software has been developed using python with which various items can be identified and can be segregated as trash and not a trash item. The autonomous trash collector robot has been designed using sold works and Roboanalyzer softwares and the results are analyzed.
Spelling Correction Using the Levenshtein Distance and Nazief and Adriani Algorithm for Keyword Search Process Indonesian Qur'an Translation

The Qur'an consists of 114 letters which has a total of 6,236 verses and 77,340 words which contains a lot of important information. The length of time used to obtain the expected information becomes an obstacle when the search is done manually. Therefore, it takes a search engine that can help to get that information. The problem with the search engine is that the input spelling of words is wrong, resulting in incorrect information. Levenshtein Distance algorithm can provide a solution to correct key input errors. The Levenshtein Distance algorithm is an edit distance algorithm that has three operations that are used to measure the distance between two strings. We conducted experiments to correct spellings that did not match the search keywords for the Indonesian translation of the Qur'an using the Levenshtein Distance and Nazief and Adriani Stemming algorithms. Based on Levenshtein Distance operation, 70% success in Insert operation, and 100 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> success in remove and replace operation while on Stemming.
Hate Speech Detection in Code-Mixed Indonesian Social Media: Exploiting Multilingual Languages Resources

Hate speech in social media is becoming a relevant issue recently. Several studies have been proposed to deal with the hate speech phenomena in online communication. However, detecting hate speech messages from social media data is not a trivial task. Previous works have mentioned the problem of code-mixed languages in hate speech detection. As a matter of fact, Indonesia consists of several regions, each with its own local languages. Naturally, Indonesians tend to mix their own local language with Bahasa Indonesia when communicating in everyday conversation, including in social media communication, which contributes to the difficulty of processing Indonesian social media data. In this study, we plan to investigate hate speech detection in code-mixed Indonesian social media by exploiting several available multilingual language resources. Our experiment shows that the current available multilingual language model could not improve the model performance compared to the models which utilized the monolingual Indonesian language model. We also found that the most recent neural-based models are able to obtain better performance than the traditional model. For future work, we plan to implement a transfer learning approach to detect hate speech in Indonesian social media, specifically to deal with the code-mixed issue.
A Performance Comparison of Hashed and Authenticated Advanced Encryption Standard

The present era is witnessing IoT as a promising upcoming field affecting almost all spheres of human lives. However, IoT is facing more significant challenges due to the constrained nature of the devices. The most crucial challenge that hinders its widespread applicability is security. The exponential rise in security attacks necessitates ensuring the confidentiality, integrity and authentication of transmitted data sensed by these resource-limited devices. Although the new field of lightweight cryptography is attempting to secure low-end devices, conventional AES block cypher is still preferred due to the absence of standard lightweight crypto primitive addressing all three security aspects. Moreover, various integrity and authentication attacks can still compromise the encrypted data, which justifies the need for hashing and MAC algorithms. In this paper, two hashing (MD5 and SHA-256) and one authentication (HMAC) techniques available in the standard and well-tested ARM mBed TLS library have been used with the AES block cypher to achieve multi-level security. All these primitives have been implemented in KEIL MDK IDE and on ARM Cortex-M3-based LPC1768 hardware development IoT platform. Moreover, these primitives have been analysed and compared in real-time regarding various design metrics such as execution time, memory footprint, power and energy consumption using ULINKpro and ULINKplus debug adapters. The results aid the designers in selecting proper encryption, hashing and MAC algorithms per the availability of resources with various IoT development platforms.
Topic Modeling for Cyber Threat Intelligence (CTI)

Topic modeling algorithms from the natural language processing (NLP) discipline have been used for various applications. For instance, topic modeling for the product recommendation systems in the e-commerce systems. In this paper, we briefly reviewed topic modeling applications and then described our proposed idea of utilizing topic modeling approaches for cyber threat intelligence (CTI) applications. We improved the previous work by implementing BERTopic and Top2Vec approaches, enabling users to select their preferred pre-trained text/sentence embedding model, and supporting various languages. We implemented our proposed idea as the new topic modeling module for the Open Web Application Security Project (OWASP) Maryam: Open-Source Intelligence (OSINT) framework. We also described our experiment results using a leaked hacker forum dataset (nulled.io) to attract more researchers and open-source communities to participate in the Maryam project of OWASP Foundation.