Reviewerly/exHarmony huggingface dataset:
Public Government Web Based Services Quality Assesment Using Webqual And Govqual in Palembang City Government

Currently, most of city government agency develop their websites without paying attention to some essential issues that are reference to support the principles of a good governance. The Ministry of State Apparatus Empowerment and Bureaucratic Reform of the Republic of Indonesia has issued a KEPMEN No. 859 year 2019 which is concerning an electronic-based public services. In this Ministry policy the evaluation of Government E-service instruments. the evaluation instruments to determine the quality of government websites in Indonesia. This instruments consist of assessments variable which are efficiency, trusty, reliability, citizen support, usability, information, and system interaction. In this research, we will assess the extent of the quality of websites from various types of public servants in the city of Palembang using the instrument, in the conclusion show that each of instruments reach the highest index grade goes to The Health Government Office and the lowest index grade was at The Disdukcapil (civilization office) of their E-Service index.
A Case Study of Digital Forensic Readiness Level Measurement Using DiFRI Model

The digital forensic readiness of an organization shows the readiness level of the organization in dealing with cybercrimes. To measure the digital forensic readiness of an institute or organization, a digital forensic assessment tool is needed. There is a digital forensic readiness assessment model proposed by Widodo called the Digital Forensic Readiness Index (DiFRI). This model has been applied to assess the readiness of digital forensics at the Computer and Information Systems Center of Sunan Kalijaga State Islamic University in Indonesia. The DiFRI model can be used to measure the digital forensic readiness level of cyber organizations. In this study, we measured the digital forensic readiness of a cyber organization called XYZ using the DiFRI model. The measurement results show that the XYZ got a digital forensic readiness index value of 2.33 from a perfect score of 10.00. This value indicates that the XYZ is in the category of “Not Ready” in digital forensic readiness. Based on the analysis of indicators on digital forensic readiness, recommendations are given for each indicator aimed at increasing digital forensic readiness while increasing the value of the DiFRI index at XYZ.
Detection of Ovarian Cancer Risk Level Using the Web-Based Dempster Shafer Method

Ovarian cancer is cancer that grows in the ovaries or ovaries. This disease occupies the seventh position among the types of cancer that most attack women. Every year, there are about 250,000 cases of ovarian cancer worldwide, which causes 140,000 deaths per year. The purpose of this study is to improve the prediction of ovarian cancer symptoms using the Dempster Shafer method where patients can get to know more about the symptoms that may occur if a patient is diagnosed with ovarian cancer. Dempster Shafer is a mathematical theory for proof based on a belief function and reasonable thought, which is used to combine separate pieces of information (proof) to calculate the probability of an event. The results of this study, it is known that the risk density value for ovarian cancer is 0.32131497 or 32.131497%. With a density value of 32.131497%, then the patient has evidence that he is not at risk of developing ovarian cancer because it is more than 50%.
Concatenate Word Embedding for Text to Image through Generative Adversarial Network

Since the explosion of deep learning, automatic image generation from natural language is highly desirable through artificial intelligence (AI) as it makes it easier for users to create visually rich images through the ease of language. One of the methods used to generate images from text is GAN, in this study using word embedding is to process natural language and develop GAN by concatenate word embedding. In this work, we carry out our early-stage research which is to explore the simple technique of concatenate word embedding as the input of the GAN neural network. We show that this model is a novelty for the GAN model with the concept of multimodal input that is able to generate text to image and is expected to improve performance on a stronger GAN. Based on the explanation of our research method, this model can be implemented and can be developed for various GAN tasks such as style transfer, image to image, face inpainting or image repair semantically, and super resolution.
Public Private Data Partnerships enabling Privacy Technologies

Data is the core element of information which helps build artificial intelligence and other modern technology advancements. Data exchanges between different companies and public bodies has been a major contributor to development of society. This data exchange between different parties, must adhere to privacy preservation regulations. Best way to preserve privacy is to use anonymization. Though truly anonymized datasets are difficult to combine and loose utility as combined dataset. This inhibits collaboration between different parties for any usage of data where personal or personally identifiable information may be involved. This paper establishes a concept on data exchange partnerships in such a way that data can be combined though privacy is still preserved. Using the experiment demonstrated in this paper, data could be combined from two different datasets, without each party having access to the original dataset. Most advanced anonymization techniques can be used using this approach to anonymize data while it is combined using the suggested framework in this paper. The experiment uses technologies like docker and edge computing, though any other technology can be used provided it is bound by the rules defined in this concept. Such an approach can enable wider exchange of information between different parties across the globe, leading to accelerated research and innovation with public private data partnerships
Comparison The New Computer Sciences Study Programs in Indonesia using PyTrends

A new study program in Computer Sciences is increasing now. This phenomenon is because a computer is one of the essential part of modern human life that can support many fields of work today. More than 500,000 diploma, bachelor, and post-graduate computer sciences students are actively studying in Indonesia. So in this paper, we propose an experimental comparison of 23 new computer sciences study programs in Indonesia using PyTrends by time and region. We want to find what did the compluter sciences study program favorite in certain regions and how the trends of computer sciences study programs over time. The first stage of our research process is selecting interesting study programs. Conducting trend analysis by using region and time is the second stage. And the last stage is to analyze the conclusion and gives a recommendation. The result shows that Multimedia, Sistem Teknologi, Teknologi Komputer, Bisnis Digital, and Rekayasa Komputer are the most popular of a new computer sciences study program sorted by their popularity of top search queries in Google Search from 2017-07-23 until 2022-07-17 based on time and based on Region.
English-to-Sundanese Translation using Neural Machine Translation: Collection and Analytics

Neural Machine Translation (NMT) performs sophisticated technology of translation between particular languages accurately and perfectly compared with traditional Statistical Machine Translation (SMT). Current models like Transformer and NMT can produce better translation results. In order to create NMT, we need a large-scale parallel corpus for training and validation of NMT data. Existing parallel corpus such as WMT English-German or WMT English-France become benchmark dataset or tasks in WMT. Conversely, ethnic language parallel corpus like English-to-Sundanese ethnic language is rarely used. This paper presents the collection and analysis of large-scale English-to-Sundanese parallel corpus. Our Parallel corpus encompasses various topics like sports, economics, politics and others. We produced a BLEU score English-to-Sundanese 38.91 that indicated our corpus valid.
BERT-based Topic Modeling Approach for Malaria Research Publication

Malaria is a communicable disease with half of the global population at risk due to its high morbidity and mortality rates. A massive number of studies are dedicated to malaria research, so it plays a key role in formulating the proper prevention strategy and effective malaria treatment. With the overwhelming number of updated publications in the field, an unsupervised text mining approach such as topic modeling may provide an alternative method for the malaria researcher to keep pace with new insights. In this work, we collect metadata of malaria publications from the PubMed database to perform BERT-based topic modeling to find well-defined topics regarding malaria research. The method is largely based on the popular BERTopic pipeline. We compare the performance of three different language models to generate document embeddings from the data. The dimension reduction and the density-based clustering algorithm are used to cluster the embeddings. The topic representation is computed based on the semantic similarity of the class TF-IDF representation. The substance of the resulting topics is then manually annotated based on the top words of each topic. We demonstrate that by merging initial topics into larger topics using hierarchical clustering and manual content-based examination, the evaluated coherence measure can be further improved, thus enhancing the topic's interpretability. Our modeling result is able to extract ten major topics recurring in the malaria research publication published from 2017–2022. The result provides preliminary insight to understand the dynamics and patterns of malaria research over the years
Model for Detect Hand Sign Language Using Deep Convolutional Neural Network for the Speech/Hearing Impaired

Sign language is commonly used by hearing or speech impaired people to communicate with others. BISINDO is one of the sign languages that is commonly used in Indonesia. In this paper, this paper proposed a convolutional neural network model to detect hand sign language using Convolutional Neural Network to facilitate the communication between hearing or speech-impaired people and normal people. To facilitate people who have speech or hearing impaired, we can use the advance of technology. Technology lay on an algorithm or method. The objective of this research is to get the best model from the dataset that has been collected before. Convolutional Neural Network is one of the methods to detect sign language. These methods are data gathering, data processing, model training and testing using Convolutional Neural Network model. By using Convolutional Neural Network getting the optimum result by 96,85%.
Robustness of Quantum Key Distribution Against Quantum Phase Error and a New Attack Strategy

Quantum key distribution (QKD) has been considered as an absolute secure mechanism with one-time pad key management. However, the quantum engineering perspective of QKD has not been well investigated. In this paper, we take a quantum phase error into the quantum channel of QKD to explore the subsequent impacts on the system performance and security analysis. Furthermore, using quantum channel phase estimation (QCPE) we restore the QKD system and suggest a new security (attack) strategy for Bob (Eve).
Comparative Analysis of Usability, Performance, and Security of Open-Source, Windows-Based Password Manager Applications Based on ISO/IEC 25010

Many workers need multiple applications to carry out their works. The number of applications makes users have to set several accounts and passwords for various application services. This creates complexity in managing it manually so that users switch to using password manager applications, both free and paid. KeePass, KeePassXC, and Password Safe are three examples of open-source password manager applications developed as low-cost solution. In general, open-source password manager applications have disadvantages compared to similar paid applications. Analysis of open-source password manager applications is necessary to find out their specifications, advantages, and disadvantages so that they can be taken into consideration by users in choosing a suitable password manager application. In this study, a comparative analysis of three open-source, Windows-based password manager applications i.e., KeePass, KeePassXC, and Password Safe was carried out using the Usability, Performance, and Security parameters based on ISO/IEC 25010. The result of this study indicates that on the parameter of Usability, the KeePassXC excels in all six categories. In parameter of Performance, the Password Safe excels in one category, the KeePass also excels in one category, and the KeePassXC and Password Safe are equivalent in one category. Furthermore, on parameter of Security, KeePass outperformed others in one category and for the rest four categories, all applications are equivalent.
Sperm Cell Classification System Carrying X or Y Chromosome In Human With CNN Algorithm

Sperm cells contain two types of chromosomes: X and Y. The sex of a child is determined by the sperm cell that fertilizes the egg containing an X or Y chromosome. The X chromosome decides the female gender, while the Y chromosome determines the gender man. To determine the type of chromosome, the process of classifying the types of chromosomes contained in sperm cells is carried out. Developing a system for classifying X and Y chromosomes using the Convolutional Neural Network (CNN) algorithm aims to classify sperm cells carrying X or Y chromosomes. This CNN algorithm was chosen because this algorithm is perfect for the object detection process in the image. The CNN algorithm in this study uses two kinds of epoch values, namely 10 and 20. And using three kinds of number of neurons, namely 16, 32 and 128. The highest accuracy obtained in testing the number of epochs is 10 and the number of neurons is 16, and the number of epochs is 20 and the number of neurons is 32, which is 75,36%.
Implementation of Rail Fence Cipher and Myszkowski Algorithms and Secure Hash Algorithm (SHA-256) for Security and Detecting Digital Image Originality

The use of digital images is increasingly widespread currently. There is a need for security in digital photos. Cryptography is a technique that can be applied to secure data. In addition to safety, data integrity also needs to be considered to anticipate the image being manipulated. The hash function is a technique that can be used to determine data authentication. In this study, the Rail Fence Cipher and Myszkowski algorithms were used for the encryption and decryption of digital images, as the Secure Hash Algorithm (SHA-256) algorithm. Rail Fence Cipher Algorithm is a transposition algorithm that is quite simple but still vulnerable. It is combined with the Myszkowski Algorithm, which has a high level of complexity with a simple key. Secure Hash Algorithm (SHA-256) is a hash function that accepts an input limit of fewer than 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∧</sup> 64 bits and produces a fixed hash value of 256 bits. The tested images vary based on image resolution and can be encrypted and decrypted well, with an average MSE value of 4171.16 and an average PSNR value of 11.96 dB. The hash value created is also unique. Keywords—Cryptography, Hash Function, Rail Fence Cipher, Myszkowski, SHA-256, Digital image.
Naive Bayes Classification Framework Model for Optimizing Prediction of Agrotourism Products Orange Gerga

One of the mainstay products of Agrotourism is Orange Gerga, one type of tangerine that is widely enj oyed by tourists and developed in various products. The production activity of Gerga Orange produces reports in the form of data which is a source of information, so it is necessary to carry out in-depth analysis by the director as a basis for making decisions. Of course, this task is not easy, mainly if it is carried out by a director with a lot of work. Therefore analytical activities can be carried out by business intelligence which is a remarkable development that can produce knowledge classifications that directors can use in decision making. Naive Bayes is one of the classification algorithms that have accuracy in making predictions and is good in reputation in classification, especially in speed in learning compared to other machine learning classification algorithms. The results showed that experiments using the nave Bayes classification framework model for optimizing the production of Gerga harvests using x-fold validation with linear sampling showed that the accuracy was 100%, and the AUC was 80%. Compared with shuffled sampling, the accuracy was 97.32% and an AUC of 90%, while stratified sampling produces an accuracy of 97.14% and an AUC of 100%. The experimental results show that testing the climate change data set using the Naive Bayes algorithm shows more accurate results in the excellent category.
Development of Mobile-Based Information Letter of Rukun Tetangga And Rukun Warga With Mern Stack Method

Basically, a certificate is proof of the validity of the activities carried out. One form of a simple certificate is a permit from the local government which in this case is represented by the Rukun Warga (RW) or the Rukun Tetangga (RT). The need for a permit certificate by residents sometimes needs to be signed quickly by the RT/RW for immediate processing. However, sometimes local authorities are not at home, so the need for a permit certificate is hampered. To overcome this problem, it is proposed that the need for a mobile-based permission information application is made to overcome this problem. The system is built using the M.E.R.N stack method. This application provides an e-signing feature to sign a permit certificate by the head of the RW and RT, which would be easier to provide services to residents including providing online request letters for people in the surrounding environment. The requesting letter process in digital means that residents no longer need to meet each other physically, including the frequency to meet physically. Based on the results of a survey of 28 respondents, 76% inform the application is very easy to use when doing a cover letter, 66.7% inform the signing process is very easy and 89.3% of respondents are helped by this application. This mobile app is built using react-native for apps and express.js for backend services
A Literature Review on the Impact of Effective Management in Cyber Security System Performance

Cyber security is a fast-growing field. As the security infrastructure in a firm grows, it needed a more complex management solution that would also scales to be able to function well. As there are security concepts to be considered, there are also management properties that cannot be ignored. While the security part helps the system function properly and securely, great management could boost productivity and eliminate unnecessary errors. Both concepts are equally important features to consider when designing an effective security management model. While it is important to have great understanding of what the system would do, it is also common that one would integrate tools to help maintain it. Tools can be used to either maintain standards or to help micromanages assets or resources available. As an example, the use of the Common Vulnerability Scoring System (CVSS), a scoring system that provides standardized way of capturing and producing quantitative score of a vulnerability would make a lot of processes easier to handle. While a ticketing or issue management tool could help manage tasks and resources laying around. In the end, the principal building blocks of the system itself could be simplified to both security and management models and integrated tools that serve the core aspect of the business process. One popular choice of such tools is Jira, an issue tracking application which can help to plan, track, and manage software development projects.
Effectiveness Analysis of Using The Vehicle Tools Feature on ERP Systems with Delone &amp; McLean Approach at PT. Putra Mulia Telecommunication

PT. Putra Mulia Telecommunication (PMT) developed Enterprise Resource Planning (ERP) to simplify business processes. The company finds that the use of the feature is not effective based on the results of the data recap generated by the ERP system. The company expect these features can be more useful cause this feature is one of core features. PT. PMT never analyzed the use of the system to find out the satisfaction and benefits. This study aims to analyze the level of effectiveness of the system, especially the Vehicle Tools feature. The measurement and evaluation method uses the DeLone & McLean approach with 6 variables: Information Quality (IQ), System Quality (SQ), Service Quality (SEQ), Use (U), User Satisfaction (US) and Net Benefit (NB). Data sourced from observations, interviews, questionnaires, and literature studies. In the questionnaire, digital questions were asked to 154 samples of the employee on the use of the Vehicle Tools feature. In conclusion, that customer satisfaction is influenced by information quality, service quality, system use, and net benefits. Based on the results, our suggestions is to socialization and retraining related to the vehicle tools feature. In addition, it is also necessary to pay attention to the Standard Operating Procedure (SOP) and the appropriate instructions. The company also needs to pay attention to the system maintenance schedule on a regular basis to anticipate possible errors and needs to conduct periodic surveys regarding the use of the system to all employees to find out if there are suggestions or other criticisms.
User Interface Design of Seminar and Workshop Information Mobile Apps for Self-Development

Increasing access to internet changed how people do things including in educational sector. Online learning is often used in times of pandemic. Students attend formal education using internet. In order to be ready for work, students need to self-develop in soft skills and hard skills. Seminar and workshop are a way to develop one skill. Based on the preliminary research, students find that searching seminar or workshop in accordance with their interest is a problem. That is because the information is scattered on social media. Students also think that awareness about self-development needs to be improved. This research aims to creating mobile application that contains curation of seminar and workshop information. By using design thinking method, this research describe the design process of mobile application. Hopefully by using this mobile application the information about self-developement can easily accessed by wider community
Object Detection in Autonomous Vehicles

In the coming years, autonomous driving will be the primary focus of the automobile industry. The great majority of accidents are caused by human mistakes, and autonomous cars can help to lower this number significantly, thus improving road safety. Object identification plays a critical part in autonomous vehicle driving, and deep learning techniques are used to implement it. YOLO is one of the most common methods for recognizing and identifying things that emerge on the road. Its popularity has developed as a result of its superior performance in terms of speed, high accuracy, and learning capabilities when compared to other object recognition approaches such as Retina-Net, fast R- CNN, and Single-Shot MultiBox Detection (SSD).