Reviewerly/exHarmony huggingface dataset:
Analysis of Community Satisfaction with the Use of PeduliLindung Applications During the Covid-19 Pandemic

The Covid-19 virus with a very fast spread requires governments in every country to act quickly to prevent the spread of the coronavirus so that it does not spread quickly. The method that is mostly used is the contact tracing application, Indonesia applies this method by creating the PeduliLindungi mobile application which can track the position of people visiting a place by scanning a QR code. To ensure the successful implementation of the application, a user satisfaction evaluation is needed. This research was conducted by conducting a questionnaire survey to users of the PeduliLindungi application to determine the level of user satisfaction, the survey data was then processed using SmartPLS. The results of this study showed that all variables are positive. Two of them, usability and reliability have a significant effect on user satisfaction. It can be concluded that the user is satisfied with the PeduliLindungi application because the application has good usability and reliability.
Classification of Sentiment Analysis Against Omnibus Law on Twitter Social Media and News Websites Using the Naïve Bayes Method

Twitter As a popular social media platform in Indonesia, it has evolved into a forum for people to express their concerns in the form of tweets. Everyone can freely express their opinions on current issues using the Twitter social media platform. The government is preparing a Job Creation Bill (RUU) using the Omnibus Law concept. However, there are several articles in the Job Creation Bill that are considered controversial, causing both pros and cons among Indonesians. The purpose of this study is to examine whether Indonesians' tweets about the Job Creation Bill contain negative or positive sentiment. This research can assist a government agency in determining public opinion on a policy that will be issued or has recently been issued by a government agency without conducting a survey directly to the public. This study focuses on public tweets on Twitter from September 16 2021 to September 20 2021 with the hashtags #omnibuslaw, #Cipatkerja, and #RUU. This study also makes use of data obtained by scraping the most popular news stories on the CNN Indonesia website from November 25 to December 2, 2022. The data analysis procedure began with crawling data from Twitter and the CNN Indonesia website, followed by data pre-processing to clean up the crawled data. The tokenizer is then used to convert words into numbers to carry out the word weighting process. The dataset used in this study contained 9849 data from Twitter, which was divided into two data sets, namely positive data of 5292 data and negative data of 4557 data, and 48 data from CNN Indonesia, which was also divided into two data sets, namely 25 positive data and negative data. as many as 23 data. The analysis is carried out by dividing the training and testing data by 60% training and 40% testing, 70% training and 30% testing, and 80% training and 20% testing. An analysis of people's tweets about the omnibus bill yielded the highest accuracy value of 56% with 80% training data and 20% testing data. While the analysis of the most popular news on CNN Indonesia's website yielded the highest accuracy value of 54% with 60% training data and 40% testing data.
A New Deep Learning-Based Mobile Application for Komering Character Recognition

The Komering script, also known as the Ulu script, is one of the scripts that must be conserved in the South Sumatra region. Some historical manuscripts are composed in the Komering script, which requires transliteration. Using technology, this study intends to preserve and revitalize the Komering script. The application technology developed can assist in reading documents written in the Komering script. The produced mobile application technology product recognizes images using deep learning technology. The Convolutional Neural Network classification algorithm is used to categorize Komering characters. The trained deep learning model can classify 336 Komering characters with a 99% accuracy rate. The learned deep learning model is subsequently incorporated into the Android application to facilitate the program's portability.
Text to Image Latent Diffusion Model with Dreambooth Fine Tuning for Automobile Image Generation

This paper will explain the generated text to image using Latent Diffusion Model while combining it with a pre-training model called DreamBooth fine tuning. There have been many studies relating text to image using generative models (GAN's), VAE, and ART. This paper will also try to generate an automobile image dataset using the newest model called Latent Diffusion model. Also, experiment to try pre-train the existing LDM model using a fine tuning method that has never been done before. The use of fine tuning is to pre-train the model and also to handle a smaller scale of dataset that this research had and to reduce the possibility of overfitting. The aim will be to generate text to image by combining 2 existence models above and to create synthetic images based on the text input that is wanted. Later on, the mini user interface will be made to let the user make an input based on the brand of the automobile and 1 or 2 conditional such as color, location, and automobile position. Furthermore, the measure of MSE and RMSE score as our parameter to determine how good the model as there is no parameter from researcher in data scientist for what is a good MSE score is, this model that it could generate low MSE score that is score below 0.3 and RMSE below 0.6 where good RMSE score determined between 0.3-06.
Camera-based Object Detection and Identification using YOLO Method for Indonesian Search And Rescue Robot Competition

Indonesian Search And Rescue Robot Contest is a competition that competes for firefighting and rescue missions by a legged robot in a burning building simulation. The robot must be able to extinguish a candle flame and rescue the victim to a safe zone area as fast as possible. Many team members were already capable of completing the firefighting mission but still had trouble in the victim rescue mission. To detect victims, mostly used cameras and object detection algorithms like color detection on HSV color space. This algorithm only detects the color without knowing what object it is. There is a possibility that the algorithm detects incorrectly if detection is carried out in places with different light conditions or if there are other objects that have a similar color. In this paper, we present the implementation of YOLOv4-tiny on a Search And Rescue robot with limited hardware specifications and real-time case conditions. The YOLO model created has an mAP value of 98.25% from the training with 458 training data and 98 validation data. The model can detect 178 of 195 objects in validation data with a percentage of IoU is 79.23%. When embedded in Search And Rescue Robot, the robot can detect objects in real-time and get an average of 17.8 FPS. The YOLO model can detect objects in variations of light conditions, with the max distance of the victim and candle object around 1.5 m. With this high accuracy and speed, this algorithm is suitable for the Indonesian Search And Rescue Robot competition.
Detection of Acute Lymphoblastic Leukemia Subtypes using YOLO and Mask R-CNN

White Blood Cell (WBC) detection and counting on Microscopic Blood Cell images automatically can support expertise in diagnosing Acute Lymphoblastic Leukemia (ALL) more easily and quickly. Previous research commonly uses conventional approaches for ALL subtype detection that needs several stages, such as WBC segmentation, touch cell separation, feature extraction, and classification. We present object detection and instance segmentation techniques that require only one learning framework without needing separate stages. In this paper, we compare the performance of the YOLO and Mask R-CNN models for the detection of ALL subtypes using evaluation metrics such as precision, recall, F1, and mAP. The results show that the YOLOv4 outperforms YOLOv5 and Mask R-CNN in the detection of ALL subtypes. The YOLOv4 model has slightly better performance than the YOLOv5 model and Mask R-CNN, with an F1 value of 89.5% and a mAP value of 93.2%, respectively.
A Recommendation Model of REST API Testing Framework based on Resource Utilization of ISO / IEC 25010

A recommendation model is needed to evaluate the REST API automated test framework based on its performance. In this study, the recommendation model has been built using the performance assessment of the testing framework based on the Resource Utilization aspect of ISO/IEC 25010. It evaluates on three types of REST API transactions: CRUD, upload file, and get massive data (GMD). In order to obtain the Resource Utilization scores, the resource usage from testing execution process of each selected test framework will be measured. Also, we need The weight value obtains from a questionnaire filled out by several experts to assess the importance of each RU aspect for each transaction type. The recommendation model will evaluate the testing frameworks as good, medium, or low. The model we propose can assess the framework well and is in line with the expectations of experts. This is evidenced by the high level of accuracy for CRUD and GMD transactions, which is 100%. However, file upload transactions show an accuracy rate of 87.5%. This is due to the incompatibility of data obtained from experts with data obtained from resource utilization. Futher research, we recommend to choose an expert who has mastered the seven frameworks well.
Network Quality Prediction with QoS and QoE Data for Digital Television Using WebGIS

Digital television is part of a rapidly growing technology. Currently, Indonesia has migrated from analog television to digital television with the broadcast standard DVB-T2 (Digital Video Broadcasting-Second Generation Terrestrial). A large number of users and the development of the services provided are challenges for increasingly complex network service problems. Therefore, it is necessary to analyze the problems that occur on the network to make improvements and increase customer satisfaction by using the QoS (Quality of Services) and QoE (Quality of Experience) parameters. To make it easier to know the quality of the network, an interactive website will be developed using WebGIS to display network quality and analysis. The results of the measurement and calculation of QoS and QoE parameters are used to predict unknown network parameter values using Linear Regression. The final test results showed that the accuracy of the prediction values for each of the QoS and QoE parameters obtained was good, with the highest accuracy value being 1 and the lowest accuracy being 0.74. Predictive values were classified as good, very good, poor, or very poor to determine network quality using Naïve Bayes. Naïve Bayes accuracy for the overall classification is quite good, which is above 0.8.
Bitcoin Investment Instrumen in Indonesia: The Impact of Perceived Risk and Benefit on Investor Behaviour

Bitcoin's revolutionary nature has been attributed to the fact that it uses constantly improving and innovating Blockchain technology. After the success of fiat currency over gold, many have speculated that this could be the next stage. The study's goal is to collect responses reflecting the range of opinions about Bitcoin. Data was collected using a quantitative method, and 244 users participated as respondents. We analyze the factors that influence users' behavioral intentions to accept Bitcoin as an alternative investment in Indonesia using an integrated methodology. In this study, we suggested a model for Bitcoin adoption that combines the insights of the Theory of Planned Behavior (TPB) with those of the Unified Theory of Acceptance and Use of Technology (UTAUT); in several prior studies, Financial Literacy factor became one of the independent variables with significant results on Behavioral Intention; however, when tested as a moderator, the results did not show that Financial Literacy significantly influenced an individual's attitude toward using Bitcoin as an investment tool.
Usage of LSTM Method On Hand Gesture Recognition For Easy Learning of Sign Language Based On Desktop Via Webcam

This paper implements hand gesture recognition for some words in Sistem Isyarat Bahasa Indonesia (SIBI). Trained hand gesture images are created based on words that can be recognized on system (‘air’, ‘api’, ‘bagaimana’, ‘burung’, ‘dengar’, ‘film’, ‘halo’, ‘jumpa’, ‘kasih’, ‘kucing’, ‘makan’, ‘mandi’, ‘nasi’, ‘pagi’, ‘sapi’, ‘saya’, ‘selamat’). The lack of interactive sign language learning media makes it difficult for the deaf to find support for interactive sign language learning. Therefore, SIBI sign language interactive learning media is needed that can support interactive learning with computers. LSTM method is used on this system to avoid vanishing gradient issues.
Extreme Learning Machine for Hourly Water Level Forecast in Madura Coastal Area

Water level forecasting is an essential thing for PT. Garam. This is closely related to the position of the sluice gate to enter raw materials for making salt. Therefore, placing water gates in the coastal area is essential in maximizing the incoming raw materials. Extreme learning machine (ELM) is used to get the maximum water level forecasting value in hourly period. In this study, data distribution is the main point that is the focus of the discussion. The MSE and RMSE values are calculated to get minor deals for the training and testing processes. The most significant results obtained for the two statistical errors are 0.1141 and 0.3377.
An Ensemble Model for Software Development Cost Estimation

Software cost estimation is a critical activity that must be fulfilled in order to ensure the successful completion of the software project. Conventional methods of software cost estimation suffer from subjectivity and overhead. To overcome these limitations in the traditional approaches, Machine learning-based techniques have been emerged as an effective solution for the problem of software cost estimation. A stacking ensemble model has been proposed in this study as an effective approach for software development cost estimation. The proposed model is composed of diverse base learners and a meta-learner which is the Random Forest. The model has been trained using the ISBSG dataset which is a high-quality, heterogenous, and well-maintained dataset. The hyperparameters of the model have been tuned using the PSO algorithm. The experiments revealed that the stacking ensemble model outperforms the single contributing model and that various data preprocessing techniques can have a considerable influence on prediction accuracy.
Sentiment Analysis using Learning Vector Quantization Method

Social media as a communication medium to connect everyone has become widely accepted by the community along with the development of information and technology. Data from social media that contains opinions from the public can be processed into information that is more important in value, one of which is by using sentiment analysis. In this Final Project research, sentiment analysis was carried out using the Learning Vector Quantization (LVQ) algorithm. The first step is retrieving Tweet data using the Twitter API. After that, the data preprocessing was done to process the data and extract the features contained in it. After that, the feature list is converted into vector form using TF-IDF weighting. The accuracy performance of the method is obtained by testing the learning rate, epoch, and amount of data used. From the results of testing the performance of the Learning Vector Quantization (LVQ) algorithm, it is known that the higher the learning rate, the smaller the accuracy obtained, then the higher the number of epochs, the greater the accuracy. The average accuracy achieved in this study was 73.15%, 66.42%, and 69.58% for each data.
Machine Learning Model using Times Series Analytics for Prediction of ATM Transactions

Automated Teller Machines (ATMs) are banking outlets that support customer activities to process regular financial transactions quickly and automatically without the help of tellers. Transaction activities carried out by customers at ATMs are often difficult to predict. Therefore, the banking sector is strongly encouraged to build an intelligent cash management system to provide opportunities for banks to lower operating costs. Customer transaction patterns are needed to make a prediction. This study aims to implement machine learning algorithms to predict transaction values at ATMs with time series algorithms. This study aims to create a machine learning model to determine the predicted value of ATM transactions using four algorithms, Linear Regression, Prophet, ARIMA, and LSTM algorithms. The dataset used is the data set of ATM transactions of XYZ bank. This dataset has around 11.588 rows and ten columns. The results of the calculations with the best evaluation model determined in the LSTM algorithm, which produces the Mean Absolute Error (MAE) value of 20,686.91, the Mean Squared Error (MSE) of 710,590,544.24, and the Coefficient of Determination (R2) 0.72.
Solving Agricultural Route Planning with Improved Particle Swarm Optimization

This research solves the optimization of agricultural machinery routes to help the agricultural sector make decisions with predetermined constraints. The purpose of Agricultural Route Planning (ARP), is to maximize the efficiency of the path used by agricultural machinery by reducing the amount of distance covered by the machine in headland regions. Particle Swarm Optimization (PSO) algorithm is utilized by representing nodes for each track in a farm field. The finding of the experiments demonstrate that the improved version of PSO is capable of achieving shorter distances than the default configuration of PSO. Moreover, the application of the proposed algorithm is superior to manual operation in terms of performance in the agricultural sector.
Radial Basis Neural Networks for Class Discovery

The non-iterative structure of radial basis function neural networks makes them more appealing for classification tasks decade after decade. In line with the growth of the training data set, the pattern layer also grows. The unprecedented growth of data with no class labels is becoming a challenge for researchers who are working in data science, big data analysis, etc. Although there is literature witnessed for many algorithms to handle data with no class label. However, neural network-based algorithms with a special characteristic of radial basis neural networks for uncovering class labels are very rare. In this paper, we propose a novel class discovery algorithm that combines the best features of radial basis function neural networks (RBFN) and self-organizing feature map (SOFM). We have taken a few datasets with class label for our experimental work. In the training phase of the network, the training instances are used without class labels. In the test phase, they are validated by combining the predicted class labels with their actual class label. The result shows that the proposed algorithm can be treated as an alternative method for class discovery.
Imbalanced Text Classification based on Corporate Culture by using Support Vector Machine, Case Study: PT XYZ, Indonesia

Corporate culture has received much attention over the last two decades because of its effects and potential impact on organizational effectiveness. Researchers and consultants are interested in the concept of corporate culture and how the values and philosophies of a business affect the achievements and behavior of its employees. It is considered that corporate culture and organizational strategy are related, particularly in the implementation of a certain plan within an organization. This study aims to develop a text classification model based on corporate culture by using the support vector machine for an imbalanced data set. The data set is message comments retrieved from a social media platform owned by the company and labeled according to five corporate cultures, i.e., integrity, collaboration, innovation, respect, and excellence. We found that the SVM model developed for Innovation value gives the best results with accuracy, and the F-1 score of the test set are 0.961 and 0.961, respectively.
Information Security Evaluation Using the Information Security Index: A Case Study In Indonesia

Information security awareness has been extensively discussed as an effective means to reduce security risks. The hacking incident experienced by the judiciary within the Supreme Court of the Republic of Indonesia in 2021. This resulted in disrupted services to the community. Therefore, it is necessary to conduct an information security audit. Information security audits were carried out using an information security evaluation using the US Index. The research methodology was conducted by interviewing resource persons at the Bureau of Public Relations Data and Information of the Supreme Court of the Republic of Indonesia and using the US index questionnaire version 3.1. The results of the study show that from 6 (six) assessment categories, the Electronic System Category has a value of “35” with a “strategic” level, Information Security Governance with a value of “32” with a status of “initial condition and starting to be implemented”, Information Security Risk Management with a value of “8” status. “Initial condition”, Information Security Management Framework value “31” status “initial condition, Information Asset Management value “86” status “implementation of basic framework”, Technology and Information Security value “63” status “initial condition and implementation commenced” Overall, the results of the final evaluation of the level of application information security standards scored “224” and the status was “inappropriate”. From the results of this study, several recommendations are then given based on SNI ISO/IEC 27001: 2013 to assist the Supreme Court in determining corrective measures.
Fog-QKD:Towards secure geospatial data sharing mechanism in geospatial fog computing system based on Quantum Key Distribution

Geospatial fog computing system offers various benefits as a platform for geospatial computing services closer to the end users, including very low latency, good mobility, precise position awareness, and widespread distribution. In recent years, it has grown quickly. Fog nodes' security is susceptible to a number of assaults, including denial of service and resource abuse, because to their widespread distribution, complex network environments, and restricted resource availability. This paper proposes a Quantum Key Distribution (QKD)-based geospatial quantum fog computing environment that offers a symmetric secret key negotiation protocol that can preserve information-theoretic security. In QKD, after being negotiated between any two fog nodes, the secret keys can be given to several users in various locations to maintain forward secrecy and long-term protection. The new geospatial quantum fog computing environment proposed in this work is able to successfully withstand a variety of fog computing assaults and enhances information security.