bluuebunny/crossref_metadata_2025_split huggingface dataset:
Fire Safety in Pre-trial Detention Facilities
Authors: Parfyonenko A 
Published: March 2021

Abstract
This article describes the main provisions for assessing the safety of people during the evacuation from the buildings and structures in the event of a fire. The analysis of normative and scientific bases in the ensuring the safety of people during the evacuation showed the absence of psychophysiological patterns of behaviour and movement of people in the buildings of pre-trial detention facilities necessary to assess the estimated values of fire risk. The data presented in the methodology for assessing fire risks do not allow to reliably assess the probability of evacuating people in the event of a fire in such institutions, since the mode of operation of the facility is not taken into account. The key factor determining timely evacuation is the time of the beginning of the evacuation, depending on the behavioural aspects of the main functional contingent. The absence of such studies determines the need for field observations and experiments in the field of evacuation of people from the buildings of pre-trial detention facilities.
Sentiment Analysis on Twitter using Neural Network: Indonesian Presidential Election 2019 Dataset
Authors: Hidayatullah Ahmad Fathan , Cahyaningtyas Siwi , Hakim Anisa Miladya 
Published: March 2021

Abstract
Due to the utilization of Twitter by Indonesian politicians ahead of the Indonesian presidential election 2019, Indonesian people have given diverse responses and sentiments to the politicians. This study aims to classify sentiment on Indonesian presidential election 2019 tweet data by using neural network algorithms and obtain the best algorithm. In our study, we train our dataset using some variants of deep neural network algorithms, including Convolutional Neural Network (CNN), Long short-term memory (LSTM), CNN-LSTM, Gated Recurrent Unit (GRU) -LSTM and Bidirectional LSTM. Moreover, as a comparison with our deep learning model, we also train our dataset using other traditional machine learning algorithms, namely Support Vector Machine (SVM), Logistic Regression (LR) and Multinomial Naïve Bayes (MNB). Our experiments showed that Bidirectional LSTM achieved the best performance with the accuracy of 84.60%.
A Method of Simplifying the Asset Dependency Cycle in Security Risk Analysis
Authors: Yunizal Edri , Santoso Judhi , Surendro Kridanto 
Published: March 2021

Abstract
In recent years, one obstacle to information security risk analysis is the complexity of asset dependency. Developing research tends to produce models with great complexity and some that do not involve dependency cycles, make the model difficult to implement. This research developed the idea of compound nodes to produce a modular risk analysis that simplify the asset dependency cycle. This study uses the following assumptions: aimed at quantitative assessments, values based on likelihood, and based on graph basic models. The stages of the method consist of identification of cyclic in the initial model, reconstructing cyclic graph into acyclic graph, and reconstruction of cyclic sub graph into acyclic sub graph. Through an illustrative example, this research produces a method that helps facilitate understanding the complexity of asset dependencies in risk analysis.
Analyzing Public’s Reaction towards Black Lives Matter Campaign using Machine learning-based Approach through Spark
Authors: Aryanto Rio Rizki , Fudholi Dhomas Hatta 
Published: March 2021

Abstract
Since the end of May 2020, there is a massive wave of protestant in the United States addressed to the government regarding the case of police violence towards black people. On May 25, 2020, George Floyd, a 46-years-old black American man was killed during an arrest for allegedly using a counterfeit bill in Minneapolis. This study analyzes the public’s reactions towards the Black Lives Matter campaign using a supervised machine learning-based approach. The proposed model uses logistic regression with word vectors as its feature. The model classifies the public’s reaction represented by tweets and crawls using Spark Streaming into sentiment class, i.e. positive, negative and neutral. In addition, named entity recognition analysis was also conducted in this study. The aim is to find who else besides George Floyd whose rights have been fought for by the public. SparkNLP is used to build the logistic regressions model, sentiment analysis and named entity recognition. This study finds that most of the public tweets had a negative tone addressed to the Floyd incident specifically and to the violence towards black people in general. Another finding is that the campaign not only fought for George Floyd, but also fought for the other victims like Rayshard Brooks, Dominique Fells and Eric Garner.
Indonesia Infrastructure Development Topic Discovery on Online News with Latent Dirichlet Allocation
Authors: Hidayatullah Ahmad Fathan , Ma’arif Muhammad Rifqi , Habibie Muhammad , Khomsah Siti 
Published: March 2021

Abstract
In the period of 2014-2019, the Indonesian government has defined infrastructure development as a priority. Related to this, various media, in both online and offline, routinely report the infrastructure development news to the public. However, it is quite difficult for people to obtain a summary of information from the internet about the infrastructure development that have been carried out by the government. This study aims to provide a brief summary about infrastructure development in Indonesia by performing topic modeling approach using Latent Dirichlet Allocation. We found that the use of bigram language model could help identify phrase from the corpus. Therefore, the keywords contained in the topics are more interpretable and acceptable. Moreover, a coherence score measurement was applied to find the best number of topics from our dataset. Based on the experiments, we obtained 40 topic models from our dataset. From those 40 topics, we inferred several topic labels, such as, oil and gas infrastructure; power plant infrastructure; information technology infrastructure and internet networks; road infrastructure; reservoir infrastructure, irrigation networks and water resources; railway infrastructure; and airport infrastructure.
Stacking Neural Network Models for Automatic Short Answer Scoring
Authors: Rajagede Rian Adam , Hastuti Rochana Prih 
Published: March 2021

Abstract
Automatic short answer scoring is one of the text classification problems to assess students’ answers during exams automatically. Several challenges can arise in making an automatic short answer scoring system, one of which is the quantity and quality of the data. The data labeling process is not easy because it requires a human annotator who is an expert in their field. Further, the data imbalance process is also a challenge because the number of labels for correct answers is always much less than the wrong answers. In this paper, we propose the use of a stacking model based on neural network and XGBoost for classification process with sentence embedding feature. We also propose to use data upsampling method to handle imbalance classes and hyperparameters optimization algorithm to find a robust model automatically. We use Ukara 1.0 Challenge dataset and our best model obtained an F1-score of 0.821 exceeding the previous work at the same dataset.
A Heuristic Load Balancing Algorithm for Cloud Computing in Heterogeneous Resource Environment
Authors: Santoso Bagus Jati , Shiddiqi Ary Mazharuddin , Kusuma Raldo 
Published: March 2021

Abstract
Cloud computing is an alternative technology to develop web-based services. The continuous increase of internet users causes high web service traffic that leads to the requirement of good infrastructure in managing servers in cloud computing. Load balancing between servers is one of the main challenges in cloud computing across multiple nodes. The objective is to ensure that no single resource is overwhelmed nor underused. Load balancing is optimal when maximizing throughput, minimizing response times, and avoiding overloading on one of the connection lines. To solve problems in cloud computing infrastructure, we propose the utilization of two well-known heuristic algorithms to distribute loads on cloud computing. Experimental results indicated that the heuristic algorithm could distribute task loads proportionally to cloud computing resources. Our heuristic algorithm can also distribute tasks to resources with different computing resource specifications and different tasks weights.
A Citation Data Collector Tool of Author’s Profiles in Scopus Based on Web and Application Programming Interface (API)
Authors: Rochim Adian Fatchur , Wijaya Tendi Nugeraha , Eridani Dania 
Published: March 2021

Abstract
Improving the quality of publications will enhance teaching, support community service, and improve the campus reputation. One of the measurements of research quality can be observed by looking at the number of citations. The more research cited, the greater the effect it will have on other research. In addition, the publication of research results is used by most researchers in relation to the promotion of their academic positions. The position of the author in the research affects the weight value of his/her publication. At this time, no application can be used to classify the author’s position of each publication produced. This paper aims to create an application that acted as a search engine to retrieve and collect researcher data along with a list of their publications from the Scopus database. The application uses a REST API or Web Service of Scopus. Web Service allows the integration of various systems with different platforms or architectures for sharing data and services. This application was built using the PHP and JavaScript programming languages. The application’s development uses the Agile Software Development method. Users can also save the search results for the researcher data and researcher documents list in the MySQL database. Furthermore, this application also allows users to download the search result in their computer devices. The System Usability Scale (SUS) method was used to test the application’s usability and gained 80 as the final score.
Developing a framework for self-isolation app: a case study of Jakarta’s COVID-19 mitigation
Authors: Gultom R , Hirzirahim A , Nugraha Y , Nursyufa Z , Hamdi  , Suherman A L 
Published: March 2021

Abstract
Self-isolation for COVID-19 suspects plays a crucial role due to a lack of medical personnel and hospital capacity in Jakarta. The ongoing COVID-19 pandemic has taken an unprecedented toll on health services because the number of infections continues to climb and is thought to be far higher because many people have not been tested. This phenomenon opens an opportunity to support innovative collaborations between government and city co-creators (e.g. start-up companies) to develop a digital application for monitoring the self-isolation activity of COVID-19 suspects. This paper proposes a conceptual framework for developing a self-isolation app by conducting in-depth interviews with 20 participants from the Jakarta Health Office and mobile health app start-up companies. The result of this paper figures out the key features of the app, such as suspects database, daily monitoring form, geotagging, chatroom, test scheduling, panic button, and daily notification. In this paper, Jakarta was selected as a case study since it is stated as a collaboration city which initiates the opportunity to business sectors for increasing access and public service quality. This paper uses design thinking methods as an evidence base to develop a self-isolation app. The conceptual framework provides the foundation for the input-process-output model and insights into what an appropriate approach for the requirements of the self-isolation app development. This paper provides a better understanding of common elements in building the app. Its conceptual framework fits in any government sector. Also, it can inspire other cities to consider the model in developing a similar application with structural and systematic phases in every development step. Further research should be carried out to build an extension model of the design thinking approach to get specific quantitative and qualitative results.
Word Embeddings Evaluation on Indonesian Translation of AI-Quran and Hadiths
Authors: Naf’an Muhammad Zidny , Sari Yunita , Suyanto Yohanes 
Published: March 2021

Abstract
Word vectors are an important part of machine learning. Word vectors are a numerical representation of text data. One of the methods that can be used to convert text into numerics is word embeddings. The word embeddings algorithm that researchers often use is Continuous Bag of Word, Skip-Gram, and FastText. This paper will discuss the transformation of textual data from Islamic knowledge domain documents into numerical forms using these three algorithms, then evaluate the word vector results using intrinsic and extrinsic evaluation techniques. We conduct intrinsic evaluations by determining the words to be evaluated, then checking for the existence of synonyms, antonyms, related words, and derived words from the nearest set of words based on vector values. We also tried to use vector words to solve word analogy problems. The best word vector in extrinsic evaluation is the result of the CBOW algorithm which is integrated with Binary Relevance and Multilayer Perceptron, with an accuracy value of 77.56% and a hamming loss value of 8.14%.
Comparative analysis of fuzzy database Tahani model and Fuzzy Multi-Attribute Decision Making TOPSIS method in cotton product for determination recommendations in textile industry (Case study: PT. Pandatex)
Authors: Prabandani Erizta Alifa , ‘Uyun Shofwatul 
Published: March 2021

Abstract
PT. Pandatex is a company engaged in the textile industry with raw materials derived from fibers. Product selection based on supplier offers makes it difficult for companies to make choices that match the quality classification and production needs. This research was made by comparing the fuzzy Tahani method and the FMADM TOPSIS method with the same parameters like fiber strength, fiber length, micronaire, price, quantity, and shipment. The fuzzy Tahani method is used to find the value of the degree of membership through the fuzzification process and the value of fire strength. Meanwhile, the FMADM TOPSIS method is used to find the best alternative from alternatives that have the shortest distance from the positive ideal solution, and have the longest distance from the negative ideal solution. The results of this study were conducted with case trials. The fuzzy Tahani method produces 4 recommendations and the FMADM TOPSIS method results in a ranking of all cotton products. From the final results of the two methods used, the calculation of the fuzzy Tahani method is more accurate with accuracy in calculating the value of fire strength.
Development of Unified Neuro-Affective Classification Tool (UNACT)
Authors: Hossain Md Farhad , Yaacob Hamwira , Nordin Azlin 
Published: March 2021

Abstract
Brain signals have been analysed to understand the affective state of different cognitive and mental conditions. For example, through the analysis, we can visualize the changes of emotion while driving, identify an autistic kid, understand the conditions that stimulate attention while studying, and many more, because emotion has a strong impact on cognitive processes in humans’ activities. This can be done through a machine learning technique, which includes data acquisition, pre-processing, feature extraction, and training. However, no existing tool integrates all supervised machine learning processes for affective state classification, which makes the process tedious and time-consuming for an analyst by doing programming. Therefore, this project aims to develop a brain analysis tool, namely Unified Neuro-Affective Classification Tool (UNACT). It consists of 3 main functions including training, classifying, and analysis. In the study of affective state electroencephalogram (EEG) signals have used, which measures brain signals. UNACT uses the Butterworth Bandpass filter for EEG signal filtering, the Power Spectral Density method for feature extraction, and the Multi-layer perceptron (MLP) for emotion classification. This tool can be used by a non-technical person to perform affective-emotional state analysis without having programming knowledge.
DCGAN-generated Synthetic Images Effect on White Blood Cell Classification
Authors: Hartanto C A , Kurniawan S , Arianto D , Arymurthy A M 
Published: March 2021

Abstract
White blood cell can give information about someone’s health. Imbalanced amount of white blood cells indicates someone’s disease. The disease detection using white blood cell can be done by using flow cytometry, however the use of the device has many drawbacks. The drawbacks can be solved by using computer aided classification. However, another problem arises, that the available white blood cell dataset has small dataset and the class distribution is imbalanced. This problem can be solved by using classic data augmentation and DCGAN to create synthetic image in order to balance the amount of white blood cell dataset. The balanced dataset then trained using ResNet50 model as the classification model. Accuracy, precision, recall and Fl-score are used as the performance measures of the classification. The result shows that the model obtain accuracy of 82.5%. ResNet50 was chosen because it can produce good accuracy when used in medical image classification.
An evidence-based culture: implementing a large-scale social restriction policy on the COVID-19 prevention protocol in Jakarta
Authors: Wiguna H , Nugraha Y , Kanggrawan J I , Suherman A L , Akbar R S , Pradana P A 
Published: March 2021

Abstract
COVID-19 has a severe and widespread impact, especially in Indonesia. One of the efforts of the DKI Jakarta Provincial Government in reducing the risk of spreading the virus is by implementing a Large-Scale Social Restriction (hereafter referred to as PSBB) policy. The hope of implementing this policy is to detain or even reduce active (positive) cases in Jakarta. In this paper, we conduct Exploratory Data Analysis (EDA) to analyze and evaluate the effect of the PSBB policy that has been made by the DKI Jakarta Provincial Government in minimizing the active cases and in increasing the number of tests in Jakarta. The results show that the PSBB policy was not directly able to limit the rate of active cases, but specific cases of people in intensive care were able to be suppressed. The results suggest that the other local governments can use EDA method in making further policies and interventions for handling COVID-19. The results also indicate that the public is expected to continue to take preventive actions, such as complying with health protocols established by the government.
Forecasting demand factors of tourist arrivals in Indonesia’s tourism industry using recurrent neural network
Authors: Anisa Melati Puspa , Irawan Herry , Widiyanesti Sri 
Published: March 2021

Abstract
Tourism is one of the sectors that have contributed to a wide variety, not just economically, but also socially political, culturally, regionally and environmentally. In Indonesia, the tourism sector has an important role in contributing to economic growth, especially in foreign exchange earnings. Therefore, it is very important to maintain and encourage the growth of tourism in Indonesia with the need for a model of forecasting the arrival of foreign tourists to Indonesia to assist the government in developing a tourism plan strategy. There are factors in demand forecasting which affect a demand in the tourism sector. In this study data in the form of fuel prices, exchange rates, per capita GDP, and the volume of bilateral trade from five countries over the span of January 2012 to December 2019 are used as variables that affect the arrival of foreign tourists. The method used to create a forecasting model is one of recurrent neural network architectures namely long short-term memory (LSTM). Three models are put in test and each model uses four types of parameters that are look-back value, hidden layer, number of epochs, and batch size. The first prediction model gives 97.21% of the highest accuracy. The second predictive model provides 99.17% of the highest accuracy. Lastly, the third prediction model provides 99.21% of the highest accuracy.
Traces Of The Brain’s Learning Potential Present Within “Uneducational” Video Games
Authors: Rahman Ayub bin Abdul , Hamzah Mohd Syarqawy bin , Yaacob Hamwira , Rahman Abdul Wahab bin Abdul 
Published: March 2021

Abstract
Video games, especially in mobile devices, have the reputation of being negative to the mass consumer. They are thought to be addictive, mind-numbing time waster. But is that really true? A study has been conducted to analyze video games design styles based on neuroaffective computational model using electroencephalogram (EEG). Although the objective is to classify abstract and realistic design styles, a different off-topic materials have been discovered. During the data collection, subjects completed an aptitude test using an online instrument provided by the Cambridge University’s Brain Lab. The EEG instruments recorded specific brain functions (cognitive, memory, verbal, creativity) separately. After classifying the data with emotional response, each brain functions give unique valence versus arousal scatter plot. What is interesting, however, is that when all of them plotted together in a single chart, they form a pattern similar to the data collected for playing video games. Could it possibly be that playing video games indeed stimulate specific brain functions? Or are these similarities in valence versus arousal plots just plain coincidences? A future research designed for brain function correlation using other model than emotions can confirm these findings. But for now, the consistency of these findings for all the subjects are hard to ignore.
Analysis and Design of UI and UX Web-Based Application in Maiproyek Startup Using User Centered Design Method in Information System Program of Telkom University
Authors: Adhitya C , Andreswari R , Alam P F 
Published: March 2021

Abstract
To develop a good application, the aspect that needs to be considered is to design a User Experience and User Interface that suits the needs of the users themselves. One of the design methods that can be used is User Centered Design method. This method focuses on designing the user’s needs where it aims to provide comfort for the user and provide a relevant interface for the user. The study was conducted in Information Systems Program of Telkom University. The design will then be tested on each lecturer and student in Information Systems Program of Telkom University. This test aims to test whether a design is in accordance with the desired objectives or not. The results of the research the author wants to achieve is to design a user interface and user experience design of a web-based application for a Maiproyek application. After conducting research, the results obtained by the author are usability scores obtained from the Maze.design application. The scores obtained are calculated in the medium category and show that the design of the application tested to each respondent already has a good usability value.