bluuebunny/crossref_metadata_2025 huggingface dataset:
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

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

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

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

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

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

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

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

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

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

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

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

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

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

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.
Indonesian Ethnicity Recognition Based on Face Image Using Gray Level Co-occurrence Matrix and Color Histogram
Authors: Putriany Dinda Mareta , Rachmawati Ema , Sthevanie Febryanti
Published: March 2021

Facial and ethnic recognition became a popular research topic. Different from face recognition, ethnic recognition classifies faces according to the general features of certain ethnic groups. Ethnic recognition in face image is increasingly becoming a necessity and is used in various fields. This paper proposed the Indonesian ethnic recognition system based on periorbital features on facial images. We use the five largest ethnic groups in Indonesia in this study, namely Banjar, Bugis, Javanese, Malay, and Sundanese. Gray Level Co-occurrence Matrix and Color Histogram were used as methods, and Random Forest was used as a classifier. Based on - cross-validation tests with optimal k values, the model achieved 98.65% accuracy.
Canvas Learning Management System Feature Analysis Using Feature-Oriented Domain Analysis (FODA)
Authors: Wicaksono Galih Wasis , Nawisworo Pamula Brian , Wahyuni Evi Dwi , Cholily Yus Muhammad
Published: March 2021

The impact of Covid-19 on the world of education requires the implementation of online learning. Information and computer technology can provide solutions in education through the Learning Management System (LMS). Canvas LMS is a form of LMS that can be utilized by lecturers and students in online learning during the Covid-19 pandemic. In this study, researchers analyzed the features of Canvas LMS using the Feature-Oriented Domain Analysis (FODA) method. Researchers also conducted a mapping of canvas LMS features with the National Standard of Higher Education (SN Dikti). This study showed two stages of validation by Education and LMS experts to verify the results of the analysis. At the end of the course, we recommended a new LMS feature that complies with SN Dikti for Lecturer and Student users.
Bilingual Lexicon Approach to English-Filipino Sentiment Analysis of Teaching Performance
Authors: Pacol Caren , Palaoag Thelma
Published: March 2021

The aim of this study is to formulate a strategy that can possibly calculate teacher performance by analyzing textual feedback. Expressing textual responses in quantitative form like average sentiment rating can actually provide opportunities for administrators to see if the numerical ratings given complement that of the comments. Our approach was designed to enable processing bilingual textual data. Findings of this study shows that there is strong correlation between teaching performance actual mean rating and average sentiment rating. Furthermore, the approach employed obtained 86% accuracy indicating that it is an encouraging technique, capable of analyzing the students' textual responses. In future work, the use of POS tagging can be explored to improve sentiment analysis accuracy. Employing machine learning methods may also be considered to discover techniques and alternative approaches to sentiment classification.
Comparison of Main Algorithms in Big Data Analysis of Telecom Customer Retention
Authors: Gu Yuanhu , Palaoag Thelma Domingo , Dela Cruz Josephine S.
Published: March 2021

In today’s fierce telecommunications market competition, customer chum is very severe. In order to retain customers, telecommunications companies have made various attempts from various data and consumption characteristics analysis to big data analysis. However, since the actual situation of customer churn is very complicated, how to predict customer churn accurately and quickly is a difficult problem. After the researchers successfully conducted big data analysis of customer churn and successfully retained customers, in this article, the researchers mainly compared several commonly used algorithms in order to find a better algorithm for big data analysis of telecommunications customer churn. Compare and analyze the accuracy and efficiency of these several algorithms and suggest that the business support staffs of telecommunications companies adopt major methods for big data analysis. The researcher found that the Decision Tree (CART) algorithm is better for the prediction of customer churn and guided other branch staffs to predict customer churn and retain customers in a timely manner. This kind of big data analysis can be used to retain customers in the telecommunications industry.
Lightweight Residual Network for Person Re-identification
Authors: Rachmadi Reza Fuad , Nugroho Supeno Mardi Susiki , Ketut Eddy Purnama I
Published: March 2021

In this paper, we investigated several lightweight convolutional neural network (CNN) classifiers for person re-identification problems. Person re-identification can be described as finding a person by performing a query with a specific person image on some defined gallery of people images. We construct the lightweight CNN classifier by utilizing the lightweight residual network architectures originally used for the CIFAR dataset. The goal is to design a lightweight CNN classifier with a maximum of 3 million number of parameters that produce good accuracy on person re-identification problems. The last layer of the residual network is detached and changed by two fully-connected layers, one for learning the discriminant features, and the other fully-connected layer for calculated the final classification score in the training process. Experiment results show that the ensemble of lightweight residual networks achieved a good performance on Market-1501 (rank-1 accuracy of 89.46% with mAP of 84.48% for single-query and rank-1 accuracy of 92.23% with mAP of 88.07% for multi-query). Although the lightweight residual network does not achieve state-of-the-art performance, our analysis shows that the lightweight residual network has higher information density comparing with other state-of-the-art models. More information density means that the classifier is more efficient in terms of performance with the number of parameters in the classifier. Implementation of these experiments are available at https://github.com/rezafuad/ person-reid-lightweight-residualnet.
Fingerprint Salt & Pepper Noisy Image Enhancement with Threshold Method
Authors: Ince Mahmut , Lu Songfeng , Alfadhli Saad Ali , Maulood Ismail , Alresheedi Shayem Saleh
Published: March 2021

We present an adaptive threshold method for salt and pepper noisy fingerprint images. At first, we applied a threshold algorithm to detect noisy and corrupt pixels. Fingerprint image optimized with window filter produced pixels values. We measured the quality of the fingerprint images with peak signal to noise ratio and other efficient noise removing techniques were compared with it. The result shows improving performance of our method with remove salt and pepper noise from a fingerprint image. Our present work is useful to reduce or minimize medium level noise in fingerprint image. In future, we will work to reduce more than 80 percent noise level in fingerprint image.
KaByahe: a mobile application focusing on the historical and cultural places within the city of manila
Authors: Cabrera G M , Caringal L P , Manocan J P , Senga J G , Estrella N
Published: March 2021

With the Philippines' rich historical and cultural heritage, tourism remains a vital sector and is one of the greatest contributors to its rising economy. Manila, its capital, has a vast list of historical and cultural places that mirrors the country’s rich historical and cultural heritage. The developers were inspired to develop a mobile application called KaByahe with the purpose to determine the different historical and cultural places within Manila and serve as its users' own guide in exploring more of its historical and cultural roots. The developers strongly believe that this project is important not only for the benefit of the tourists in exploring Manila, but also, for its local people, to aid them in knowing what has transpired beyond these places and promote appreciation and understanding of the city’s origins. The developers used Agile Development Methodology since the strategy is to develop using short development cycles to focus more on addressing potential issues and produce quality output per cycle. With this, the developers were able to work more efficiently and collaboratively. The developers were able to gather substantial information through various testing procedures conducted to address both functional and nonfunctional requirements. Based on the results of these, KaByahe was deemed to successfully meet the objectives set by the developers.
Ensemble convolutional neural network for robust batik classification
Authors: Azhar Yufis , Mustaqim Moch. Chamdani , Minarno Agus Eko
Published: March 2021

Some researchers propose using the Convolutional Neural Network (CNN) method to classified batik images. It can extract features automatically without the need to define feature manually from the image. However, CNN’s weakness is that its accuracy is quite low, especially for small-sized datasets, compared to machine learning methods that use handcrafted feature extraction. In this research, an ensemble CNN method is proposed to improve the accuracy of the CNN method in classifying batik images. This method will train several CNN models at once, and then by voting and averaging techniques, the output label will be determined. Test results for two different datasets show this method can improve the accuracy of the CNN method and get an accuracy value of 100%. This method is also proven to extract features faster than the state-of-the-art method like MTCD+SVM, which is included in the hand-crafted feature extraction category.
Predicting COVID-19 Confirmed Case in Surabaya using Autoregressive Integrated Moving Average, Bivariate and Multivariate Transfer Function
Authors: Saikhu A , Hudiyanti C V , Buliali J L , Hariadi V
Published: March 2021

In March 2020, the first case of Covid-19 was found in Indonesia. The increase of confirmed, suspected, and exposed in Surabaya has also significantly. Some studies show there is a relation among temperature, humidity, suspected, and exposed patients in an area with the number of confirmed COVID-19. Several statistical techniques that can be used to determine this relationship are to analyze and predict it using the ARIMA, bivariate, and multivariate transfer functions. The aim of this study is the performance of three models and determine the best model. The performance on the training data for ARIMA is 0.376, which shows that the accuracy of the model is 37.6%. The bivariate transfer function accuracy is 0.409, and the accuracy of the multivariate transfer function is 0.478. The result performance of ARIMA testing is 0.074, the bivariate transfer function is 0.055, and the multivariate transfer function is 0.108. The multivariate transfer function forecasting model is a technique in this case with the best performance.
Realization of Ball on Plate Control using PID Controller and Webcam
Authors: Pahlawan Dahnur Agus , Wibowo Agung Surya , Susanto Erwin
Published: March 2021

This study aims to apply the PID controller to the ball on plate control realisation to reach a stable position of ball on plate. The ball on plate props adjust the position at the desired coordinates on a flat square plate. The targeted position is set as set point coordinates in the form of X pixels and Y pixels. The input used is in the form of coordinates obtained from the webcam through image processing methods and sent serially to the microcontroller. The output from the microcontroller is fed into the actuator, namely the servo motor. From the experiment results and its implementation, it is shown that ball on plate can move towards to the desired set point. For PWM signal set point, the value referred to centre of plate is 250 and the response of X-Y axes have errors 3.64% and 8%, respectively.
Usability Evaluation of a Fintech Lending Mobile Application for University Student: A Case Study
Authors: Emanuela Ellen , Widyanti Ari , Pratama Gradiyan Budi
Published: March 2021

The rapid development of information technology has changed various sector of our life, including economy. A number of startup companies in Indonesia has been utilized technology to deliver financial services to its customer. One of them attempts to answerthe needs of university students in Indonesia for an alternative source of funding, who develop a lending mobile application for student. An initial study using interview and secondary data found that the users still find difficulties to use the mobile application. Usability is one approach that can be used to minimize the problem. The purpose of this study is to increase a fintech mobile application’s usability by conducting evaluation of several features including, 'creating a new account', 'completing profile registration', 'applying for university tuition lending', and 'applying for goods lending.' Usability evaluation was conducted by testing the effectiveness, efficiency, and satisfaction through a set of remote usability testing involving 20 participants. The results shows that the mobile application is not optimized in terms of effectiveness, efficiency, and satisfaction as yet. Using the Concept Generation Method, 21 recommendations were proposed to increase the usability of the lending mobile application.
Vehicle Classification Based on CCTV Video Recording Using Histogram of Oriented Gradients, Local Binary Patterns, and Hierarchical Multi-SVM
Authors: Navastara D A , Maulana M F , Suciati N , Sarena S T
Published: March 2021

Vehicle classification is a crucial element in a dynamic traffic management system. The system is needed since the number of vehicles gradually increase every year. There are many methods for vehicle classification, one of those is the use of CCTV camera. As it is multifunction and cheaper than other types of camera, the government tend to use the CCTV camera as monitoring equipment of vehicles on the road. Hence, an accurate vehicle classification system from CCTV records is needed for traffic management system as well as other systems. In this study, the method contains four steps: object detection using Gaussian Mixture Model, features extraction using Histogram of Oriented Gradients and Local Binary Patterns, model training and classification using Hierarchical Multi-SVM. The experimental results show that our proposed method works well with the accuracy of 80.28%, precision of 94.27%, recall of 73.79%, and F-measure of 82.76%.
Scrum Adoption Challenges in Higher Education in Indonesia: Case Study of Board of Information System, Universitas Islam Indonesia
Authors: Setiawan Mukhammad Andri , Sujono
Published: March 2021

The level of success in project development information systems or software in many countries is still low. A study of 110 information system projects in Indonesia found that most information system projects are problematics, and 18% were canceled. Even though it has been around for a while, Scrum as an approach is relatively new in Indonesia’s software development. Many efforts to adopt agile in various organizations are not running well. This research aims to identify the challenges in agile adoption in a higher education organization in Indonesia using the Scrum Adoption Challenges Detection Model (SACDM) theory. The study found that even though Scrum is performing well, there are still some works needed. Especially in improving the experiences and transforming the organizational culture and structure to make the scrum adoption more effective.
Analysis of the dual-fuel combined cycle power plants with a parallel operating scheme efficiency
Authors: Voronkova A S , Borush O V
Published: March 2021

The article addresses the problems of local power plants operating on fossil fuels such as gas and coal. As a solution to the problem, it is proposed to consider dual-fuel combined cycle power plants with a parallel operating scheme. This type of combined cycle technology is good variant for integration into coal predominating districts. Therefore, the efficiency of dual-fuel combined cycle power plants operating at subcritical (p
0 = 13 MPa, t
0 = 540 °C) and supercritical (p
0 = 24 MPa, t
0 = 545 °C) initial steam parameters are discussed in the article. The thermal model and a calculation methodology for the installation under consideration are proposed. The results of the installation’s technical and economic indicators analysis are presented. Additionally article sets out the assessment of capital costs, Net Present Value and the possibility of using the technology under study within the framework of the considering region.
Optimization of refueling times in fast neutron reactors
Authors: Tashlykov O L , Dolgii Yu F , Sesekin A N
Published: March 2021

The importance of solving issues of increasing energy efficiency and energy saving for the development of the Russian economy was emphasized. The main directions of increasing the energy efficiency of the nuclear power industry (increasing the efficiency of using primary fuel and reducing the shutdown time of the power unit for repair and reloading of nuclear fuel) are highlighted. The importance of route optimization in solving applied problems of nuclear power is updated, in particular, in minimizing the time of nuclear fuel reloading, increasing the utilization factor of the installed capacity of NPP power units. The necessity of periodic reloading of nuclear fuel in reactors has been substantiated. The features of nuclear fuel reloading in thermal and fast neutron reactors are presented. The necessity of using in fast neutron reactors a system for guiding a capture on a fuel assembly using eccentrically located rotary plugs is substantiated. The necessity and importance of optimization of the process of guidance and movement of the overload mechanism in order to reduce the time to stop the power unit is shown. Mathematical models for the mechanisms of reloading fuel assemblies have been built. Time-optimal algorithms for the operation of overload mechanisms with two and three rotating plugs are proposed.
Organizing the information security in Modbus TCP interfaces for use in the energy complex
Authors: Ametov F R , Bekirov E A , Asanov M M
Published: March 2021

A modern automated control system at electric power companies is a complex of many systems, modules and interfaces. One of the most important and, at the same time, vulnerable is the switching module, which is a set of devices and interfaces for transmitting, processing and analyzing data. The Modbus protocol is considered in this work as one of the most common interfaces for control systems at power plants. Its advantages and disadvantages are analyzed. Modern control interfaces are susceptible to cyber attacks, which poses a threat to the entire energy complex. The study of Modbus problems is considered, the main solutions for their elimination are proposed and analyzed in order to increase resistance to cyberthreats. A proprietary solution for modernizing the Modbus protocol was proposed based on the implementation of its own authentication mechanism, as well as data transmission using cryptographically strong encryption algorithms. General functional and structural schemes, as well as schemes that take into account the use of the proposed solution, have been formed. The calculation of the cryptographic strength of the proposed cryptography algorithms is made. Conclusions regarding the potential increase in resilience to cyber threats were formulated based on the data collected during the analysis.
Development and modeling of LED indicator of high voltage insulator status
Authors: Balobanov R N , Amirov D I , Razina A G
Published: March 2021

The article discusses a non-contact method for monitoring the state of high-voltage insulation. The principle of operation of circuits of devices built using various electronic components is described. The results of modeling the operation of the device in the software complex of finite element modeling COMSOL Multiphysics are presented. The modeling was carried out by constructing and analyzing three-dimensional models of insulators of the LK70/35 and LK 70/110 brands with indicators installed on them. The presented method of insulation control will allow, during visual inspection of overhead lines, to additionally receive information about the change in the electric field and the appearance of partial discharges in the insulation. The main results of this article are the determination of the parameters and requirements for the electronic components of the considered circuits of indicators of the state of high-voltage insulators.
Transformation Justification of types of filter compensating devices in 6 kV mine networks with powerful nonlinear electric receivers
Authors: Zhilin E V , Prasol D A , Savvin N Yu
Published: March 2021

In this paper presents the features and disadvantages of networks of mining enterprises with a voltage of 6 kV with high-power thyristor DC electric drives of hoisting installations, made according to the TP-D system. In most of these networks, negative phenomena are observed due to the presence of powerful nonlinear electrical receivers. Various power filters are widely used to eliminate negative phenomena and reduce additional power losses in mine networks. Moreover, the choice of a specific type of filter and its design is a complex multicriteria task. The paper presents an in-depth analysis of various types of power filters, and considers factors that influence the choice of FCD. The disadvantages of various existing methods of selecting FCD are revealed. Justification of the types of filter-compensating devices is based on the application of a multi-criteria approach and optimization of the structure of the FCD using the fuzzy set apparatus. The optimal design of FCD for use in 6 kV mine networks with powerful thyristor DC electric drives of hoisting installations, made according to the TP-D system, has been determined. At the same time, the use of the developed FCD design in 6 kV mine networks will allow to compensate for the most pronounced HH currents and voltages, significantly reduce additional power losses, improve the quality of electrical energy and improve the overall electromagnetic environment.
Adsorption Purification of Phenol-Containing Wastewater from Oil Refineries
Authors: Nikolaeva L A , Aikenova N E
Published: March 2021

In this paper, the possibility of adsorptive purification of industrial wastewater from oil refineries from phenols by industrial wastes is considered. It is proposed to use carbonate sludge for water treatment of natural water as one of the most accessible materials, which is a large-tonnage waste of energy, for sewage treatment. Phenols in wastewater are hazardous toxic substances. In legal order, sanitary and hygienic standards establish the maximum permissible concentration (MPC) of phenol in water, which is 0.001 mg / dm3. Industrial wastewater containing phenols has a number of difficulties faced by technologists when selecting a technological scheme for cleaning these pollutants, or their disposal. The adsorption isotherm, the kinetic curve of phenol adsorption, the output curve of phenol adsorption under dynamic conditions were constructed, the dynamic sorption capacity of the “GrSM-1” sorption material was determined, which prove the effectiveness of using carbonate sludge as an adsorbent. A flowchart for the purification of wastewater from phenols is proposed, the calculation of the adsorption filter with the loading of carbonate sludge in the scheme for the purification of wastewater from phenols is performed.
Calculation of electric energy savings and simulation of tank operation with variable extraction of steam and liquid phases of propane-butane mixtures
Authors: Rulev A V , Kuznezov S S , Sevostiyanov V O
Published: March 2021

Currently, when consumers use various categories of propane-butane mixtures of liquefied petroleum gas as the main or backup energy carrier, natural and artificial regasification systems based on electric or direct fire evaporators with intermediate heat transfer agent or solid-state intermediate heat transfer agent of various evaporation capacity, which operate depending on the design in various evaporation modes, are widely used. An important element for reducing energy consumption in the artificial liquefied petroleum gas regasification systems is the switching of tank to the combined regasification mode. The combined cycle provides two main periods of tank operation: the initial period with natural regasification of liquefied gas in the day tank, ground tube evaporator, and the subsequent period with artificial regasification of the propane-butane mixture in the evaporator with an intermediate heat transfer agent. For the purpose of theoretical justification of the proposed cycle, a mathematical model of combined regasification of liquefied petroleum gases was developed, which comprehensively takes into account the main periods of tank operation: with natural regasification mode in the day tank, ground heat exchanger and artificial regasification mode in the evaporator. The results of the conducted research show that the implementation of the combined regasification cycle in the liquefied gas supply can significantly reduce the energy costs of its evaporation, increases the energy efficiency of liquefied petroleum gas supply systems and provides an average annual savings of 33.7% in electric energy for regasification.
Research and development of ice storage in building energy supply system
Authors: Yavorovsky Y V , Sultanguzin I A , Khristenko B A , Kruglikov D A , Chaikin V Y
Published: March 2021

In this work, the most suitable type of heat exchanger for an ice accumulator was determined based on studies of the dynamics of ice growth on heat exchange surfaces of various shapes and its configuration was determined. The calculation of the accumulating capacity of the ice accumulator has been made. The task is set to create a mathematical model in the Ansys Fluent and TRNSYS environment, as well as BIM and BEM models of the main elements of the system. A ready-made design solution for the introduction of an ice storage device into the existing heat supply system of an energy-efficient house with an assessment of the cost of costs and required materials has been developed. An assessment was made of the cost of various solutions for heating a house with a load of 10 kW. A test bench layout has been developed for monitoring and analyzing the operation of the system, as well as the impact on the environment. The functionality of the current system for recording and processing the operating parameters of the equipment allows not only to confirm the correctness of calculations and the operability of the mathematical model, but also to assess the impact of the ice accumulator on the environment.
Studying the kinetics mechanism of emulsified petroleum products recovery from waste waters in dynamic conditions
Authors: Starostina I V , Polovneva D O , Porozhnyuk E V , Shevtsova R G
Published: March 2021

Petroleum products belong to the most common man-made pollutants of surface watercourses, and sometimes even underground drinking water sources. They get to the environment at the extraction and transportation of oil and oil products, as well as at industrial disasters. The oil-contaminated sewage waters produce the general-toxic, embryotoxic and carcinogenic effect, and weaken the immune system. To purify wastewaters from petroleum products the most efficient and environmentally friendly is the sorption method. This paper considers using a carbon-containing material, obtained by thermal modification at 450°C of waste kieselguhr sludge from vegetable oil extraction industry, as petroleum sorbent. The designed sorbent is characterized with mesoporous structure, high hydrophobic properties and the presence of oxygen-containing functional groups – carboxyl, hydroxyl and lactone – on the carbon layer surface. Research has been carried out in purification of test water media from emulsified petroleum products in dynamic conditions with the use of the designed sorbent. The number of passed sorption volumes to the «breakthrough» and the full saturation of the sorbent with oil products were determined. The calculated dynamic exchange capacity of the sorbent for petroleum products is 2.9 mg/g, the total exchange capacity – 8.4 mg/g. The limiting stage of petroleum products sorption process kinetics with the carbon-containing sorption material in the studied conditions is diffusion inside the sorbent grains.