bluuebunny/crossref_metadata_2025 huggingface dataset:
Sub-kelvin temperature management in ion traps for optical clocks
Authors: Nordmann T. , Didier A. , Doležal M. , Balling P. , Burgermeister T. , Mehlstäubler T. E.
Published: November 2020

The uncertainty of the ac Stark shift due to thermal radiation represents a major contribution to the systematic uncertainty budget of state-of-the-art optical atomic clocks. In the case of optical clocks based on trapped ions, the thermal behavior of the rf-driven ion trap must be precisely known. This determination is even more difficult when scalable linear ion traps are used. Such traps enable a more advanced control of multiple ions and have become a platform for new applications in quantum metrology, simulation, and computation. Nevertheless, their complex structure makes it more difficult to precisely determine its temperature in operation and thus the related systematic uncertainty. We present here scalable linear ion traps for optical clocks, which exhibit very low temperature rise under operation. We use a finite-element model refined with experimental measurements to determine the thermal distribution in the ion trap and the temperature at the position of the ions. The trap temperature is investigated at different rf-drive frequencies and amplitudes with an infrared camera and integrated temperature sensors. We show that for typical trapping parameters for In+, Al+, Lu+, Ca+, Sr+, or Yb+ ions, the temperature rise at the position of the ions resulting from rf heating of the trap stays below 700 mK and can be controlled with an uncertainty on the order of a few 100 mK maximum. The corresponding uncertainty of the trap-related blackbody radiation shift is in the low 10−19 and even 10−20 regime for 171Yb+(E3) and 115In+, respectively.
Research on acoustic conduction mechanism of underwater acoustic channel based on metamaterials
Authors: Zhao Xinsa , Yang Peng , Zhao Rongrong , Han Jianning
Published: November 2020

In underwater acoustic communication, there are problems of low transmission efficiency, severe loss, and inaccurate point-to-point transmission. To realize the efficient transmission of underwater acoustic communication based on finite element analysis, a metamaterial-based underwater acoustic channel model was designed using COMSOL Multiphysics 5.3 software, and its acoustic conduction mechanism was studied. It was found that the directional transmission of sound waves with low loss was achieved when the sound waves generated by the point sound source pass through the underwater acoustic channel. In addition, the periodic structure of local resonance was proposed to generate the multi-directional acoustic waves, which can control the acoustic wave transmission direction arbitrarily and improve the transmission efficiency of underwater acoustic communication. This indicates a new direction for long-distance and efficient transmission of underwater acoustic communication.
The color center singlet state of oxygen vacancies in TiO2
Authors: Chen Ji , Bogdanov Nikolay A. , Usvyat Denis , Fang Wei , Michaelides Angelos , Alavi Ali
Published: November 2020

Oxygen vacancies are ubiquitous in TiO2 and play key roles in catalysis and magnetism applications. Despite being extensively investigated, the electronic structure of oxygen vacancies in TiO2 remains controversial both experimentally and theoretically. Here, we report a study of a neutral oxygen vacancy in TiO2 using state-of-the-art quantum chemical electronic structure methods. We find that the ground state is a color center singlet state in both the rutile and the anatase phases of TiO2. Specifically, embedded coupled cluster with singles, doubles, and perturbative triples calculations find, for an oxygen vacancy in rutile, that the lowest triplet state energy is 0.6 eV above the singlet state, and in anatase, the triplet state energy is higher by 1.4 eV. Our study provides fresh insights into the electronic structure of the oxygen vacancy in TiO2, clarifying earlier controversies and potentially inspiring future studies of defects with correlated wave function theories.
Long term impacts of Phytophthora cinnamomi infestation on heathy woodland in the Great Otway National Park in south-eastern Australia
Authors: Wilson B. A. , Annett K. , Laidlaw W. S. , Cahill D. M. , Garkaklis M. J. , Zhuang-Griffin L.
Published: November 2020

The significant impacts of the introduced plant pathogen Phytophthora cinnamomi on native Australian vegetation have been well documented, but there is less knowledge of long-term effects. We assessed long-term (26 years) disease progression and impacts on vegetation floristics and structure at a heathy woodland site in the Great Otway National Park, eastern section. Disease progressed dramatically between 1989 and 2005 and by 2015 only 0.08% of the site was non-diseased. There were significant declines in plant species richness and numbers of susceptible species; and increases in percentage cover of resistant sedges and grasses overall, and in cover of Leptospermum continentale (prickly tea-tree) in post-disease areas. There were significant declines of Xanthorrhoea australis (Austral grass-tree), a keystone species that contributes greatly to vegetation structure and fauna habitat. There is an urgent need to map the distribution of P. cinnamomi affected vegetation and identify floristically important non-diseased vegetation in the national park. It is imperative that quarantine of non-infested areas, phosphite application, track closures and vehicle wash-downs be implemented to reduce disease extension and protect the significant biodiversity of the region including species rich heathlands and nationally listed threatened plant and fauna species and their habitats.
How roots of Picea abies and Fraxinus excelsior plantations contribute to soil strength and slope stability: evidence from a study case in the Hyrcanian Forest, Iran
Authors: Esmaiili Marzieh , Abdi Ehsan , Nieber John L. , Jafary Mohammad , Majnounian Baris
Published: November 2020

Although the effects of different vegetation cover on soil reinforcement due to their roots have been addressed in the literature, there remains a lack of assessment and comparison of root biomechanical properties of hardwood and softwood plantations for similar site conditions. To address this gap, two adjacent hillslopes with similar site conditions but different forest plantations, a Picea abies (softwood) and a Fraxinus excelsior (hardwood), were selected to assess and compare their effectiveness in protecting sloping soils. The profile trench method was used to obtain root distribution from both upslope and downslope sides of tree samples and on each side at two horizontal distances from the tree stems. Root tensile strength of live root samples was measured using a standard Instron Universal Testing Machine. A modified Wu and Waldron root reinforcement model was used to calculate root cohesion for the two plantations. The root tensile strength for was significantly greater for softwood than for hardwood trees (19.31 ± 2.64 vs 16.98 ± 1.01 MPa). Interestingly, the number of roots, root area ratio values, and the root tensile strength of the two species did not significantly differ between the upslope and downslope sides of trees. The results also showed a higher root cohesion for the softwood than the hardwood species (1.56 ± 0.34 vs 1.03 ± 0.21 kPa). In addition, softwood trees extended their contribution to soil protection to a larger horizontal distance compared with hardwood. However, our findings generally revealed that the values of root cohesion of both studied plantations were surprisingly lower than those found in earlier reports.
Effects of plant invaders on rhizosphere microbial attributes depend on plant identity and growth stage
Authors: Kapagianni Pantelitsa D. , Topalis Ioannis , Gwynn-Jones Dylan , Menkissoglu-Spiroudi Urania , Stamou George P. , Papatheodorou Efimia M.
Published: November 2020

Invasive species are considered a serious threat to local biodiversity and ecosystem functioning. Invasive success is often dictated via a plant’s capacity to influence belowground processes. We considered the role of two invasive plants – Solanum elaeagnifolium Cav, a perennial, and Conyza bonariensis L, an annual species – on the rhizosphere soil microbial communities and soil functionality. This study included the cultivation of each of the invasive species and bare soil as a control, in a mesocosm experiment. Rhizosphere soil was collected at three samplings during plant growth. Phospholipid fatty acids and the activities of β-glucosidase, N-acetylglucosaminidase, urease, peroxidase, polyphenol oxidase and acid phosphomonoesterase were analysed. Soil planted with invasive plants exhibited different enzyme activities and microbial biomasses compared with controls. Also, the plant species had soil enzyme profiles that differed from each other, mostly at the vegetative stage, lesser at the maturity stage, with no difference recorded at the middle of the annual cycle. The differences between plant species were significant for microeukaryotes, actinomycetes and the fungi/bacteria and Gram-positive/Gram-negative bacteria ratios but not significant for total microbial biomass and biomasses of Gram-positive and -negative bacteria and fungi in all samplings. The network of interactions among the microbial community and enzymes was affected by plant presence, plant species as well as plant growth stage. This study suggests that life-cycle stage is important in relation to invasive species influence on belowground processes.
Large-scale dissociations between views of objects, scenes, and reachable-scale environments in visual cortex
Authors: Josephs Emilie L. , Konkle Talia
Published: November 2020

Space-related processing recruits a network of brain regions separate from those recruited in object processing. This dissociation has largely been explored by contrasting views of navigable-scale spaces to views of close-up, isolated objects. However, in naturalistic visual experience, we encounter spaces intermediate to these extremes, like the tops of desks and kitchen counters, which are not navigable but typically contain multiple objects. How are such reachable-scale views represented in the brain? In three human functional neuroimaging experiments, we find evidence for a large-scale dissociation of reachable-scale views from both navigable scene views and close-up object views. Three brain regions were identified that showed a systematic response preference to reachable views, located in the posterior collateral sulcus, the inferior parietal sulcus, and superior parietal lobule. Subsequent analyses suggest that these three regions may be especially sensitive to the presence of multiple objects. Further, in all classic scene and object regions, reachable-scale views dissociated from both objects and scenes with an intermediate response magnitude. Taken together, these results establish that reachable-scale environments have a distinct representational signature from both scene and object views in visual cortex.
Rapid trial-and-error learning with simulation supports flexible tool use and physical reasoning
Authors: Allen Kelsey R. , Smith Kevin A. , Tenenbaum Joshua B.
Published: November 2020

Many animals, and an increasing number of artificial agents, display sophisticated capabilities to perceive and manipulate objects. But human beings remain distinctive in their capacity for flexible, creative tool use—using objects in new ways to act on the world, achieve a goal, or solve a problem. To study this type of general physical problem solving, we introduce the Virtual Tools game. In this game, people solve a large range of challenging physical puzzles in just a handful of attempts. We propose that the flexibility of human physical problem solving rests on an ability to imagine the effects of hypothesized actions, while the efficiency of human search arises from rich action priors which are updated via observations of the world. We instantiate these components in the “sample, simulate, update” (SSUP) model and show that it captures human performance across 30 levels of the Virtual Tools game. More broadly, this model provides a mechanism for explaining how people condense general physical knowledge into actionable, task-specific plans to achieve flexible and efficient physical problem solving.
Aging and the encoding of changes in events: The role of neural activity pattern reinstatement
Authors: Stawarczyk David , Wahlheim Christopher N. , Etzel Joset A. , Snyder Abraham Z. , Zacks Jeffrey M.
Published: November 2020

When encountering unexpected event changes, memories of relevant past experiences must be updated to form new representations. Current models of memory updating propose that people must first generate memory-based predictions to detect and register that features of the environment have changed, then encode the new event features and integrate them with relevant memories of past experiences to form configural memory representations. Each of these steps may be impaired in older adults. Using functional MRI, we investigated these mechanisms in healthy young and older adults. In the scanner, participants first watched a movie depicting everyday activities in a day of an actor’s life. They next watched a second nearly identical movie in which some scenes ended differently. Crucially, before watching the last part of each activity, the second movie stopped, and participants were asked to mentally replay how the activity previously ended. Three days later, participants were asked to recall the activities. Neural activity pattern reinstatement in medial temporal lobe (MTL) during the replay phase of the second movie was associated with detecting changes and with better memory for the original activity features. Reinstatements in posterior medial cortex (PMC) additionally predicted better memory for changed features. Compared to young adults, older adults showed a reduced ability to detect and remember changes and weaker associations between reinstatement and memory performance. These findings suggest that PMC and MTL contribute to change processing by reinstating previous event features, and that older adults are less able to use reinstatement to update memory for changed features.
Therapeutic candidates for the Zika virus identified by a high-throughput screen for Zika protease inhibitors
Authors: Abrams Rachel P. M. , Yasgar Adam , Teramoto Tadahisa , Lee Myoung-Hwa , Dorjsuren Dorjbal , Eastman Richard T. , Malik Nasir , Zakharov Alexey V. , Li Wenxue , Bachani Muzna , Brimacombe Kyle , Steiner Joseph P. , Hall Matthew D. , Balasubramanian Anuradha , Jadhav Ajit , Padmanabhan Radhakrishnan , Simeonov Anton , Nath Avindra
Published: November 2020

Significance

Infection with Zika virus can cause severe consequences in a subset of the patient population. When infection occurs during pregnancy, congenital abnormalities can occur. Additionally, the onset of Guillain–Barré syndrome, encephalitis, and myelitis are associated with Zika virus infection. We have identified several small molecules with favorable clinical profiles, including the five-lipoxygenase–activating protein inhibitor, MK-591, and the tetracycline antibiotic, methacycline, as inhibitors of Zika virus infection, the latter of which reduced neurological deficits in a Zika virus mouse model. These compounds have the potential to be used as prophylactics or for the treatment of the neurological complications of Zika virus infection.
Natural gas shortages during the “coal-to-gas” transition in China have caused a large redistribution of air pollution in winter 2017
Authors: Wang Siwen , Su Hang , Chen Chuchu , Tao Wei , Streets David G. , Lu Zifeng , Zheng Bo , Carmichael Gregory R. , Lelieveld Jos , Pöschl Ulrich , Cheng Yafang
Published: November 2020

Significance

Improving air quality is an important driving force for China’s move toward clean energy and the extensive implementation of the “coal-to-gas” policy. Our analysis shows, however, that a shortage of natural gas during the implementation of the action in northern China has led to the transfer of pollution emissions and deterioration of air quality for large areas and populations in southern China during winter 2017. Our finding highlights the importance and necessity of synergy between environmental and energy policymaking to address the grand challenge of an actionable future to achieve the cobenefits of air quality, human health, and climate.
A general model of hippocampal and dorsal striatal learning and decision making
Authors: Geerts Jesse P. , Chersi Fabian , Stachenfeld Kimberly L. , Burgess Neil
Published: November 2020

Significance

A central question in neuroscience concerns how humans and animals trade off multiple decision-making strategies. Another question pertains to the use of egocentric and allocentric strategies during navigation. We introduce reinforcement-learning models based on learning to predict future reward directly from states and actions or via learning to predict future “successor” states, choosing actions from either system based on the reliability of its predictions. We show that this model explains behavior on both spatial and nonspatial decision tasks, and we map the two model components onto the function of the dorsal hippocampus and the dorsolateral striatum, thereby unifying findings from the spatial-navigation and decision-making fields.
Studies in bank voles reveal strain differences between chronic wasting disease prions from Norway and North America
Authors: Nonno Romolo , Di Bari Michele A. , Pirisinu Laura , D’Agostino Claudia , Vanni Ilaria , Chiappini Barbara , Marcon Stefano , Riccardi Geraldina , Tran Linh , Vikøren Turid , Våge Jørn , Madslien Knut , Mitchell Gordon , Telling Glenn C. , Benestad Sylvie L. , Agrimi Umberto
Published: November 2020

Significance

Chronic wasting disease (CWD) is a highly contagious disease caused by prions that affects several cervid species and is relentlessly spreading across North America. Very recently, CWD was detected for the first time in Europe. In this study, we found that Norwegian CWD strains are distinct from those causing the epidemic in North America. Moreover, we show that Norwegian reindeer and moose are affected by different CWD strains, revealing an unprecedented prion strain variation in Norwegian wild cervid populations. These findings indicate that North American CWD prions are not the proximate cause of the newly discovered Norwegian CWD cases and have implications for CWD control strategies in Europe, as well as for the safety of humans.
Green energy by recoverable triple-oxide mesostructured perovskite photovoltaics
Authors: Schneider Avi , Efrati Ariel , Alon Stav , Sohmer Maayan , Etgar Lioz
Published: November 2020

Significance

In the progress toward a more sustainable world, wind and solar energy production are the leading renewable contenders. The emerging field of perovskite solar cells has achieved remarkable efficiencies and is gaining a steady foothold among other solar technologies. However, a worldwide deployment of perovskite solar panels poses environmental and economic problems due to degradation over time. The degrading photoactive materials are usually situated between other layers, complicating or inhibiting cell recycling. Here, we investigated a structure for perovskite solar cells composed of chemically and thermally stable oxides which includes the application of the photoactive perovskite material as a final step. This structure allows for the removal and replacement of degraded perovskite, with a full restoration of photovoltaic characteristics.
Enhanced trace element mobilization by Earth’s ice sheets
Authors: Hawkings Jon R. , Skidmore Mark L. , Wadham Jemma L. , Priscu John C. , Morton Peter L. , Hatton Jade E. , Gardner Christopher B. , Kohler Tyler J. , Stibal Marek , Bagshaw Elizabeth A. , Steigmeyer August , Barker Joel , Dore John E. , Lyons W. Berry , Tranter Martyn , Spencer Robert G. M. ,   the SALSA Science Team
Published: November 2020

Significance

Trace elements are integral to biogeochemical processes at the Earth’s surface and play an important role in the carbon cycle as micronutrients to support biological productivity. We present data from the Greenland and Antarctic ice sheets to demonstrate the importance of subglacial biogeochemical processes in mobilizing substantial quantities of these elements. Usually immobile elements are found in subglacial meltwaters at elevated concentrations compared with typical rivers, with most exhibiting distinctive size fractionation due to adsorption onto nanoparticles. Our findings suggest that ice sheets need to be included in models of global biogeochemical cycles of trace elements and studies of the fertilization of adjacent marine systems, especially the Southern Ocean, due to large export fluxes of micronutrients, most notably iron.
Triclosan leads to dysregulation of the metabolic regulator FGF21 exacerbating high fat diet-induced nonalcoholic fatty liver disease
Authors: Yueh Mei-Fei , He Feng , Chen Chen , Vu Catherine , Tripathi Anupriya , Knight Rob , Karin Michael , Chen Shujuan , Tukey Robert H.
Published: November 2020

Significance

While exploring triclosan-induced hepatoxicity, this study discovered that exposure to triclosan, in combination with a high-fat diet, causes nutritional imbalance, resulting in the pathogenesis of hepatic steatosis. The underlying mechanism through which triclosan imposes an impact on critical metabolic networks was delineated. The significance of this study lies in the fact that the ubiquitous presence of the environmental contaminant triclosan in conjunction with the prevalence of high consumption of dietary fat, which constitutes a good recipe for the development of fatty liver disease, are common factors encountered in everyday life. The finding that triclosan exacerbates high-fat diet-induced metabolic disorders by disrupting regulation of FGF21 expression provides a basis for a therapeutic approach in treating nonalcoholic fatty liver disease and steatohepatitis.
Oncogenic activation of PI3K-AKT-mTOR signaling suppresses ferroptosis via SREBP-mediated lipogenesis
Authors: Yi Junmei , Zhu Jiajun , Wu Jiao , Thompson Craig B. , Jiang Xuejun
Published: November 2020

Significance

This work demonstrates that oncogenic activation of the PI3K-AKT-mTOR signaling pathway suppresses ferroptosis via the sterol regulatory element-binding protein-mediated lipogenesis, and that inhibition of this pathway potentiates the cancer therapeutic effect of ferroptosis induction. This finding unveils mechanisms for the regulation of ferroptosis and provides a potential cancer therapeutic approach for treating cancer patients bearing tumorigenic mutations in the PI3K-AKT-mTOR pathway.
Combination of the SAW and TOPSIS Method For Determining The Best Marketplace Recommendations
Authors: Frieyadie  , Sukmawati Adriana Hadi , Nurajijah
Published: November 2020

A marketplace is a place where sellers can sell merchandise online without the need to create a website. From the many available marketplaces, it appears that consumers’ desires in choosing a marketplace are different. Therefore a decision support system is needed to help resolve this problem. In this study, the author combines two methods, namely Simple Additive Weighting (SAW) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The SAW method is used to find normalized matrix values, and the TOPSIS method to find normalized weighted matrices up to ranking. This is because the two methods are simple, easy to understand, efficient, and can measure the relative performance of decision alternatives in a simple mathematical form. The final result of this research is to determine the best alternative of a marketplace with criteria: application appearance, features, interactivity, transaction security, and customer service.
Understanding Impact of M-banking on Individual Performance of the DeLone & McLean Method and TTF Perpective
Authors: Azizah Qudsiah Nur , Hidayat Taopik , Riana Dwiza , Dwiantoro Tino , Suhardoyo  , Fitriana Saghifa
Published: November 2020

Currently the public has a curiosity about e-commerce applications that are growing rapidly, such as Mobile banking (m-banking). The development of m-banking can be seen from user satisfaction, end-user interest and the success of everyone using m-banking. This study adds the Task Technology Fit (TTF) model to the DeLone & McLean model for the purpose of knowing the success of individuals using m-banking. We used 102 respondents to answer all the questions in the questionnaire. The results show that of the 14 hypotheses used there were 8 accepted hypotheses. This shows that in individual achievement, usage and user satisfaction are not important. Users consider the importance of individual performance to simplify the use of TTF. User satisfaction is influenced by system quality, information quality, and service quality.
Clustering Based Undersampling for Handling Class Imbalance in C4.5 Classification Algorithm
Authors: Nugraha Wahyu , Maulana Muhammad Sony , Sasongko Agung
Published: November 2020

Machine Learning is very difficult to make an effective learning model if the distribution of classes in the training data set that is used is not balanced. The problem of class imbalance is mostly found during classifications in the real world where one class is very small in number (minority class) while the other classes are very numerous (majority in class). Building a learning algorithm model without considering the problem of class imbalance causes the learning model to be flooded by majority class instances so that it ignores minority class predictions. Random undersampling and oversampling techniques have been widely used in various studies to overcome class imbalances. In this study using the undersampling strategy with clustering techniques while the classification model uses C4.5. Clustering is used to group data and the undersampling process is performed on each data group. The goal is that sample samples that are useful are not eliminated. Statistical test results from experiments using 10 imbalance datasets from KEEL-repository dan Kaggle dataset with various sample sizes indicate that clustering-based undersampling produces satisfactory performance. Improved performance can be seen from the sensitivity and AUC values that increased significantly.
Comparing Classification Algorithm With Genetic Algorithm In Public Transport Analysis
Authors: Aryanti Riska , Saryoko Andi , Junaidi Agus , Marlina Siti , Wahyudin  , Nurmalia Lia
Published: November 2020

Congestion major cities in Indonesia caused by the proliferation of the use of private vehicles. Some people express their opinions and its opinion regarding public transport users through social media sites and other websites. This opinion can be used as a sentiment analysis material to find out whether the public transport service is positive or negative. The results of the sentiment analysis can help in the assessment and evaluation of the use of public transportation, it is also expected to improve services and facilities from public transportation so that the public tends to have a positive opinion. Based on the results of the sentiment analysis, it is expected that the community will switch to using public transportation which will certainly reduce congestion. In this study also added preprocessing stages by using the GataFramework framework to complete processes that cannot be done on RapidMiner tools. The method used in this study is sentiment analysis with the method of applying genetic algorithms for feature selection with comparative classification algorithms. Performed by testing the composition of various data. From the results of testing for the case in this study, it was found that the Support Vector Machine classification algorithm based on Genetic Algorithms had a fairly good average accuracy of 76.11% and AUC value of 0.778% with the Fair Classification diagnosis level compared to the three methods such as Naive Bayes, Support Vector Machine and Naive Bayes based on Genetic Algorithms. So that in this study Support Vector Machine classification algorithm based on Genetic Algorithm can be recommended as an algorithm classification good enough to analyze land transportation public sentiment. Based on the analysis it is expected that the public sentiment will switch to using public transport which would reduce congestion.
Stock price prediction using artificial neural network integrated moving average
Authors: Suryani I , Buani D C P
Published: November 2020

Stock prices are always interesting to be a research topic because stock prices always change at any time. Stock price index is a benchmark for shareholders to sell, buy or maintain it. As in this study, the data used is the closing price of ANTM’s share price which is then processed to predict future stock prices. The proposed method in this study is an integrated moving average which is used to transform data in order to improve data quality. So that it can improve the accuracy of predictions on the neural network. Based on the experiment conducted using 10 combinations of parameters on the neural network using integrated moving average, has been able to produce the RMSE value. And validation based on t-test also showed a significant difference compared to the previous model. So from the result of experiment use an integrated moving average proved to be able to improve neural network performance.
Information Technology Governance in Al Kautsar Islamic Elementary School Using COBIT 5 Framework
Authors: Haryani  , Misriati T , Hidayat R , Puspitasari D , Muthia DA , Elyana I
Published: November 2020

This study aims to assess the level of maturity of the use of information technology and obtain a gap value which is then analyzed to achieve the expected maturity value. Management of information technology today is very important, not only focused on profit organizations or companies. With good management of information technology, information within an institution can be achieved optimally, so that institutional goals can be achieved in accordance with the established vision and mission. With reference to COBIT 5 Enabling Process on Enterprise Goals and reality in the field after initial research conducted at Al Kautsar Islamic Elementary School, the scope of the framework for developing Information Technology management models focused on operational and staff productivity in the Balanced Scorecard included in the internal section. The focus of this research was chosen because at Al Kautsar Islamic Elementary School the Information Technology process is still in the development stage. From enterprise goals, IT-related goals can be generated, namely 8 applications, information and adequate information technology solutions and 16 competent and highly motivated IT staff. After collecting and processing data, the results of calculations for each process contained in the customer perspective that determine the achievement value of 2 of the targets set, it is expected that the management can meet these targets. Based on the analysis of audit findings using the COBIT 5 framework, for the six IT processes related to previous goals, the operator staff and the system used at Al Kautsar Islamic Elementary School are still largely in the reactive stage. This means that the application of information technology is still based on current needs, without first doing a careful IT governance planning.
Comparison of Naïve Bayes Algorithm with Genetic Algorithm and Particle Swarm Optimization as Feature Selection for Sentiment Analysis Review of Digital Learning Application
Authors: Ernawati Siti , Wati Risa , Nuris Nuzuliarini , Marita Lita Sari , Yulia Eka Rini
Published: November 2020

The problem examined in this study is about the user’s trust in using digital learning applications that are downloaded on playstore. Many reviews are given by the public about the application that has been downloaded on playstore. This review is very influential on their trust in using the application. The purpose of this study is to classify data according to labels and find out the best choice between the classification method and the proposed selection feature as a consideration in determining the use of digital learning applications. This study compares the classification method, the Naïve Bayes algorithm and the genetic algorithm (GA) as feature selection with the Naïve Bayes algorithm classification method and the particle swarm optimization (PSO) as feature selection to categorize the reviews in the playstore. The experimental results show that the Naïve Bayes algorithm and PSO as feature selection is the best model between the two models proposed in this study. Reviews can be classified into positive and negative labels well. The accuracy is 98.00%. The results of the classification are expected to help in making decisions when going to use digital learning application.
Generalized Reduced Gradient Approach for Solving Periodic Heterogeneous Vehicle Routing Problem with Side Constraints
Authors: Mawengkang Herman , Sutarman
Published: November 2020

In order to transport customers’ request economically, such as, goods, meals etc it is frequently the delivery company depend on a model which is called vehicle routing problem (VRP). This model consists of a customer population with deterministic demands, and a central station which acts as the base of a set of vehicles. The purpose is to design the vehicle routes starting and terminating at the assigned central station, such that the customer demands is met, within a time windows This paper develops an optimization model for the management of periodic deliveries of a given commodity. Due to the geographic location of customers the company requires to have diverse type of vehicles with diverse capacity to serve the commodity for customers. This kind of problem is called Periodic Heterogeneous Vehicle Routing Problem (PHVRP). The purpose is to schedule periodically the deliveries according to feasible combinations of delivery days and to determine the scheduling of fleet and driver and routing policies of the vehicles. The aim is to minimize the total costs of all routes over the planning horizon. We propose a combined approach of reduced gradient and heuristic algorithm to solve the problem.
Efficiency Measurement of Operations Management of Clean Water Company using DEA
Authors: Hardiyan  , Fridayanthie Eka Wulansari , Septiani Noer Azni , Sayfulloh Asep , Kusumaningrum Aliffah , Wahyudin
Published: November 2020

Clean water is the most fundamental need for humans, it can be said to be the source of life. Water is an important factor in the future development process, if the management of water resources is wrong, it can cause a socio-economic development crisis for all countries. In 2017, water needs for residents of the city of Jakarta only reached 60% that served by clean water. The problem that arises is the lack of clean water supply, therefore research is needed to measure the level of efficiency, so that companies become a reference for overcoming unserved areas. Therefore DEA method is needed to measure the efficiency relative of a company. DMUs are based on annual data comparisons from 2014 to 2018 refer to homogeneous data. Efficiency measurements of clean water companies show a positive trends every year. In 2017 and 2018, the company showed extraordinary achievements with a relative level of efficiency reaching 100% or a value of 1, so that it can be a reference for the following years.
Analysis of the AMARI COVID-19 application with the Technology Acceptance Model Method
Authors: Nuryanto A , Setyawan O , Riana D , Hadianti S , Aji AMB , Pujiastuti E
Published: November 2020

The purpose of this research is to know the influence of the application of AMARI COVID-19 to how well the use of the application is received in the community to prevent the spread of the virus Covid-19. This type of research is explanatory research with a quantitative approach. To this end, their responses to an online questionnaire (n=113) were analyzed using IBM SPSS Statistics 21 as software. The sampling techniques used are simple random sampling. Descriptive analysis is used to analyze the research hypothesis. The test results of the five hypothesis in the study stated that the AMARI COVID-19 application has a significant effect on the prevention of the spread of Covid-19 viruses, meaning it is accepted by the wider community as one of the tools that can help prevent the spread of COVID-19 diseases.
Data mining for cryptocurrencies price prediction
Authors: Fatah Haerul , Anggraini Recha Abriana , Supriadi Deddy , Pertiwi Melisa Winda , Warnilah Ai Ilah , Ichsan Nurul
Published: November 2020

Electronic payments is something that is currently in high demand by investors today, but transactions are often constrained due to various problems, especially from third parties. For this reason, cryptocurrency emerged, which is one of the solutions for conducting electronic payment transactions. Some types of cryptocurrency that are most in demand by investors are bitcoin, ethereum, and ripple. The fluctuation value of cryptocurrency is very difficult to predict so that investors often experience losses when making transactions. This study aims to predict cryptocurrency prices such as bitcoin, ethereum and ripple using data mining algorithms. The data mining algorithm used in this prediction process is K-NN, Neural Network, SVM, Linear Regression, Random Forest and Decision Tree. Data mining modeling is done by dividing the dataset into each type of commodity and then analyzed using each algorithm. The results of this study indicate that the accuracy value obtained from some data mining algorithms is good enough to predict cryptocurrency prices
Improved Accuracy of Sentiment Analysis Movie Review Using Support Vector Machine Based Information Gain
Authors: Maulana Reza , Rahayuningsih Panny Agustia , Irmayani Windi , Saputra Dedi , Jayanti Wanty Eka
Published: November 2020

The quality of a movie can be known from the opinions or reviews of previous audiences. This classification of reviews is grouped into positive opinions and negative opinions. One of the data mining algorithms that are most frequently used in research is the Support Vector Machine because it works well as a method of classifying text but has a very sensitive deficiency in the selection of features. The Information Gain method as feature selection can solve problems faster and more stable convergence levels. After testing on two movie review datasets are Cornell and Stanford datasets. The results obtained on the Cornell dataset is the Support Vector Machine algorithm to produce an accuracy of 83.05%, while for the Support Vector Machine based on Information Gain, the accuracy value is 85.65%. Increased accuracy reached 2.6%. Then, the results obtained on the Stanford dataset is the Support Vector Machine algorithm yields a value of 86.46%, while for the Support Vector Machine based on Information Gain, the accuracy value is 86.62%. Increased accuracy reached 0.166%. Support Vector Machine based Information Gain on the problem of movie review sentiment analysis proved to provide more accurate value.
Determining the Eligibility of Providing Motorized Vehicle Loans by Using the Logistic Regression, Naive Bayes and Decission Tree (C4.5)
Authors: Rianto Harsih , Amrin  , Rudianto  , Pahlevi Omar , Kusumawardhani Paramita , Hadi Seno Sudarmono
Published: November 2020

Evaluating in determining the eligibility of giving credit is very important. Errors in providing credit worthiness assessments can result a bad credit risk. The problem that often occurs is not the application of the system by financial parties but more on HR when making predictions about the determination of consumer credit worthiness. Research in the field of computers has been done to reduce credit risk resulting in losses to the company. In this research a comparison of Logistic Regression (LR), Naïve Bayes (NB) and Decision Tree (C4.5) algorithms is performed to predict the feasibility of granting credit. In order to produce a prediction of the feasibility of granting credit to new consumers, credit data used by the company is used. The data used in this study consists of 481 consumer records that have been classified as consumers with current credit and bad credit. After testing using the same dataset on the three algorithms by comparing the AUC and Confusion Matrix values, it was found that the appropriate algorithm to be applied to the credit worthiness dataset was Logistic Regression with an Area Under Curve (AUC) value of 0.972 and Accuracy or Confusion Matrix of 93.14%. As for the Decision Tree Algorithm (C4.5) from the test results, the AUC value is 0.926 and the Accuracy is 90.85% and the Algortima Naïve Bayes AUC value is 0.905 and the Accuracy is 82.75%.
Hybrid approximation for solutions of high-order integro-differential equations including variable delay
Authors: Gürbüz Burcu
Published: November 2020

In this study, a numerical technique with hybrid approximation is developed for solving high-order linear integro-differential equations including variable delay under the initial conditions. These type of problems are of applications in mathematical physics, mechanics, natural sciences, electronics and computer science. The aim of this work is to investigate an approximation with the matrix forms of Taylor and Laguerre polynomials along with standard collocation points. By the reduction of the solution of this problem with regard to the matrix relations, the solution of a system of algebraic equations has been obtained. The usefulness of this algorithm has been demonstrated by numerical experiments together with an error analysis technique.
Entrepreneurial Mindfulness Based on Artificial Intelligence
Authors: Indrianti Yasinta , Sasmoko  , Mohd Amin Nor Fadila , Rabiha Sucianna Ghadati , Setiadi Nugroho Juli , Handrimurtjahjo Agustinus Dedy , Waspodo Muktiono
Published: November 2020

The ability to self-diagnose to build capacity as an entrepreneur is something that is important in order to bridge the development of self-competence needed in the industrial era 4.0. This ability is expected to bring up a condition in which a person has full active awareness of the awakening of the entrepreneurial spirit within himself to then produce a productive person called Entrepreneurial Mindfulness. This study aims to develop self-detection tools about Entrepreneurial Mindfulness through an artificial intelligence-based website for students and alumni who are entrepreneurs. The research method used is the Neuroresearch method for developing Entrepreneurial Mindfulness measurement tools and the waterfall method for developing Artificial Intelligence-based websites. The results of research in the form of an Artificial Intelligence-based website about Entrepreneurial Mindfulness where the website will be able to provide profiling based on diagnoses made related to the level of Entrepreneurial Mindfulness owned by students and alumni in implementing entrepreneurial practices.
K-Means Algorithm for Clustering The Location Of Accident-Prone On The Highway
Authors: Puspitasari Diah , Wahyudi Mochamad , Rizaldi Muhammad , Nurhadi Acmad , Ramanda Kresna , Sumanto
Published: November 2020

In Indonesia, there is a highway which connects Jakarta and Bogor, some accidents have occurred at the highway every year. This paper aims to analyze the location of the accident to classify the high and low levels of accident vulnerability in the Jakarta Bogor Highway. First, the extracted data is then grouped based on some of the same characteristics in the dataset namely cause, location, minor injuries, serious injuries and death. Second, the grouping results are visualized in the form of highway maps that can help highway managers in identifying and evaluating several accident-prone points on Jakarta Bogor Highway. The method to be used in data processing in this study is the K-Means clustering algorithm which is expected to produce useful information for Jakarta Bogor Highway managers. The results of this study indicate that accidents that often occur are in cluster 3 with a total of 80 accidents and at least there are in cluster 2 with a total of 57 accidents, the location of accidents that often occurs in cluster 1 is in KM 24, while cluster 2 is in KM 41 and cluster 3 located at KM 10.6.
Analysis of Perceptron Quantum Artificial Neural Networks to Classify the Feasibility of Prospective Debtors
Authors: Pujiastuti Lise , Wahyudi Mochamad , Solikhun
Published: November 2020

Bank is a business entity whose activities are collecting funds from the public in the form of deposits and channeling them to the public in the form of loans or other forms to improve the lives of many people. This study aims to classify the feasibility data of prospective borrowers with the perceptron algorithm using quantum computing to facilitate the bank in determining the prospective debtor. The results of this study are an analysis of the feasibility of prospective borrowers using the perceptron algorithm with quantum computing.
Generation of Rectangular Matrix Key for Hill Cipher Algorithm Using Playfair Cipher
Authors: Alawiyah Tuti , Hikmah Agung Baitul , Wiguna Wildan , Kusmira Mira , Sutisna Herlan , Simpony Bambang Kelana
Published: November 2020

Hill cipher is a cryptographic algorithm that uses matrix as a key by utilizing modulo operations. The key matrix used is usually made randomly as a square matrix. In this study, a key matrix was created using the Playfair Cipher algorithm as a rectangular matrix. The use of the Playfair Cipher algorithm makes it easy for users to remember key matrices, while remaining safe when distributed. Beside the key matrix security, use of a rectangular key matrix produces a longer and more complicated ciphertext to find the linear equation.
Gojek and Grab User Sentiment Analysis on Google Play Using Naive Bayes Algorithm And Support Vector Machine Based Smote Technique
Authors: Hermanto  , Kuntoro Antonius Yadi , Asra Taufik , Pratama Eri Bayu , Effendi Lasman , Ocanitra Ridatu
Published: November 2020

As the online Ojek services, people often talk about them by giving their opinions and opinions through various media, one of which is Google Play opinion given by the public to the services of online Ojek also diverse. Users provide review reviews or comments about the application, of course users will choose an app that has a good review. But monitoring the reviews of the general public is not easy, because the amount is very much to be processed so that researchers want to know the extent of the user review analysis of Gojek and Grab applications based on the review of user comments using the classification technique is using the NB algorithm and SVM based technique Smote. The results of the test with the highest accuracy result 81.09% and AUC value = 0.922 is the application Gojek while for application test results grab accuracy value of 73.20% and AUC value = 0.848. To that end, the implementation of the Support Vector Machine based Smote technique in this study has higher accuracy so that it can be used to provide solution to the sentiment analysis problems in the review user comments online Ojek application
Popular Content Prediction Based on Web Visitor Data With Data Mining Approach
Authors: Iskandar I D , Basjaruddin N Ch , Supriadi D , Ratningsih  , Purnia D S , Wibisono T
Published: November 2020

A quality website has five parameters that must be considered are: information, security, convenience, comfort, quality of service. But of course, the fifth parameter does not always guarantee the amount through its Web page will increase, from that problem. So research is conducted to predict website content based on visitor data with a data mining approach, this research aims to improve the quality of content on target according to the interest of website visitors. Evaluation of random forest algorithm has the value the accuracy of classification of 71 percent by value of Kappa 0.712 whereas the k-NN algorithm has higher accuracy values of random Forest algorithm i.e. worth 84.88 percent and kappa values of the mean 0.847 the k-NN algorithm performs data processing process predictions against data of web content more effectively than the random forest