bluuebunny/crossref_metadata_2025_split huggingface dataset:
Mathematic
Authors: Nasution Mahyuddin K M , Salim Sitompul Opim , Nasution Sawaluddin , Aulia Indra , Elveny Marischa 
Published: July 2020

Abstract
This paper provides a classical interpretation of mathematics from terms to categories such as arithmetic, geometry, algebra, and trigonometry. The background of interpretation is based on mathematics itself as reasoning, which contains systematic and structure. However, the mathematical term is recognized by them, but it comes with a variety of symbols and ways of interpretation. That interpretation is accumulated into universe of discourse which is a means of mathematics itself, and it states the mathematic.
Interaction Mixed Reality Rare Animal Indonesia with Voice Input Using Microsoft Hololens
Authors: Syahputra M F , Kurniawan D S , Siregar B 
Published: July 2020

Abstract
Indonesia is one country that is known for its natural wealth. Various types of flora and fauna living in this country. Examples of rich fauna that exist in Indonesia and can still be found at this point that one-horned rhino (Java rhinoceros), Sumatran elephant, Sumatran tiger, deer Bawean even dragons. However, over the times that make more and more people are not responsible for expanding residential land for private purposes and poaching that occurred in Indonesia, the animals are increasingly difficult to find because of habitats and their populations are dwindling. In this research, researchers applied a mixed reality technology with voice input to bring the Indonesian endangered species that has begun difficult to be found with the actual scale without having to disturb the habitat or environment. How it works is a mixed reality which brings together the virtual world with the real world. So that thanks to the development of today’s technological world, users can feel the direct presence of these animals without having to hunt or look for them.
Data Science
Authors: Nasution Mahyuddin K M , Salim Sitompul Opim , Budhiarti Nababan Erna 
Published: July 2020

Abstract
The presence of new science does not necessarily occur just like that. Every science starts from interests, discussion, and looks for a basic foundation, but in general the main foundation of science is mathematics. Data science includes structured and systematic knowledge about data. However, many other sciences that has a relationship with the data in question, ranging from statistics to computer science. This paper aims to reveal the obstacle and limitations of other science into a data science completely, on that basis the definition of data sciences needs to be elaborated, then confirm data science as new science and not depend directly on several other sciences.
Feature Selection Approach for Solving Imbalanced Data Problem in Single Nucleotide Polymorphism Discovery
Authors: Nurhasanah R , Hasibuan L S , Kusuma W A 
Published: July 2020

AbstractSingle Nucleotide Polymorphism (SNP) is a type of molecular marker which constitutes the phenotypic variations between individuals in certain species. In recent years, the advantages of SNP were widely considered in many fields, for instance in designing precision medicine in humans and assembling superior cultivars in plant breeding. The main challenge in SNP discovery is imbalanced data distribution between classes, where the number of true SNPs in question is much fewer than false SNPs. While the study in observing the benefit of feature selection in classification problem was widely reported, the use of this technique in solving imbalanced class problem still become interesting topic for research. In this study, we selected the features that most contribute in identifying SNP using Feature Assessment by Sliding Thresholds (FAST) method. FAST evaluates the contribution of each feature in identifying SNPs based on the Area under ROC Curve (AUC) value. SNP identification using 4 best features resulted in improved classifier performance in terms of G-Means compared to using 24 features. In addition, using feature selection techniques can reduce computational time and save resource needed.
Waspmote-Based Landslide Early Disaster Detection System with GSM Communication
Authors: Fachrizal F , Zarlis M , Efendi S 
Published: July 2020

Abstract
Landslide early detection system consists of a sensing system and an alarm system that is not in the same location (remote area). The sensing system is located in a vulnerable area, while the alarm system is placed in a residential area. The sensing system uses sensors found in Plug and Sense from Libelium to read rainfall, soil moisture and soil movement and use a 3G module. The alarm system uses Arduino Uno R3 as a controller and has a Relay and SIM800l module. The installation area of the system depends on the area that has an internet network. From the overall test results, the system successfully measured rainfall, soil moisture, and soil movement. The system also managed to activate an alarm if there is a potential for landslides and successfully sends measurement data to the website. The activation of the alarm system has a delay of about ± 30 seconds. Keywords: Landslide Early Warning, Plug and Sense Smart, 3G, Arduino, Waspmote.
Spatial Modelling to Optimize the Positioning of Air Defense Surveillance Radars with Minimal Shadow Zone
Authors: Wijaya S D R , Bura R O , Prihanto Y 
Published: July 2020

Abstract
Indonesia’s strategic position between two continents and two oceans has resulted in an obligation for Indonesia to be involved in various international cooperation and international conflicts. The declaration of the development of the maritime sector to be the cornerstone of Indonesia’s main strength requires support from the air defense sector. The current condition of Indonesia’s air defense sector, especially the Air Surveillance Radar has not worked optimally resulting in an air defense operation that has not been maximized. The properties of high-frequency electromagnetic waves used in Doppler radar have a straight line of sight. Mountains, hills, and artificial objects/buildings can become obstacles, create blank areas (areas that cannot be covered by radar) behind them, and reduce the performance of the radar coverage. The use of sensing technology, in particular, the geographic information system to determine the position of air surveillance radar placing is an effective solution to optimize the coverage radars exist. The rectangular rule approach method combined with the land surveying technique is a combination of methods that can be used to measure the volume of air defense radar coverage. The level optimization of radar position planning point can be determined by processing the obstacle volume, land slope, earthquake and landslide vulnerability, and land cover conditions into the calculation of the fuzzy inference system technique. This study examines the concept of using Geographic Information Systems to measure the level of radar optimization when placed in a planned position. This research aims to produce a method review on optimizing the performance of air surveillance radar coverage by utilizing remote sensing technology to determine the radar placement position.
SSAM: A Haskell Parallel Programming STM Based Simple Actor Model
Authors: Kim Y , Cheon J , Hur T , Byun S , Woo G 
Published: July 2020

Abstract
As computers with manycore architecture are being widely spread, parallel programming becomes a pending issue. While parallel programming has been a challenging issue, Haskell is known to be of the best available one. The immutability and declarative nature of Haskell suit well the need for parallelism. In our previous work, we implemented the SAM, a parallel programming model based on Haskell. In this paper, we propose the SSAM, an updated version of SAM, by solving the performance overhead of SAM socket. We discuss the architecture of SSAM and its major functions and show the 14% scalability upgrade in a performance test.
Nikkei Stock Market Price Index Prediction Using Machine Learning
Authors: Harahap Lukman A , Lipikorn Rajalida , Kitamoto Asanobu 
Published: July 2020

Abstract
Stock market prediction has always been a difficult process, most of the prediction rely solely on the data of the corresponding stock market. Relationship of gold and oil price with stock market performance has been proven significant in some major world stock index. Prediction of stock market price index using machine learning methods is expected to perform well, with the ability of machine learning method to predict using nonlinear inputs. The methods were commonly able to predict relatively well in predicting the values of Japanese Nikkei 225 and Japanese Nikkei 400 indexes.
Ant Colony Optimization (ACO) Algorithm for Determining The Nearest Route Search in Distribution of Light Food Production
Authors: Fahmi H , Zarlis M , Nababan E B , Sihombing P 
Published: July 2020

Abstract
At this time the development of technology that forces everything must move fast, the efficiency of time becomes a measure of the success of a distribution or production. the continuity of production and the success of production itself is related to distribution. When the distribution fails, production will fail. Indonesian people especially in North Sumatra often consume snacks when free time and snacks are the most consumed food by the Indonesian population until now the production of snacks in Indonesia rose to 50% in 2019, causing the food production to have a concept in timely distribution without experiencing delays in distributing goods that have been produced. One of the expeditions in Indonesia experienced problems in terms of late distribution of goods up to 15-20%, this would have a bad impact on the industry in the future, so to overcome the problems above the application of the ant colony optimization algorithm can help the above problems by placing 4 ants and the ants will spread from point A to point H. In the results of this study, there are 4 ants that move together where the best results are obtained by ant 2, closest to the point H, where the value obtained reaches 0,00015
Prediction of Vocational Students Behaviour using The k-Nearest Neighbor Algorithm
Authors: Nasution Fadliansyah , Muiza Zamzami Elviawaty 
Published: July 2020

Abstract
This article discusses the implementation of the k-NN algorithm in predicting student behavior. The school is a management unit that has data that correlate with students. All student data is stored in an academic information system that can be processed to predict student behavior. One of the data assessing student behavior is in the database of counseling guidance. Some data that will be processed include attendance, lateness notes of problems, teacher responses, tuition payments, broken home. The sample being tested was the data of 100 vocational students from various classes and various majors and divided into two categories. From this experiment, it can be seen that the most accurate K value is the value of K = 1, 3 and 4. The accuracy of the testing data generated is 94.9%.
Analysis of Decision Support Systems on Recommended Sales of the Best Ornamental Plants by Type
Authors: Rusdiyanto R. , Karman Joni , Toyib Hidayat Asep , Muli Peranginangin Adat , Tambunan Frinto , Hutahaean Jeperson 
Published: July 2020

Abstract
The research objective is to recommend the sale of the best ornamental plant species to lovers of ornamental plants by using decision support system techniques. Ornamental plants are a type of ornamental plants that can survive in various conditions. In this case ornamental plants are also very vulnerable to disease such as root rot, shoot rot and others. Many types of ornamental plant sellers do not know how to handle ornamental plants that have diseases or symptoms when they occur. The study was conducted in Simalungun District with a sample of thirteen traders selling ornamental plants using observation and questionnaire distribution techniques. Selection of ornamental plants based on the types in this study include: Orchid Plants (A1), Tulips (A2), Bougenvil (A3) and Jasmine (A4). The assessment criteria used include: Plant height (C1), Plant quality (C2), Plant type (C3) and Plant age (C4). The method used in this research is Simple Additive Weighting. The results showed that Orchid Plant (A1) was the best alternative for selling ornamental plant species (V1: 0.91) and Jasmine (A4) as a second alternative (V2: 0.8).
Feature engineering strategies based on a One-point Crossover for fraud detection on Big Data Analytics
Authors: Soleh M , Djuwitaningrum E R , Ramli M , Indriasari M 
Published: July 2020

Abstract
A wide range of new opportunities for fraudulent online activities has arisen with the growing popularity of online shopping and big data issue. E-payment fraud schemes are collecting billions of dollars from customers, distributors and service providers every year. A lot of machine learning methods for fraud detection problems have been proposed which can be categorized into supervised, unsupervised and semi-supervised methods. In this paper, we proposed biologically inspired technic in the feature engineering phase for handling imbalanced data to increase the total data of a small number of classes by oversampling. One-point crossover used to generate the new data of minority classes. The best algorithm performance obtained to predict the fraud transaction from various machine learning models is Classification and Regression Tree with the corresponding accuracy, precision, recall, and F-1 Score are 96%.
Application of Scrum Maturity Model: A Case Study in a Telecommunication Company
Authors: Setiyawan J , Gunawan F , Raharjo T , Hardian B 
Published: July 2020

Abstract
Digitalization and internet penetration force telecommunication companies to perform a transformation to survive, to face competitions, and to gain opportunities. PT XYZ is a telecommunication company that has introduced a new leaner and agile working method for digital transformation. The organization is undergoing a transformation to implements an agile approach using a scrum framework. There are two divisions that use scrum that currently plagued with time estimation difficulties and inability to create an agile backlog due to the old change request mechanism. Scrum Maturity Assessment (SMM) is used to measure current scrum practice maturity. The data is retrieved using a questionnaire that was given to two Scrum Master from two divisions which have been implementing Scrum. There are 83 questions derived from SMM assessment questions. SMM assessment indicated the scrum practice is Level 1 (Initial). There are 31.76% of scrum practices in IT division and 36.25% of scrum practices in CS division need to be improved to achieve Level 5 Optimizing. In order to achieve Level 5, we proposed 38 steps and six recommendations which will guide the scrum practice improvement process.
An Indonesian Adaptation of the E-Learning Usability Scale
Authors: Hasibuan D P , Santoso H B , Yunita A , Rahmah A 
Published: July 2020

Abstract
This paper investigates how the instruments of E-Learning Usability Scale adapted in the Indonesian context. The need for adapting the original version arises while assessing the usability of e-learning in a higher degree in Indonesia, namely Student-Centered e-Learning Environment or (SCELE). In the process of usability evaluation, a questionnaire is needed. E-learning Usability Scale (EUS), which is created by Sandoval (2016) will be used as the usability scale. However, the EUS was originally built in the English version and it has not been developed in the Indonesian version. Overall, there are five steps to adapt the usability scale to Indonesian culture which are forward translation, synthesis of the translation from the first step, back translation, consulting to the experts, and reliability testing. The results show that Croncbach’s Alpha of the adaptation Indonesian of EUS was 0.922. It can be concluded that the proposed version is reliable to be used by usability researchers. In the future, the adapted version that is proposed in this study is beneficial to help usability researchers, academicians and practitioners in Indonesia while conducting usability testing for e-Learning applications.
Deep Learning approach for predicting the therapeutic usage of Jamu
Authors: Wijaya S H , Saumnuari M , Nasution A K , Ramadhan D A , Hasibuan L S 
Published: July 2020

Abstract
As a country with the largest number of medicinal plants in the world, Indonesian uses medicinal plants as a composition of herbal medicine. The ingredient of herbal medicine is generally made based on experiences and hereditary. This research aims to build a scientific background of Jamu through analysis of the relationship between medicinal plants used as the composition of Jamu and its therapeutic usage. Deep Learning was chosen as a classifier because it shows good effectiveness in generating predictive models in many studies. As a comparison, we also applied the Random Forest and Support Vector Machine as classifiers and examined the classifier performances while predicting the therapeutic usage of Jamu. To handle the imbalanced data between efficacy classes, the Synthetic Minority Oversampling Technique was applied before model generation. The result shows that the highest accuracy for Deep Learning is 88.74%, relatively higher than Random Forest and Support Vector Machine, which obtain accuracy values of 78.84% and 77.60%, respectively. Variable importance of the best prediction model using Deep Learning identified 105 medicinal plants, and 39 of them were selected as potential plants for 14 therapeutic usages.
A comparison of SARIMA and LSTM in forecasting dengue hemorrhagic fever incidence in Jambi, Indonesia
Authors: Khaira U , Utomo P E P , Aryani R , Weni I 
Published: July 2020

Abstract
Dengue Hemorrhagic Fever (DHF) is one of the common and fatal diseases in Indonesia. Jambi city is one of the dengue-endemic areas in Jambi province. To reduce the incidence rate of dengue, an early warning based on forecasting is necessary. Time-series forecasting of DHF can provide useful information to support and help public health officers for planning on DHF prevention. This paper compares two methods for Time-series forecasting of DHF incidence, namely seasonal autoregressive integrated moving average (SARIMA) and Long Short-Term Memory (LSTM). The forecasting performance is assessed using the monthly number of DHF incidence data from January 2012 to April 2019 were acquired from the district health offices. To show the effectiveness of the model, the performances are evaluated based on two metrics: mean absolute error (MAE) and root mean square error (RMSE). In the first analysis, we found that the SARIMA ((1,0,0) (1,0,0)12) is the most suitable model to predict the number of monthly DHF incidence with RMSE value of 30.07 and MAE 18.97, and the second one used the LSTM with one hidden layer (1-64-1) architecture with RMSE of 30.41 and MAE 18.27. Based on the experiment between SARIMA and LSTM perform relatively well to predict the future.
The Development of Self-Service Application at Martabak Air Mancur Restaurant
Authors: Sagala N T M , Andreas  , Putro E 
Published: July 2020

Abstract
The amount of time spent in requesting and waiting for the meal in Martabak Air Mancur restaurant may permanently multiply the number of customers in the line waiting especially during peak hours and on weekend. It will affect the quality service of the restaurant and the loyalty of the customer. From the observation, most of the customers spent a great deal of time making an order when faced with many proportionate choices. The study proposed a system by using self-service technology and the waterfall model. In the beginning, the time needed from making an order to finishing the payment is 15 minutes. The system built can reduce the transaction time to 10 minutes.
Analysis of Euclidean Distance and Manhattan Distance in the K-Means Algorithm for Variations Number of Centroid K
Authors: Suwanda R , Syahputra Z , Zamzami E M 
Published: July 2020

Abstract
K-Means is a clustering algorithm based on a partition where the data only entered into one K cluster, the algorithm determines the number group in the beginning and defines the K centroid. The initial determination of the cluster center is very influential on the results of the clustering process in determining the quality of grouping. Better clustering results are often obtained after several attempts. The manhattan distance matrix method has better performance than the euclidean distance method. The author making the result of conducted testing with variations in the number of centroids (K) with a value of 2,3,4,5,6,7,8,9 and the authors having conclusions where the number of centroids 3 and 4 have a better iteration of values than the number of centroids that increasingly high and low based on the iris dataset.
Analysis of Attribute Reduction Effectiveness on The Naive Bayes Classifier Method
Authors: Syafira D , Suwilo S , Sihombing P 
Published: July 2020

Abstract
Naïve Bayes is a prediction method that contains a simple probabilistic based on the application of the Bayes theorem (Bayes rule) with the assumption that the dependence is substantial. Classification is a technique in data mining to form a model from a predetermined data set. Data mining techniques are the choices that can overcome in solving this problem. Therefore, researchers make a comparison of Naïve Bayes by modifying using PCA. Improving the comparative performance of the Naive Bayes method by weighting using Principal Component Analysis (PCA) and Naive Bayes was carried out in this study. Research conducted on the classification model of Naïve Bayes (PCA + Naïve Bayes) using the Iris dataset that simplified into two attributes, four classes and 147 instances with an accuracy rate of 97.78% with a classification error rate of 2.22%. Meanwhile, the Conventional Naïve Bayes classification model uses four attributes with four classes from the Iris dataset, which has an accuracy rate of 95.45% with a classification error rate of 4.55%. Based on the test results of the classification model, it can be concluded that the success of the PCA can be used as a reference to improve the accuracy performance of the Naïve Bayes classification model.
Analysis of Accuracy Improvement in K-Nearest Neighbor using Principal Component Analysis (PCA)
Authors: Lubis A H , Sihombing P , Nababan E B 
Published: July 2020

Abstract
This research conducted to simplify and eliminate attributes or features that are less relevant without reducing the intent and purpose of the original data using Principal Component Analysis (PCA) to improve the performance (accuracy) of the K-NN (K-Nearest Neighbour) classification method. Modifying the method yields an average accuracy of 88%, where the K value is at K = 3 to K = 9. While the lowest accuracy value generated from K-Nearest Neighbors Conventional with PCA + K-NN classification models for the Pekanbaru Air Quality dataset has an average accuracy of 87% where when the K value is at K = 2, K = 4, K = 6 and K = 8. The highest average error is 0.11% where when the value of K is at K = 2, K = 4, K = 6 and K = 9. While the lowest average error value generated from K-Nearest Neighbors Conventional with PCA + K-NN classification model of the Pekanbaru Air Quality dataset is 0.08% where when the K value was at K = 5.
Utilization of Augmented Reality to Showcase Historical Buildings in Medan City
Authors: Syahputra M F , Umaya A , Rahmat R F 
Published: July 2020

Abstract
Medan is the capital of the province of North Sumatera, Indonesia. Medan city is known as a city that has a lot of old buildings heritage legacy of the former Dutch. It is unfortunate as a result of the lack of maintenance and attention from the government and community making the historic buildings gradually demolished and replaced with new buildings. By using Image-Based-Modelling in the process of making 3D buildings. This application of Augmented Reality Tour is expected to be a recognition media, and can increase the interest in studying history.