bluuebunny/crossref_metadata_2025_split huggingface dataset:
Incident and reflected wave separation on wave propagation over breakwater
Authors: Magdalena I , Atras M F 
Published: January 2021

AbstractThe quantification of the reflection waves is of paramount importance in coastal engineering. The reflection wave that affects the incoming waves over time will gradually affect the calculation of the transmitted waves after passing through a submerged breakwater. In this study, the reflection waves that affect the incident waves will be separated so that the appropriate transmission coefficient is obtained. The accuracy of this separation method of the incident waves and reflections will be evaluated using a numerical scheme. The numerical scheme that we use here is a staggered finite volume method. A small error in the comparison indicates the wave separation process is good enough to produce a reflection coefficient following the numerical results.
Survival analysis with the Cox Proportional Hazard Method to determine the factors that affect how long the Large-Scale Social Distancing (LSSD) will applied in various areas affected by the covid-19 pandemic
Authors: Fauziah A A , Budiman  , Safitri D L , Meiza A 
Published: January 2021

Abstract
This study will determine the length of time for large-scale social restrictions, in Indonesia is known as the Large-Scale Social Distancing (LSSD), which requires in a region to reduce the number of different covid-19 cases. Associated with the implementation of the LSSD, it turns out that many things can influence the LSSD to be able to run effectively in the community, from internal and external factors. This research was conducted for 15 days from 1 June 2020 to 15 June 2020, to find out the factors that had a significant influence on the effectiveness of LSSD using Cox Proportional Hazard Regression. The dependent variable used in the study is the length of time the LSSD system (Y). From the results can concluded that the socialization variable (X1) has a significant effect to the effectiveness of LSSD.
An Agent-Based Model of Contagion Effects in Affected Depression and Its Recovery Process
Authors: Andarlia H T , Gunawan I 
Published: January 2021

Abstract
Depression, as the most common mental worldwide disorder, has been the major contributor to suicide deaths. In this paper, we model how depression is contagious among the population using Agent-Based Modeling approach. A sufferer can be affected due to contagion effects of social interactions, particularly from close relationships, then undergo a depressive episode that categorized by mild, moderate, severe. Every episode can be recovered by doing therapy. Those transition processes are described based on gender in a flowchart diagram and simulations, which depend on time using software. Our simulation results represent that the greater contacts rate with someone who affected depression, the more severe of depressive episodes that will be experienced. Further, the greater probability of someone who is going to a therapy can reduce the number of depressed persons over time, especially for contagious depression. Thus, these program simulations and modeling method will be used to calculate and handle the contagion of depression, helping for sufferers’ recovery, and decreasing number of depressed people who commit suicide.
Survival Analysis Using Cox Proportional Hazard Regression Approach in Dengue Hemorrhagic Fever (DHF) Case in Abdul Moeloek Hospital Bandar Lampung in 2019
Authors: Irfan M , Usman M , Saidi S , Warsono  , Kurniasari D , Widiarti  
Published: January 2021

Abstract
Survival analysis is one of the statistical procedures analyzing data in the form of survival time and variables that affect survival time, namely survival time data starting from the beginning of the study (time origin/start point) until the time an event or endpoint occurs. In the field of health data is obtained from observations of patients who were observed and recorded the time event of each individual. The event in question can be in the form of death, recurrence of new diseases, or recovery. This study will discuss the application of cox proportional hazard regression to determine the cox proportional hazard model on DHF patients. Determine the factors that affect the recovery rate of DHF patients and determine the hazard ratio value of DHF patients at Abdul Moeloek Hospital in Bandar Lampung in 2019. Regression Cox proportional hazard is used because the cox proportional hazard model does not depend on the assumption of the distribution of the time of occurrence; the results are almost the same as the parametric model. They can estimate the hazard ratio without knowing the baseline hazard. Based on the selection of the best model with backward elimination and the Akaike Information Criterion (AIC), the best model is obtained with a four-variable model, namely Leukocyte, Hemoglobin, Hematocrit, and Thromobocyte. These four variables are factors that have a significant effect on the patient’s length of stay. Then this study also looked at the value of the hazard ratio which thrombocyte are the variables with the largest hazard ratio value, which means the thrombocyte variable has the highest risk level.
Application of Vector Autoregressive with Exogenous Variable: Case Study of Closing Stock Price of PT INDF.Tbk and PT ICBP.Tbk
Authors: Putri A R , Usman M , Warsono  , Widiarti  , Virginia E 
Published: January 2021

Abstract
Multivariate time series are widely used in various fields such as finance, economics, and the stock market. One analysis model that is widely used for multivariate time series data is the VAR model. Vector autoregressive (VAR) is a model used to describe the relationship between several variables. The VAR model provides an alternative approach that is very suitable for forecasting purposes and is very suitable for solving economic data problems. The variables used in this study consisted of endogenous variables with closing prices of ICBP and INDF shares and exogenous variables with exchange rates collected from January 2017 to July 2020. In this study, the best model, VARX (1,0), was obtained. also the relationship between variables through the impulse response function and granger causality. Furthermore, forecasting is also carried out for the next 30 days using the best model, VARX (1,0).
Modeling Multivariate Time Series by Vector Error Correction Models (VECM) (Study: PT Kalbe Farma Tbk. and PT Kimia Farma (Persero) Tbk)
Authors: Loves L , Usman M , Warsono  , Widiarti  , Russel E 
Published: January 2021

Abstract
Time series analysis (time series) is one method with the aim to find out events that will occur in the future based on data and past circumstances. Time series are widely used in economics, business, environmental science, and finance. The analytical tool that is widely used to answer quantitative research problems is the Autoregressive Vector (VAR). The VAR model is used if the data is stationary. If the variable has cointegration and stationary at the first difference value, the VAR model is modified to become the Error Correction Model (VECM). Then we can find out the influence of variables with other variables by looking at the Impulse Response Function and Granger Causality. In this research, PT Kalbe Farma Tbk’s stock data will be analyzed. (KLBF) and PT Kimia Farma (Persero) Tbk (KAEF). The data used are weekly data from January 2010 to June 2020. Based on data analysis, it is known that the data is not stationary and there are unit roots. Furthermore, first differencing is done to make the data stationary. Because there was cointegration, a VECM analysis was performed and a VECM (p) was obtained with a lag of p = 4. So the best model for this research is VECM (4) with rank = 2. Causal relationships between variables using Granger Causality showed that KLBF influenced KAEF in the past. Based on IRF analysis, each variable gives a fluctuating response with itself and with other variables.
Dynamic Modeling Data Return by Using BEKK-GARCH (Study: PT. Indofarma Tbk (INAF) and PT. Kimia Farma Tbk (KAEF) from June 2015 to July 2020)
Authors: Indryani N , Usman M , Warsono  , Nabila S U , Widiarti  , Kurniasari D 
Published: January 2021

AbstractThe Vector Autoregressive (VAR) model is a statistical model that can be used for modeling multivariate time series data which is commonly applied in the fields of finance, management, business and economics. However, economic data, especially return values, have quite high data fluctuations, so we need to add the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model in the analysis to obtain efficient results. This study will discuss the formation of the best model for multivariate time series data, namely return data of PT. Indofarma Tbk. (INAF) and PT. Kimia Farma Tbk. (KAEF) from June 2015 to July 2020, where data retuned for the two variables tended to have a high volatility shock at some time and low volatility at other times which characterizes the data as having an ARCH effect so that the GARCH model will be used in this analysis, namely the BEKK-model. GARCH. This model proposes a new parameterization which is easily given a restriction, namely the requirement that H_t must be positive for all values of ε_t and x_t in sample room. Based on the selection of the best model using the AICC, HQC, AIC and SBC criteria, it is found that the VAR (1)-GARCH (1,1) model is the best model for the data used. Then this research will also examine the behavior and relationship between INAF and KAEF based on Granger Causality and Impulse Response. In addition, based on the forecasting results of the VAR (1)-GARCH (1,1) model, it shows that this model is good for short-term forecasting.
Dynamic Modeling Data Time Series By Using Constant Conditional Correlation-Generalized Autoregressive Conditional Heteroscedasticity
Authors: Nabila S U , Usman M , Warsono  , Indryani N , Widiarti  , Kurniasari D 
Published: January 2021

AbstractThe Constant Conditional Correlation-Generalized Autoregressive Conditional Heteroscedasticity (CCC-Garch) model as one of the multivariate time series models is used to model economic variables, especially in stock price data with high volatility characteristics that result in heterogeneous variations. The higher the volatility, the higher the level of uncertainty of the stock returns that can be obtained. The CCC-Garch Multivariate model is the simplest model in its class. The principle of this model is to decompose the conditional covariance matrix into conditional standard deviation and correlation. In this study, we will discuss and determine the best model that can describe the relationship between two vector data timeseries, namely stock return data for companies engaged in mining and construction in Indonesia, namely United Tractor Tbk (UNTR) and Petrosea Tbk (PTRO) where the data is the daily stock return data for the period July 2015 to August 2020. Several models that involve modeling the mean and variance with CCC-GARCH parameterization are applied to data such as the VAR (1)-Garch (1,1), VAR (2)-Garch (1) model., 1), VAR (3) Garch (1,1) and VAR (4)-Garch (1,1). The result was that the VAR (1)-Garch (1,1) model was selected as the best model with the criteria for selecting the AICC, SBC, AIC and HQC models. The dynamic behavior of both UNTR and PTRO stock return variables is explained by Granger Causality and Impulse Response. Furthermore, the forecasting of this data was carried out for some time in which the VAR (1)-Garch (1,1) model which was selected as the best model was only suitable for forecasting in a short time.
The Locating-Chromatic Number of Certain Barbell Origami Graphs
Authors: Irawan A , Asmiati  , Suharsono S , Muludi K 
Published: January 2021

Abstract
The locating-chromatic number of a graph combined two graph concept, coloring vertices and partition dimension of a graph. The locating-chromatic number, denoted by χL
(G), is the smallest k such that G has a locating k-coloring. In this paper, we discuss the locating-chromatic number for certain barbell Origami graphs.
Asymptotically Unbiased, Efficient, and Consistent Properties of the Bayes estimator in the Binomial Distribution with Prior Beta
Authors: Simanjuntak E G , Widiarti  , Kurniasari D , Amanto  
Published: January 2021

Abstract
This study will examine the characteristics of the Bayes estimator in the Binomial distribution with prior Beta theoretically and empirically. The theoretical result shows that the Bayes estimator in this distribution is an asymptotically unbiased and consistent, but inefficient estimator. Meanwhile, empirically, Bayes’s estimator is an unbiased estimator, efficient, and consistent.
Determining the Number of Disconnected Vertices Labeled Graphs of Order Six with the Maximum Number Twenty Parallel Edges and Containing No Loops
Authors: Putri D , Wamiliana  , Fitriani  , Faisol A , Dewi K S 
Published: January 2021

Abstract
If there exist two vertices on a given graph that are not connected by a path, then we call that graph is disconnected. Given a graph with n vertices and m edges, then a lot of graphs can be constructed. In this paper, we discuss the number of disconnected vertices labeled graphs of order six (n = 6) with the maximum number of parallel edges is twenty. Moreover, a maximum number of edges that connect different pair of vertices is ten (parallel edges are counted as one) and containing no loops (isomorphic graphs are counted as one).
Calculating the Number of vertices Labeled Order Six Disconnected Graphs which Contain Maximum Seven Loops and Even Number of Non-loop Edges Without Parallel Edges
Authors: Pertiwi F A , Amanto  , Wamiliana  , Asmiati  , Notiragayu  
Published: January 2021

Abstract
A labeled graph is a graph where each vertex or edge is given a value or label. A line/edge with the same starting and ending vertex is called a loop. If given n vertices and m edges, then there are lot disconnected vertices labeled graphs that can be formed. Among those graphs, there are graphs with maximum seven loops and whose non-loop edges are even. In this research, we will discuss the formula for finding that kinds of graphs.
Mathematical Modeling of Heat Transper in Agricultural Drying Machine Room (Box Dryer)
Authors: Ruby T , Sutrisno A 
Published: January 2021

Abstract
Differential equations arise in many fields of science and technology, when the deterministic relationships involving a continuously changing quantity (modeled by a mathematical function) and its rate of change (expressed as a derivative) are known or postulated. This can be seen for example in the problem of heat transfer. The problems raised in this study are how to model the heat equation and how to solve the heat equation model. The research was conducted to find a model of the heating equation in a rice dryer and solve the heat equation using the variable separation method. This discussion is carried out in two conditions, namely the steady state (constant time) and unsteady (changing time).
Risk Analysis in the Application of Financore Information Systems Using FMEA Method
Authors: Triana Y S , Pangabean R A M 
Published: January 2021

Abstract
In the banking world maintaining customer data is very important so as not to experience various risks, including leakage of customer data. Leakage of customer data is one of the big risks that may occur in the banking world, whereas customer confidentiality should be guaranteed. Possible risks such as misuse of identity or theft of transaction data by unauthorized parties. This will certainly harm both the customer and the bank. So that customer data is not leaked, lost or misused, it is necessary to have a regulation or verification system of customer data that can be reviewed for security and use. The solution that can be done to prevent this event is to use the FMEA methods that can be used to reduce errors and system failures. Information system security has a positive and very significant effect on decision making, therefore it is necessary to have an information system risk analysis that supports it comprehensively so that it can help in decision making.
Effect of mono corpus quantity on statistical machine translation Indonesian – Lampung dialect of nyo
Authors: Abidin Z , Permata  , Ahmad I , Rusliyawati  
Published: January 2021

Abstract
Lampung Province is located on the island of Sumatera. For the immigrants in Lampung, they have difficulty in communicating with the indigenous people of Lampung. As an alternative, both immigrants and the indigenous people of Lampung speak Indonesian. This research aims to build a language model from Indonesian language and a translation model from the Lampung language dialect of nyo, both models will be combined in a Moses decoder. This research focuses on observing the effect of adding mono corpus to the experimental statistical machine translation of Indonesian - Lampung dialect of nyo. This research uses 3000 pair parallel corpus in Indonesia language and Lampung language dialect of nyo as source language and uses 3000 mono corpus sentences in Lampung language dialect of nyo as target language. The results showed that the accuracy value in bilingual evalution under-study score when using 1000 sentences, 2000 sentences, 3000 sentences mono corpus show the accuracy value of the bilingual evaluation under-study, respectively, namely 40.97 %, 41.80 % and 45.26 %.
Abstract Classification Using Support Vector Machine Algorithm (Case Study: Abstract in a Computer Science Journal)
Authors: Lumbanraja F R , Fitri E , Ardiansyah  , Junaidi A , Prabowo Rizky 
Published: January 2021

Abstract
Jurnal Komputasi is an online journal written by researchers and published by the Department of Computer Science, University of Lampung. Specific scientific information contained in journals is difficult to find because journals have not been structured and are classified into more specialized categories of computer science. Text mining can convert the shape of a journal into structured by homogeneous data form in it. 144 journal abstracts are collected into one corpus document in CSV format used as a research dataset. Journal abstract classification is done using one of the supervised machine learning methods, namely Support Vector Machine (SVM) so that the classification process is faster than the manual method. The TF-IDF technique is used to transform sentences in the abstract into vector so that they can be modelled with SVM. The classification model will be validated by applying the 10-fold cross validation technique. From these classifications a calculation of the resulting performance will be calculated based on the confusion matrix calculation of the resulting performance will be calculated based on the confusion matrix calculation and the use of 3 SVM kernels. The conclusion based on this research is that there are two factors that affect classification accuracy, that is the number of members between scientific classes that are not balanced and the number of features generated from text mining. The highest accuracy of testing result obtained on the use of 205 features and SVM Linear kernel with a value of 58,3%.
Identification of SNPs Associated with Iron Toxicity Tolerance in Rice
Authors: Chrisnawati L , Miftahudin  , Utami D W 
Published: January 2021

Abstract
Iron (Fe) toxicity is one of the limiting factors that can lead to the decrease of rice yield in paddy fields. Association studies to identify potential alleles or markers linked to iron toxicity tolerant trait can be carried out using high throughput single nucleotide polymorphisms (SNPs). We conducted an association study for Fe toxicity tolerance characters, using Forty-five double haploid lines derived from reciprocal double-crossing, i.e. IR54 / Parekaligolara // Bio110 / Markuti in high Fe wetland rice field. Genome-wide association study was carried out using 384 SNP-plex markers distributed on 12 rice chromosomes. A total of 77 SNPs were significantly associated with the Fe toxicity tolerance-related traits. Functional annotation allowed us to shortlist four SNP markers associated with Fe toxicity tolerance trait, i.e.: TBGI204006, TBGI310247, id9006377, and id10000498. The research suggests that association studies followed by functional annotation can effectively detect potential alleles and candidate genes for the trait. The identified QTL and genes provided valuable sources for future genetic improvement of Fe tolerant rice lines.
Altering Physical Characteristics of Sinking Fish-Feed through Sub-Optimal Fermentation Using Tempeh Mould without Mechanical Extrusion
Authors: Sriherwanto C , Rahmanisa H , Yunita E , Suja’i I , Nadaviana A 
Published: January 2021

AbstractSinking pellets disintegrates easily in water, hence reducing water quality and fish productivity due to oxygen-consuming biodegradation of the unconsumed feed. Modifying sinking pellets into more stable floating pellets using an edible tempeh mould through solid fermentation has previously been studied as an alternative to the more expensive extrusion method. However, the fermentation resulted in the uncontrolled growth of the fungal mycelium, causing the individual pellets to aggregate into a single compact mass. The fermentation also contributed to the dry weight loss of the initial pellets. Thus, using a laboratory-scale bioreactor, this study aimed at generating water-stable and floating properties on sinking-pellets through sub-optimal fermentation using tempeh mould. The conditions varied were the amount of sinking pellets substrate, fermentation duration, and aeration-humidification. Results showed that less pellet aggregation was observed with shorter fermentation time. Based on 40-minute laboratory tests in water, the best 80% floating ability was achieved for 10 g sinking pellets fermented for 72 h with humidified bubble aeration. The fermented pellets showed 42% water stability and 4.86% dry weight loss.
Monosex male formation of juvenile redclaw crayfish using natural steroid hormone from gamma sea cucumber and different doses of honey bee
Authors: Susanto G N , Sutyarso  , Widianto W 
Published: January 2021

AbstractIn a new commercial commodity aquaculture, tropical freshwater crayfishCherax quadricarinatus, it is known that female has a slower growth rate than male. One way to increase production rate is through monosex cultures comprising all-male population by using genital reversion technique. This biotechnology method can be applied by using hormones and natural products. The natural hormones extracted from gamma sea cucumber,Stichopus variegatushas bioactive substances including steroid compounds, while honey bee contains chrysin compounds that act as aromatase inhibitors. Therefore, the combination of both substances expected can increase in testosterone level and accelerate production in the male monosex population of juvenile crayfish. The purpose of the study is to determine the most effective dose of honey bee in masculinization of juvenile freshwater crayfish combined with 2 mg. L−1steroid extract of gamma sea cucumber. Furthermore, a Completely Randomized Design (CRD) was designed in the experiment with five different dose of honey bee treatment and dipping method in 2 mg. L−1steroid extract of gamma sea cucumber for 18 h. Each treatment with four replications. The highest male formation was found in the dose of 20 ml. L−1 honey bee at 83.75% and the lowest was in control. The present of honey bee at different doses in 2 mg. L−1of gamma steroid extract did not significantly give an effect on percentage of female, intersex formation, survival, and growth rate of juvenile redclaw crayfish.