bluuebunny/crossref_metadata_2025_split huggingface dataset:
Implementation of lyapunov method to analyze the stability of pompano, cantang growth and nutrition dynamical systems
Authors: Widowati  , Putro S P , Maan N , Sulpiani R 
Published: June 2019

Abstract
This study aims to determine the analysis stability of dynamical model of pompano, cantang growth, and nutrition on the Integrated Multi-Trophic Aquaculture (IMTA) systems by using the Lyapunov method. The analytical analysis was given to discuss the dynamic behavior of this model. Global stability analysis was performed based on the Lyapunov theory, i.e., construction the function V (x) that is a definite positive scalar function and the derivative of V (x) is defa inite negative. As a verification of the Lyapunov method, we conducted numerical simulations with data taken from IMTA systems in Sea Farming region, Kepulauan Seribu, Indonesia.
Quadratic programming model for optimal decision making of supplier selection problem integrated with inventory control problem
Authors: Hakim D U H E , Sutrisno  , Widowati  
Published: June 2019

Abstract
In this paper, we propose a quadratic programming model to determine the optimal decision for integrated supplier selection and inventory control. The corresponding optimization is solved by using constrained quadratic optimization method i.e. Karush-Kuhn-Tucker Method performed in LINGO 17.0 to determine the optimal joint decisions which are the optimal supplier and the optimal inventory. A numerical experiment was performed to analyze the model with a multi-product, multi-supplier, multi-carrier and multi-period inventory system. From the results we have found, we can find the optimal supplier for each product and each period.
Potential environmental pressures on water availability in Gembong reservoir in Pati District for the development of agropolitan area
Authors: Kartono  , Purwanto  , Suripin  
Published: June 2019

Abstract
Land-use management is the key parameter in developing an agropolitan area. The Spatial Plan of Pati District 2010-2030 contains plans for the Gembong Subdistrict to be developed into an agropolitan area. The Gembong Reservoir is one of the reservoirs in the region that has a vital role in supporting the area development. The main problem in managing the Gembong reservoir is the decreasing availability of reservoir water. Increased sedimentation affects the availability of reservoir water, which is used as a supply of irrigation areas. Management of water availability must be based on optimization analysis, because the volume of storage is limited and increasing inflow to the reservoir to increase the water level requires a long time. Therefore, the optimization model of reservoir water management is urgent to be formulated, so that the reservoir operator can be utilized to determine the allocation of supplementary water. This article presents a study of the results of observations in the field and secondary data, as well as reviews of related scientific articles, to be able to identify potential environmental stresses on reservoir water availability and its management.
Serret-Frenet Multi-Agent System with optimal control approach
Authors: Tjahjana R H , Utomo R H S 
Published: June 2019

Abstract
Multi-agents systems is a system consisting of several agents, from two to hundreds agents. These agents are connected to each other. The main problem is how to control such systems. This paper isnpired from some facts as follows. In nature, can be viewed animals perfom multi agent system in the land and in the air. In avionic world, can be observed that there are many cases where pilots must fly their aircrafts in some particular formation. Some ships also very often form a convoy. In smaller scales, can be viewed that there are many robot designer would like to create multi-robot system for conducting some task. In particular, this paper focuses on the robotics utilizations. That is we would like to steer the multi-agent system to go from one particular position to the other position. Moreover, the agents move in some formations is required. The first objective of this paper is to create mathematical model of multi-agent system applicable in robotic. The model would consist of Serret-Frenet control systems. The second objective is to model the performance index of the multi-agent system. Thus, we would like the multi-agent system move from one place to another and the whole group of agents works collaboratively for optimizing the performance index. This is the idea behind multi-agent control system design. The third objective is to analyze controllability the above models. The last objective is to find the optimal control of each agent. The Pontryagin Maximumm Principle is used to search this optimal control. The result of this paper, optimal control can be used to model the multi agent system Serret-Frenet system.
The characteristics of the Moore-Penrose inverse using the Drazin inverse
Authors: Mansuri F A , Udjiani SRRM T , Sutimin  , Suryoto  , Tarmizi U 
Published: June 2019

Abstract
In this paper, we present several new characteristics of the Moore-Penrose inverse in rings with involution. We use the concept of the Drazin inverse to build these characteristics in purely algebraic terms. We only discuss on the symmetric element of Moore-Penrose invertible.
The feed forward neural network with genetic algorithm for daily stock prediction
Authors: Dipinto R , Santoso R , Prahutama A 
Published: June 2019

Abstract
The power of Feed Foward Neural Network (FFNN) in conjungtion with Genetic Algorithm (GA) was applied in this research to predict daily stock price. Finance time series data has a high complexity, so that the acurate prediction is hard to be gained by standard model. Machine learning becomes the new prediction tool which is often used because of its adaptive properties. Neural Network (NN) is one of the machine learning which able to complete inference tasks such as prediction, especially in large data sets. FFNN is one of the NN models that has simple network architecture. In the standard optimization method, the initial weights is randomly selected to desire the optimum solution. But It usually raises the problem of unsteady estimation. The GA optimization method was applied in this research to overcome this lack. GA optimizes any function effectively and seeks a global optimum solution efficiently. GA implementation on the FFNN was aimed to obtain optimum weights that minimizing the error. The daily stock price prediction of PT. Adhi Karya Tbk had RMSE of training and testing data at 51.2531 and 44.8706 respectively. This result was equivalent with MAPE values at 1.5714% and 1.5501%.
Forecasting with Feed Forward Neural Network model and adaptive simulated annealing algorithm (Case: world crude oil prices that was published by OPEC)
Authors: Hanafie A , Sugito  , Sudarno  , Hakim A R 
Published: June 2019

Abstract
Today, crude oil trading industry became an important industry in the world, it was caused by highly fuel oil consumption. The price of crude oil has a changing price trend, it makes the prediction of crude oil in the coming periods to be challenging. Various methods can use to forecast price of crude oil, it is using ARIMA Box-Jenkins model with OLS method to estimate the parameter, but this method has several assumptions that must be complete. Overtime, many methods were develop, one of them is artificial neural network can be combine with various parameter optimization methods such as Adaptive Simulated Annealing algorithm. Adaptive Simulated Annealing algorithm is an optimization method it was inspired by the process of crystallization, the advantages of this algorithm has a running time faster than similar algorithms. The combination of artificial neural networks and Adaptive Simulated Annealing algorithms can be used to model the historical data without requiring assumptions in the analysis. Based on the analysis on this research, the best model is obtained FFNN 2-5-1 with MAPE value of 1.0042%.
Formation of stock portfolio using Markowitz method and measurement of Value at Risk based on generalized extreme value (Case study: company’s stock The IDX Top Ten Blue 2017, Period 2 January - 29 December 2017)
Authors: Situmorang R E , Maruddani D A I , Santoso R 
Published: June 2019

Abstract
In financial investment, investors will try to minimize risk and increase returns for portfolio formation. One method of forming an optimal portfolio is the Markowitz method. This method can reduce the risk and increase returns. The performance portfolio is measured using the Sharpe index. Value at Risk (VaR) is an estimate of the maximum loss that will be experienced in a certain time period and confidence level. The characteristics of financial data are the extreme values that are alleged to have a heavy tail and cause financial risk to be very large. The existence of extreme values can be model Generalized Extreme Value (GEV). This study uses company stock data of The IDX Top Ten Blue 2017 which forms an optimal portfolio that is a combination of TLKM and BMRI shares with the expected return rate of 0.0111 and a standard deviation of 0.01057. Portfolio performance measured by the Sharpe index is 1.6190 indicating the return obtained from investing in the portfolio above the average risk-free investment return rate of -0.01010. Risk calculation is obtained based on Generalized Extreme Value (GEV) if you invest both of these stocks with a 95% confidence level is 0.206 or 2.06% of the current assets.
Busy period density of M/

C
2
b

/1 queueing system through lattice path approach: a special case
Authors: Slamet I , Hidayati P N , Wibowo S , Zukhronah E 
Published: June 2019

Abstract
We consider M/

C
2
b

/ lqueueing system which is a queueing system with arrival rate follows a Poisson distribution, service rate follows an exponential distribution which is approximated by Coxian 2-phase distribution, service is performed in
b
-size, and the number of service facility is one. This paper aims to derive the properties of M/

C
2
b

/ 1 queueing system when the system is in busy pea riod using latticea path combinatorics approach. We consider a special case which is called pure incomplete busy period (PIBP) Through this approach, the queue system is represented in the form of lattice path. Furthermore, we have determined the number of lattice paths and finally, the probability function in the queue system M/

C
2
b

/ 1 in PIBP is derived.
Random forest prognostic factor in colorectal cancer
Authors: Anuraga G , Fernanda J W , Pebrianty  
Published: June 2019

Abstract
In developing countries such as Indonesia, colorectal cancer cases in women are the third largest after breast cancer and cervical cancer, whereas, in men, cancer ranks second after lung cancer, followed by the third is prostate cancer. This study aims to determine the factors that affect the survival of colorectal cancer patients in the city of Makassar, Indonesia. The data used in this study including colon cancer patients diagnosed first in 2012 in 4 hospitals in Makassar City and observed survival until 2015. Predictor variables consisted of comorbidity, stage of cancer, age, treatment status, the location of cancer, sex, and history of metastasis of patients with colorectal cancer. The samples used in this study were as many as 38 cancer patients. In this study, we are using random forest which is an algorithm used in data classification through tree merging by training on sample data. Random Forest also an ensemble method consisting of several decision trees as classifiers. In a nutshell, the accuracy of this models can be justified by the value of classification by Area Under Curve (AUC) equal to 50%. Moreover, the most influential variable on the survival of colorectal cancer patient is a history of metastasis of colorectal cancer patient, cancer location and gender respectively.
Robust geographically weighted regression with least absolute deviation (case study: the percentage of diarrhea occurrence in semarang 2015)
Authors: Nurhayati I C , Warsito B , Yasin H , Rusgiyono A 
Published: June 2019

Abstract
Diarrhea is one of many health issues in the developing country like Indonesia because the sickness and the death number are still high. According to the health profile of Semarang City, the people who suffer from diarrhea from 2010-2015 are decreasing. The lowest point happened in the year 2013 with the total case of 38.001. However, there is an increasing number from 2014-2015. The distribution data of diarrhea is spatial data. The differences between environment and sanitation could cause spatial heterogeneity. The spatial heterogeneity could cause the produced variant value no longer constant, but instead, it is different in each region. Therefore, the regression model that involves the effects of spatial heterogeneity is needed, which are Geographically Weighted Regression (GWR) that is built by Weighted Least Square (WLS) adjuster. Although, GWR parameter adjuster that used WLS is very sensitive with the existence of outliers. The existence of the outlier in the data will create a vast residual. Thus, a more robust method is needed, which is the Least Absolute Deviation (LAD) methods in order to estimate the parameter on model GWR. This model is called Robust GWR (RGWR). The result shows that the model events of diarrhea on each region in Semarang City are different. Furthermore, the model events of diarrhea with the RGWR model generate MAPE 16.3396% which means the performance of RGWR is formed well.
Credit scoring analysis using pseudo nearest neighbor
Authors: Pratiwi H , Mukid M A , Hoyyi A , Widiharih T 
Published: June 2019

Abstract
Credit scoring is one of the crucial task and a core responsibility for financial institutions in their risk management. This study aims to apply the pseudo nearest neighbour (PNN) method as a tool to identify which prospective borrowers are eligible for their loan proposals. If a new borrower has characteristics closer to a good historical borrower then the loan proposal is worthy to approval. But if not, the proposed loan will be refused. The historical data in this paper are credit data from a national bank in Indonesia. The characteristics of historical debtors consist of age, amount of a child, length time of business, income, loans amount, and the period of credit. The best classification of k-NN is using k = 1, because it makes the smallest error 1,89%. While the best classification of PNN is using k = 13 with the smallest error 20,75%. Based on total accuracy of classification shows that the credit classification of debtors using k-NN is more appropriate than PNN.
Particle swarm optimization versus gradient based methods in optimizing neural network
Authors: Warsito B , Yasin H , Prahutama A 
Published: June 2019

Abstract
Neural network modelling has become a great interest for many statisticians to be utilized in various types of data as classification, regression, and time series. It also has been applied in many fields as environment, financial, medical, agriculture and climate change. A lot of parametric methods have been developed to predict time series data such as ARIMA and exponential smoothing. However, requirement of residual assumptions causes limitedness of the models. Time series prediction by using neural network been an interesting study in the forecasting problem. In this model, one of the most interesting discussion is about how to get the optimal weights. Various gradient and non-gradient based methods have been applied in obtaining the network weights. Particle swarm optimization is one non-gradient based algorithm inspired by the behaviour of birds and fish flocks, which move to form certain formations without colliding to get the best position in a multi-dimensional space. In neural network modelling, the number of input and hidden unit give influence to the network architecture. The more complex an architecture, the more network weights must be estimated. In this study, a comparison of particle swarm optimization and some gradient based methods on the optimizing neural network was studied. Comparative studies were performed on both stationary and non-stationary data. Experiments were conducted several times to obtain optimal accuracy and stability of results, through statistics of mean and variance of MSE values.
Spatial autoregressive with a spatial autoregressive error term model and its parameter estimation with two-stage generalized spatial least square procedure
Authors: Saputro D R S , Muhsinin R Y , Widyaningsih P , Sulistyaningsih  
Published: June 2019

Abstract
Spatial relationship models often use dependence relationships into covariance structures through the autoregressive model. The autoregressive process is shown through the dependence relationship between a set of observations or a location from now on called the dependent spatial model. The Spatial dependent model is divided into two categories: spatial lag and spatial error. The spatial lag regression model is a model that considers dependent variables on an area with other areas associated with it, and the spatial error regression model is a model that takes into account the dependency of error values of an area with errors in other areas associated with it. Models with both dependencies are expressed as spatial autoregressive models with a spatial autoregressive error term (SAR-SAR). These dependencies resulted in the estimation of parameters by the ordinary least square method (OLS) resulting in inconsistent estimators. Therefore a special estimation method is required which results in a consistent estimate of the generalized spatial two-stage least square (GS2SLS). In this paper, we review the parameter estimation of SAR-SAR model with GS2SLS. To complete this paper, we also applicate of SAR-SAR model in dengue hemorrhagic fever (DHF) case in Surakarta, Central Java.
Budgeting school operational assistance in Central Java using three spatial process modelling
Authors: Wasono R , Karim A , Darsyah M Y , Suwardi  
Published: June 2019

Abstract
The formulation of Indonesian school operational assistance is a complex issue, as each region has different characteristics. The aim of this research was to find out the mapping of the distribution of school operational assistance budget in Central Java Province, to develop the equalization model of Indonesian school operational assistance of Central Java Province using spatial through three spatial processes namely spatial lag X (SLX), spatial autoregressive (SAR), and spatial error model (SEM). Spatial modeling is expected to be a tool of educational development planning so that the development is more directed to equitable distribution of Indonesian school operational assistance in Central Java. The distribution of funds for the program is one form of government expenditures in the form of subsidies for the education sector as the compensation from reduced subsidies for fuel oil. The research results show that the SEM model is the best model, and the estimation results show that the school development budget, school management and human development index can significantly be a determinant of the distribution of Indonesian school operational assistance.
Spatial modelling for rice production analysis in Central Java province Indonesia
Authors: Karim A , Sarra D S , Wasono R , Utami T W , Toheri  
Published: June 2019

Abstract
Rice is one of staple food in Central Java province because rice is the main carbohydrate and calorie source for society in general. From year to year rice production in various regions in Indonesia shows a significant increase. Central Java is one of the provinces in Indonesia which has the agricultural sector as its main sector. However, in the last five years, the average rice production in Central Java showed a stagnant decline in value. This study was aimed to model the spatial effects on rice productivity in the cities in Central Java along with the factors that influence it. The method used is spatial modeling approach. The results of the analysis show that spatial lag X (SLX) model has the smallest AIC value, estimation result shows that rice production and harvest area have significant effect on rice productivity in Central Java.
Application of decision support system using the K-Nearest Neighbor and Weighted Product method for determining the recipients of low-income family scholarship (GAKIN) (case study: Poltekkes Kemenkes Semarang)
Authors: Nasher L A , Bahtiar N 
Published: June 2019

Abstract
Poltekkes Kemenkes Semarang is a university of health that has a scholarship facility for underprivileged students or often referred as Beasiswa Keluarga Miskin (Gakin). At this time the determination of Gakin scholarship recipients is still in the form of manual selection.
Decision-Making is a systematic approach to a problem with the gathering of facts, a mature determination of the alternatives to be faced, and the taking of actions that are calculated to be the most appropriate action. To simplify the process of determining a decision, present an application of decision support system with input and output required. This research makes an application of decision support system with K-Nearest Neighbor method to recommend the decision in the system. It aims to shorten the time and provide convenience to policymakers (decision makers) in Poltekkes Kemenkes Semarang in selecting recipients of Poor Family Scholarships (GAKIN). Based on existing data, KNN method can handle all new data from potential recipients with a value of neighbor is 5 to produce an accuracy of 99.61%, precision 99.85% and recall 99.42% with data of scholarship recipients last five years.
The early detection system of pulmonary tuberculosis disease using learning vector quantization 2 (lvq2)
Authors: Widyasari L A , Sasongko P S , Sutikno  , Suhartono  , Reynaldhi E 
Published: June 2019

Abstract
Tuberculosis is an infectious disease caused by the Mycobacterium tuberculosis virus and infects the pulmonary. While in Indonesia, Central Java province was on the third-ranked for the highest number of new case of pulmonary tuberculosis disease. This disease can cause dangerous complications until death if not immediately detected and not treated completely. To help the community do early detection of pulmonary tuberculosis disease easily, this research aims to make an early detection system of pulmonary tuberculosis disease using Artificial Neural Network algorithm Learning Vector Quantization 2 (LVQ2). The variable that was used consisted of 8 symptoms of pulmonary tuberculosis disease. The research data obtained from health record data of pulmonary tuberculosis patients at Puskesmas Karangawen II Kab. Demak as much as 80 data. The distribution of training data and testing data was obtained from the application of k-fold cross-validation with the value of k = 8. The results showed that the best LVQ2 architecture for early detection system was obtained in combination of parameters learning rate (α) 0,06; smallest learning rate 0,001; window (ε) 0,3; and maximum epoch 500. The best architecture in this research produced 87,5% accuracy, 12,5% error rate, 85% sensitivity, and 90% specificity with a processing time of 8-fold was 60,68 seconds.