In the spirit of promoting decision-based analytics through OR/MS, the conference theme is In this process, OR/MS has provided significant support for increasing the productivity of the economies of different countries.
Literature Review
This statement was taken from the interview with the company's production manager. The aim of this project is to obtain a strategy for optimizing the loading and unloading time of the forklift in the warehouse by building an animated process for evaluating the current state of the system and for simulating the changes to see the results.
Research Methodology
Insufficient amounts of these resources can result in increased process times and reduced efficiency, while zero availability of resources will result in extreme delays and process times as the required task cannot be completed on time (Al-bazi & Emery, 2013). The process of data analysis is the process of reviewing, cleaning, transforming and modeling data with the goal of discovering useful information, proposing conclusions and supporting decision-making.
Results and Discussion 1 Introduction
The simulation model of the current layout consists of seven stations, which are Station A, Station B, Station C, Station D, Station E, Station F and Station G. The simulation model of the alternative layout consists of eight stations, which is Station A, Station B1 , station B2, station C, station D, station E, station F and station G.
The Padovan-like sequence raised from Padovan Q-matrix
Introduction
By substituting the value m in equation (10) and (11), Sokhuma [2] obtains the general form of the Padova sequence numbers in equation (1) and the Perrin sequence numbers in equation (2). By substituting the value m of equation (12) and (13), Wijayanti [4] to the general form of the sequence of Padova numbers in equation (1) and the sequence of Perrin numbers in equation (2).
Research methodology
Result and discussion
The general formulas are the sets of the Padovan, Perrin and similar Padovan numbers Sn that can be generated from the matrix Q Pn. From the result of the first matrix line multiplication Qm with the first column of the matrix.
Prediction of the number of international tourist arrival to West Java using Holt Winter method
Holt Winter
Forecasting the number of international tourist arrivals in West Java using the Holt Winter method.
Data and Method
Result and Discussion
Proceeding of the 2017 IORA International Conference on Operations Research Universitas Terbuka, Tangerang Selatan, Indonesia, October 12, 2017. Proceeding of the 2017 IORA International Conference on Operations Research Universitas Terbuka, Tangerang Selatan, Indonesia, 12th October 11 using a method result October 20; (b) Graph of Ǻ௧ towards month-year;. Based on the Holt Winter method, it was known that the forecast of international tourist arrivals in West Java in 2017, as shown in Table 3, with MAPE error of and MAE error of 2239, was 26005 international tourist arrivals in West Java for December 2017.
1] Encik R, Sigit S, Gamal M D H Metode Holt-Winter Forecasting untuk Memprediksi Jumlah Pengunjung Perpustakaan Universitas Riau, Pekanbaru, 2016, marrë nga (http://repository.unri.ac.id/xmlui/bitstream/ handlearticle%20lagi .pdf?seq uence=1 5 korrik 2017.
Dynamic models of provision non-classical raw water on village level to support smart village (case on Bendungan
- Material and Method
- Condition of Research Sites
- Result and Discussion 1. Dynamic Modeling
- Conclusion and Recommendation 1. Conclusion
Poor management, will lead to the supply of raw water in rural areas is not sustainable, leading to the village experienced prone to clean water. This research uses a new paradigm in the provision of raw water that is non-classical, where the village is a water basin. Provision of non-classical raw water at village level with Water Smart Village approach.
The existing condition and the result of modeling raw water supply in Bendungan village are shown in Figure 5.
Application of matrix and numerical methods in the
Methodology
In this subsection the solution of the matrix equation (20) is performed using the Gauss-Jordan method, referred to (10). In this subsection the solution of the matrix equation (20) is performed using the LU decomposition method. In this subsection the solution of the matrix equation (20) is performed using the Gauss-Seidel method.
Considering the estimated values of the parameter coefficients summarized in Table 2, whether done using Gauss-Jordan methods, LU decomposition and Gauss-Seidel, the results are indeed different.
Analysis of quality of service (QoS) traffic network of Pakuan University website with queue system model
Research Steps
Analysis of the Quality of Service (QoS) traffic network of Pakuan University website with queuing system model. Measuring process to determine how good the service of the traffic network traffic is using QoS. In Little's law, the relationship between E (L), E (S) and λ is stated as follows: E(L) = λE(S), which means that the capacity of the system is sufficient, or the number of customers in the system does not grow indefinitely.
Results and discussion
This function is used to refine the graph data more accurately, the graph can be seen with the bandwidth 0.7 (red graphic), bandwidth 0.5 (black graphic) and bandwidth 0.2 (green color map). From the calculation of E (L) it can be seen that the average number of packets in 1 minute in the system is 0.851. The system in the network can serve the average of each incoming packet for about 0.016.
The calculation of E(Lq) shows that the average number of packets in 1 minute in the queue is 0.39 packets.
Conclusions
Cost control of drugs in primary healthcare facilities: from health information to quality control
When using the approach, the role of the control chart is crucial in monitoring the current production process. Examples of the use of control cards in the primary care process can be seen in table 1 (source: Pohan, 2004) [5]. Some examples of the use of control cards in the care process.
In this study, the median price of the drug for upper respiratory tract infections was Rp.
Implementation of fuzzy multiple attribute decision making (FMADM) model using analytic hierarchy process (AHP)
Research Methods
System development life cycle is a series of activities implemented by user of information system to develop and implement information system [1,3].
Result and Discussion 1. Program Implementation
Calculations of the results, which were done manually or with the system, give a value of 0.3243 according to AHP and a value of 0.324324 return according to the system. The manual validation of AHP and ELECTRE is usually the same, but there was only a small difference due to the number of decimal places between the system and manual calculation results. When various tests were performed both manually and with the system, it was found that the method used produced different final values as indicated in this case study.
Assessment standards using the AHP method produced the final value in the form of priorities, while using ELECTRE compares each criterion to produce some criteria to be prioritized.
Conclusion
2 0.192492 RPP Evaluasi Proses Pembelajaran Hasil Pembelajaran Kelompok Kegiatan Sekolah Kegiatan Ekstrakurikuler Surat Keterangan Surat Keterangan Dosen. Sistem pendukung keputusan penetapan prioritas peningkatan standar akreditasi program studi sarjana dengan menggunakan metode proses hierarki analitik.
The integrated academic information system support for Education 3.0 in higher education institution: students’
- Literature review
- Methodology
- Analysis and Discussion
- Conclusion
As in Figure 1, Education 3.0 is made possible by Education 2.0, which is Internet-enabled learning, and by centuries of experience with memorization in Education 1.0. Based on analysis and discussion, this research has strengthened the problems that the students face when the HEI implements Education 3.0 in their learning process. 7] Utomo H P, Bon A T and Hendayun M 2017 Academic Information System Support in the Era of Education 3.0 IOP Conf.
Available from https://www.cisco.com/web/learning/le21/le34/downloads/689/educause/. 15] Keats D and Schmidt J P 2007 The Genesis and Emergence of Education 3.0 in Higher Education and Its Potential for Africa Fst.
![Figure 1. Education 3.0 paradigm [14]](https://thumb-ap.123doks.com/thumbv2/123dok/11436190.0/72.892.194.724.608.966/figure-1-education-3-0-paradigm-14.webp)
Regression model of simple recirculating aquaculture system
Multivariate Linear Regression
The data were used to find the correlation with the Multivariate Linear Regression method and implemented with a linear equation system solved using the LU matrix decomposition [4]. In this study, the relationship between the dependent variable y and the independent variables x1, x2, x3, .., xk is obtained so that the regression of y on x1, x2, x3, .., xk, this regression line is called Multivariate Linear Regression.
Matrix Decomposition
The data retrieval method was conducted by direct observation at the experimental site (primary data) for 92 days.
Aquaculture System
The result of observation and study of the optimization model based on regression can be concluded that the standard error estimate is 2.314886 or 2.3% and the stability of the normal pond ecosystem if the parameter value of x1, x2, x3 and x4 is in accordance with the filter conditions. media area (SSA), and the sustainability of the ecosystem in accordance with expectations, which graphs can be seen in Figure 1. It is further suggested that observations for fish growth and certain fish species can be distinguished, so that other comparisons for certain fish design of types and application programs will be based on the filter media area (SSA). Pattillo, Water Quality Management for Recirculating Aquaculture, Aquaculture Extension Specialist, Iowa State University Extension and Outreach, 2014.
Wramner, Integrated dynamic aquaculture and wastewater treatment modeling for recirculating aquaculture systems, journal website: www.elsevier.com/locate/aqua-on line, Aquaculture.
Algorithm design model and formulation for recirculating aquaculture system
Design Model and Formulation
The ammonia removal rate by nitrifying bacteria is 0.2-2 g per square meters per day [1], assuming that the water flowing from the entire pool passes through the average filter media within 1 hour to 3 hours [5]. The first step based on [1] can be designed algorithm to calculate ammonia nitrification process in unit of SSA area is:. The formulation can be used as a starting point to determine the simple pond design with the desired SSA area as well as to maximize the number of fish and feed quantities with conditions for parameters: temperature, ph, tds, ammonia and DO according to standard [ 6].
Algorithm Design
50 kg is the maximum value, in a pond ecosystem there is a growth rate of other parameters that are very influential, therefore the initial stocking of fish should be much less than the maximum value so that the maximum target can be achieved as desired. period. Allen Pattillo, Water Quality Management for Recirculating Aquaculture, Extension Aquaculture Specialist, Iowa State University Extension and Extension, 2014. Correlation between Water Quality Parameters in Ground Basin and Concrete Tank,” Department of of Aquaculture and Fisheries, University of Ibadan, 2015.
Implementation of artificial neural networks in vehicle registration number detection by region based on digital image.
Implementation of artificial neural networks in detection of vehicle registration number by region based on digital image
Results and Discussion
Application license plate identification based on region-based image using Artificial Neural Network (ANN) is structured as the flow diagram shown in Figure 3. The application displays the value of the image feature extraction from test data that the value of entropy, energy, contrast and homogeneity. Application Identification of vehicle license plate based on region using Artificial Neural Network (ANN) based on digital image processing aims to identify license plate using image divided into 4 classes namely class 1 for Jakarta (B), class 2 for Purwakarta (T) grade 3 for Bandung (D), and Grade 4 for Bogor (F).
The identification results show test data with the region code based on the value closest to the training data.
