Linear Regression Analysis To Predict The Length Of Thesis Completion

Students who carry out knowledge in the undergraduate program will certainly be faced with the preparation of a thesis at the end of their study period. However, every year students still find it takes longer than the time specified in completing their thesis. This is caused by several things, such as students who are working, working hours that do not support the implementation of thesis preparation, students who already have families and other factors. This of course makes universities have to prepare special strategies in order to reduce the number of students who cannot complete their thesis on time in the future, one of which is with a decision support. This can be done by utilizing university big data. Prediction of the length of time for completion of college student thesis can be done by utilizing data mining and a simple linear regression approach. Using 1 independent variable, namely the average inhibiting factor (Working Status, Working Hours, Work Sip, Guidance Media, Status) (X1) and the number of days of thesis completion being the dependent variable (Y). After looking for the regression value of b and constant a, then the simple linear regression equation model is: Y = 280.450 + 1.650 X

The Best Employee Selection Decision Support System at The Tribeca Condominium Apartment with the Promethee Method

Employee development is highly dependent on company performance appraisal. Therefore we need a system that can provide the right decisions in determining the best employees so that employees are motivated to improve their work. For the selection of the best employees, the Preference Ranking Organization Method For Enrichment Evaluation (Promethee) method is used to determine the ranking of employees. The Promethee method is a method of determining the order (priority) in a multicriteria analysis. This method is used in this study because this method is quite good at taking into account the characteristics of the data and its multicriteria (can calculate many criteria) so that it can help decision makers assess the performance of each employee according to existing criteria

Forecasting the Number of Students in Multiple Linear Regressions

The most important element of higher education was students, therefore every university must continue to improve services in the future, and one of them was by using decision support. This case could be done by utilizing the University of Big Data. Predicting the number of prospective students in higher education was done by utilizing data mining and multiple linear regression approaches. By using 2 independent variables, namely administration costs (X1), accreditation score (X2), and the number of students who was registered each year as dependent variable (Y). For the test data, it used database for the last 13 years. By using multiple linear regression, the intercept value was sought and the coefficient of determination until the regression coefficient was obtained with the equation Y = 45.28 + -0.02.X1 + 121.58.X2, noted that if X2 was constant, the increasing of one unit was in X1 would have the effect of increasing -0.02 units on Y. Secondly, if X1 was constant, the increasing of one unit was in X2, would have the effect of increasing 121.58 units in Y. Thirdly, if X1 and X2 were equal to zero, the magnitude of Y was 45.28 units. Therefore, the proposed approach could be provided the acceptable predictive results

Sistem Pakar untuk Identifikasi Kandungan Formalin dan Boraks pada Makanan dengan Menggunakan Metode Certainty Factor

Tujuan dari penelitian ini adalah untuk mengetahui identifikasi kandungan zat pengawet berbahaya boraks dan formalin pada makanan. Metode yang digunakan untuk mengidentifikasi kandungan zat berbahaya pada makanan dengan menggunakan Certainty Factor dengan teknik pemberian bobot pada setiap premis (gejala) hingga memperoleh persentase keyakinan untuk mengidentifikasi makanan yang mengandung formalin dan boraks. Hasil penelitian ini adalah Kandungan boraks pada makanan, dari 4 sampel makanan (100%) yaitu 4 sampel atau seluruh sampel tidak mengandung boraks dengan persentase sebesar 100%. Kandungan formalin pada makanan, dari 4 sampel makanan (100%) yaitu ada 2 sampel makanan positif mengandung formalin dengan persentase sebesar 50% dan ada 2 makanan negative mengandung formalin dengan persentase sebesar 50%. Dari hasil pemeriksaan menggunakan spektrofoto meter UV-VIS kadar formalin yang terendah terdapat pada sampel (Ikan Segar) dengan nilai 0,6631 mg/l. Kadar formalin yang tertinggi terdapat pada sampel C (Mi Bakso) dengan nilai 1,7140 mg/l

Developing an Enhanced Algorithms to Solve Mixed Integer Non-Linear Programming Problems Based on a Feasible Neighborhood Search Strategy

Engineering optimization problems often involve nonlinear objective functions, which can capture complex relationships and dependencies between variables. This study focuses on a unique nonlinear mathematics programming problem characterized by a subset of variables that can only take discrete values and are linearly separable from the continuous variables. The combination of integer variables and non-linearities makes this problem much more complex than traditional nonlinear programming problems with only continuous variables. Furthermore, the presence of integer variables can result in a combinatorial explosion of potential solutions, significantly enlarging the search space and making it challenging to explore effectively. This issue becomes especially challenging for larger problems, leading to long computation times or even infeasibility. To address these challenges, we propose a method that employs the "active constraint" approach in conjunction with the release of nonbasic variables from their boundaries. This technique compels suitable non-integer fundamental variables to migrate to their neighboring integer positions. Additionally, we have researched selection criteria for choosing a nonbasic variable to use in the integerizing technique. Through implementation and testing on various problems, these techniques have proven to be successful

A stochastic approach for evaluating production planning efficiency under uncertainty

Planning production is an essential component of the decision-making process, which has a direct bearing on the effectiveness of production systems. This study’s objective is to investigate the efficiency performance of decision-making units (DMU) in relation to production planning issues. However, the production system in a manufacturing environment is frequently subject to uncertain situations, such as demand and labor, and this can have an effect not only on production but also on profit. The robust stochastic data envelopment analysis model was proposed in this study with maximizing the number of outputs as the objective function thus means of handling uncertainty in input and output in production planning problems. This model, which is based on stochastic data envelopment analysis and a method of robust optimization, was proposed with the intention of providing an efficient plan of production for each DMU of stage production. The model is applied to small and medium-sized businesses (SMEs), with inputs consisting of the cost of labor, the number of customers, and the quantity of raw materials, and the output consisting of profit and revenue. It has been demonstrated through implementation that the proposed model is both efficient and effective