Every year, all the colleges hold new student enrollment. It is needed to start a new school academic year. Unfortunately, the number of students who resigned is considerably high to reach 837 students and caused 324 empty seats. The college’s stakeholders can minimize the resignation number if the selection phase of new students is done accurately. Making a machine learning-based model can be the answer. The model will help predict which candidates who potentially complete the enrollment process. By knowing it in the first place will help the management in the selection process. This prediction is based on historical data. Data is processed and used to train the model using the Adaboost algorithm. The performance comparison between Adaboost and Decision Tree model is performed to find the best model. To achieve the maximum performance of the model, feature selection is performed using chi-square calculation. The results of this research show that the performance of Decision Tree is lower than the performance of the Adaboost algorithm. The Adaboost model has f-measure score of 90.9%, precision 83.7%, and recall 99.5%. The process of analyzing the data distribution of prospective new students was also conducted. The results were obtained if prospective students who tended to finish the enrollment process had the following characteristics: graduated from an Islamic school, 19-21 years old, parents' income was IDR 1,000,000 to IDR. 5,000,000, and through the SBMPTN program.
SPBE (Electronic Based Government System) is a legal protection as new breakthroughs in the reform of Indonesian government bureaucracy. The issuance of Presidential Regulation (Perpres) 95/2018 concerning SPBE is expected to be a reference for the transformation from e-Government into i-Government (integrated Government). In the meantime, the government through the Ministry of Administrative and Bureaucratic Reform (PANRB) is drafting an academic paper on the SPBE Bill. Of the 10 elements contained in Presidential Regulation (Perpres) 95/2018, the second element namely SPBE architecture is a concept known in the world of Information Systems as Enterprise Architecture.Enterprise architecture is a conceptual framework that describes how an enterprise is constructed by defining its primary components and the relationships among these components. In SPBE, the main component is defined as domain, consisting of 6 parts, namely: business process architecture domain, data and information, infrastructure, SPBE applications, security, and SPBE services. Unfortunately, the Presidential Regulation (Perpres) 95/2018 has not regulated the concept of Digital Enterprise Architecture, since between Enterprise Architecture (EA) and Digital Enterprise Architecture (DEA) are two things that are significantly different. If EA merely focuses on structuring the company based on the main frame of reference, then DEA focuses on utilizing digital repositories to create living documents as according to the EA framework so that they are easily accessed, modified and managed at any time following the company's development. This study created a DEA model for SPBE in Indonesia. The model created is adapted to the SPBE architecture by carrying out the concept of a digital repository. With digital repositories, time efficiency, paper savings and change management will be easier to achieve. The model created in this study is expected to be utilized to make SPBE much more efficient and green-minded.
Intense competition at the present time requires producers to increase the effectiveness and efficiency of human resources and product material resources. Meatball X production is one of the small industrial productions engaged in the culinary field. In the production process and distribution of meatball X production is still not optimal. This study aims to make simulation modeling to maximize production and distribution in meatball X production. The results of the research of the meatball manufacturing process go through 4 stages namely milling, emulsification, printing, and packaging. With a total production of 1200 pieces per day and produces a maximum income of Rp. 1,800,000 with a net profit of Rp. 1,025,000. To determine the shortest route and distribution path using the Dijkstra algorithm. From the Dijkstra calculation results the shortest route and path as far as 14.1 Km with this route X meatball production can shorten the distribution time and reduce costs for fuel.
The successful implementation of information technology (IT) is largely determined by the level of acceptance of each individual user. Therefore, understanding and prediction are needed to increase user acceptance by changing the nature of the system and the implementation process used. This also applies to the Sistem Informasi Enterprise Soetomo untuk Transparansi dan Akuntabilitas (SIESTA) developed by RSUD Dr. Soetomo, which until now has never measured the level of acceptance of the system. This study aims to determine the factors that influence the acceptance of the modul Kepegawaian SIESTA using the UTAUT2 model so that the research results can be used by RSUD Dr. Soetomo Surabaya in implementing other SIESTA modules. The sampling method taken is the disproportionate stratified random sampling method using 5% error tolerance and obtained a total sample of 359 respondents. The data analysis uses Structural Equation Modeling-Partial Least Square (SEM-PLS). Hypothesis test results state that the variable social influence and price value significantly influence the dependent variable behavioral intention. While use behavior is influenced by the variable facilitating conditions and habits significantly. This is supported by the value of the path coefficient which shows that behavioral intention is positively influenced by social influence and price value with a value of 0.321 and 0.350. While the use behavior is influenced by facilitating conditions and habits of 0.261 and 0.563. Based on the results of the hypothesis testing the T Statistics values for the three moderating variables age, gender, and experience did not meet the significance standard. This means that age, gender, and length of work have no effect on the acceptance of the modul Kepegawaian SIESTA.
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