预测新大学生Fti Usn Kolaka使用单次平滑法

Aliniy, Yuwanda Purnamasari Pasrun, Andi Tenri Sumpala
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引用次数: 0

摘要

FTI学生每年的录取目标往往达不到。这是因为FTI的位置发生了变化,从位于Kolaka的USN Kolaka到位于Tanggetada的USN Kolaka。此外,还有其他原因,大量的学生没有按时毕业,造成教师和学生的比例不平衡。这将减少认证时的评估。做出预测是为了帮助FTI计划和做出决定,以确定每年将接受多少潜在学生的优先事项。所使用的观测数据是2013-2021学年(9个学期)信息系统研究项目和2018-2021学年(4个学期)计算机科学研究项目的新FTI学生人数的数据。数据处理采用单指数平滑法和MAPE (Mean Absolute Percent Error)精度检验。对该方法的测试结果表明,对于信息系统专业2022/2023学年的预测结果为141人,MAPE最小值为26.67%,表明该模型的预测能力较好(合理)。同时,对于计算机科学研究项目,在2022/2023学年获得的预测结果为116人,MAPE最小值= 18.52%,表明具有较好的预测模型能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prediksi Jumlah Mahasiswa Baru Fti Usn Kolaka Menggunakan Metode Single Exponential Smoothing
The FTI student admission target every year is often not achieved. This happened because of the change in FTI's location, from USN Kolaka which was in Kolaka to USN Kolaka which was in Tanggetada. In addition, there are also other causes in terms of the large number of students who do not graduate on time, causing an unbalanced ratio of lecturers and students. This will reduce the assessment at the time of accreditation. Predictions are made to assist FTI in planning and making decisions to determine priorities for how many prospective students will be accepted each year. The observational data used are data on the number of new FTI students for the 2013-2021 academic year (9 periods) for the Information Systems study program and the 2018-2021 academic year (4 periods) for the Computer Science study program. Data processing is carried out using the Single Exponential Smoothing Method and MAPE (Mean Absolute Percent Error) accuracy testing. The results of testing this method are that for the information systems study program the predicted results obtained in the 2022/2023 academic year are 141 people, and the smallest value of MAPE = 26.67% which shows the ability of the forecasting model to be quite good (Reasonable). Meanwhile, for the computer science study program, the prediction results obtained in the 2022/2023 academic year were 116 people, and the smallest value of MAPE = 18.52%, which indicates a good forecasting model ability.
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