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All Journal Indonesian Journal of Electrical Engineering and Computer Science Proceeding of International Conference Health, Science And Technology (ICOHETECH)
Rahmat Gernowo
Diponegoro University
Computer Science & IT Decision Sciences, Operations Research & Management Health Professions Industrial & Manufacturing Engineering Medicine & Pharmacology

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Implementation of the K-Nearest Neighbor Method to determine the Classification of the Study Program Operational Budget in Higher Education Gufron; Bayu Surarso; Rahmat Gernowo
Proceeding of International Conference on Science, Health, And Technology Proceeding of the 1st International Conference Health, Science And Technology (ICOHETECH)
Publisher : LPPM Universitas Duta Bangsa Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (304.117 KB) | DOI: 10.47701/icohetech.v1i1.803

Abstract

Sultan Agung Islamic University annually designs operational costs for work programs in the study program and of course determines the amount of financial budget for the study program work program. K-Nearest Neighbor algorithm is needed to determine the required operational budget classification based on the number of active students, the number of financial admissions, the number of employees and the percentage of work program realization programs. The results of this study are to facilitate the leadership of higher education in the budget field to classify the amount of the budget required by study programs in the classification of up, or fixed. The purpose of this study is expected to facilitate the leadership of the financial budget department to classify the budget needed by the study program and as an awareness system in the work program of the study program with a classification value of 79.96% for the operational budget of the college study program.
Rainfall prediction model in Semarang City using machine learning Carissa Devina Usman; Aris Puji Widodo; Kusworo Adi; Rahmat Gernowo
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 2: May 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i2.pp1224-1231

Abstract

The erratic distribution of rainfall greatly affects people's daily activities, especially in Semarang City, so it is necessary to predict rainfall. Correct prediction of rainfall can improve community preparedness in dealing with natural disasters. Algorithms for machine learning and data mining have been extensively utilized in research involving rainfall data from various regions. The primary objectives of this study are to find the best regression algorithm and use machine learning algorithms to predict rainfall in Semarang. The dataset used is daily rainfall data for the City of Semarang from the meteorological, climatological, and geophysical agency (BMKG). Machine learning algorithms such as multiple linear regression, random forest regression, and replicated neural networks will be used to conduct regression analysis on this dataset. The mean absolute error and Root mean squared error techniques are utilized to evaluate the performance of machine learning algorithms. With an error rate of 13.055 for root mean squared error (RMSE) and 6.621 for mean absolute error (MAE), the results of the research indicate that the performance of the neural network algorithm is superior to that of other algorithms.
Co-Authors Aris Puji Widodo Bayu Surarso Carissa Devina Usman Gufron Kusworo Adi