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Journal : Indonesian Journal of Electrical Engineering and Computer Science

Implementation of eigenface method and support vector machine for face recognition absence information system Chakim Annubaha; Aris Puji Widodo; Kusworo Adi
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i3.pp1624-1633

Abstract

The student attendance system is what is needed in the process of recording attendance in learning and the development of student achievement. Currently several modern educational institutions have implemented a student attendance system using QR codes or fingerprints, but many still use the traditional system by calculating the number of students attending class. Based on these problems, the solution that can be given is to implement a student attendance system through face matching in the Android mobile application with Eigenface algorithm and support vector machine (SVM) algorithm. Eigenface using the principal component analysis (PCA) method can be used to reduce the dimensions of facial images so that they produce fewer variables and are easier to handle. The results obtained are then entered into a pattern classifier to determine the identity of the owner of the face. This study used 100 facial data as test data and training data. The system test results show that the use of Eigenface with SVM as a classifier can provide a fairly high level of accuracy. For facial images that were included in the training, 91% of the identification was correct.
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.
Big five personality with fuzzy approach to feasibility assessment and loan determination for peer-to-peer lending Iwan Purwanto; Rizal Isnanto; Aris Puji Widodo
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i3.pp1770-1786

Abstract

Bad credit is an uncollectible receivable because the debtor has difficulty repaying. In May 2023, the number of loans will increase by 3.36%. This is due to the inaccuracy of creditors in assessing prospective debtors. Several methods of valuation of prospective debtors have been widely used, but the use of the test big five personality (TBFP) method for the assessment of prospective debtors has not been found. This study will use TBFP as an input variable that will be calculated using fuzzy-Mamdani. The output of the system is in the form of a recommended percentage (%) of the loan amount. This research needs to be done to provide an assessment of prospective debtors to be more objective so that bad credit problems can be reduced. The results of this study are taken into consideration to be used as input in assessing prospective debtors that are more appropriate so that it has an impact on increasing income. For the community can increase business activities. For the government to help people’s economic activities. Our research still needs to be developed by adding variables such as the financial condition of prospective debtors, psychological values, and loan history. Apart from that, it is necessary to carry out an in-depth study regarding recommendations for loan amounts for bad credit