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All Journal Jurnal Informatika
A R Taufani
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Computer Science & IT

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Random forest algorithm for algorithm for prediction of high school science students acceptance snmptn based on students assesment report U Pujianto; A R Taufani; J A Aziz
Jurnal Informatika Vol 15, No 3 (2021): September 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jifo.v15i3.a25413

Abstract

National Selection for State University (SNMPTN) is one of the selectionlines for admission of new students in Indonesia to enter State Universities byinvitation. Report card grades are one component of the assessment ofadmission of new students to enter state universities on this pathway. Thedifference in standards between universities in determining the admission ofSNMPTN applicants, causing the need to predict based on several relatedfactors. This research uses data mining techniques with Random forestalgorithm. From the results of research that has been done, it was found thatthe Random Forest algorithm can be used to predict students who are acceptedat SNMPTN based on report card grades, obtained from the results of theclassification process with the student report card report survey datasetreceived by SNMPTN, This is indicated by the accuracy, precision, and recallvalues of 93%. Optimization of the random forest algorithm using theoversampling technique with the SMOTE method can improve the classifier'sperformance due to the imbalanced class problem.
Forecasting chicken meat and egg in indonesia using ARIMA and SARIMA M D Wisodewo; H A Rosyid; A R Taufani
Jurnal Informatika Vol 16, No 1 (2022): January 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jifo.v16i1.a25416

Abstract

Abstract. Chicken meat and eggs are part of the main commodities in Indonesia. Indonesian people's consumption of chicken meat per capita per year continues to increase. Indonesian government is trying to lure investments to help fund these growing needs. However, inflation has never been positively affected investments. Furthermore, the price of chicken meat and eggs in Indonesia are vulnerable to such a fluctuation. This price hike causes losses to society, due to higher costs, and to the country: inflation affects the future of investment. So, if ones can forecast both commodities, could help decision makers optimizing their policies. This research forecasts the price of chicken meat and egg using the ARIMA and SARIMA methods. Price forecasting is done on chicken meat and egg because they are interrelated, as seen from the Pearson Correlation Test of 0.92 in the datasets and 0.87 in the forecasting results. The selection of the best model is based on the smallest MSE, MAE, and MAPE. The best chicken meat price forecasting results using the ARIMA(3, 1, 2) with MAPE value of 2.31%, while the best chicken egg price forecasting results is the SARIMA[(2, 1, 1)(2, 0, 2, 0), n] with MAPE value of 3.44%.
Co-Authors H A Rosyid J A Aziz M D Wisodewo U Pujianto