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All Journal JOURNAL OF SCIENCE AND SOCIAL RESEARCH JSAI (Journal Scientific and Applied Informatics)
Jorry Karim
Universitas Pohuwato
Computer Science & IT Economics, Econometrics & Finance Education Social Sciences

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COMPARASI ALGORITMA FORECASTING SVM, K-NN DAN NN UNTUK PREDIKSI HARGA CABAI KOTA GORONTALO Abdul Yunus Labolo; Andi Bode; Ivo Colanus Rally Drajana; Jorry Karim
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol 6, No 2 (2023): June 2023
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v6i2.1112

Abstract

The high demand for chilies, especially in Gorontalo, is a driving force for chilli cultivating farmers. The price of chili which is uncertain every day can fluctuate. The Gorontalo City Food Service cannot make predictions to estimate prices in the following month. Prediction is defined as the use of statistical techniques in the form of a picture of the future based on the processing of historical figures. Due to the many algorithms that can be used in predictions, this study will compare forecasting algorithms namely Support Vector Machine (SVM), K-Nearest Neighbor (K-NN) and Neural Network (NN). Experiments that have been carried out, on chili price prediction with forecasting algorithms have been successfully carried out. The root mean square error (RMSE) result of the SVM algorithm is 0.233, the K-NN algorithm is 0.223 and the NN algorithm is 0.206. Of the three forecasting algorithms used, the best results are produced by the Neural Network algorithm with the smallest RMSE value of 0.206. So it can be concluded that the proposed model is close to perfection, because a comparison of the results of implementing chili price predictions for the next three months produces an accuracy value of 99.25% on average
APLIKASI MOBILE MONITORING KEHADIRAN SISWA MENGGUNAKAN TEKNOLOGI QUICK RESPONSE Misrawati Aprilyana Puspa; Jorry Karim; Marlin Lasena; Zufrianto Dunggio
JSAI (Journal Scientific and Applied Informatics) Vol 6 No 2 (2023): Juni
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v6i2.5281

Abstract

Attendance is an important part of every educational institution. Where attendance is one of the main supports that can support and motivate every activity carried out in it. Schools are educational institutions designed for student instruction under the supervision of teachers. Most countries have a formal education system, which is generally mandatory, as well as attendance at SMK Negeri 5 Gorontalo which still uses a manual method (signature), this method is very vulnerable for an educational institution because the level of discipline that cannot be controlled and can be misused by irresponsible people, another disadvantage that may arise in the manual attendance system is data recapitulation which still takes a lot of time and effort. Another disadvantage, parents of students cannot find out about their son's attendance at school so it is considered less effective and efficient. For this reason, the purpose of this research is to create and design an information system for monitoring student attendance using an android-based QR Code at SMK 5 Gorontalo so that it can make it easier for parents to monitor attendance. This application is designed using java programming language software, namely Android Studio, for PHP programming language using Dreamwever software, and for data storage using Mysql, as well as a tool for modeling using UML (Unified Modeling Language).
COMPARASI ALGORITMA FORECASTING SVM, K-NN DAN NN UNTUK PREDIKSI HARGA CABAI KOTA GORONTALO Abdul Yunus Labolo; Andi Bode; Ivo Colanus Rally Drajana; Jorry Karim
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol. 6 No. 2 (2023): June 2023
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v6i2.1112

Abstract

The high demand for chilies, especially in Gorontalo, is a driving force for chilli cultivating farmers. The price of chili which is uncertain every day can fluctuate. The Gorontalo City Food Service cannot make predictions to estimate prices in the following month. Prediction is defined as the use of statistical techniques in the form of a picture of the future based on the processing of historical figures. Due to the many algorithms that can be used in predictions, this study will compare forecasting algorithms namely Support Vector Machine (SVM), K-Nearest Neighbor (K-NN) and Neural Network (NN). Experiments that have been carried out, on chili price prediction with forecasting algorithms have been successfully carried out. The root mean square error (RMSE) result of the SVM algorithm is 0.233, the K-NN algorithm is 0.223 and the NN algorithm is 0.206. Of the three forecasting algorithms used, the best results are produced by the Neural Network algorithm with the smallest RMSE value of 0.206. So it can be concluded that the proposed model is close to perfection, because a comparison of the results of implementing chili price predictions for the next three months produces an accuracy value of 99.25% on average
Co-Authors Abdul Yunus Labolo Abdul Yunus Labolo Andi Bode Andi Bode Ivo Colanus Rally Drajana Marlin Lasena Misrawati Aprilyana Puspa Zufrianto Dunggio