PREDIKSI TINGKAT PENGGUNA NARKOBA DENGAN METODE REGRESI LINEAR BERGANDA BERBASIS WEB
Abstract
Drug cases, are not decreasing from day to day but are increasing, both as dealers, users, sellers, even as dealers. Drug users range from the elderly to the younger generation and children. Based on these problems, the researcher intends to help people related to law and ordinary people by creating an application to predict the level of drug users with multiple linear regression methods. Data on drug users used from 2015 to 2019 were taken at the Aceh Tamiang Police (Polres). To perform calculations using this multiple linear regression method using 619 user data. In its application, the multiple linear regression method results in an equation Y’ = -4,492312 + 1,018444 X1 + 1,181143 X2 where X1 is methamphetamine and X2 is marijuana. If the number of methamphetamine users is 108 and marijuana is 76, it can be predicted that the number of drug users is 195 cases and MAPE is 68.48%.
Keyword: drugs, predictions, multiple linear regression.
Full Text:
PDF (Bahasa Indonesia)References
Ramadhani Sartika, “Perilaku Pecandu Narkoba Pasca Rehabilitasi Pada Badan Narkotika Nasional,” Skripsi, (Sulawesi Selatan: Universitas Islam Negeri Alauddin Makassar, 2016).
Katemba, Djoh. Prediksi Tingkat Produksi Kopi Menggunakan Metode Regresi Linear, 2017.
Kinaswara, T.A., Nasrol R.H. dan Fatima Nugratani. Cadangan Bangun Aplikasi Inventaris Berbasis Web Pada Kelurahan Bantengan. Seminar Nasional Teknologi Informasi & komunikasi (2019).
Putra Pradipta Duwila, ”Tinjauan Sosiologi Hukum Terhadap Ujaran Kebencian di Media Sosial,” Skripsi, (Makasar: Universitas Hasanudin, 2016).
DOI: https://doi.org/10.29103/techsi.v13i2.3738
Article Metrics
Abstract Views : 574 timesPDF (Bahasa Indonesia) Downloaded : 35 times
Refbacks
- There are currently no refbacks.
Copyright (c) 2021 Dahlan Abdullah, Maryana Maryana, muli ani
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Indexed by:
© Copyright of Journal TECHSI, (e-ISSN:2614-6029, p-ISSN:2302-4836).
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.