Data Mining Untuk Estimasi Sidang Perkara Narkotika Menggunakan Metode Regresi Linier Berganda
Abstract
Narcotics cause unrest in the community because it has a very bad impact on society. The number of reports of narcotics cases has an impact on the number of executions in the trial of these cases. From the number of trial executions, it is necessary to follow up efforts to anticipate the handling of narcotics cases by knowing in advance the trend/pattern of increasing/decreasing narcotics cases as supporting information in efforts to handle these cases. The purpose of the research is to help speed up the process of calculating and managing the information contained in the data into new knowledge so that an estimate of the trial of narcotics cases is produced based on information on the pattern/trend of increasing/decreasing narcotics. The case uses multiple linear regression which is then tested for the coefficient of determination and the simultaneous significant test. The case data used is a time series from January 2021 to December 2021. The resulting regression model is Y = 39.777 – 0.035 X1 – 0.065 X2. The calculation of the regression results shows that the estimation of the implementation of the number of stages of narcotics cases with stage I and stage II variables has a negative effect on the implementation of narcotics cases based on the results of hypothesis testing conducted.
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References
Y. Mardi, “Data Mining : Klasifikasi Menggunakan Algoritma C4.5,” J. Edik Inform., vol. 2, no. 2, p. 7, 2016, [Online]. Available: https://ejournal.upgrisba.ac.id/index.php/eDikInformatika/article/view/1465.
I. L. L. Gaol, S. Sinurat, and E. R. Siagian, “Implementasi Data Mining Dengan Metode Regresi Linear Berganda Untuk Memprediksi Data Persediaan Buku Pada Pt. Yudhistira Ghalia Indonesia Area Sumatera Utara,” KOMIK (Konferensi Nas. Teknol. Inf. dan Komputer), vol. 3, no. 1, pp. 130–133, 2019, doi: 10.30865/komik.v3i1.1579.
A. Putri, Y. Syafrialdi, and Mustakim, “Analisa Pengaruh Temperatur Terhadap Titik Embun, Jarak Pandang, Kecepatan Angin, Dan Curah Hujan Metode Regresi Linier Berganda,” Semin. Nas. Teknol. Inf. Komun. dan Ind., 2017, [Online]. Available: http://ejournal.uin-suska.ac.id/index.php/SNTIKI/article/view/3269/2140.
A. A.-F. N. Wahyudin, A. Primajaya, and A. S. Y. Irawan, “Penerapan Algoritma Regresi Linear Berganda Pada Estimasi Penjualan Mobil Astra Isuzu,” Techno.COM, vol. 19, no. 4, pp. 364–374, 2020.
E. Duha, D. Rahmadiansyah, and E. Affandi, “Implementasi Data Mining Dalam Mengestimasi Hasil Penjualan Menggunakan Algoritma Regresi Linear Berganda,” vol. 1, pp. 480–486, 2022.
Y. Rokhayati, N. S. Utomo, and Sartikha, “Prediksi Kelayakan Operasional Mesin Rivet Menggunakan Regresi Linear Berganda,” J. Sustain. J. Has. Penelit. dan Ind. Terap., vol. 10, no. 1, pp. 10–15, 2021, doi: 10.31629/sustainable.v10i1.2336.
A. Ikhwan, D. Nofriansyah, and Sriani, “Penerapan Data Mining dengan Algoritma Fp-Growth untuk Mendukung Strategi Promosi Pendidikan ( Studi Kasus Kampus STMIK Triguna Dharma),” Saintikom, vol. 14, no. 3, p. 16, 2015, [Online]. Available: https://prpm.trigunadharma.ac.id/public/fileJurnal/hpqZ6 Ali Ikhwan .pdf.
M. Iqbal and Muatin, “Analisa Keranjang Belanja Konsumen Pada Data Penjualan Bulan Ramadhan Menggunakan Algoritma Apriori (Studi Kasus: Distro Coffepark Clothes Pekanbaru),” SNTIKI (Seminar Nas. Teknol. Inf. Komun. dan Ind., 2017, [Online]. Available: http://ejournal.uin-suska.ac.id/index.php/SNTIKI/article/view/3202/2159.
Amrin, “Data Mining Dengan Regresi Linier Berganda Untuk Peramalan Tingkat Inflasi,” J. Techno Nusa Mandiri, vol. XIII, no. 1, p. 6, 2016, [Online]. Available: https://ejournal.nusamandiri.ac.id/index.php/techno/article/view/220/196.
D. Firdaus, “Penggunaan Data Mining dalam Kegiatan Sistem Pembelajaran Berbantuan Komputer,” J. Format, vol. 6, no. 2, 2017, [Online]. Available: https://media.neliti.com/media/publications/224659-penggunaan-data-mining-dalam-kegiatan-si-f3afe53d.pdf.
T. S. Korting, “GeoDMA : a toolbox integrating data mining with object-based and multi- temporal analysis of satellite remotely sensed imagery Geodma : A Toolbox Integrating Data Mining With Object-Based And Multi-Temporal Analysis Of Satellite Remotely Sensed Imagery Th,” no. August 2012, 2017, doi: 10.13140/RG.2.2.33016.34565.
U. Fayyad, G. Piatetsky-Shapiro, and P. Smyth, “The KDD Process for Extracting Useful Knowledge from Volumes of Data,” Commun. ACM, vol. 39, no. 11, pp. 27–34, 1996, doi: 10.1145/240455.240464.
M. Iqbal, “Pengolahan Data dengan Regresi Linier Berganda,” in Perbanas Institute Jakarta, vol. 4, 2000.
Sugiyono, Metode Penelitian kuantitatif, kualitatif dan R & D. Bandung, 2014.
A. Asra, P. Bodro Irawan, and A. Purwoto, “Metode Penelitian Survei,” Bogor, 2015.
Rahmadeni and D. Anggreni, “Analisis Jumlah Tenaga Kerja Terhadap Jumlah Pasien RSUD Arifin Achmad Pekanbaru Menggunakan Metode Regresi Gulud,” J. Sains, Teknol. dan Ind., vol. 12, no. 1, p. 10, 2014, [Online]. Available: http://ejournal.uin-suska.ac.id/index.php/sitekin/article/view/773/722.
B. Setiawan, “Teknik Hitung Manual Analisis Regresi Linear Berganda Dua Variabel Bebas,” 2017.
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