ABIDIN, RAHMAT ZAINUL (2024) KLASIFIKASI TINGKAT PERAWATAN PENGGUNAAN NARKOBA MENGGUNAKAN ALGORITMA MULTINOMIAL NAIVE BAYES (STUDI KASUS di BNN TANGERANG). S1 thesis, Universitas Mercu Buana Jakarta.
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Abstract
This research aims to classify the level of treatment of drug users using the Multinomial Naïve Bayes algorithm, with a case study at BNN Tangerang. Data includes demographic information, types of drugs, symptoms and treatment measures from BNN Tangerang during the 2020-2023 period. The Multinomial Naïve Bayes algorithm was chosen because it is suitable for data with a multinomial distribution. The results show that this algorithm has good performance with an average accuracy, precision and recall of 89% based on manual calculation results, showing effectiveness in classifying the level of maintenance required. Data analysis reveals that the 18-25 year age group uses drugs the most, with the majority of cases being men. The type of drug most often used is methamphetamine with varying symptoms. This research contributes to the classification of levels of care and provides insights for improving rehabilitation programs. Suggestions for future research include the use of other algorithms, improving data quality, a focus on adolescent interventions, and ongoing training for medical personnel. Keywords: Rehabilitation, Multinomial Naïve Bayes, Classification, BNN Tangerang, Types of Drugs Penelitian ini bertujuan mengklasifikasikan tingkat perawatan pengguna narkoba menggunakan algoritma Multinomial Naïve Bayes, dengan studi kasus di BNN Tangerang. Data mencakup informasi demografi, jenis narkoba, gejala, dan tindakan perawatan dari BNN Tangerang selama periode 2022-2023. Algoritma Multinomial Naïve Bayes dipilih karena sesuai untuk data dengan distribusi multinomial. Hasilnya menunjukkan algoritma ini memiliki performa baik dengan akurasi, precision, dan recall rata-rata sebesar 89% berdasarkan hasil hitungan manual, menunjukkan efektivitas dalam mengklasifikasikan tingkat perawatan yang diperlukan. Analisis data mengungkapkan bahwa kelompok usia 18-25 tahun paling banyak menggunakan narkoba, dengan mayoritas kasus pada laki-laki. Jenis narkoba yang paling sering digunakan adalah methamphetamine dengan gejala bervariasi. Penelitian ini berkontribusi dalam klasifikasi tingkat perawatan dan memberikan wawasan untuk peningkatan program rehabilitasi. Saran untuk penelitian selanjutnya mencakup penggunaan algoritma lain, peningkatan kualitas data, fokus pada intervensi remaja, dan pelatihan berkelanjutan bagi tenaga medis. Kata Kunci: Rehabilitasi, Multinomial Naïve Bayes, Klasifikasi, BNN Tangerang, Jenis Narkoba
| Item Type: | Thesis (S1) |
|---|---|
| Call Number CD: | FIK/INFO. 24 153 |
| Call Number: | SIK/15/24/112 |
| NIM/NIDN Creators: | 41520010178 |
| Uncontrolled Keywords: | Rehabilitasi, Multinomial Naïve Bayes, Klasifikasi, BNN Tangerang, Jenis Narkoba |
| Subjects: | 300 Social Science/Ilmu-ilmu Sosial > 390 Customs of People/Adat Istiadat dan Kebiasaan Masyarakat Berdasarkan Status Ekonomi dan Kelas Sosial > 394 General Customs/Adat Istiadat Umum > 394.1 Eating, Drinking; Using Drugs/Makan, minum; Menggunakan Obat-obatan 500 Natural Science and Mathematics/Ilmu-ilmu Alam dan Matematika > 510 Mathematics/Matematika > 518 Numerical Analysis/Analisis Numerik, Analisa Numerik > 518.1 Algorithms/Algoritma |
| Divisions: | Fakultas Ilmu Komputer > Informatika |
| Depositing User: | khalimah |
| Date Deposited: | 14 Aug 2024 03:12 |
| Last Modified: | 14 Aug 2024 03:12 |
| URI: | http://repository.mercubuana.ac.id/id/eprint/90219 |
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