Analisa Efektifitas Kebijakan PPKM terhadap Pertumbuhan Kasus COVID-19 Menggunakan Algoritma Naïve Bayes
DOI:
https://doi.org/10.30865/mib.v6i3.4356Keywords:
Implementation of the Policy for Enforcement of Community Activity Restrictions (PPKM), Machine Learning, Naïve Bayes, RapidminerAbstract
The pandemic that is being experienced by Indonesia, namely the outbreak of the COVID-19 virus, has led to the implementation of large-scale social restrictions in order to accelerate the handling of the spread of the virus. As a result of the increasing number of COVID-19 cases in Indonesia, including in Riau Province, where every City and Regency has increased, especially Pekanbaru, the implementation of Community Activity Restrictions (PPKM). This study tries to answer this question by using the Naïve Bayes Algorithm. Naïve Bayes Classifier is a probabilistic and statistical method of classification technique that predicts future opportunities based on previous experience. The use of the Naïve Bayes algorithm to predict the growth of Covid-19 cases in Pekanbaru obtained a good performance score with 90.00% accuracy, 90.24% precision and 91.90% recall. Based on the results of the classification of the datasets used in this study, it can be concluded that the implementation of the Policy for Enforcement of Community Activity Restrictions (PPKM) in the city of Pekanbaru has proven to be effective where there has been a decline in the category of growth of Covid-19 cases with a high category from 70% to 25%. Vice versa, the growth of Covid-19 cases in the low category has increased from 30% to 65%.
References
O. Económico and para a C. e D, “Innovation , development and COVID-19 : Challenges , opportunities and ways forward,†pp. 1–14, 2020.
Y. Yanis et al., “PENGEMBANGAN MODAL SOSIAL SEBAGAI STRATEGI MENGATASI DAMPAK PANDEMI COVID-19 DI KOTA,†vol. 9, no. 2, pp. 427–432, 2021.
R. M. Amin, R. F. Envelope, and B. Wicaksono, “Handling COVID-19 from a Collaborative Governance Perspective in Pekanbaru City,†vol. 13, pp. 1–13, 2021.
P. K. Pekanbaru, “Wali Kota Aktifkan Seluruh Pos PPKM di Kota Pekanbaru,†2021. .
D. Suhardi, S. P. Insinyur, U. M. Malang, and K. Malang, “PREDIKSI DIABETES BERDASARKAN PENGUKURAN MAGE ( MEAN AMPLITUDE GLYCEMIC ) DENGAN,†pp. 48–59, 2021.
A. Purwanto and E. A. Darmadi, “Perbandingan Minat Siswa Smu Pada Metode Klasifikasi Menggunakan 5 Algoritma,†ikraith-informatika, vol. 2, no. 1, pp. 43–47, 2018.
S. Lalmuanawma, J. Hussain, and L. Chhakchhuak, “Applications of machine learning and artificial intelligence for Covid-19 (SARS-CoV-2) pandemic: A review,†Chaos, Solitons & Fractals, p. 110059, 2020.
M. H. D. M. Ribeiro, R. G. da Silva, V. C. Mariani, and L. dos Santos Coelho, “Short-term forecasting COVID-19 cumulative confirmed cases: Perspectives for Brazil,†Chaos, Solitons & Fractals, vol. 135, p. 109853, 2020.
Z. Malki, E.-S. Atlam, A. E. Hassanien, G. Dagnew, M. A. Elhosseini, and I. Gad, “Association between weather data and COVID-19 pandemic predicting mortality rate: Machine learning approaches,†Chaos, Solitons & Fractals, vol. 138, p. 110137, 2020.
L. K. Shrivastav and S. K. Jha, “A gradient boosting machine learning approach in modeling the impact of temperature and humidity on the transmission rate of COVID-19 in India,†Appl. Intell., vol. 51, no. 5, pp. 2727–2739, 2021.
M. J. Maloney, N. J. Rhodes, and P. R. Yarnold, “Mask mandates can limit COVID spread: Quantitative assessment of month-over-month effectiveness of governmental policies in reducing the number of new COVID-19 cases in 37 US States and the District of Columbia,†medRxiv, 2020.
M. L. Feb, “Analysis of the Effectiveness of Face-Coverings on the Death Rate of COVID-19 Using Machine Learning,†pp. 1–24, 2021.
S. Widaningsih, “Perbandingan Metode Data Mining Untuk Prediksi Nilai Dan Waktu Kelulusan Mahasiswa Prodi Teknik Informatika Dengan Algoritma C4,5, Naïve Bayes, Knn Dan Svm,†J. Tekno Insentif, vol. 13, no. 1, pp. 16–25, 2019, doi: 10.36787/jti.v13i1.78.
B. S. Prakoso, D. Rosiyadi, D. Aridarma, H. S. Utama, F. Fauzi, and M. A. N. Qhomar, “Optimalisasi Klasifikasi Berita Menggunakan Feature Information Gain Untuk Algoritma Naive Bayes Terhubung Random Forest,†J. Pilar Nusa Mandiri, vol. 15, no. 2, pp. 211–218, 2019, doi: 10.33480/pilar.v15i2.684.
F. Tempola, M. Muhammad, and A. Khairan, “Perbandingan Klasifikasi Antara KNN dan Naive Bayes pada Penentuan Status Gunung Berapi dengan K-Fold Cross Validation,†J. Teknol. Inf. dan Ilmu Komput., vol. 5, no. 5, p. 577, 2018, doi: 10.25126/jtiik.201855983.
R. Hayami and I. Gunawan, “Klasifikasi Jamur Menggunakan Algoritma Naïve Bayes,†J. CoSciTech (Computer Sci. Inf. Technol., vol. 3, no. 1, pp. 28–33, 2022.
R. Setiawan and A. Triayudi, “Klasifikasi Status Gizi Balita Menggunakan Naïve Bayes dan K- Nearest Neighbor Berbasis Web,†vol. 6, no. 2, pp. 777–785, 2022, doi: 10.30865/mib.v6i2.3566.
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).
