Penerapan Metode Naïve Bayes Untuk Mengetahui Kualitas Air Di Jakarta
DOI:
https://doi.org/10.22441/fifo.2021.v13i2.010Keywords:
nave bayes, quality, water, JakartaAbstract
Water is a chemical compound that is needed for the survival of living things on earth. The largest area on planet earth is water which covers almost 71% of the area on earth. Water is also a very important substance on earth that is needed by all living things ranging from plants, animals and humans. It is necessary to monitor and treat the environment around the water source so that it can produce clean water quality. In this study, the Naïve Bayes algorithm is applied to determine water quality in the Jakarta area and produces an accuracy rate of water classification results in the DKI Jakarta area of 50.6%.
Downloads
References
Alfa Saleh. 2015. Implementasi metode klasifikasi naive bayes dalam memprediksi besarnya penggunaan listrik rumah tangga. Creative Information Technology Journal, 2(3):207–217
Ali Masduqi, Eddy S Soedjono, Noor Endah, and Wahyono Hadi. 2009. Prediction of rural water supply systems sustainability using a mathematical model. Jurnal Purifikasi, 10(2):155–164
Alieja Muhammad Putrada, Maman Abdurohman, and Aji Gautama Putrada. 2018. Increasing smoke classifier accuracy using naive bayes method on internet of things. Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, 4(1):19–26
Budi Santosa. Data mining teknik pemanfaatan data untuk keperluan bisnis. Yogyakarta: Graha Ilmu, 978(979):756, 2007.
Burhan Alfironi Muktamar, Noor Akhmad Setiawan, and Teguh Bharata Adji. 2015. Analisis perbandingan tingkat akurasi algoritma naïve bayes classifier dengan correlated-naïve bayes classifier. SEMNASTEKNOMEDIA ONLINE, 3(1):2–1
Chongsheng Zhang, Changchang Liu, Xiangliang Zhang, and George Almpanidis. 2017. An up-to-date comparison of state-of-the-art classification algorithms. Expert Systems with Applications, 82:128–150
Daniela Xhemali, Chris J Hinde, and Roger G Stone. 2009. Naïve bayes vs. decision trees vs. neural networks in the classification of training web pages
Dewan Md Farid and Mohammad Zahidur Rahman. 2010. Anomaly network intrusion detection based on improved self adaptive bayesian algorithm. JCP, 5(1):23–31
Hartatik Hartatik, Andri Syafrianto, and Wiwi Widayani. 2018Perbandingan klasifikasi pecemaran air sungai dengan metode backpropagation dan naïve bayes. Data Manajemen dan Teknologi Informasi (DASI), 18(4):67–71
Hefni Effendi.2003. Telaah kualitas air, bagi pengelolaan sumber daya dan lingkungan perairan. Kanisius
Kusnawi. 2007. “Pengantar Solusi Data Mining”. STMIK AMIKOM, Yogyakarta.
Kustiyahningsih Yeni, dkk. 2013. “Sistem Pendukung Keputusan untuk Menentukan Jurusan pada Siswa SMA Menggunakan Metode KNN dan SMART”. Universitas Trunojoyo, Madura.
Kusumadewi, Sri. 2009. “Klasifikasi Status Gizi Menggunakan Naive Bayesian Classification”. Universitas Islam Indonesia, Yogyakarta.
Lestiana, Mila. 2015. “Perbandingan Algoritma Decision Tree (C4.5) dan Naive Bayes pada Data Mining untuk Identifikasi Tumbuh Kembang Anak Balita”. Universitas Muhammadiyah, Surakarta.
Peraturan Menteri Republik Indonesia nomor 492 tahun 2010 tentang Persyaratan Kualitas Air minum. Jakarta : Kementrian Kesehatan Republik Indonesia. Peraturan Pemerintah Republik Indonesia nomor 82 tahun 2001 tentang Pengelolaan Kualitas Air dan Pengendalian Pencemaran Air. Jakarta.
Mohamad Sakizadeh. 2015. Assessment the performance of classification methods in water quality studies, a case study in karaj river. Environmental monitoring and assessment, 187(9):573
Pablo Bermejo, José A Gámez, and José M Puerta. 2014. Speeding up in cremental wrapper feature subset selection with naïve bayes classifier. Knowledge-Based Systems
Riyadh Arridha. 2018. Design and implementation of iot-big data analytic for smart environment monitoring system.
Santoso. 2016. “Perbandingan Metode KNearest Neighbor(K-NN) dan Learning Vector Quantization (LVQ) untuk Permasalahan Klasifikasi Tingkat Kemiskinan”. Institut Teknologi Sepuluh November, Surabaya.
Situmorang, Manihar. 2017. “Kimia Lingkungan”. Depok: PT RajaGrafindo
Tri Vulandari, Retno. 2017. “Data Mining Teori dan Aplikasi Rapidminer”. Surakarta.
Saeful Bachrein. 2015. Pengembangan daerah aliran sungai (das) cikapundung: Diagnostik wilayah. Jurnal Bina Praja: Journal of Home Affairs Governance, 4(4):227–236
Sritrusta Sukaridhoto, Dadet Pramadihanto, Muhammad Alif, Andrie Yuwono, Nobuo Funabiki, et al. 2015. A design of radio- controlled submarine modification for river water quality monitoring. In 2015 International Seminar on Intelligent Technology and Its Applications (ISITIA), pages 75–80. IEEE
Sritrusta Sukaridhoto, Rahardhita Widyatra Sudibyo, Widi Sarinastiti, Rizky Dharmawan, Atit Sasono, Ahmad Andika Saputra, and Shiori Sasaki. 2016. Design and development of a portable low-cost cots-based water quality monitoring system. In 2016 International Seminar on Intelligent Technology and Its Applications (ISITIA), pages 635–640. IEEE
Shumei Wang, Zhaoji Zhang, Zhilong Ye, Xiaojun Wang, Xiangyu Lin, and Shaohua Chen. 2013. Application of environmental internet of things on water quality management of urban scenic river. International Journal of Sustainable Development & World Ecology, 20(3):216–222
Xindong Wu and Vipin Kumar. 2009. The top ten algorithms in data mining. CRC press
Downloads
Published
How to Cite
Issue
Section
License
The copyright to this article is transferred to Universitas Mercu Buana (UMB) if and when the article is accepted for publication. The undersigned hereby transfers any and all rights in and to the paper including without limitation all copyrights to UMB. The undersigned hereby represents and warrants that the paper is original and that he/she is the author of the paper, except for material that is clearly identified as to its original source, with permission notices from the copyright owners where required. The undersigned represents that he/she has the power and authority to make and execute this assignment.
We declare that this paper has not been published in the same form elsewhere.
Furthermore, I/We hereby transfer the unlimited rights of publication of the above-mentioned paper as a whole to UMB. The copyright transfer covers the right to reproduce and distribute the article, including reprints, translations, photographic reproductions, microform, electronic form (offline, online) or any other reproductions of similar nature.
The corresponding author signs for and accepts responsibility for releasing this material on behalf of any and all co-authors. This agreement is to be signed by at least one of the authors who have obtained the assent of the co-author(s) where applicable. After submission of this agreement signed by the corresponding author, changes of authorship or in the order of the authors listed will not be accepted.
Retained Rights/Terms and Conditions
Although authors are permitted to re-use all or portions of the Work in other works, this does not include granting third-party requests for reprinting, republishing, or other types of re-use.
Our Articles are licensed under CC BY-NC
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
