Perancangan sistem pendukung keputusan dalam pengalokasian dana bantuan sosial di kabupaten pinrang dengan menggunakan metode AHP
Abstract
Penelitian ini bertujuan untuk membuat sistem yang dapat membantu pemerintah kabupaten Pinrang dalam menentukan penerima bantuan sosial yang layak.Sistem yang digunakan adalah sistem pendukung keputusan dengan menggunakan metode AHP berbasis website.Dalam Sistem ini terdapat 6 Kriteria-kriteria yang dapat membantu pemerintah untuk dapat memperhitungkan manfaat dan resiko dari setiap keputusannya, Kriteria-kriteria tersebut dianalisis menggunakan metode AHP menggunakan berbasis website.Penelitian ini berusaha untuk membentuk suatu sistem pendukung keputusan yang diharapkan dapat membantu pengambil keputusan untuk melaksanakan pertimbangannya. Sistem yang dibangun akan memudahkan pengambil keputusan untuk membuat, menghapus, ataupun mengedit model-model penilaian yang ada. Dengan mengetahui model yang paling tepat untuk masing-masing kelompok ataupun usulan, diharapkan pengalokasian dana Bantuan sosial usaha khususnya di Kabupaten Pinrang Propinsi Sulawesi Selatan dapat diperoleh oleh masyarakat dan wilayah yang benar- benar membutuhkannya
Downloads
References
M. M. Baharuddin, T. Hasanuddin, and H. Azis, “Analisis Performa Metode K-Nearest Neighbor untuk Identifikasi Jenis Kaca,” Ilk. J. Ilm., vol. 11, no. 28, pp. 269–274, 2019.
A. Fitria and H. Azis, “Analisis Kinerja Sistem Klasifikasi Skripsi menggunakan Metode Naïve Bayes Classifier,” Pros. Semin. Nas. Ilmu Komput. dan Teknol. Inf., vol. 3, no. 2, pp. 102–106, 2018.
A. A. Karim, H. Azis, and Y. Salim, “Kinerja Metode C4.5 dalam Penyaluran Bantuan Dana Bencana 1,” Pros. Semin. Nas. Ilmu Komput. dan Teknol. Inf., vol. 3, no. 2, pp. 84–87, 2018.
L. Nurhayati and H. Azis, “Perancangan Sistem Pendukung Keputusan Untuk Proses Kenaikan Jabatan Struktural Pada Biro Kepegawaian,” Semin. Nas. Teknol. Inf. dan Multimed., pp. 6–7, 2016.
H. Azis, R. D. Mallongi, D. Lantara, and Y. Salim, “Comparison of Floyd-Warshall Algorithm and Greedy Algorithm in Determining the Shortest Route,” Proc. - 2nd East Indones. Conf. Comput. Inf. Technol. Internet Things Ind. EIConCIT 2018, pp. 294–298, 2018.
N. Fadhillah, Huzain Azis, and D. Lantara, “Validasi Pencarian Kata Kunci Menggunakan Algoritma Levenshtein Distance Berdasarkan Metode Approximate String Matching,” Pros. Semin. Nas. Ilmu Komput. dan Teknol. Inf., vol. 3, no. 2, pp. 3–7, 2018.
S. Chugh, K. Arivu Selvan, and R. K. Nadesh, “Prediction of heart disease using apache spark analysing decision trees and gradient boosting algorithm,” IOP Conf. Ser. Mater. Sci. Eng., vol. 263, no. 4, pp. 0–10, 2017.
M. Lestari, “Penerapan Algoritma Klasifikasi Nearest Neighbor (K-NN) Untuk Mendeteksi Penyakit Jantung,” Fakt. Exacta, vol. 7, no. September 2010, pp. 366–371, 2014.
V. Chaurasia, “Early Prediction of Heart Diseases Using Data Mining,” Caribb. J. Sci. Technol., vol. 1, no. December, pp. 208–217, 2013.
Rosmasari et al., “Usability Study of Student Academic Portal from a User’s Perspective,” Proc. - 2nd East Indones. Conf. Comput. Inf. Technol. Internet Things Ind. EIConCIT 2018, pp. 108–113, 2018.
Hasran, “Klasifikasi Penyakit Jantung Menggunakan Metode K-Nearest Neighbor,” Indones. J. Data Sci., vol. 1, no. 1, pp. 1–4, 2020
A. Tharwat, “Classification assessment methods,” Appl. Comput. Informatics, 2018, doi: 10.1016/j.aci.2018.08.003.
P. A. Flach and M. Kull, “Precision-Recall-Gain curves: PR analysis done right,” Adv. Neural Inf. Process. Syst., vol. 2015-Janua, pp. 838–846, 2015.
L. Nurhayati and H. Azis, “Perancangan Sistem Pendukung Keputusan Untuk Proses Kenaikan Jabatan Struktural Pada Biro Kepegawaian,” Semin. Nas. Teknol. Inf. dan Multimed., pp. 6–7, 2016.
J. D. Kelleher, B. Mac Namee, and A. D. Arcy, Fundamentals of Machine Learning For Predictive Data Analytics Algorithms, Worked Examples, and Case Studies. London: The MIT Press, 2015.
K. H. Brodersen, C. S. Ong, K. E. Stephan, and J. M. Buhmann, “The balanced accuracy and its posterior distribution,” Proc. - Int. Conf. Pattern Recognit., pp. 3121–3124, 2010, doi: 10.1109/ICPR.2010.764.
Copyright (c) 2020 Indonesian Journal of Data and Science
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
License and Copyright Agreement
In submitting the manuscript to the journal, the authors certify that:
- They are authorized by their co-authors to enter into these arrangements.
- The work described has not been formally published before, except in the form of an abstract or as part of a published lecture, review, thesis, or overlay journal.
- The work is not under consideration for publication elsewhere.
- The work has been approved by all the author(s) and by the responsible authorities – tacitly or explicitly – of the institutes where the work has been carried out.
- They secure the right to reproduce any material that has already been published or copyrighted elsewhere.
- They agree to the following license and copyright agreement.
Copyright
Authors who publish with Indonesian Journal of Data and Science agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. (CC BY-NC 4.0) that allows others to share the work with an acknowledgement 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 acknowledgement 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.