Data Mining Applications for Violence Pattern Analysis with FP-Growth Algorithm
DOI:
https://doi.org/10.33633/jais.v6i1.4444Abstract
Violence is a crime that is one of the problems the principal experienced by each country. Violence can be interpreted as a behavior that causes harm to someone. According to the results of DP3AKB research in Central Java Province in 2017, there are less many than 200 people in Central Java province experienced acts of violence. By because of the many acts of violence that occur in various forms of violence, it requires definite information about the form of violence that happens most often, in obtaining that information Data mining techniques are needed by using the FP-Growth algorithm. The application of the FP-Growth algorithm to produce form association patterns violence. Hardness data is 420 data, the best 7 rules have been obtained with min value support 50% and min value support 60%. On the best rule results have given a recommendation (solution) so that the DP3AKB can handle the problem of violence well and on target.References
T. Santoso, Kekuasaan Dan Kekerasan, vol. 14, no. 4. 2001.
F. Gorunescu, Data Mining - Concepts, Models and Techniques, vol. 12. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011.
I. H. Witten, E. Frank, M. A. Hall, and C. J. Pal, Data Mining - Practical Machine Learning Tools and Techniques (Fourth Edition), Fourth Edi., vol. 53, no. 9. Morgan Koufmann & Elsevier, 2017.
M. Bramer, Principles of data mining fourth edition, vol. 30, no. 7. 2020.
A. Luthfiarta, J. Zeniarja, E. Faisal, and W. Wicaksono, “Prediction on Deposit Subscription of Customer based on Bank Telemarketing using Decision Tree with Entropy Comparison,” J. Appl. Intell. Syst., vol. 4, no. 2, pp. 57–66, 2019.
J. Zeniarja, K. Widia, and R. R. Sani, “Penerapan Algoritma Naive Bayes dan Forward Selection dalam Pengklasifikasian Status Gizi Stunting pada Puskesmas Pandanaran Semarang,” JOINS (Journal Inf. Syst., vol. 5, no. 1, pp. 1–9, 2020.
A. Luthfiarta et al., “Classification of Governer’s Public Report from SMS LaporGub Using Naive Bayes Classifier Method,” Proc. - 2018 Int. Semin. Appl. Technol. Inf. Commun. Creat. Technol. Hum. Life, iSemantic 2018, pp. 214–218, 2018.
I. Purnamasari, F. Handayanna, E. Arisawati, L. S. Dewi, E. G. Sihombing, and Rinawati, “The Determination Analysis of Telecommunications Customers Potential Cross-Selling with Classification Naive Bayes and C4.5,” J. Phys. Conf. Ser., vol. 1641, no. 1, 2020.
B. Changkakati and C. Das, “Data mining techniques in hr analytics: A review of domain specific concepts and technicalities,” Int. J. Sci. Technol. Res., vol. 9, no. 3, pp. 4358–4362, 2020.
O. Stit, J. Riffi, A. Yahyaouy, and H. Tairi, “Comparative Study of Different Association Rule Methods,” in Colloquium in Information Science and Technology, CIST, 2018, vol. 2018-Octob, pp. 323–327.
W. N. Ismail, M. M. Hassan, and H. A. Alsalamah, “Context-Enriched Regular Human Behavioral Pattern Detection from Body Sensors Data,” IEEE Access, vol. 7, no. 1, pp. 33834–33850, 2019.
T. P. Hong, C. Y. Lin, W. M. Huang, K. S. M. Li, L. S. L. Wang, and J. C. W. Lin, “Using Tree Structure to Mine High Temporal Fuzzy Utility Itemsets,” IEEE Access, vol. 8, pp. 153692–153706, 2020.
P. Naresh and R. Suguna, “Association rule mining algorithms on large and small datasets: A comparative study,” in 2019 International Conference on Intelligent Computing and Control Systems, ICCS 2019, 2019, pp. 587–592.
D. Winarti et al., “Analisis Data Mining Dengan Algoritma Fp-Growth Dalam Mendukung Strategi Promosi,” Simtika, vol. 1, no. 1, pp. 27–31, 2018.
G. Gunadi and D. I. Sensuse, “Penerapan Metode Data Mining Market Basket Analysis Terhadap Data Penjualan Produk Buku Dengan Menggunakan Algoritma Apriori Dan Frequent Pattern Growth ( Fp-Growth ) :,” Telematika, vol. 4, no. 1, pp. 118–132, 2012.
E. Faisal, J. Zeniarja, and D. A. N. Sa’adah, “Pola Beli Konsumen menggunakan Algoritma FP-Growth untuk Rekomendasi Promosi penjualan pada Batik Nadya Pekalongan,” in SeNTIK - STMIK JAKARTA STI&K, 2017.
M. Hossain, A. H. M. S. Sattar, and M. K. Paul, “Market basket analysis using apriori and FP growth algorithm,” in 2019 22nd International Conference on Computer and Information Technology, ICCIT 2019, 2019.
Islamiyah, P. L. Ginting, N. Dengen, and M. Taruk, “Comparison of Priori and FP-Growth Algorithms in Determining Association Rules,” in ICEEIE 2019 - International Conference on Electrical, Electronics and Information Engineering: Emerging Innovative Technology for Sustainable Future, 2019, pp. 320–323.
M. S. Kaysar, M. A. Bin Khaled, M. Hasan, and M. I. Khan, “Word Sense Disambiguation of Bengali Words using FP-Growth Algorithm,” in 2nd International Conference on Electrical, Computer and Communication Engineering, ECCE 2019, 2019, pp. 1–5.
R. Fitria, W. Nengsih, and D. H. Qudsi, “Implementasi Algoritma FP-Growth Dalam Penentuan Pola Hubungan Kecelakaan Lalu Lintas,” J. Sist. Inf., vol. 13, no. 2, p. 118, 2017.
I. Astrina, M. Z. Arifin, and U. Pujianto, “Penerapan Algoritma FP-Growth dalam Penentuan Pola Pembelian Konsumen pada Kain Tenun Medali Mas,” Matrix J. Manaj. Teknol. dan Inform., vol. 9, no. 1, pp. 32–40, 2019.
Fadlina, “Data Mining Untuk Analisa Tingkat Kejahatan Jalanan Dengan Algoritma Association Rule Metode Apriori,” Inf. dan Teknol. Ilm., vol. 3, no. 1, pp. 144–154, 2014.
D. Y. Hardiyanti, H. Novianti, and A. Rifai, “Penerapan Algoritma Fp-Growth Pada Sistem Informasi Perpustakaan,” J. Comput. Eng. Syst. Sci., vol. 3, no. 1, pp. 75–77, 2018.
Downloads
Published
Issue
Section
License
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution 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 (See The Effect of Open Access).
