Density-Based Spatial Clustering, K-Means and Frequent Pattern Growth for Clustering and Association of Malay Cultural Text Data in Indonesia
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Mustakim Mustakim
*
Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru,
Indonesia
- Febi Nur Salisah Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia (*) Corresponding Author
Abstract
Several studies state the need to develop information technology to disseminate information related to culture in Indonesia. There are many similar studies but they still have weaknesses, one of which is that they do not use machine learning and intelligent computing. This research answers the challenges of previous researchers, namely developing machine learning-based learning applications using the Density-Based Spatial Clustering of Application Noise (DBSCAN) and Frequent Pattern Growth (FP-Growth) algorithms. The results of the modeling of the two algorithms are deemed to still require improvement in the future, as it is proven that DBSCAN does not yet have optimal validity. So in this research, one of the comparison algorithms is used, namely K-Means Clustering, with a better evaluation than DBSCAN. The modeling results were implemented into mobile programming as a cultural learning application in Indonesia, especially Riau Malay Culture, the black box testing results had an accuracy of 100% and the User Acceptance Test (UAT) was 86%. Thus, it is concluded that this application can be used effectively and efficiently for general users.
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References
M. P. Sari and A. R. Hidayatulloh, “Pengenalan Kebudayaan Indonesia melalui Fotografi pada Akun Instagram ‘KWODOKIJO,’” Edsence J. Pendidik. Multimed., vol. 2, no. 2, pp. 111–120, 2020, doi: 10.17509/edsence.v2i2.27460.
N. D. B. Setyaningrum, “Budaya Lokal Di Era Global,” Ekspresi Seni, vol. 20, no. 2, p. 102, 2018, doi: 10.26887/ekse.v20i2.392.
Y. Yulisman and S. Serdiansah, “Aplikasi Pengenalan Kebudayaan Provinsi Riau Berbasis Android,” J. Teknol. Sist. Inf. dan Apl., vol. 2, no. 3, p. 79, 2019, doi: 10.32493/jtsi.v2i3.3294.
G. Riau, “Peraturan Daerah Provinsi Riau Nomor 9 Tahun 2015 Tentang Pelestarian Kebudayaan Melayu Riau,” pp. 1–28, 2015.
2017 UU Nomor 5 Tahun, “UU 5 tahun 2017 tentang Pemajuan Kebudayaan,” 2017, UU Nomor 5 Tahun, p. 57, 2017.
H. Wazni; Zulfa, “Relasi Kuasa Negara dan Adat dalam Mengembangkan Pariwisata Budaya Melayu Kabupaten Siak,” J. PolGov, vol. 3, no. 2, pp. 361–392, 2021.
M. J. Saputra and N. Hamdi, “Rancang Bangun Aplikasi Sejarah Kebudayaan Aceh Berbasis Android Studi Kasus Dinas Kebudayaan Dan Pariwisata Aceh,” vol. 5, no. 2, pp. 147–158, 2019.
A. R. Dayat and Liza Angriani, “Perancangan Aplikasi Pengenalan Kebudayaan Khas Papua Berbasis Augmented Reality,” JISKa, vol. 5, no. 1, pp. 42–55, 2020.
A. Suryadi, N. M. Rosa, and E. Subandriyo, “Perancangan Aplikasi Pengenalan Suku Dan Kebudayaan Berbasis Android,” Semin. Nas. Ris. dan Teknol. (SEMNAS RISTEK), vol. 4, no. 1, pp. 186–192, 2020.
P. Nerurkar, A. Shirke, M. Chandane, and S. Bhirud, “Empirical Analysis of Data Clustering Algorithms,” Procedia Comput. Sci., vol. 125, pp. 770–779, 2018, doi: 10.1016/j.procs.2017.12.099.
A. C. Benabdellah, A. Benghabrit, and I. Bouhaddou, “A survey of clustering algorithms for an industrial context,” Procedia Comput. Sci., vol. 148, pp. 291–302, 2019, doi: 10.1016/j.procs.2019.01.022.
S. Reddy et al., “Use and validation of text mining and cluster algorithms to derive insights from Corona Virus Disease-2019 (COVID-19) medical literature,” Comput. Methods Programs Biomed. Updat., vol. 1, no. April, p. 100010, 2021, doi: 10.1016/j.cmpbup.2021.100010.
S. Jun, S. S. Park, and D. S. Jang, “Document clustering method using dimension reduction and support vector clustering to overcome sparseness,” Expert Syst. Appl., vol. 41, no. 7, pp. 3204–3212, 2014, doi: 10.1016/j.eswa.2013.11.018.
J. Rejito, A. Atthariq, and A. S. Abdullah, “Application of text mining employing k-means algorithms for clustering tweets of Tokopedia,” J. Phys. Conf. Ser., vol. 1722, no. 1, 2021, doi: 10.1088/1742-6596/1722/1/012019.
Mustakim, M. Z. Fauzi, Mustafa, A. Abdullah, and Rohayati, “Clustering of Public Opinion on Natural Disasters in Indonesia Using DBSCAN and K-Medoids Algorithms,” J. Phys. Conf. Ser., vol. 1783, no. 1, p. 012016, 2021, doi: 10.1088/1742-6596/1783/1/012016.
Mustakim et al., “Unsupervised learning as a data sharing model in the fp-growth algorithm in determining the best transaction data pattern,” J. Theor. Appl. Inf. Technol., vol. 99, no. 11, pp. 2679–2689, 2021.
M. T. Furqon and L. Muflikhah, “Clustering the potential risk of tsunami using Density-Based Spatial clustering of application with noise (DBSCAN),” J. Environ. Eng. Sustain. Technol., vol. 3, no. 1, pp. 1–8, 2016.
Mustakim, E. Rahmi, M. R. Mundzir, S. T. Rizaldi, Okfalisa, and I. Maita, “Comparison of DBSCAN and PCA-DBSCAN Algorithm for Grouping Earthquake Area,” in 2021 International Congress of Advanced Technology and Engineering, ICOTEN 2021, 2021, pp. 0–4, doi: 10.1109/ICOTEN52080.2021.9493497.
A. Fauzan, A. Novianti, R. R. M. A. Ramadhani, and M. A. S. Adhiwibawa, “Analysis of Hotels Spatial Clustering in Bali: Density-Based Spatial Clustering of Application Noise (DBSCAN) Algorithm Approach,” EKSAKTA J. Sci. Data Anal., pp. 25–38, 2022, doi: 10.20885/eksakta.vol3.iss1.art4.
S. N. Yanti and E. Budiyati, “Aplikasi Pengenalan Budaya Provinsi Bagian Wita Di Indonesia Berbasis Android,” pp. 1–11, 2020.
S. S. Tafui, “Aplikasi Pengenalan Kebudayaan Kabupaten Belu Berbasi Android,” J. Mhs. Tek. Inform., vol. 1, no. 2, pp. 61–66, 2017.
S. Hozeng and A. Syam, “Aplikasi Pengenalan Kebudayaan Khas Toraja ( UKIRAN ) Berbasis Android,” Semnasteknomedia, vol. 5, no. 1, pp. 55–60, 2017.
A. Atan, Z. Indra, and A. Febtriko, “Perancangan Game Berbasis Android Untuk Memperkenalkan Adat Melayu Riau,” Rabit J. Teknol. dan Sist. Inf. Univrab, vol. 5, no. 1, pp. 54–66, 2020, doi: 10.36341/rabit.v5i1.963.
N. Metafani and D. Djamaludin, “Aplikasi Pengenalan Cagar Budaya Tangerang Berbasis Android Di Dinas Kebudayaan Dan Pariwisata Kota Tangerang,” JIMTEK J. Ilm. Mhs. Fak. Tek., vol. 1, no. 1, pp. 66–73, 2020.
Mustakim et al., “DBSCAN algorithm: Twitter text clustering of trend topic pilkada pekanbaru,” J. Phys. Conf. Ser., vol. 1363, no. 1, 2019, doi: 10.1088/1742-6596/1363/1/012001.
K. N. S. Behara, A. Bhaskar, and E. Chung, “A DBSCAN-based framework to mine travel patterns from origin-destination matrices: Proof-of-concept on proxy static OD from Brisbane,” Transp. Res. Part C Emerg. Technol., vol. 131, no. August 2020, p. 103370, 2021, doi: 10.1016/j.trc.2021.103370.
Q. Zhu, X. Tang, and A. Elahi, “Application of the novel harmony search optimization algorithm for DBSCAN clustering,” Expert Syst. Appl., vol. 178, no. April, p. 115054, 2021, doi: 10.1016/j.eswa.2021.115054.
R. Novita, Mustakim, and F. N. Salisah, “Determination of the relationship pattern of association topic on Al-Qur’an using FP-Growth Algorithms,” IOP Conf. Ser. Mater. Sci. Eng., vol. 1088, no. 1, p. 012020, 2021, doi: 10.1088/1757-899x/1088/1/012020.
L. Shabtay, P. Fournier-Viger, R. Yaari, and I. Dattner, “A guided FP-Growth algorithm for mining multitude-targeted item-sets and class association rules in imbalanced data,” Inf. Sci. (Ny)., vol. 553, pp. 353–375, 2021, doi: https://doi.org/10.1016/j.ins.2020.10.020.
J. Wang and Z. Cheng, “FP-Growth based Regular Behaviors Auditing in Electric Management Information System,” Procedia Comput. Sci., vol. 139, pp. 275–279, 2018, doi: 10.1016/j.procs.2018.10.268.
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