Optimasi Rekomendasi Sustainable Development Goals (SDGs) di Indonesia menggunakan Content-Based Filtering dan Algoritma Machine Learning
Abstract
Abstrak−Lahirnya program tentang Tujuan Pembangunan Berkelanjutan atau Sustainable Development Goals (SDGs) pada tahun 2015 membuat masyarakat di semua negara mulai memandang penting pembangunan berkelanjutan untuk diimplementasikan. Indonesia, sebagai bagian dari komunitas global, juga telah mengadopsi SDGs ini sebagai kerangka kerja dalam upaya mencapai Indonesia Emas 2045. Dengan visi ini, Indonesia bercita-cita menjadi negara maju yang berdaulat, adil, dan makmur tepat pada peringatan 100 tahun kemerdekaannya. Untuk mencapai tujuan secara efektif, penting untuk menerapkan sistem rekomendasi berbasis Artificial Intelligence (AI) yang mempertimbangkan tantangan sosial, ekonomi, dan lingkungan hidup yang dihadapi oleh negara Indonesia di masa mendatang. Content-Based Filtering (CBF) adalah teknik yang populer untuk membangun sistem tersebut. Penelitian ini membahas teknik untuk optimasi CBF menggunakan beberapa algoritma machine learning tradisional yaitu SVM, KNN, DT dan algoritma Deep Learning yaitu MLP. Teknik pengambilan sample dan penyetelan hiperparameter juga diperhatikan dalam penelitian ini. Algoritma Deep Learning MLP menghasilkan akurasi tertinggi yaitu 84%.
Downloads
References
A. Slamet Rusydiana and M. Handi Khalifah, “Islamic Social Instruments and Sustainable Development Goals (SDGs) Framework Islamic Social Instruments and Sustainable Development Goals (SDGs) Framework. Management and Sustainability. 2.2.. Rusydiana & Khalifah Islamic Social Instruments and Sustainable Development Goals (SDGs) Framework.” [Online]. Available: http://journals.smartinsight.id/index.php/MS
L. Rosidah and P. Dellia, “Library Book Recommendation System Using Content-Based Filtering,” Internet of Things and Artificial Intelligence Journal, vol. 4, no. 1, pp. 42–65, Feb. 2024, doi: 10.31763/iota.v4i1.693.
J. Leander and A. Wicaksana, “Optimizing a Personalized Movie Recommendation System with Support Vector Machine and Content-Based Filtering,” Journal of System and Management Sciences, vol. 14, no. 1, pp. 490–501, 2024, doi: 10.33168/JSMS.2024.0128.
M. Zhang, “Applications of Deep Learning in News Text Classification,” Sci Program, vol. 2021, 2021, doi: 10.1155/2021/6095354.
V. Rolanda, T. S. Gunawan, and Wanayumini, “Content-Based Filtering Recommendation System Using Categories Search Engine,” International Journal of Research in Vocational Studies (IJRVOCAS), vol. 2, no. 4, pp. 120–125, Jan. 2023, doi: 10.53893/ijrvocas.v2i4.177.
A. Baroqah Pohan and A. Kurniasih, “Journal of Artificial Intelligence and Engineering Applications Optimization of Classification Algorithm with GridSearchCV and Hyperparameter Tuning for Sentiment Analysis of the Nusantara Capital City,” 2024. [Online]. Available: https://ioinformatic.org/
Z. Alhaq, A. Mustopa, S. Mulyatun, and J. D. Santoso, “PENERAPAN METODE SUPPORT VECTOR MACHINE UNTUK ANALISIS SENTIMEN PENGGUNA TWITTER,” Journal of Information System Management (JOISM), vol. 3, no. 2, pp. 44–49, Jul. 2021, doi: 10.24076/joism.2021v3i2.558.
M. Ibrohim and I. Budi, “Multi-label Hate Speech and Abusive Language Detection in Indonesian Twitter,” Jul. 2019, pp. 46–57. doi: 10.18653/v1/W19-3506.
F. Tala, “A Study of Stemming Effects on Information Retrieval in Bahasa Indonesia,” Jul. 2003.
P. M. Prihatini, “IMPLEMENTASI EKSTRAKSI FITUR PADA PENGOLAHAN DOKUMEN BERBAHASA INDONESIA The Implementation of Extraction Feature on Indonesian Documents’ Processing,” 2016.
M. Sokolova and G. Lapalme, “A systematic analysis of performance measures for classification tasks,” Inf Process Manag, vol. 45, no. 4, pp. 427–437, 2009, doi: https://doi.org/10.1016/j.ipm.2009.03.002.
D. H. Wahid and A. Sn, “Peringkasan Sentimen Esktraktif di Twitter Menggunakan Hybrid TF-IDF dan Cosine Similarity,” Indonesian Journal of Computing and Cybernetics Systems, vol. 10, pp. 207–218, 2016, [Online]. Available: https://api.semanticscholar.org/CorpusID:63834394
V. Rattan, R. Mittal, J. Singh, and V. Malik, “Analyzing the Application of SMOTE on Machine Learning Classifiers,” Apr. 2021, pp. 692–695. doi: 10.1109/ESCI50559.2021.9396962.
P. Cunningham and S. J. Delany, “k-Nearest Neighbour Classifiers: 2nd Edition (with Python examples),” Apr. 2020, doi: 10.1145/3459665.
E. Dritsas, N. Fazakis, O. Kocsis, K. Moustakas, and N. Fakotakis, “Optimal Team Pairing of Elder Office Employees with Machine Learning on Synthetic Data,” Apr. 2021. doi: 10.1109/IISA52424.2021.9555511.
R. Awad Mariette and Khanna, “Support Vector Machines for Classification,” in Efficient Learning Machines: Theories, Concepts, and Applications for Engineers and System Designers, Berkeley, CA: Apress, 2015, pp. 39–66. doi: 10.1007/978-1-4302-5990-9_3.
A. K. Narayan Vipul and Daniel, “RBCHS: Region-Based Cluster Head Selection Protocol in Wireless Sensor Network,” in Proceedings of Integrated Intelligence Enable Networks and Computing, V. B. and B. V. and C. R. G. Singh Mer Krishan Kant and Semwal, Ed., Singapore: Springer Singapore, 2021, pp. 863–869.
D. D. Lewis, “Naive (Bayes) at forty: The independence assumption in information retrieval,” in Machine Learning: ECML-98, C. Nédellec and C. Rouveirol, Eds., Berlin, Heidelberg: Springer Berlin Heidelberg, 1998, pp. 4–15.
C. M. Bhatt, P. Patel, T. Ghetia, and P. L. Mazzeo, “Effective Heart Disease Prediction Using Machine Learning Techniques,” Algorithms, vol. 16, no. 2, Feb. 2023, doi: 10.3390/a16020088.
M. D. Mohanty and M. N. Mohanty, “Chapter 5 - Verbal sentiment analysis and detection using recurrent neural network,” in Advanced Data Mining Tools and Methods for Social Computing, S. De, S. Dey, S. Bhattacharyya, and S. Bhatia, Eds., in Hybrid Computational Intelligence for Pattern Analysis. , Academic Press, 2022, pp. 85–106. doi: https://doi.org/10.1016/B978-0-32-385708-6.00012-6.
T. Menzies, E. Kocagüneli, L. Minku, F. Peters, and B. Turhan, “Chapter 24 - Using Goals in Model-Based Reasoning,” in Sharing Data and Models in Software Engineering, T. Menzies, E. Kocagüneli, L. Minku, F. Peters, and B. Turhan, Eds., Boston: Morgan Kaufmann, 2015, pp. 321–353. doi: https://doi.org/10.1016/B978-0-12-417295-1.00024-2.
P. Atanasova, “A Diagnostic Study of Explainability Techniques for Text Classification,” in Accountable and Explainable Methods for Complex Reasoning over Text, Cham: Springer Nature Switzerland, 2020, pp. 155–187. doi: 10.1007/978-3-031-51518-7_7.
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Optimasi Rekomendasi Sustainable Development Goals (SDGs) di Indonesia menggunakan Content-Based Filtering dan Algoritma Machine Learning
Pages: 1045-1058
Copyright (c) 2024 Alfajri Hulvi, Kusrini Kusrini
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).