KOMPARASI METODE K-NEAREST NEIGHBORS DAN LONG SHORT TERM MEMORY PADA KLASIFIKASI TERJEMAHAN AL-QUR’AN
Keywords:
Accuracy, F1-Score, K-Nearest Neighbors, Long Short Term Memory, Terjemahan Al-Qur’an
Abstract
Al-Qur’an merupakan kitab suci yang diturunkan untuk umat islam. Secara harfiah, Al-Qur'an berasal dari kata qara’a yang artinya membaca atau mengumpulkan. Namun untuk memahami terjemahan dari Al-Qur’an tidaklah mudah. Salah satu cara yang dapat dilakukan untuk memahami dan mempelajarinya adalah melakukan klasifikasi terhadap terjemahan ayat Al-Qur’an. Penelitian ini mengklasifikasikan terjemahan Al-Qur'an bahasa Indonesia ke enam kelas yang berbeda. Metode yang digunakan adalah K-Nearest Neighbor (KNN) dan Long Short Term Memory (LSTM) dan membandingkan kedua metode untuk mendapatkan hasil performa klasifikasi yang tertinggi. Hasil klasifikasi menunjukkan model LSTM menghasilkan performa klasifikasi yang lebih tinggi yaitu berupa rata-rata F1-Score sebesar 65% dan rata-rata accuracy 96% dibandingkan model KNN dengan nilai rata-rata F1-Score sebesar 55% dan rata-rata accuracy 93%.
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[2] R. Ananda Pane and M. Syahrul Mubarok, “Klasifikasi Multi-Label Pada Topik Ayat Al-Quran Terjemahan Bahasa Inggris Menggunakan Multinomial Naive Bayes,” e-Proceeding of Engineering, vol. 5, no. 1, pp. 1551–1555, 2018.
[3] M. R. Choirulfikri, K. M. Lhaksamana, and S. Al Faraby, “A Multi-Label Classification of Al-Quran Verses Using Ensemble Method and Naïve Bayes,” Building of Informatics, Technology and Science (BITS), vol. 3, no. 4, pp. 473–479, Mar. 2022, doi: 10.47065/bits.v3i4.1287.
[4] B. Arkok and A. M. Zeki, “Classification of Qur’anic topics based on imbalanced classification,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 22, no. 2, pp. 678–687, Apr. 2021, doi: 10.11591/ijeecs.v22.i2.pp678-687.
[5] T. H. Putrisanni, A. Adiwijaya, and S. Al Faraby, “Klasifikasi Ayat Al-Quran Terjemahan Bahasa Inggris Menggunakan K-Nearest Neighbor (Knn) Dan Information Gain,” KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer), vol. 3, no. 1, pp. 362–369, Nov. 2019, doi: 10.30865/komik.v3i1.1614.
[6] M. Fauzan, H. Junaedi, and E. Setyati, “Klasifikasi Al-Qur’an Terjemahan Bahasa Indonesia Dengan Menggunakan Algoritma Support Vector Machine,” KONVERGENSI, vol. 18, no. 2, pp. 42–49, 2022, doi: https://doi.org/10.30996/konv.v18i1.6912.
[7] C. P. Kirana, S. P. Fachri, and R. F. S. Nuraini, “Meningkatkan Akurasi Long-Short Term Memory (LSTM) pada Analisis Sentimen Vaksin Covid-19 di Twitter dengan Glove,” Jurnal Telematika, vol. 16, no. 2, pp. 85–90, 2021, doi: 10.61769/telematika.v16i2.400.
[8] E. H. Mohamed and W. H. El-Behaidy, “An Ensemble Multi-label Themes-Based Classification for Holy Qur’an Verses Using Word2Vec Embedding,” Arab J Sci Eng, vol. 46, no. 4, pp. 3519–3529, Apr. 2021, doi: 10.1007/s13369-020-05184-0.
[9] A. Abdullahi, N. A. Samsudin, M. H. A. Rahim, S. K. A. Khalid, and R. Efendi, “Multi-label classification approach for Quranic verses labeling,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 24, no. 1, pp. 484–490, Oct. 2021, doi: 10.11591/ijeecs.v24.i1.pp484-490.
[10] A. Adeleke, N. A. Samsudin, Z. A. Othman, and S. K. Ahmad Khalid, “A two-step feature selection method for quranic text classification,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 16, no. 2, pp. 730–736, 2019, doi: 10.11591/ijeecs.v16.i2.pp730-736.
[11] N. Sabri Elmitwally and A. Alsayat, “The Multi-Class Classification for the First Six Surats of the Holy Quran,” IJACSA) International Journal of Advanced Computer Science and Applications, vol. 11, no. 1, pp. 327–332, 2020, doi: 10.14569/IJACSA.2020.0110141.
[12] S. Shahriar and U. Tariq, “Classifying Maqams of Quranic Recitations using Deep Learning,” IEEE Access, vol. 9, pp. 117271–117281, 2021, doi: 10.1109/ACCESS.2021.3098415.
[13] A. Naas, S. Na’iema, H. Mulyo, and A. Widiastuti, “Klasifikasi penerima bantuan program rehabilitasi rumah tidak layak huni menggunakan algoritme K-Nearest Neighbor,” Jurnal Teknologi dan Sistem Komputer, vol. 10, no. 1, pp. 32–37, 2022, doi: 10.14710/jtsiskom.2022.14110.
[14] A. Hanafi, A. Adiwijaya, and W. Astuti, “Klasifikasi Multi Label pada Hadis Bukhari Terjemahan Bahasa Indonesia Menggunakan Mutual Information dan k-Nearest Neighbor,” Jurnal Sisfokom (Sistem Informasi dan Komputer), vol. 9, no. 3, pp. 357–364, Sep. 2020, doi: 10.32736/sisfokom.v9i3.980.
[15] N. Alvi Hasanah, Nanik Suciati, and Diana Purwitasari, “Pemantauan Perhatian Publik terhadap Pandemi COVID-19 melalui Klasifikasi Teks dengan Deep Learning,” Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 5, no. 1, pp. 193–202, Feb. 2021, doi: 10.29207/resti.v5i1.2927.
[16] M. Ihsan, B. S. Negara, and S. Agustian, “Metode LSTM (Long short term memory) untuk Klasifikasi Sentimen Vaksin Covid-19 pada Twitter,” Digital Zone: Jurnal Teknologi Informasi dan Komunikasi, vol. 13, no. 1, pp. 79–89, May 2022, doi: 10.31849/digitalzone.v13i1.9950.
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[18] A. Nikmatul Kasanah and U. Pujianto, “Penerapan Teknik SMOTE untuk Mengatasi Imbalance Class dalam Klasifikasi Objektivitas Berita Online Menggunakan Algoritma KNN,” JURNAL RESTI (Rekayasa Sistem dan Teknologi Informasi, vol. 1, no. 3, pp. 196–201, 2019, doi: 10.29207/resti.v3i2.945.
[19] R. D. Mendrofa, M. H. Siallagan, J. Amalia, and D. P. Pakpahan, “Credit Risk Analysis With Extreme Gradient Boosting and Adaptive Boosting Algorithm,” Journal of Information System,Graphics, Hospitality and Technology, vol. 5, no. 1, pp. 1–7, Mar. 2023, doi: 10.37823/insight.v5i1.233.
[20] D. Sugiarto, E. Utami, and A. Yaqin, “Perbandingan Kinerja Model TF-IDF dan BOW untuk Klasifikasi Opini Publik Tentang Kebijakan BLT Minyak Goreng,” Jurnal Teknik Industri, vol. 12, no. 3, pp. 272–277, 2022, doi: 10.25105/jti.v12i3.15669.
[21] E. Mulyani, F. P. B. Muhamad, and K. A. Cahyanto, “Pengaruh N-Gram terhadap Klasifikasi Buku menggunakan Ekstraksi dan Seleksi Fitur pada Multinomial Naïve Bayes,” JURNAL MEDIA INFORMATIKA BUDIDARMA, vol. 5, no. 1, p. 264, Jan. 2021, doi: 10.30865/mib.v5i1.2672.
[22] A. M. Alashqar, “A Classification of Quran Verses Using Deep Learning,” International Journal of Computing and Digital Systems, vol. 14, no. 1, pp. 189–201, 2023, doi: 10.12785/ijcds/XXXXXX.
[23] A. Saputra and I. Z. Yadi, “Klasifikasi Ayat Al-Quran Terjemahan Menggunakan Metode Support Vector Machine Dan K-Nearest Neighbors,” Bina Darma Conference on Computer Science, vol. 2, no. 4, pp. 449–466, 2020, Accessed: Oct. 30, 2023. [Online]. Available: https://conference.binadarma.ac.id/index.php/BDCCS/issue/view/19
[24] A. Wahdan, S. Hantoobi, S. A. Salloum, and K. Shaalan, “A systematic review of text classification research based on deep learning models in Arabic language,” International Journal of Electrical and Computer Engineering, vol. 10, no. 6, pp. 6629–6643, Dec. 2020, doi: 10.11591/IJECE.V10I6.PP6629-6643.
[25] A. Naldi and S. Agustian, “Klasifikasi sentimen Vaksin Covid-19 menggunakan K-Nearest Neighbor Berdasarkan Word Embeddings Fasttext Pada Twitter,” ZONAsi: Jurnal Sistem Informasi, vol. 5, no. 2, pp. 323–333, 2023, doi: 10.31849/zn.v5i2.12548.
Published
2024-05-12
How to Cite
Nurul Fatiara, Nazruddin Safaat H, Surya Agustian, Yusra, & Iis Afrianty. (2024). KOMPARASI METODE K-NEAREST NEIGHBORS DAN LONG SHORT TERM MEMORY PADA KLASIFIKASI TERJEMAHAN AL-QUR’AN. ZONAsi: Jurnal Sistem Informasi, 6(2), 332 - 345. https://doi.org/10.31849/zn.v6i2.19863
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