Classification of regional language diversity in the maluku region using decision trees
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Abstract
Regional languages are an important part of cultural heritage that reflect the identity, values, and character of a community. In Maluku Province, there is a high degree of linguistic diversity because the region consists of many islands with different community characteristics. However, the passage of time, modernization, and population mobility have led to a decline in the number of speakers in some areas, threatening the extinction of a number of regional languages. This study aims to classify and visualize the diversity of regional languages in Maluku Province using the Decision Tree algorithm. This method was chosen because it is capable of recognizing patterns and relationships between variables, such as region, number of speakers, and language vitality. The research data was obtained from the compilation of the Language Agency and field observations, then processed using Python with the help of the pandas, scikit-learn, matplotlib, and Streamlit libraries to produce an interactive analytical dashboard. The results showed that regional languages on Seram Island, such as Tana, Alune, and Wemale, had higher vitality levels than languages in other regions. The Decision Tree model built was able to classify language status with an accuracy rate of 92%. The resulting visualization provided a clear picture of the actual condition of regional languages in Maluku and could be used as a basis for regional language preservation and development efforts by local governments.
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How to Cite
Tomhisa, G. G. ., Latuny, W. ., Makaruku, Y. N. ., Manuhuttu, J. V. . and Hawurubun, H. . (2026) “Classification of regional language diversity in the maluku region using decision trees”, Jurnal Mantik, 9(4), pp. 1397-1409. doi: 10.35335/mantik.v9i4.6935.
References
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Septiani, B., Hasanuddin, T., & Astuti, W. (2025). Classification of Lontara Script Using K-NN Algorithm, Decision Tree, and Random Forest Based on Hu Moments and Canny Segmentation. Indonesian Journal of Data and Science, 6(2), 163–174. https://doi.org/10.56705/ijodas.v6i2.281
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Zaky, U., Naswin, A., Sumiyatun, S., & Murdiyanto, A. W. (2023). Performance Analysis of the Decision Tree Classification Algorithm on the Water Quality and Potability Dataset. Indonesian Journal of Data and Science, 4(3), 145–150. https://doi.org/10.56705/ijodas.v4i3.113
Aulia, Y., Andriyansyah, A., Suharjito, S., & Nensi, S. W. (2024). Analisis Prediksi Stroke dengan Membandingkan Tiga Metode Klasifikasi Decision Tree, Naïve Bayes, dan Random Forest. Jurnal Ilmu Komputer Dan Informatika, 3(2), 89–98. https://doi.org/10.54082/jiki.90
Baharudin, M. N., & Dwi Nuryana, I. K. (2022). Implementasi Algoritma Decision Tree untuk Klasifikasi Surat pada Aplikasi Mobile E-Surat Dinas Komunikasi dan Informatika Kota Kediri Berbasis Android. Journal of Informatics and Computer Science (JINACS), 4(01), 76–85. https://doi.org/10.26740/jinacs.v4n01.p76-85
Helmud, E., Helmud, E., Fitriyani, F., & Romadiana, P. (2024). Classification Comparison Performance of Supervised Machine Learning Random Forest and Decision Tree Algorithms Using Confusion Matrix. Jurnal Sisfokom (Sistem Informasi Dan Komputer), 13(1), 92–97. https://doi.org/10.32736/sisfokom.v13i1.1985
Iskandar, M. Y., & Nugroho, H. W. (2025). Comparative Evaluation of Decision Tree and Random Forest for Lung Cancer Prediction Based on Computational Efficiency and Predictive Accuracy. Jurnal Teknik Informatika (Jutif), 6(5), 3392–3404. https://doi.org/10.52436/1.jutif.2025.6.5.4877
Istiqomah, K., & Sofica, V. (2025). Penerapan Data Mining Menggunakan Algoritma Decision Tree Untuk Menganalisis Penggunaan Media Sosial Dengan Konsentrasi Belajar Mahasiswa. RIGGS: Journal of Artificial Intelligence and Digital Business, 4(4), 53–67. https://doi.org/10.31004/riggs.v4i4.3228
Katriani, N. (2022). Klasifikasi Bahasa Daerah Toraja, Halmahera, Dan Kalimantan Menggunakan Metode Decision Tree Dan Gradient Boots. JATISI (Jurnal Teknik Informatika Dan Sistem Informasi), 9(2), 930–940. https://doi.org/10.35957/jatisi.v9i2.1670
Kusumawardani, H. H., Rosyadi, I., Artanto, F. A., Arzha, F. I., & Rachmayani, N. A. (2022). Analisis decision tree dalam pengaruh digital marketing terhadap penerimaan siswa baru. Remik: Riset Dan E-Jurnal Manajemen Informatika Komputer, 6(2), 225–231. http://doi.org/10.33395/remik.v6i2.11494
Mao, R., Shi, X., & Shi, Z. (2025). A Decision Tree Classification Algorithm Based on Two-Term RS-Entropy. Entropy, 27(10). https://doi.org/10.3390/e27101069
Meng, L., Bai, B., Zhang, W., Liu, L., & Zhang, C. (2023). Research on a Decision Tree Classification Algorithm Based on Granular Matrices. Electronics (Switzerland), 12(21), 1–14. https://doi.org/10.3390/electronics12214470
Mienye, I. D., & Jere, N. (2024). A Survey of Decision Trees: Concepts, Algorithms, and Applications. IEEE Access, 12(June), 86716–86727. https://doi.org/10.1109/ACCESS.2024.3416838
Nugraha, A. B., & Romadhony, A. (2023). Identification of 10 Regional Indonesian Languages Using Machine Learning. Sinkron, 8(4), 2203–2214. https://doi.org/10.33395/sinkron.v8i4.12989
Nurain Jalaludin, Rahma Do Subuh, Ismail Maulud, & Bakhtiar Haerullah. (2024). Language Vitality Indicator Towards The Use Of Ternate Language On Ternate Language Enclave In Sahu District, West Halmahera Regency. IJOLEH?: International Journal of Education and Humanities, 3(1), 15–25. https://doi.org/10.56314/ijoleh.v3i1.209
Patasik, E. S., & Yulianto, S. (2023). Classification of Regional Languages Using Methods Gradient Boots and Random Forest. Jurnal Teknik Informatika (Jutif), 4(5), 1249–1255. https://doi.org/10.52436/1.jutif.2023.4.5.1459
Ramadhon, R. N., Ogi, A., Agung, A. P., Putra, R., Febrihartina, S. S., & Firdaus, U. (2024). Implementasi Algoritma Decision Tree untuk Klasifikasi Pelanggan Aktif atau Tidak Aktif pada Data Bank. Karimah Tauhid, 3(2), 1860–1874. https://doi.org/10.30997/karimahtauhid.v3i2.11952
Saifudin, Fadlilah, N. I., Nouvel, A., & Sunanto, S. (2025). Penerapan Algoritma Decision Tree Dengan Optimasi Parameter Dalam Memprediksi Reaksi Autoimun Akibat Obat. Informatics and Computer Engineering Journal, 5(2), 81–85. https://doi.org/10.31294/icej.v5i2.9157
Septiani, B., Hasanuddin, T., & Astuti, W. (2025). Classification of Lontara Script Using K-NN Algorithm, Decision Tree, and Random Forest Based on Hu Moments and Canny Segmentation. Indonesian Journal of Data and Science, 6(2), 163–174. https://doi.org/10.56705/ijodas.v6i2.281
Sharma, D. N., & Iqbal, S. I. M. (2023). Applying Decision Tree Algorithm Classification and Regression Tree (CART) Algorithm to Gini Techniques Binary Splits. International Journal of Engineering and Advanced Technology, 12(5), 77–81. https://doi.org/10.35940/ijeat.e4195.0612523
Shoaib, L. A., Safii, S. H., Idris, N., Hussin, R., & Sazali, M. A. H. (2024). Utilizing decision tree machine model to map dental students’ preferred learning styles with suitable instructional strategies. BMC Medical Education, 24(1), 1–13. https://doi.org/10.1186/s12909-023-05022-5
Sri Septiana, D. F., Triyanto, W. A., & Muzid, S. (2025). Penerapan Metode Decision Tree Algoritma C4.5 Dalam Sistem Rekomendasi Jurusan Bagi Calon Mahasiswa Baru. Jurnal Unitek, 18(1), 167–178. https://doi.org/10.52072/unitek.v18i1.1322
Sujaini, H., & Putra, A. B. (2024). Analysis of language identification algorithms for regional Indonesian languages. IAES International Journal of Artificial Intelligence, 13(2), 1741–1752. https://doi.org/10.11591/ijai.v13.i2.pp1741-1752
Surya, R., Dar, M. H., & Aini Nasution, F. (2024). Implementation of Decision Tree Method to Predict Customer Interest in Internet Data Packages. International Journal of Science, Technology & Management, 5(4), 947–952. https://doi.org/10.46729/ijstm.v5i4.1155
Syatriawan, A., Fadlisyah, & Kurniawati. (2025). Penerapan Metode Decision Tree Cart Untuk Klasifikasi Penyakit Pada Tanaman Kelapa Sawit. Rabit?: Jurnal Teknologi Dan Sistem Informasi Univrab, 10(2), 1191–1199. https://doi.org/10.36341/rabit.v10i2.6544
Wahyudi, I., Annisa, A., Nst, S. A. E., & Manurung, A. S. (2025). The Role of Regional Languages as Markers of Cultural Identity. HORIZON: Indonesian Journal of Multidisciplinary, 3(1), 38–45. https://doi.org/10.54373/hijm.v3i1.2427
Zaky, U., Naswin, A., Sumiyatun, S., & Murdiyanto, A. W. (2023). Performance Analysis of the Decision Tree Classification Algorithm on the Water Quality and Potability Dataset. Indonesian Journal of Data and Science, 4(3), 145–150. https://doi.org/10.56705/ijodas.v4i3.113
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