Optimasi Support Vector Machine (SVM) Menggunakan Naive Bayes dan Decision Tree untuk Klasifikasi Tema Tugas Akhir Mahasiswa Sistem Informasi
DOI:
https://doi.org/10.58477/cj.v2i2.179Keywords:
Support Vector Machine, Naive Bayes, Decision TreeAbstract
-The School of Informatics and Computer Management (STMIK) Abulyatama faces challenges in classifying student thesis topics. The current manual classification process is inefficient and ineffective. Therefore, the implementation of data mining techniques is needed to manage this data, specifically to automate the classification process. This study aims to optimize the Support Vector Machine (SVM) model by integrating Naive Bayes and Decision Tree algorithms to improve the accuracy of thesis topic classification. Based on the analysis, it can be concluded that both SVM and K-Means can be utilized by decision-makers to categorize thesis topics as a decision support system. Naive Bayes and Decision Tree were shown to optimize SVM and enhance its accuracy. Naive Bayes achieved the highest accuracy at 82.50%, while Decision Tree recorded an accuracy of 62.50%, making Naive Bayes the most suitable model for thesis classification.
Downloads
References
Afkar, M. K., Wali, M., & Imilda. (2024). Aplikasi prediksi produksi cabai dengan algoritma C.45 untuk dinas pertanian provinsi Aceh berbasis web. Jurnal Ilmu Komputer Dan Teknologi Informasi, 1(1), 1–13. https://doi.org/10.35870/jikti.v1i1.732
Amini, T. A., & Setiawan, K. (2024). Application of the Naive Bayes algorithm in Twitter sentiment analysis of 2024 vice presidential candidate Gibran Rakabuming Raka using Rapidminer. International Journal Software Engineering and Computer Science (IJSECS), 4(1), 234–246. https://doi.org/10.35870/ijsecs.v4i1.2236
Apriansyah, F. A., Hermawan, A., & Avianto, D. (2024). Optimization of K value in KNN algorithm for spam and HAM classification in SMS texts. International Journal Software Engineering and Computer Science (IJSECS), 4(2), 767–779. https://doi.org/10.35870/ijsecs.v4i2.2681
Azis, I. M. A., & Wahyudi, T. (2024). Analisa sentimen rencana pemindahan ibu kota Nusantara dari Jakarta ke Kalimantan Timur menggunakan algoritma Naïve Bayes. Jurnal Indonesia : Manajemen Informatika Dan Komunikasi, 5(3), 2668-2679. https://doi.org/10.35870/jimik.v5i3.969
Azzahra, Y. A., & Akbar, Y. (2024). Komparasi penerapan algoritma C4.5 dan Naïve Bayes untuk ketepatan waktu pengiriman barang pada PT. Rtrans Logistik Artamandiri. Jurnal Indonesia : Manajemen Informatika Dan Komunikasi, 5(3), 2768-2780. https://doi.org/10.35870/jimik.v5i3.1003
Baidowi, A., & Sutisna. (2024). Implementasi data mining klasifikasi fuel surcharge menggunakan algoritma Naive Bayes studi kasus PT Pelabuhan Indonesia (Persero) Regional 2 Tanjung Priok. Jurnal Indonesia : Manajemen Informatika Dan Komunikasi, 5(3), 2854-2863. https://doi.org/10.35870/jimik.v5i3.960
Efendi, A. N., Triayudi, A., & Winarsih, W. (2022). Sistem pakar mendeteksi penyakit ikan arwana Asia menggunakan metode Naïve Bayes. Jurnal JTIK (Jurnal Teknologi Informasi Dan Komunikasi), 6(2), 243–249. https://doi.org/10.35870/jtik.v6i2.415
Efendi, R., Fauziah, F., & Gunaryati, A. (2021). Diagnosa penyakit tanaman cabai menggunakan metode forward chaining dan Naïve Bayes. Jurnal JTIK (Jurnal Teknologi Informasi Dan Komunikasi), 5(2), 164–172. https://doi.org/10.35870/jtik.v5i2.208
Fahmi, H., & Sutisna. (2024). Implementasi data mining klasifikasi gejala penyakit TB menggunakan algoritma Naive Bayes pada studi kasus Puskesmas Pegangsaan Dua B. Jurnal Indonesia : Manajemen Informatika Dan Komunikasi, 5(3), 2888-2898. https://doi.org/10.35870/jimik.v5i3.970
Farhani, A., & Sutisna. (2024). Analisis sentimen terhadap kendaraan listrik di Indonesia menggunakan metode klasifikasi Naïve Bayes. Jurnal Indonesia : Manajemen Informatika Dan Komunikasi, 5(3), 2680-2690. https://doi.org/10.35870/jimik.v5i3.983
Fatmasari, R., Widodo, A. Z. P., Hakim, V. F., Gata, W., & Saputra, D. D. (2023). Pengkategorian komentar Instagram terhadap layanan akademik dan non-akademik Universitas Terbuka. Jurnal JTIK (Jurnal Teknologi Informasi Dan Komunikasi), 7(1), 59–70. https://doi.org/10.35870/jtik.v7i1.669
Firmansyah, D. R., & Lestariningsih, E. (2024). Analisis sentimen ulasan aplikasi Smart Campus Unisbank di Google Playstore menggunakan algoritma Naive Bayes. Jurnal JTIK (Jurnal Teknologi Informasi Dan Komunikasi), 8(2), 498–507. https://doi.org/10.35870/jtik.v8i2.1882
Fuad, M., Wattimena, F. Y., Rizani, A., & Yuswardi. (2023). Investment decision making in digital business using Tsukamoto fuzzy logic. International Journal Software Engineering and Computer Science (IJSECS), 3(2), 144–150. https://doi.org/10.35870/ijsecs.v3i2.1525
Hafid, I., Gata, W., Hilyati, K., Hakim, V. F., & Rahayu, S. (2023). Sentimen analisis masyarakat Indonesia terhadap presiden Rusia pada komentar media berita online. Jurnal JTIK (Jurnal Teknologi Informasi Dan Komunikasi), 7(1), 172–178. https://doi.org/10.35870/jtik.v7i1.698
Hanif, J., Farid, M. N., & Hasanah, B. (2023). Penerapan Natural Language Processing untuk klasifikasi bidang minat berdasarkan judul tugas akhir. Jurnal Sistim Informasi Dan Teknologi, 5(1), 41–49. https://doi.org/10.37034/jsisfotek.v5i1.196
Jaya, D. R. P., & Lestari, S. (2024). Analisis sentimen naturalisasi tim nasional Indonesia U-23 di era Shin Tae-yong menggunakan algoritma Naïve Bayes dan K-Nearest Neighbors. Jurnal Indonesia : Manajemen Informatika Dan Komunikasi, 5(3), 3262-3277. https://doi.org/10.35870/jimik.v5i3.1024
Julianto, M. R., Akbar, Y., & Wahyudi, T. (2024). Analisis sentimen respon publik terhadap program internet gratis di platform X melalui pendekatan algoritma Naïve Bayes. Jurnal Indonesia : Manajemen Informatika Dan Komunikasi, 5(3), 2940-2950. https://doi.org/10.35870/jimik.v5i3.981
Legito, R., Nuraini, R., Judijanto, L., & Lubis, A. I. (2023). The application of convolutional neural networks in floristic recognition. International Journal Software Engineering and Computer Science (IJSECS), 3(3), 520–528. https://doi.org/10.35870/ijsecs.v3i3.1827
Lestari, D., & Lestari, S. (2024). Penerapan data mining klasifikasi tingkat pemahaman siswa pada kegiatan belajar mengajar dengan metode decision tree (Studi kasus SDN Malaka Jaya 11 Duren Sawit). Jurnal Indonesia: Manajemen Informatika Dan Komunikasi, 5(2), 1260–1268. https://doi.org/10.35870/jimik.v5i2.662
Lestari, S. A., & Sugiyono. (2024). Analisis sentimen pada media sosial X (Twitter) terhadap tumor jinak payudara menggunakan metode Naïve Bayes. Jurnal Indonesia : Manajemen Informatika Dan Komunikasi, 5(3), 3336-3348. https://doi.org/10.35870/jimik.v5i3.1015
Limanauw, G. C. T., & Oetama, R. (2024). Analysis of the millennial view on insurance as a key financial priority using K-Means and Decision Tree. Jurnal JTIK (Jurnal Teknologi Informasi Dan Komunikasi), 8(1), 202–209. https://doi.org/10.35870/jtik.v8i1.1445
Mardiani, E., Rahmansyah, N., Ningsih, S., Lantana, D. A., Wulandana, N. P., Lombu, A. A., & Budyarti, S. (2024). Classification of potential tsunami disaster due to earthquakes in Indonesia based on machine learning. International Journal Software Engineering and Computer Science (IJSECS), 4(1), 13–23. https://doi.org/10.35870/ijsecs.v4i1.2084
Nofiar Am, A., & Nasari, F. (2023). Android smartphone damage diagnosis expert system by web-based forward chain method. International Journal Software Engineering and Computer Science (IJSECS), 3(1), 11–18. https://doi.org/10.35870/ijsecs.v3i1.1019
Nugraha, D., & Said, F. (2024). Implementasi algoritma C4.5 dan Naive Bayes untuk analisis sentimen publik terhadap platform live streaming Dukov di Indonesia. Jurnal Indonesia : Manajemen Informatika Dan Komunikasi, 5(3), 3326-3335. https://doi.org/10.35870/jimik.v5i3.1011
Pasaribu, N. A., & Sriani. (2023). The Shopee application user reviews sentiment analysis employing Naïve Bayes algorithm. International Journal Software Engineering and Computer Science (IJSECS), 3(3), 194–204. https://doi.org/10.35870/ijsecs.v3i3.1699
Pernama, B., & Purnomo, H. D. (2023). Analisis risiko pinjaman dengan metode Support Vector Machine, Artificial Neural Network, dan Naïve Bayes. Jurnal JTIK (Jurnal Teknologi Informasi Dan Komunikasi), 7(1), 92–99. https://doi.org/10.35870/jtik.v7i1.693
Puteri, D. W., Buana, P. W., & Sukarsa, I. M. (2024). Komparasi metode decision tree dan deep learning dalam meramalkan jumlah mahasiswa drop out berdasarkan nilai akademik. Journal of Internet and Software Engineering, 1(2), 12. https://doi.org/10.47134/pjise.v1i2.2327
Qubra, R., & Saputra, R. A. (2024). Classification of hoax news using the Naïve Bayes method. International Journal Software Engineering and Computer Science (IJSECS), 4(1), 40–48. https://doi.org/10.35870/ijsecs.v4i1.2068
Salam, A., Albahri, F. P., & Fathurrahmad. (2022). Sistem rekomendasi tugas akhir mahasiswa pada AMIK Indonesia untuk mendukung Merdeka Belajar-Kampus Merdeka menggunakan metode Collaborative Filtering (CF). Jurnal JTIK (Jurnal Teknologi Informasi Dan Komunikasi), 6(2), 281–288. https://doi.org/10.35870/jtik.v6i2.420
Samantri, M., & Afiyati. (2024). Perbandingan algoritma Support Vector Machine dan Random Forest untuk analisis sentimen terhadap kebijakan pemerintah Indonesia terkait kenaikan harga BBM tahun 2022. Jurnal JTIK (Jurnal Teknologi Informasi Dan Komunikasi), 8(1), 1–9. https://doi.org/10.35870/jtik.v8i1.1202
Sari Siregar, Y., Handoko, D., Khairani, M., Syahputri, N. I., & Harahap, H. (2024). Implementasi data mining klasifikasi algoritma Chaid dalam menentukan pola penerima mahasiswa baru. Digital Transformation Technology, 3(2), 978–989. https://doi.org/10.47709/digitech.v3i2.3612
Setiawan, K., & Saputry, Y. Y. A. (2024). Clustering data calon siswa baru menggunakan metode K-Means di pusat pengembangan anak Fajar Baru Cengkareng. Jurnal JTIK (Jurnal Teknologi Informasi Dan Komunikasi), 8(1), 75–83. https://doi.org/10.35870/jtik.v8i1.1426
Sugiyono, Haryati, Sarimole, F. M., & Tundo. (2024). Data mining modeling using the K-Means algorithm to analyze the impact of new media on early childhood psychology at Bimba Rainbow Kids Sukmajaya. International Journal Software Engineering and Computer Science (IJSECS), 4(2), 647–655. https://doi.org/10.35870/ijsecs.v4i2.2874
Supendi, Kumala, D., & Yulianti, M. L. (2024). Implications of deep learning for stock market forecasting. International Journal Software Engineering and Computer Science (IJSECS), 4(1), 68–80. https://doi.org/10.35870/ijsecs.v4i1.2281
Syah, F., Fajrin, H., Afif, A. N., Saeputra, M. R., Mirranty, D., & Saputra, D. D. (2023). Analisa sentimen terhadap Twitter IndihomeCare menggunakan perbandingan algoritma Smote, Support Vector Machine, AdaBoost dan Particle Swarm Optimization. Jurnal JTIK (Jurnal Teknologi Informasi Dan Komunikasi), 7(1), 53–58. https://doi.org/10.35870/jtik.v7i1.686
Tundo, & Rachmawati, D. N. (2024). Implementasi algoritma Naive Bayes untuk analisis sentimen terhadap program makan siang gratis. Jurnal Indonesia: Manajemen Informatika Dan Komunikasi, 5(3), 2925–2939. https://doi.org/10.35870/jimik.v5i3.978
Tundo, Eldina, R., Setiawan, K., & Fajri, R. (2024). Sentiment analysis of cigarette use based on opinions from X using Naive Bayes and SVM. Jurnal Indonesia : Manajemen Informatika Dan Komunikasi, 5(3), 2561-2569. https://doi.org/10.35870/jimik.v5i3.947
Tyar, F., & Wahyuddin, M. I. (2022). Sistem pakar menggunakan metode Naïve Bayes dan certainty factor untuk mendeteksi hama pada tanaman alpukat berbasis web. Jurnal JTIK (Jurnal Teknologi Informasi Dan Komunikasi), 6(4), 488–496. https://doi.org/10.35870/jtik.v6i4.519
Wibiyanto, A., & Wibowo, A. (2023). Penerapan algoritma Multiclass Support Vector Machine dan TF-IDF untuk klasifikasi topik tugas akhir. SKANIKA: Sistem Komputer dan Teknik Informatika, 6(1), 42–50.
Widyawati, D., Faradibah, A., & Belluano, L. L. (2023). Comparison analysis of classification model performance in lung cancer prediction using Decision Tree, Naive Bayes, and Support Vector Machine. Indonesian Journal of Data and Science, 4(2), 78-86. https://doi.org/10.56705/ijodas.v4i2.76
Yansyah, H., Fauziah, S., & Maulana, D. (2023). Classification of production machine spare part stock data request needs using the K-Nearest Neighbor method. International Journal Software Engineering and Computer Science (IJSECS), 3(3), 457–466. https://doi.org/10.35870/ijsecs.v3i3.1878
Yesisca, F., Ratnawati, D. E., & Rahayudi, B. (2022). Analisis perbandingan klasifikasi topik skripsi mahasiswa menggunakan K-Nearest Neighbor dan Support Vector Machine (Studi Kasus: Jurusan Sistem Informasi, Fakultas Ilmu Komputer, Universitas Brawijaya). Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 6(5), 2328–2335. Diambil dari https://j-ptiik.ub.ac.id/index.php/j-ptiik/article/view/11045
Yumansyah, Q., Fatchan, M., & Turmudi Zy, A. (2023). Design and development of an information system for indemnity claim box recapitulation using SDLC method at Mandiri Inhealth Insurance. International Journal Software Engineering and Computer Science (IJSECS), 3(3), 553–559. https://doi.org/10.35870/ijsecs.v3i3.1970
Zendrato, G. F. S., Triayudi, A., & E, E. T. (2022). Analisis clustering dokumen tugas akhir mahasiswa sistem informasi Universitas Nasional menggunakan metode K-Means clustering. Jurnal JTIK (Jurnal Teknologi Informasi Dan Komunikasi), 6(1), 70–76. https://doi.org/10.35870/jtik.v6i1.389.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Computer Journal
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.