Implementation of Artificial Neural Networks in an Android-Based Online Course Recommendation Application

Muhamad Syaiful Anwar

doi:10.33650/coreai.v7i1.13177

Implementation of Artificial Neural Networks in an Android-Based Online Course Recommendation Application

DOI: https://doi.org/10.33650/coreai.v7i1.13177

Authors

(1) * Muhamad Syaiful Anwar

(Gunadarma University)
Indonesia
(*) Corresponding Author

Abstract

The growing interest in online courses has caused information overload. Users now struggle to choose suitable programs. Machine Learning (ML) can address this problem through a Recommendation System Model based on an artificial neural network (ANN). This research aims to address this issue by designing an intelligent, ML-based recommendation system integrated within an Android app. The system uses a TensorFlow-built ML model to provide personalized course recommendations. The native Android application is developed with Kotlin and Jetpack Compose. Results show that the developed model provides recommendations that match user preferences and is successfully implemented in the online course recommendation application 'Rekomendasi Kursus Online.' Supporting features include Google account authentication, cloud storage, synchronization for favorite course lists, and dark mode. These features work as expected. User testing with the System Usability Scale (SUS) yielded a score of 83.17. This places the app in the Grade B category with a "Good" rating, showing the application is well-designed and suitable for use.

Keywords

Jaringan Syaraf Tiruan; Rekomendasi; Android

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References

Cania, O., & Jehwae, A. (2025). Implementasi Perancangan Sistem Informasi Berbasis Website di SD Negeri 18 Angge Palimbatan. Journal of Information System and Education Development, 3(4), 4–8. https://doi.org/10.62386/JISED.V3I4.152

Chandani, C., Migunani, M., & Putra, T. W. A. P. (2022). RANCANG BANGUN SISTEM INFORMASI AKADEMIK BERBASIS WEB MOBILE. Jurnal Teknik Informatika Dan Teknologi Informasi, 1(3), 08–19. https://doi.org/10.55606/JUTITI.V1I3.62

Khodijah, K., Sriyanto, S., Aziz, R. Z. A., & Suhendro, S. (2024). PERBANDINGAN KINERJA LIMA ALGORITMA KLÀSIFIKASI DASAR UNTUK PREDIKSI PENYAKIT JANTUNG “CLASSIFIER: NB, DTC4. 5, KNN, ANN & SVM”. Jaringan Sistem Informasi Robotik-JSR, 8(2), 230–234.

Nopriandi, H., & Haswan, F. (2022). Analisis Klasterisasi Mahasiswa Baru dalam Memilih Program Studi dengan Menggunakan Algoritma K-Means. Journal of Information System Research (JOSH), 3(4), 666–671. https://doi.org/10.47065/JOSH.V3I4.1986

Nuryansyah, A., & Ratnawati, D. (2020). Pengembangan Sistem Informasi Sekolah Berbasis Website Di SMK Taman Karya Madya Ngemplak. JINTECH: Journal Of Information Technology, 1(2), 21–31. https://doi.org/10.22373/JINTECH.V1I2.593

Siregar, J., & Wijayanti, D. (2025). Integrasi Model Neural Network dengan SVM dan KNN untuk Deteksi Dini Penyakita Jantung pada Penderita Hypertensi. Technologia: Jurnal Ilmiah, 16(1), 104–109.

Al-Ghuribi, S. M., & Noah, S. A. M. (2021). A comprehensive overview of recommender system and sentiment analysis. arXiv:2109.08794.

Hidayatullah, F. A., Haviluddin, H., & Alfred, R. (2021). Systematic review of information overload on social media. Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), 5(2), 387-394.

Houssein, E. H., et al. (2022). Deep learning for recommender systems: A review. Neural Computing and Applications, 34, 21423-21442. https://doi.org/10.1007/s00521-022-07445-5

Ko, H., Lee, S., Park, Y., & Choi, A. (2022). A survey of recommendation systems: Recommendation models, techniques, and application fields. Electronics, 11(1), 141. https://doi.org/10.3390/electronics11010141

Ricci, F., Rokach, L., & Shapira, B. (2022). Recommender systems: Techniques, applications, and challenges. In Recommender systems handbook (pp. 1-42). Springer. https://doi.org/10.1007/978-1-0716-2197-4_1

Roy, D., & Dutta, M. (2022). A systematic review and research perspective on recommender systems. Journal of Big Data, 9(1), 59. https://doi.org/10.1186/s40537-022-00592-5

Saputro, N. H., Aripradono, A. D., & Baqi, M. P. A. (2020). Perancangan sistem informasi pendaftaran siswa baru berbasis website (Studi kasus: SMAN 12 Tangerang Selatan). Jurnal Informatika Universitas Pamulang, 5(4), 525-531.

Sib, M. (2021). Pengujian black box pada aplikasi perpustakaan menggunakan metode equivalence partitions. Jurnal Teknologi Sistem Informasi dan Aplikasi, 4(2), 94-99. https://doi.org/10.32493/jtsi.v4i1.8519

Wibowo, A. S. (2021). Perancangan aplikasi point of sales (POS) pada platform Android dengan arsitektur MVVM (Model-View-ViewModel). Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 5(7), 2807-2814.

Yaseen, Q. I., Al-Slamy, N. M., & Al-Tememi, A. A. (2022). A comprehensive review of Google Firebase in mobile and web applications. International Journal of Interactive Mobile Technologies (iJIM), 16(10), 4-22.

Khodijah, K., Sriyanto, S., Aziz, R. Z. A., & Suhendro, S. (2024). PERBANDINGAN KINERJA LIMA ALGORITMA KLÀSIFIKASI DASAR UNTUK PREDIKSI PENYAKIT JANTUNG "CLASSIFIER: NB, DTC4. 5, KNN, ANN & SVM". Jaringan Sistem Informasi Robotik-JSR, 8(2), 230-234.

Siregar, J., & Wijayanti, D. (2025). Integrasi Model Neural Network dengan SVM dan KNN untuk Deteksi Dini Penyakita Jantung pada Penderita Hypertensi. Technologia: Jurnal Ilmiah, 16(1), 104-109. https://doi.org/10.31602/tji.v16i1.17046

Zheng, Y., et al. (2023). A survey on deep learning for recommender systems. arXiv.

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