PENGOPTIMALAN INTEROPERABILITAS SISTEM NAIVE BAYES DALAM PENINGKATAN DATA MINING DI EKOSISTEM MULTI-LAYANAN GOJEK
DOI:
https://doi.org/10.34151/technoscientia.v17i1.4624Keywords:
Data Mining, Gojek Multi-service, Naive Bayes, System InteroperabilityAbstract
This research aims to optimize the performance of Data Mining in Gojek's multi-service ecosystem through improving the interoperability of the Naive Bayes system. As a leading multi-service platform, Gojek faces complexity in extracting value from data involving different types of services. The Naive Bayes data classification method, which has been proven effective, is integrated with a focus on optimizing system interoperability. The research involves developing technical solutions that facilitate better integration between services, enabling efficient and accurate data exchange. The literature study investigated the concept of interoperability, Naive Bayes, and similar implementations, while Gojek's needs analysis established the basis of the solution design. The prototype was implemented and evaluated in a test environment replicating Gojek's ecosystem conditions. Results show improved performance with classification accuracy metrics and data exchange efficiency. With trials in the production environment, the optimized solution is widely implemented. The findings of this research are expected to make a positive contribution to the effectiveness of the Gojek system in utilizing data, opening new opportunities for service development, and improving user experience.
References
A. Mustopa and D. Risdiansyah, “Analysis of User Reviews for the PeduliLindungi Application on Google Play Using the Support Vector Machine and Naive Bayes Algorithm Based on Particle Swarm Optimization; Analysis of User Reviews for the PeduliLindungi Application on Google Play Using the Support Vector Machine and Naive Bayes Algorithm Based on Particle Swarm Optimization,” 2020, doi: 10.1109/ICIC50835.2020.9288655.
Jazi Eko Istiyanto and Edhy Sutanta, “MODEL INTEROPERABILITAS ANTAR APLIKASI E-GOVERNMENT,” JURNAL TEKNOLOGI TECHNOSCIENTIA . Accessed: Jan. 26, 2024. [Online]. Available: https://ejournal.akprind.ac.id/index.php/technoscientia/article/view/501/377
Ditha Damayanti, “The Effect of Technology of Acceptance Model on the Gojek 4.0 Application on Repurchase Intention,” Institute Of Computer Science. Accessed: Jan. 26, 2024. [Online]. Available: https://iocscience.org/ejournal/index.php/mantik/article/view/1479/1044
A. Yadi Kuntoro, T. Asra, E. Bayu Pratama, L. Effendi, and R. Ocanitra, “Gojek and Grab User Sentiment Analysis on Google Play Using Naive Bayes Algorithm And Support Vector Machine Based Smote Technique,” J Phys Conf Ser, vol. 1641, p. 12102, 2020, doi: 10.1088/1742-6596/1641/1/012102.
Yessy Asri, “JURNAL KA]IAN ILMU DAN TEKNOLOGI,” Oct. 2015.
Gunawan, Hanes, and Catherine, “C4.5, K-Nearest Neighbor, Naïve Bayes and Random Forest Algorithms Comparison to Predict Students’ On Time Graduation,” Indonesian Journal of Artificial Intelligence and Data Mining (IJAIDM), vol. 4, no. 2, pp. 62–71, 2021, doi: 10.24014/ijaidm.v4i2.10833.
Marwiyah, Pipit Puji Arti, and Taufik Hidayat, “An Analysis of Online Transportation Applications Between Gojek and Grab for Students,” International Journal of Science Education and Cultural Studies. Accessed: Jan. 26, 2024. [Online]. Available: https://ejournal.sultanpublisher.com/index.php/ijsecs/article/view/28/6
A. A. Azzuhri, F. Tanoto, Y. Universitas, G. Mada, and R. Amalia, “A Creative, Innovative, and Solutive Transportation for Indonesia with Its Setbacks and How to Tackle Them: A Case Study of the Phenomenal GOJEK”, Accessed: Jan. 26, 2024. [Online]. Available: http://buscompress.com/journal-home.html
Hermanto, A. Y. Kuntoro, T. Asra, E. B. Pratama, L. Effendi, and R. Ocanitra, “Gojek and Grab User Sentiment Analysis on Google Play Using Naive Bayes Algorithm And Support Vector Machine Based Smote Technique,” J Phys Conf Ser, vol. 1641, no. 1, p. 012102, Nov. 2020, doi: 10.1088/1742-6596/1641/1/012102.
V. Oktaviani, B. Warsito, H. Yasin, R. Santoso, and Suparti, “Sentiment analysis of e-commerce application in Traveloka data review on Google Play site using Naïve Bayes classifier and association method,” J Phys Conf Ser, vol. 1943, no. 1, p. 012147, Jul. 2021, doi: 10.1088/1742-6596/1943/1/012147.
H. Haryono, P. Palupiningsih, Y. Asri, and A. N. S. Handayani, “KLASIFIKASI PESAN GANGGUAN PELANGGAN MENGGUNAKAN METODE NAIVE BAYES CLASSIFIER,” KILAT, vol. 7, no. 2, pp. 100–108, Oct. 2018, doi: 10.33322/KILAT.V7I2.354.
A. Mustopa, Hermanto, Anna, E. B. Pratama, A. Hendini, and D. Risdiansyah, “Analysis of user reviews for the pedulilindungi application on google play using the support vector machine and naive bayes algorithm based on particle swarm optimization,” 2020 5th International Conference on Informatics and Computing, ICIC 2020, Nov. 2020, doi: 10.1109/ICIC50835.2020.9288655.
J. Mantik and D. Damayanti, “The Effect of Technology of Acceptance Model on the Gojek 4.0 Application on Repurchase Intention,” Jurnal Mantik, vol. 5, no. 2, pp. 1147–1152, Aug. 2021, doi: 10.35335/JURNALMANTIK.VOL5.2021.1479.PP1147-1152.
Hermanto, A. Y. Kuntoro, T. Asra, E. B. Pratama, L. Effendi, and R. Ocanitra, “Gojek and Grab User Sentiment Analysis on Google Play Using Naive Bayes Algorithm And Support Vector Machine Based Smote Technique,” J Phys Conf Ser, vol. 1641, no. 1, p. 012102, Nov. 2020, doi: 10.1088/1742-6596/1641/1/012102.
Agung Wijoyo, Asep Jalal, Dina Miranti, Muhammad Aliansah, and Sovie Velisa, “Sistem Informasi Manajemen PT. Gojek Indonesia,” JRIIN: Jurnal Riset Informatika dan Inovasi. Accessed: Jan. 26, 2024. [Online]. Available: https://jurnalmahasiswa.com/index.php/jriin/article/view/425/350
