Analisis Sentimen Aplikasi Adiraku di Google Play Store Menggunakan Metode Support Vectore Machine
DOI:
https://doi.org/10.37859/jf.v15i1.8510
Abstract
Penelitian ini bertujuan untuk menganalisis sentimen ulasan pengguna aplikasi Adiraku yang diambil dari Google Play Store dengan menggunakan algoritma Support Vector Machine (SVM). Data ulasan yang diperoleh kemudian melalui proses preprocessing, seperti casefolding, filtering, tokenizing, dan stemming, untuk mempersiapkan data sebelum dilakukan klasifikasi. Hasil klasifikasi menunjukkan bahwa model SVM berhasil mengklasifikasikan ulasan dengan akurasi sebesar 91%. Pada sentimen positif, model menunjukkan kinerja yang sangat baik dengan precision 0.94 dan recall 0.93, sementara pada sentimen negatif, meskipun masih baik, terdapat peluang untuk peningkatan dengan precision 0.84 dan recall 0.86. Visualisasi menggunakan wordcloud digunakan untuk mengidentifikasi kata-kata dominan pada kedua kategori sentimen tersebut, dengan kata-kata positif seperti "mudah", "bantu", dan "bagus" mendominasi ulasan positif, sedangkan kata-kata negatif seperti "gak", "kontrak", dan "error" muncul lebih sering pada ulasan negatif. Evaluasi model menggunakan classification report menunjukkan bahwa model SVM memiliki performa yang baik, meskipun terdapat ruang untuk perbaikan pada kategori negatif. Berdasarkan hasil analisis ini, disarankan agar pengembangan aplikasi lebih difokuskan pada peningkatan fitur aksesibilitas, transparansi kontrak, dan pengalaman pengguna secara keseluruhan.
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References
A. Kovari, “AI for Decision Support: Balancing Accuracy, Transparency, and Trust Across Sectors,” Inf., vol. 15, no. 11, 2024, doi: 10.3390/info15110725.
C. Collins, D. Dennehy, K. Conboy, and P. Mikalef, “Artificial intelligence in information systems research: A systematic literature review and research agenda,” Int. J. Inf. Manage., vol. 60, no. November 2020, p. 102383, 2021, doi: 10.1016/j.ijinfomgt.2021.102383.
D. Angraina and A. Putri, “Analisis Sentimen Pengguna Aplikasi Google Meet Menggunakan Algoritma Support Vector Machine,” vol. 3, no. 3, pp. 472–478, 2022, doi: 10.37859/coscitech.v3i3.4260.
D. Safryda Putri and T. Ridwan, “Analisis Sentimen Ulasan Aplikasi Pospay Dengan Algoritma Support Vector Machine,” vol. 11, no. 01, pp. 32–40, 2023, doi: 10.33884/jif.v11i01.6611.
L. Rajaoarisoa, R. Randrianandraina, G. J. Nalepa, and J. Gama, “Decision-making systems improvement based on explainable artificial intelligence approaches for predictive maintenance,” Eng. Appl. Artif. Intell., vol. 139, no. PB, p. 109601, 2025, doi: 10.1016/j.engappai.2024.109601.
M. Asrol, M. Yani, Machfud, P. Papilo, S. Mursida, and Marimin, “Design of intelligent decision support system for supply chain sustainability assessment,” Procedia Comput. Sci., vol. 227, pp. 659–669, 2023, doi: 10.1016/j.procs.2023.10.570.
I. Maulana, W. Apriandari, and A. Pambudi, “Analisis Sentimen Berbasis Aspek Terhadap Ulasan Aplikasi Mypertamina Menggunakan Support Vector Machine,” vol. 6, no. 2, pp. 172–181, 2023, doi: 10.36080/idealis.v6i2.3022.
S. Behmel, M. Damour, R. Ludwig, and M. J. Rodriguez, “Intelligent decision-support system to plan, manage and optimize water quality monitoring programs: design of a conceptual framework,” J. Environ. Plan. Manag., vol. 64, no. 4, pp. 703–733, 2021, doi: 10.1080/09640568.2020.1782858.
M. Yazdani, S. Shahriari, and M. Haghani, “Progress in Disaster Science Real-time decision support model for logistics of emergency patient transfers from hospitals via an integrated optimisation and machine learning approach,” Prog. Disaster Sci., vol. 25, no. May 2024, p. 100397, 2025, doi: 10.1016/j.pdisas.2024.100397.
K. Coussement and D. F. Benoit, “Interpretable data science for decision making,” Decis. Support Syst., vol. 150, no. August, p. 113664, 2021, doi: 10.1016/j.dss.2021.113664.
M. A. Mochinski et al., “Developing an Intelligent Decision Support System for large-scale smart grid communication network planning,” Knowledge-Based Syst., vol. 283, no. October 2023, p. 111159, 2024, doi: 10.1016/j.knosys.2023.111159.
M. Oktafani and P. T. Prasetyaningrum, “Implementasi Support Vector Machine Untuk Analisis Sentimen Komentar Aplikasi Tanda Tangan Digital,” vol. 15, no. 1, pp. 10–19, 2022, doi: 10.33005/sibc.v15i1.4.
E. Rizqi Mar’atus Sholiihah, I. G. Susrama Mas Diyasa, and E. Yulia Puspaningrum, “Perbandingan Kinerja Kernel Linear Dan RBF Support Vector Machine Untuk Analisis Sentimen Ulasan Pengguna Kai Access Pada Google Play Store,” vol. 8, no. 1, pp. 728–733, 2024, doi: 10.36040/jati.v8i1.8800.
S. Wang, J. Ren, and R. Bai, “A Regularized Attribute Weighting Framework for Naive Bayes,” IEEE Access, vol. 8, pp. 225639–225649, 2020, doi: 10.1109/ACCESS.2020.3044946.
D. Li, J. Sun, H. Yang, and X. Wang, “An Enhanced Naive Bayes Model for Dissolved Oxygen Forecasting in Shellfish Aquaculture,” IEEE Access, vol. 8, pp. 217917–217927, 2020, doi: 10.1109/ACCESS.2020.3042180.
S. Shitharth, P. R. Kshirsagar, P. K. Balachandran, K. H. Alyoubi, and A. O. Khadidos, “An Innovative Perceptual Pigeon Galvanized Optimization (PPGO) Based Likelihood Naïve Bayes (LNB) Classification Approach for Network Intrusion Detection System,” IEEE Access, vol. 10, pp. 46424–46441, 2022, doi: 10.1109/ACCESS.2022.3171660.
S. Ruan, H. Li, C. Li, and K. Song, “Class-specific deep feature weighting for naïve bayes text classifiers,” IEEE Access, vol. 8, pp. 20151–20159, 2020, doi: 10.1109/ACCESS.2020.2968984.
N. Rachmawati Oktaria Mardiyanto, K. Kusrini, and N. Ferry Wahyu Wibowo, “Analisis Sentimen Pengguna Aplikasi Bank Syariah Indonesia Dengan0menggunakan0algoritma Support Vector Machine (Svm),” vol. 4, no. 1, pp. 9–15, 2023, doi: 10.46764/teknimedia.v4i1.85.
R. Wahyudi and G. Kusumawardana, “Analisis Sentimen Pada Aplikasi Grab Di Google Play Store Menggunakan Support Vector Machine,” vol. 8, no. 2, pp. 200–207, 2021, doi: 10.31294/ji.v8i2.9681.
J. Fasilkom, “Peningkatan Akurasi Klasifikasi Tutupan Lahan Menggunakan Random Forest pada Data Sentinel-2 di Jambi Author : Akhiyar Waladi,” vol. 15, no. 1, pp. 17–24, 2025.
B. C. Octariadi, “Penerapan Algoritma ( Naïve Bayes ) Untuk Memprediksi Penyakit Diare,” vol. 15, no. 1, pp. 49–56, 2025.
J. Sanjaya, B. Priyatna, and S. S. Hilabi, “Analisis Sentimen Terhadap Opini Proyek Kereta Cepat Menggunakan Metode Naïve Bayes Classifier,” J. Fasilkom, vol. 14, no. 1, pp. 263–270, 2024.
S. Naiem, A. E. Khedr, A. M. Idrees, and M. I. Marie, “Enhancing the Efficiency of Gaussian Naïve Bayes Machine Learning Classifier in the Detection of DDOS in Cloud Computing,” IEEE Access, vol. 11, no. October, pp. 124597–124608, 2023, doi: 10.1109/ACCESS.2023.3328951.
B. Han, H. Shin, Y. Kim, J. Choi, and Y. Lee, “HEaaN-NB: Non-Interactive Privacy-Preserving Naive Bayes Using CKKS for Secure Outsourced Cloud Computing,” IEEE Access, vol. 12, no. August, pp. 110762–110780, 2024, doi: 10.1109/ACCESS.2024.3438161.
A. Abraham et al., “Naïve Bayes Approach for Word Sense Disambiguation System with a Focus on Parts-of-Speech Ambiguity Resolution,” IEEE Access, vol. 12, no. August, pp. 126668–126678, 2024, doi: 10.1109/ACCESS.2024.3453912.
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