PENINGKATAN KOMPETENSI PELAKU UMKM DALAM STRATEGI BRANDING BERBASIS TEKNOLOGI
DOI:
https://doi.org/10.56127/jammu.v3i3.1883Keywords:
Quick commerce, Segari, sentiment analysis, user reviews, Google Play Store, BERT, machine learning, model accuracyAbstract
This community service program aims to provide training and guidance for Micro, Small, and Medium Enterprises (MSMEs) in implementing technology-based branding strategies. The activity is a collaboration between Gunadarma University and the Indonesian Young Lecturers Association (ADMI), conducted throughout the PTA 2024-2025 academic year. The program employs a hybrid implementation method (online and offline), tailored to the needs of MSMEs. The primary focus of this program is to enhance MSMEs’ understanding and skills in utilizing technology for digital branding, covering creative content creation, social media utilization, and market target analysis using digital tools.
This initiative involves six fields of study—Accounting, Management, Industrial Engineering, Mechanical Engineering, Information Systems, and English Literature—to provide a comprehensive interdisciplinary approach. The expected outcomes include improving MSMEs' competencies in building a strong brand identity, managing digital branding campaigns, and expanding market reach through technology. Program outputs include journal publications, activity documentation in the form of videos, and increased lecturer participation. All program results will be reported to Gunadarma University’s Community Service Institution (LPM-UG) as part of program evaluation.
References
M. Schorung, “Quick commerce: will the disruption of the food retail industry happen? Investigating the quick commerce supply chain and the impacts of dark stores,” 2023.
Simanungkalit, J. P. P. Naibaho, and A. De Kweldju, “Analisis Sentimen Berbasis Aspek Pada Ulasan Aplikasi Shopee Menggunakan Algoritma Naïve Bayes,” Jutisi J. Ilm. Tek. Inform. dan Sist. Inf., vol. 13, no. 1, p. 659, 2024.
D. A. Pradana and A. P. Wibowo, “Analisis Sentimen Ulasan Produk Sepatu Compass Di E- Commerce Tokopedia Menggunakan Algoritma Naive Bayes Classifier ( NBC ) Analisis Sentimen Ulasan Produk Sepatu Compass Di E- Commerce Tokopedia Menggunakan Algoritma Naive Bayes Classifier ( NBC ),” Inov. Pembang. – J. KELITBANGAN, vol. 12, no. 3, pp. 1–12, 2024.
K. Dewi, “Analisis Sentimen Ekspedisi Sicepat Dari Ulasan Google Play Mennggunakan Algoritma Naïve Bayes,” JATISI (Jurnal Tek. Inform. dan Sist. Informasi), vol. 9, no. 2, pp. 796–805, 2022.
D. N. I. Huda and C. Prianto, “Analisis Sentimen Layanan Jasa Pengiriman Pada Ulasan Play Store: Systematic Literature Review,” J. Inform. dan Teknol. Komput., vol. 4, no. 2, pp. 87–98, 2023.
K. F. E. D. W. R. Permatasari, “Analisis Sentimen Berbasis Aspek Pada Ulasan Aplikasi Indrive Menggunakan Bidirectional Encoder Representations From Transformers (Bert),” J. Ilm. Wahana Pendidik., vol. 10, no. 20, pp. 105–115, 2024.
N. B. Sidauruk and N. Riza, “Sentimen Analisis Data Pengguna Terhadap KAI Access Systematic Literature Review,” J. Mhs. Tek. Inform., vol. 7, no. 2, pp. 1297–1303, 2023.
D. Jovanoski, V. Pachovski, and P. Nakov, “Sentiment analysis in twitter for Macedonian,” Int. Conf. Recent Adv. Nat. Lang. Process. RANLP, vol. 2015-January, pp. 249–257, 2015.
R. M. R. W. P. K. Atmaja and W. Yustanti, “Analisis Sentimen Customer Review Aplikasi Ruang Guru dengan Metode BERT (Bidirectional Encoder Representations from Transformers),” Jeisbi, vol. 02, no. 03, pp. 55–62, 2021.
M. Senjaya, A., & Achmad Benny, “Analisis Sentimen Aplikasi Vidio Berdasarkan Review Pada Google Play Store Menggunakan Metode Bidirectional Encoder Representations from Transformers (BERT),” 2022.
R. Kusnadi, Y. Yusuf, A. Andriantony, R. Ardian Yaputra, and M. Caintan, “Analisis Sentimen Terhadap Game Genshin Impact Menggunakan Bert,” Rabit J. Teknol. dan Sist. Inf. Univrab, vol. 6, no. 2, pp. 122–129, 2021.
C. A. Putri, “Analisis Sentimen Review Film Berbahasa Inggris Dengan Pendekatan Bidirectional Encoder Representations from Transformers,” JATISI (Jurnal Tek. Inform. dan Sist. Informasi), vol. 6, no. 2, pp. 181–193, 2020.
L. Qadrini, H. Hikmah, and M. Megasari, “Oversampling, Undersampling, Smote SVM dan Random Forest pada Klasifikasi Penerima Bidikmisi Sejawa Timur Tahun 2017,” J. Comput. Syst. Informatics, vol. 3, no. 4, pp. 386–391, 2022.
V. V. Verianto, “Factors Influencing Segari E-Grocery’s Purchase Intention Moderated by Gender and Age,” J. Bus. Manag. Rev., vol. 4, no. 2, pp. 149–167, 2023.
B. Liu, “Sentiment Analysis: Mining Opinions, Sentiments, and Emotions,” in Sentiment Analysis: Mining Opinions, Sentiments, and Emotions, vol. 11, no. 3, 2015, pp. 277–278.
M. F. Ramadhan, B. Siswoyo, and S. I. Cirebon, “Mengenal Model BERT dan Implementasinya untuk Analisis Sentimen Ulasan Game,” in Prosiding Seminar Nasional Sistem Informasi dan Teknologi (SISFOTEK) ke, 2024, pp. 395–398.
B. G. A. Ghodsi, Neural Network Compression and Knowledge Distillation : Tutorial and Survey. 2024.
T. A. S. Rohmah and W. Maharani, “Personality Detection on Twitter Social Media Using IndoBERT Method,” Build. Informatics, Technol. Sci., vol. 4, no. 2, pp. 448–453, 2022.
D. R. Wijaya and G. M. A. S. W. O. Vihikan, “Sentiment Analysis of Indonesian Citizens on Electric Vehicle Using FastText and BERT Method,” J. Inf. Syst. Informatics, vol. 6, no. 3, pp. 1360–1372, 2024.
