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All Journal Pro Bisnis Jurnal Abdimas BSI: Jurnal Pengabdian Kepada Masyarakat JURTEKSI BERNAS: Jurnal Pengabdian Kepada Masyarakat Madani : Indonesian Journal of Civil Society Nusantara Hasana Journal Jurnal Algoritma Jurnal Pengabdian Mitra Masyarakat (JPMM) Journal of Multimedia Trend and Technology
Luzi Dwi Oktaviana
Universitas Amikom Purwokerto
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Dentistry Economics, Econometrics & Finance Education Environmental Science Languange, Linguistic, Communication & Media Mathematics Medicine & Pharmacology Neuroscience Nursing Public Health Social Sciences Veterinary Other

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SENTIMENT ANALYSIS OF PEGIPEGI.COM ON GOOGLE PLAYSTORE WITH NAÏVE BAYES ALGORITHM Riski Hardian; Luzi Dwi Oktaviana; Aulia Hamdi
JURTEKSI (Jurnal Teknologi dan Sistem Informasi) Vol 10, No 3 (2024): Juni 2024
Publisher : STMIK Royal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v10i3.3201

Abstract

Abstract: Today, many users use online platforms rather than offline platforms for ticket bookings, involving a wide range of services such as flights, hotels, trains, buses, and entertainment. PegiPegi.com, as one of the fastest growing online travel agencies in Indonesia, demonstrates success by understanding the value of technology and maintaining strong partnerships. Users of this platform often provide reviews, viewing user reviews can be done manually but this will have a less effective impact, so it needs to be done automatically with sentiment analysis. This research the Naïve Bayes method in sentiment analysis of PegiPegi.com reviews, with a focus on understanding customer satisfaction and service improvement. By combining these approaches, this research contributes to a deeper understanding of user responses to OTA services and presents the evaluation results of the Multinomial Naive Bayes classification model with an accuracy rate of 89.5%. The high precision in the Negative class demonstrates the model's ability to identify negative reviews. However, there are challenges in classifying the Neutral class, indicating the potential for further improvement. Nevertheless, the F1 score of 0.522 reflects a good balance between overall precision, recall so it can be concluded the naïve bayes algorithm is successful for performing sentiment analysis. Keywords: Sentiment analysis; naïve bayes algorithm; pegipegi.com; playstore  Abstract: Saat ini banyak pengguna platform online dibandingkan offline untuk pemesanan tiket, yang melibatkan berbagai layanan seperti penerbangan, hotel, kereta api, bus, dan hiburan. PegiPegi.com, sebagai salah satu agen perjalanan online yang berkembang pesat di Indonesia, menunjukkan keberhasilan dengan memahami nilai teknologi dan mempertahankan kemitraan yang kuat. Pengguna platform ini sering memberikan ulasan, melihat ulasan pengguna bisa saja dilakukan secara manual tetapi hal ini akan memberikan dampak yang kurang efektif, sehingga perlu dilakukan secara otomatis dengan analisis sentiment. Penelitian ini bertujuan untuk menerapkan metode klasifikasi Naïve Bayes dalam analisis sentimen ulasan PegiPegi.com, dengan fokus pada pemahaman kepuasan pelanggan dan peningkatan layanan. Dengan menggabungkan pendekatan ini, penelitian ini berkontribusi pada pemahaman yang lebih dalam tentang tanggapan pengguna terhadap layanan OTA dan menyajikan hasil evaluasi model klasifikasi Multinomial Naive Bayes dengan tingkat akurasi 89,5%. Presisi tinggi di kelas Negatif menunjukkan kemampuan model untuk mengidentifikasi ulasan negatif. Namun, ada tantangan dalam mengklasifikasikan kelas Netral, menunjukkan potensi untuk perbaikan lebih lanjut. Namun demikian, skor F1 0,522 mencerminkan keseimbangan yang baik antara presisi keseluruhan dan daya ingat sehingga dapat disimpulkan algoritma naïve bayes berhasil untuk melakukan analisis sentimen. Keywords: Analisis sentimen; naïve bayes; pegipegi.com; playstore
Evaluasi Kinerja Penjualan dan Efisiensi Iklan Kampanye GMV Max pada TikTok Shop Garage Fortress Rachman Hidayat; Jeffri Prayitno Bangkit Saputra; Luzi Dwi Oktaviana
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3445

Abstract

The growth of social commerce has driven changes in digital marketing strategies that are increasingly data-driven and automated on e-commerce platforms. One of the features utilized in TikTok Shop is GMV Max, an automated campaign system designed to optimize sales performance through platform-based advertising management. This study aims to describe and evaluate the sales performance of TikTok Shop Garage Fortress under the GMV Max campaign using a descriptive quantitative approach based on secondary data obtained from campaign reports covering the period from October 9, 2025, to April 6, 2026. The analysis focuses on GMV, number of orders, advertising costs, ROAS, and conversion rate indicators without examining causal relationships among variables. The results show that the GMV Max campaign generated a total GMV of IDR 25,608,081 with 772 orders, advertising expenditure of IDR 2,324,547, a weighted ROAS of 11.02×, and a conversion rate of 6.15 percent. The GLASSWOOL product campaign contributed the largest share of sales value and number of orders. Based on advertising content type, video advertisements demonstrated higher performance in terms of GMV and ROAS, while product cards achieved a higher conversion rate. Overall, the findings indicate that the GMV Max campaign within the research dataset produced a positive ROAS and measurable conversion rate, although the interpretation of the results should still consider data quality and potential attribution anomalies within the TikTok Shop platform.
Co-Authors Aan Riyani Aulia Hamdi Bangkit, Jeffri Prayitno Desty Sandra Utami Fiby Nur Afiana Fiby Nur Nur Afiana Ika Romadoni Yunita Muliasari Pinilih Prayoga Pribadi Rachman Hidayat Retno Waluyo Riski Hardian Shodiq Khalidy Suliswaningsih Tri Muryanti Trias Bratakusuma Uswatun Hasanah Waidi Zanuar Rifai Zanuar Rifai Zanuar Rifa’i