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All Journal Jurnal Sains Manajemen Malcom: Indonesian Journal of Machine Learning and Computer Science
Entis Sutisna
Akademi Digital Bandung
Computer Science & IT Decision Sciences, Operations Research & Management Social Sciences

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Loyalitas Sebagai Mediator Kepuasan Pelanggan Dan Word-Of-Mouth Pengguna Shopee Entis Sutisna
Jurnal Sains Manajemen Vol. 8 No. 1 (2026): Jurnal Sains Manajemen
Publisher : LPPM Universitas Adhirajasa Reswara Sanjaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51977/cyvq1r07

Abstract

Pertumbuhan e-commerce di Indonesia telah mendorong persaingan yang semakin ketat antar platform digital, di mana kualitas pengalaman pelanggan menjadi penentu utama keberhasilan jangka panjang. Meskipun hubungan antara kepuasan pelanggan dan word-of-mouth telah banyak diteliti, mekanisme bagaimana loyalitas pelanggan memediasi hubungan tersebut masih jarang dieksplorasi secara eksplisit dalam konteks platform e-commerce Indonesia. Penelitian ini bertujuan menguji peran Loyalitas Pelanggan sebagai mediator dalam hubungan antara Kepuasan Pelanggan dan Perilaku Word-of-Mouth pada pengguna Shopee di Indonesia, dengan Customer Experience Quality sebagai anteseden. Data yang digunakan merupakan data sekunder dari dataset publik Handoko dan Yustian yang tersedia di repositori Mendeley Data. Penelitian menggunakan metode Partial Least Square Structural Equation Modeling dengan perangkat lunak SmartPLS. Konstruk Customer Experience Quality diukur melalui empat dimensi second-order: Peace of Mind, Moment of Truth, Outcome Focus, dan Product Experience. Hasil penelitian menunjukkan bahwa seluruh hipotesis didukung: Customer Experience Quality berpengaruh positif terhadap Kepuasan Pelanggan, Loyalitas, dan Word-of-Mouth; Kepuasan Pelanggan berpengaruh positif terhadap Loyalitas dan Word-of-Mouth; serta Loyalitas terbukti memediasi hubungan antara Kepuasan Pelanggan dan Word-of-Mouth. Dimensi Outcome Focus merupakan dimensi yang paling dominan dalam membentuk loyalitas dan perilaku rekomendasi pengguna. Novelty penelitian ini terletak pada pengujian eksplisit loyalitas sebagai mediator hubungan kepuasan dan word-of-mouth dalam konteks Shopee Indonesia, suatu posisi yang belum diujikan dalam penelitian terdahulu. Temuan ini memberikan implikasi manajerial bagi platform e-commerce untuk mengoptimalkan efisiensi transaksi sebagai kunci membangun loyalitas organik dan mendorong rekomendasi mulut ke mulut yang berkelanjutan.
The Lexicon-Based Sentiment Analysis for Stock Price Prediction in Islamic Fashion Industry Entis Sutisna; Rahma Dafitri; Dian Daryani
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 6 No. 2 (2026): MALCOM April 2026
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v6i2.2586

Abstract

This study develops a lexicon-based sentiment analysis method to examine the relationship between social media sentiment and stock price movements in Indonesia's Islamic fashion industry. We collected 6,087 social media comments from TikTok, Instagram, and Twitter over 396 days, alongside daily stock data from Elzatta (ZATA). Using an Indonesian fashion-specific lexicon with 42 positive and 38 negative indicators validated through inter-annotator agreement (Cohen's Kappa = 0.82), we employed ordinary least squares regression with heteroskedasticity-robust standard errors to test sentiment-return relationships. Results show no economically significant relationship between social media sentiment and stock returns (correlation = -0.06, p = 0.240), with models explaining only 2.09% of return variance. We identify three boundary conditions limiting sentiment-based prediction: (1) feature mismatch 82% of comments discuss product attributes rather than business fundamentals, (2) demographic disconnect social media users (78% female, aged 18-29) differ markedly from investors (68% male, aged 35-55), and (3) market microstructure constraints 43% zero-volume days and 2.3% bid-ask spreads impede price discovery. This study provides the first Indonesian fashion-specific sentiment lexicon and establishes actionable validation guidelines for practitioners: sentiment features must be verified for demographic alignment and market liquidity before deployment. For machine learning applications in emerging markets
Co-Authors Dian Daryani Rahma Dafitri