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All Journal Dinamik Intechno Journal : Information Technology Journal
Itishom Al Khoiry
Universitas Persatuan Guru Republik Indonesia Semarang
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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VIKOR-Based Assessment of Indonesia’s Sharia-Compliant Peer-to-Peer Lending Platforms Itishom Al Khoiry; Ika Menarianti; Noventia Karina Putri
Dinamik Vol 31 No 2 (2026)
Publisher : Universitas Stikubank

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35315/dinamik.v31i2.10432

Abstract

This study applies the VIKOR method to evaluate six Sharia compliant peer to peer (P2P) lending platforms in Indonesia: Ethis, Qazwa, Ammana, Dana Syariah, Alami Sharia, and Duha Syariah. The evaluation focuses on five key criteria: default risk, platform reputation, regulatory compliance and legal protection, return on investment and transparency, and platform sustainability. Criteria weights were determined using the Rank Sum Weighting method, and platform scores were obtained through structured user evaluations. VIKOR, a multi criteria decision making method, ranks alternatives by measuring their closeness to an ideal solution, enabling fair comparison when trade offs exist between conflicting criteria. The method reveals clear distinctions among platforms, enabling balanced, compromise based evaluation aligned with Sharia principles. It offers a practical decision support tool for investors and contributes to the broader application of multi criteria methods in ethical fintech ecosystems across emerging markets.
Aspect-Level User Sentiment Patterns in Shopee Mobile App Reviews Using TF-IDF and Logistic Regression Itishom Al Khoiry
Intechno Journal : Information Technology Journal Vol. 8 No. 1 (2026): July
Publisher : Universitas AMIKOM Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24076/intechnojournal.2026v8i1.2871

Abstract

Purpose: This study examines recent Shopee mobile application reviews from a service diagnostic perspective by linking sentiment classification with aspect-level interpretation. The aim is to identify not only whether user feedback is positive or negative, but also which service dimensions are associated with satisfaction and dissatisfaction. Methods: A total of 160,492 Google Play Store reviews of the Shopee Android application were collected from the 2026 review period. After cleaning, rating-based sentiment labeling, and preprocessing, a stratified random sample of 50,000 reviews was used for modeling. TF-IDF unigram and bigram features were applied with a majority class baseline, Naive Bayes, Logistic Regression, and Linear SVM. Performance was evaluated using accuracy, Macro F1-score, confusion matrix, classification report, and 5-fold stratified cross-validation. A keyword-based aspect mapping was then used to interpret sentiment across six service dimensions. Result: The TF-IDF and Logistic Regression model achieved 0.9194 accuracy, 0.9096 Macro F1-score, and 0.899 cross-validated Macro F1-score. Aspect-level findings show that positive sentiment was concentrated in Promotion and Voucher, Product and Seller, and Customer Service, while negative sentiment was concentrated in Payment, Delivery, and Application Performance. Advertising and delivery-related expressions emerged as key negative signals. Novelty: This study proposes a service diagnostic task with aspect-level analysis, which includes sentiment classification, term-weight interpretation, and service-aspect mapping, to position Shopee review analysis. The strategy transforms bulk app reviews into actionable data to track platform service quality.
Co-Authors Ika Menarianti Noventia Karina Putri