Abstract:This study aims to analyze the role of consumer trust in mediating the effects of affiliate marketing and online customer ratings on purchase decisions for fashion products among Shopee users in Surabaya. A quantitative…
approach was used in this research, with data collected through questionnaires from 100 Shopee users in Surabaya. The data analysis was conducted using the Partial Least Squares (PLS) method to examine the direct and mediating relationships between the variables: affiliate marketing, online customer ratings, consumer trust, and purchase decisions. Validity and reliability tests of the instruments were also performed to ensure the accuracy of the variable measurements in this research model.The findings indicate that affiliate marketing does not significantly drive consumer purchase decisions. However, consumer trust plays a key role in mediating the relationship between affiliate marketing and purchase decisions. Without trust, affiliate marketing promotions have limited influence. Additionally, online customer ratings positively influence purchase decisions, and consumer trust is crucial in mediating the relationship between online customer ratings and purchase decisions. Positive ratings from other customers enhance trust in both the product and the seller.
Abstract:This study addresses the limitations of traditional Customer Relationship Management (CRM) systems by analyzing the adoption and impact of Artificial Intelligence (AI) integration (AI-Powered CRM). Informed by the Technology…
logy Acceptance Model (TAM) for employee perception and the Resource-Based View (RBV) for strategic capability, the primary objective is to evaluate how AI-driven automation enhances customer service processes and, subsequently, impacts marketing efficiency. The research employs an exploratory qualitative case study design, utilizing in-depth interviews, document analysis, and system observation on a single organization to gather rich, contextual data. The results demonstrate that AI integration significantly accelerated service, with chatbots handling 65–70% of routine queries and drastically reducing response times. Operationally, these improvements fostered high employee acceptance (TAM). Strategically, the AI-Powered CRM generated refined predictive analytics, resulting in a 12–18% improvement in campaign conversion rates and efficient resource allocation, confirming that AI creates a valuable and difficult-to-imitate strategic capability (RBV). The study concludes that AI-Powered CRM is a critical enabler for both operational efficiency and long-term strategic competitiveness in digital markets.
Abstract:This study investigates the influence of artificial intelligence (AI) integration on strategic financial management in large corporations. Focusing on a sample of 20 Fortune 500 companies from diverse industries, the research…
earch employs a quantitative, descriptive-analytical approach utilizing secondary data from financial reports and AI system logs. The findings reveal that AI adoption significantly enhances forecasting accuracy, risk identification, and operational efficiency, while also enabling financial managers to redirect resources toward creative and strategic initiatives. However, the study also identifies challenges related to data quality, ethical considerations, and skill gaps in AI utilization. The results highlight the importance of a balanced approach that combines AI-driven insights with managerial intuition to maximize value creation in the digital age.
Abstract:Penggunaan dompet digital yang terus meningkat menghasilkan banyak ulasan pengguna yang dapat dimanfaatkan untuk mengevaluasi kualitas layanan. Penelitian ini bertujuan meningkatkan akurasi klasifikasi sentimen pengguna…
dompet digital menggunakan metode Stacking Ensemble Machine Learning yang mengombinasikan Support Vector Machine (SVM), Multinomial Naïve Bayes (MNB), dan AdaBoost dengan Logistic Regression sebagai meta-learner. Data ulasan diproses melalui tahapan text preprocessing meliputi case folding, cleaning, tokenizing, stopword removal, stemming, dan pembobotan fitur menggunakan TF-IDF. Penyeimbangan data dilakukan dengan SMOTE, sedangkan evaluasi model menggunakan 5-Fold Cross-Validation. Hasil penelitian menunjukkan bahwa model Stacking Ensemble memperoleh akurasi rata-rata 80,55%, lebih tinggi dibandingkan algoritma dasar. Evaluasi menggunakan Confusion Matrix, Classification Report, dan ROC Curve juga menunjukkan peningkatan nilai precision, recall, F1-score, dan kemampuan diskriminasi model. Hasil ini menunjukkan bahwa pendekatan Stacking Ensemble Machine Learning efektif untuk meningkatkan akurasi klasifikasi sentimen pengguna dompet digital serta mendukung evaluasi kualitas layanan berbasis opini pengguna.
The rapid growth of digital wallet usage has generated a large volume of user reviews that can be utilized to evaluate service quality. This study aims to improve the accuracy of digital wallet user sentiment classification using a Stacking Ensemble Machine Learning approach that combines Support Vector Machine (SVM), Multinomial Naïve Bayes (MNB), and AdaBoost with Logistic Regression as the meta-learner. User reviews were processed through text preprocessing stages, including case folding, text cleaning, tokenization, stopword removal, stemming, and TF-IDF feature weighting. Synthetic Minority Over-sampling Technique (SMOTE) was employed to address class imbalance, while model performance was evaluated using 5-Fold Cross-Validation. The experimental results show that the proposed Stacking Ensemble model achieved an average accuracy of 80.55%, outperforming the individual base learners. Furthermore, evaluations based on the Confusion Matrix, Classification Report, and Receiver Operating Characteristic (ROC) Curve demonstrated improvements in precision, recall, F1-score, and the model's discriminative capability. These findings indicate that the proposed Stacking Ensemble Machine Learning approach is effective in improving the accuracy of digital wallet user sentiment classification and can serve as a reliable tool for supporting service quality evaluation based on user opinions.
Abstract:The community service program implemented in Cibodas Tourism Village, Pasirjambu District, Bandung Regency, aimed at improving local waste management practices, reducing environmental impacts of tourism, and creating livelihood…
elihood opportunities through circular-economy activities. The program combines participatory methods and workshops on waste management by applying the 3R principles (Reduce, Reuse, and Recycle). Measurable outcomes included improved source segregation accuracy, an operational community composting facility, and income streams from recyclable materials. The initiative, aligned with Sustainable Development Goals (SDG), demonstrates that integrated education and infrastructure interventions can produce environmental and socio-economic co-benefits in rural tourism settings.
Abstract:This study explores the application of AI-based technology in assessing students' practical skills through an experimental approach at MI NU Manbaul Huda. The background of this research stems from the challenges of traditional…
itional assessment methods, which are often subjective, time-consuming, and inconsistent. The study aims to evaluate the effectiveness of an AI-based assessment system in improving the accuracy and efficiency of practical skill evaluation. The methodology involves a quasi-experimental design, comparing an experimental group using the AI system with a control group using traditional methods. Data were collected through observation, questionnaires, and interviews with teachers and students. The results show that the AI system achieved an accuracy rate of 89%, significantly higher than the traditional method's 75%. Additionally, 85% of teachers and 90% of students expressed positive perceptions of the AI system. The study concludes that AI-based technology has the potential to transform practical skill assessment by providing objective, real-time feedback. This research contributes to the field of educational technology by demonstrating the feasibility and benefits of AI in primary education, particularly in Islamic-based schools. Recommendations include teacher training and infrastructure improvements to support broader implementation.
Keywords: AI-based assessment, practical skills, primary education, Islamic schools, educational technology.
Abstract:This study aims to evaluate the accuracy of the interpretation of the Qur'an text through a semiotic approach, which can provide a holistic understanding of the mechanism of interpretation and the validity of the interpretation…
etation method used by the mufasir. This study examines three main works of interpretation and applies qualitative content analysis to uncover the semiotic aspects underlying the interpretation of sacred texts. This research also seeks to answer research questions regarding the validity of the interpretation methodology in representing the meaning of the Qur'an text as a whole. The results of the study show that there are certain patterns in the use of symbols and signs that significantly affect interpretation, thus demanding an in-depth understanding of the semiotic framework in the study of interpretation. The contribution of this research is expected to enrich the study of interpretation and provide strategic recommendations for further research in the field of Qur'an studies.
Abstract:PayLater services are one of the rapidly growing digital financial innovations widely utilised in fintech apps in Indonesia, including Kredivo and Akulaku. User reviews on the Google Play Store reflect a range of experiences,…
nces, from satisfaction with the ease of use of the service to complaints regarding bills, interest rates, late payment fees, credit limits, and app performance. This study aims to classify the sentiment of user reviews regarding PayLater services on the Kredivo and Akulaku apps using the Multinomial Naïve Bayes algorithm. Data was collected via web scraping from the Google Play Store and automatically labelled based on user ratings, with ratings of 1-2 classified as negative sentiment and ratings of 4-5 as positive sentiment, whilst a rating of 3 was excluded as it was considered ambiguous. Following a preprocessing stage comprising cleaning, case folding, tokenisation, stopword removal, and stemming, as well as feature extraction using TF-IDF, 3,652 reviews were obtained with a training-to-test data split ratio of 80:20. The results indicate that positive sentiment dominates the dataset at 56.49%, whilst negative sentiment accounts for 43.51%. Analysis by application revealed that Kredivo was dominated by positive sentiment (68.20%), whilst Akulaku was dominated by negative sentiment (51.70%). The Naïve Bayes multinomial model achieved an accuracy of 84.13%, with average precision, recall, and F1-score values of 0.84, demonstrating good and balanced classification performance across both sentiment classes.
Abstract:Islamic economics operates as a normative framework prioritizing social justice, equitable wealth redistribution, and collective wellbeing. Despite its robust philosophical foundations, empirical consensus regarding how…
these tenets translate into measurable welfare outcomes across heterogeneous developing nations remains highly fragmented. This study addresses this critical gap by executing a systematic literature review guided by the PRISMA 2020 statement to synthesize empirical evidence on the operationalization of Zakat, Waqf, and Islamic Social Finance (ISF) as structural instruments for poverty reduction. Departing from traditional descriptive reviews, this paper introduces an original analytical taxonomy that maps the operational mechanics of ISF against micro-level and macro-level development outcomes. Based on a rigorous multi-stage screening of peer-reviewed empirical studies published between 2020 and 2025 across emerging economies, a final synthesized sample of $n = 10$ high-quality primary articles was evaluated. The qualitative narrative synthesis reveals that integrated ISF instruments exert a structurally positive impact on poverty alleviation, income optimization, and socio-economic empowerment, particularly when embedded within digital financial ecosystems and formal financial inclusion frameworks. However, the analysis uncovers substantial outcome heterogeneity, demonstrating that welfare efficacy is highly conditional upon institutional governance quality, targeting accuracy, and localized implementation designs. The structural novelty of this research lies in its empirical crystallization of the explicit boundary conditions under which normative faith-based capital successfully disrupts poverty traps, offering an evidence-based operational blueprint for policymakers and Sharia social institutions in the Global South.
Abstract:The Indonesian telecommunication industry is currently experiencing saturation in the Business-to-Consumer (B2C) market segment, prompting PT Telkom Indonesia (Persero) Tbk to aggressively execute business transformation…
by shifting toward a Business-to-Business (B2B) Digital model to maintain relevance amidst increasingly competitive and dynamic global business competition. This shifting phenomenon demands comprehensive internal readiness, particularly regarding resource orchestration and marketing ambidexterity maturity to balance traditional connectivity business with the exploration of high-value digital service innovation. This research employs a quantitative methodology with descriptive and causal approaches to dissect and objectively measure the level of organizational readiness in facing such market disruptions. Primary data collection was conducted with 385 respondents consisting of employees and strategic stakeholders involved in the transformation process at PT Telkom Indonesia using a nonprobability sampling technique with a purposive sampling method. All collected data were subsequently processed and tested using the SmartPLS version 4 analysis tool to ensure accuracy in modeling the complex relationships between the variables. The data analysis techniques utilized include descriptive statistics to provide a general overview of the data and variance-based Structural Equation Modeling (PLS-SEM) to test the significance of relationships between latent variables within the research model. The research findings project that corporate strategy significantly and positively influences business transformation success and B2B Digital model development, while simultaneously providing a strong direct impact on strengthening the company's competitiveness at the global level. The analysis results also indicate that internal transformation effectiveness and the implementation of B2B digital solutions are primary determinants capable of substantially enhancing the company's ability to compete across borders in the digital platform era. Furthermore, the mediating roles of business transformation and B2B Digital model variables have proven to be crucial in reinforcing the link between corporate strategic orientation and the achievement of sustainable international competitive advantage. Overall, this study provides an empirical foundation regarding the importance of precise resource orchestration for Telkom Indonesia to realize its vision as a preferred digital telco capable of winning competition in international markets.