The research aims to examine the antecedents of Fintech Peer-to-Peer (P2P) lending adoption in Indonesia by extending the UTAUT3 framework with financial risk tolerance and artificial intelligence literacy. It also explores the moderating role of green self-identity and gender differences in P2P lending adoption. Data were collected from 421 respondents in Indonesia using a non-probability voluntary response sampling approach and analyzed using the PLS-SEM method. The results indicate that performance expectancy, effort expectancy, social influence, and AI literacy have a positive and significant influence on behavioral intention toward P2P lending. Facilitating conditions positively and significantly affect use behavior. Green self-identity weakens the relationship between behavioral intention and use behavior. No significant gender differences were found in the correlations between behavioral intention, use behavior, and financial inclusion. This study advances the literature on financial innovation by bridging theory and practice in the context of AI-enabled Fintech P2P lending. By integrating empirical evidence with policy considerations, the findings highlight the importance of collaboration between regulators and Fintech providers in fostering responsible innovation. The results provide actionable implications for designing user-centered lending systems that enhance financial inclusion. In this regard, the study contributes to the advancement of inclusive digital finance and supports Indonesia’s broader strategic objective of expanding sustainable and equitable access to financial services.
This study investigates the determinants of FinTech adoption and its role in supporting financial inclusion among micro, small, and medium enterprises (MSMEs) in South Sumatra, Indonesia. The analysis applies an extended Unified Theory of Acceptance and Use of Technology (UTAUT) framework that incorporates digital financial literacy, artificial intelligence literacy, green self-identity, and perceived green finance. Data from 632 MSMEs, comprising 377 rural and 255 urban enterprises, were analyzed using partial least squares structural equation modeling (PLS-SEM), multi-group analysis (MGA), and importance performance map analysis (IPMA). The results indicate that facilitating conditions represent the most influential determinant of FinTech adoption among rural MSMEs, while effort expectancy emerges as the dominant factor in urban enterprises. FinTech adoption also significantly strengthens both FinTech continuance intention and financial inclusion across the two groups, highlighting the role of digital financial technologies in promoting inclusive economic development. In addition, the IPMA shows that rural MSMEs place strong emphasis on facilitating conditions as the key driver of FinTech adoption, whereas urban MSMEs prioritize effort expectancy. By extending the UTAUT framework with sustainability-related constructs, this study provides new evidence on how digital financial innovation can support inclusive growth and contribute to Sustainable Development Goal 8.
To achieve the 2030 SDGs, this study will examine the acceleration of social development through the use of digital technology. This is done in an effort to reduce the disparity between urban and rural areas. Using qualitative methods and data analysis assisted by NVivo 12 Plus software, this study analyzes available digital infrastructure, digital literacy, access to services, and digital economic opportunities. The results show that digital infrastructure is a crucial component in equalizing access to information and public services. However, uneven availability remains a problem, especially in 3T (remote and underdeveloped) regions. Digital literacy in rural communities also needs to be strengthened so that technology can be optimally utilized to support improvements in quality of life. In addition, digital services such as e-government, digital payments, and e-commerce have been proven to open more efficient service access and new business opportunities for local MSMEs in supporting inclusive economic growth. However, this study has limitations in the scope of secondary data and does not include an in-depth field perspective. Therefore, further research is recommended to conduct a comprehensive analysis using mixed methods and expand the research object to different rural areas to obtain a more representative picture of the acceleration of digital transformation in Indonesia.
Digitalisasi koleksi museum merupakan upaya strategis dalam mendukung pelestarian warisan budaya benda melalui pemanfaatan teknologi informasi. Museum Sultan Mahmud Badaruddin II (SMB II) Palembang sebagai institusi pelestarian budaya memiliki berbagai koleksi benda bersejarah yang merepresentasikan identitas budaya masyarakat, khususnya Palembang. Namun, pengelolaan inventaris masih menghadapi kendala berupa keterbatasan dokumentasi digital, belum tersusunnya data koleksi secara sistematis, serta terbatasnya kemampuan pengelola dalam pengarsipan digital. Kegiatan pengabdian ini bertujuan untuk mendukung revitalisasi pengelolaan koleksi melalui pendampingan digitalisasi inventaris berbasis teknologi digital. Metode pelaksanaan meliputi observasi, identifikasi koleksi, dokumentasi visual, pencatatan metadata, serta pengelolaan data koleksi menggunakan Microsoft Excel. Selain itu, dilakukan pendampingan teknis kepada staf museum dalam digitalisasi visual koleksi museum. Hasil kegiatan pendampingan menunjukkan bahwa sebanyak 100 benda budaya koleksi museum SMB II berhasil didokumentasikan secara sistematis. Kegiatan ini sebagai langkah awal dalam degitalisasi koleksi museum SMBII dan diharapkan dapat meningkatkan efisiensi pengelolaan koleksi, mendukung pelestarian budaya benda, serta diharapkan dapat memperluas akses informasi budaya bagi pengelola museum secara berkelanjutan. Assistance in the Digitalization of the Collections of the Sultan Mahmud Badaruddin II Museum in Palembang Abstract Digitizing museum collections is a strategic effort to support the preservation of tangible cultural heritage through the use of information technology. The Sultan Mahmud Badaruddin II Museum (SMB II) Palembang, as a cultural preservation institution, has a diverse collection of historical objects that represent the cultural identity of the community, especially Palembang. However, inventory management still faces obstacles such as limited digital documentation, the lack of systematic collection data, and limited management skills in digital archiving. This community service activity aims to support the revitalization of collection management through assistance with digital technology-based inventory digitization. Implementation methods include observation, collection identification, visual documentation, metadata recording, and collection data management using Microsoft Excel. In addition, technical assistance was provided to museum staff in the visual digitization of museum collections. The results of the assistance activity indicate that 100 cultural objects from the SMB II museum collection have been successfully documented systematically. This activity is the first step in digitizing the SMBII museum collection and is expected to improve the efficiency of collection management, support the preservation of tangible cultural objects, and is expected to expand access to cultural information for museum managers in a sustainable manner.
The IEC 61850 protocol uses GOOSE messages for fast, reliable power system communication, but is vulnerable to DoS attacks that can disrupt protection and control communications. To address this vulnerability, an LSTM model can learn sequence-based communication patterns, but its performance depends on hyperparameter configuration. Therefore, an approach is needed to optimize the LSTM configuration. The present study proposes an optimized LSTM system for detecting DoS attacks in GOOSE communications. The proposed approach integrates GOOSE-specific feature extraction, feature selection, class imbalance handling, and hyperparameter optimization to enhance the model's detection capabilities. The novelty of this research lies in the use of GOOSE communication attribute characteristics, such as stNum, sqNum, and timeAllowedToLive, within the Optimized LSTM approach to improve the reliability of DoS attack detection in GOOSE. The evaluation results demonstrate that the Optimized LSTM model exhibits a high degree of accuracy, with a mean accuracy of 99.76%, a precision of 99.84%, a recall of 99.69%, and an F1-score of 99.76%. These findings are corroborated by statistical analysis, which reveals significant disparities between models in terms of accuracy, F1-score, and ROC-AUC (p-value < 0.0001). Moreover, the Optimized LSTM model has been demonstrated to surpass competing models, including the Convolutional Neural Network (CNN), Gated Recurrent Unit (GRU), and Transformer models (adjusted p-value 0.0059–0.0176). These results support the conclusion that optimizing the LSTM model's hyperparameters can enhance DoS detection performance and advance deep learning-based detection in GOOSE communications.
