The tourism sector plays a crucial role in the global economy, encompassing both physical infrastructure and cultural engagement. Indonesia has a wide range of attractions and has experienced remarkable growth, with Bali as a notable example of this. With the rapid advancements in technology, travelers now have the freedom to explore independently, while online travel agencies (OTAs) serve as important resources. Reviews from tourists significantly impact the service quality and perception of destinations, and text mining is a valuable tool for extracting insights from unstructured review data. This research integrates multiclass text classification and a network analysis to uncover tourists’ behavioral patterns through their perceptions and movement. This study innovates beyond conventional sentiment and cognitive image analysis to the tourists’ perceptions of cognitive dimensions and explores the sentiment correlation between different cognitive dimensions. We find that destinations generally receive positive feedback, with 80.36% positive reviews, with natural attractions being the most positive aspect while infrastructure is the least positive aspect. We highlight that qualitative experiences do not always align with quantitative cost-effectiveness evaluations. Through a network analysis, we identify patterns in tourist mobility, highlighting three clusters of attractions that cater to diverse preferences. This research underscores the need for tourism destinations to strategically adapt to tourists’ varied expectations, enhancing their appeal and aligning their services with preferences to elevate destination competitiveness and increase tourist satisfaction.

The growth of the tourism industry in Southeast Asia, particularly in Indonesia, Thailand, and Vietnam, establishes the region as a leading global tourism destination. Numerous studies have explored tourist behavior within specific regions. However, the question of whether tourists’ experience perceptions differ based on their cultural backgrounds is still insufficiently addressed. Previous articles suggest that an individual’s cultural background plays a significant role in shaping tourist values and expectations. This study investigates how tourists’ cultural backgrounds, represented by their geographical regions of origin, impact their entertainment experiences, sentiments, and mobility patterns across the three countries. We gathered 387,010 TripAdvisor reviews and analyzed them using a combination of advanced text mining techniques and network analysis to map tourist mobility patterns. Comparing sentiments and behaviors across cultural backgrounds, this study found that entertainment preferences vary by origin. The network analysis reveals distinct exploration patterns: diverse and targeted exploration. Vietnam achieves the highest satisfaction across the cultural groups through balanced development, while Thailand’s integrated entertainment creates cultural divides, and Indonesia’s generates moderate satisfaction regardless of cultural background. This study contributes to understanding tourism dynamics in Southeast Asia through a data-driven, comparative analysis of tourist behaviors. The findings provide insights for destination management, marketing strategies, and policy development, highlighting the importance of tailoring tourism offerings to meet the diverse preferences of visitors from different global regions.

A Train Control and Monitoring System (TCMS) is a vital part of monitoring sensors in a train. The data output of sensors is sent wirelessly to the data server for monitoring. However, as the wireless channel used to send the data is a shared public network, the transmitted data are prone to hackers and attacks. This paper proposes the Securebox architecture to manage secure data transfer from the onboard Vehicle Control Unit (VCU) to the data server in TCMS. The architecture is comprised of four main functions: network management, buffer management, data management, and security management. The architecture has been successfully developed in an HSM (Hardware Security Modul) and verified using alpha and beta software testing to form a secure TCMS. From the real-time testing phase in an electric-diesel train, the average performance of the AES-based HSM showed 55% faster time processing with unnoticed 0.1% added memory usage compared to the 3DES. The secure TCMS also withstands MITM attack and provides end-to-end data security compared to the (Mobile Station) MS to Base Station (BS) only in GSM-R.

Predicting financial distress is essential in Indonesia's rapidly evolving economy, characterized by diverse business environments and regulatory challenges. This study evaluates four machine learning classifiers, XGBoost, Random Forest (RF), Support Vector Classification (SVC), and Long Short-Term Memory (LSTM), to predict financial distress among Indonesian companies. Two sampling methods, Random Under-Sampling (RUS) and Synthetic Minority Over-Sampling Technique (SMOTE), were used to address class imbalance. Empirical results indicate that the RF model trained with SMOTE sampling was the most effective, achieving an F1 score of 0.9632 and an accuracy of 0.96, while the XGBoost classifier with RUS sampling achieved a precision of 0.9716. These findings provide valuable insights into Indonesia's financial sector, guiding the selection of appropriate models for timely financial distress prediction and intervention.

Artificial intelligence (AI) technology has grown rapidly in customer service, including AI chatbots. Tokopedia has adopted an AI chatbot for customer service, but there are still many users who do not trust it because it lacks reliability. This study aims to examine the effect of AI chatbot dimensions on customer trust in Tokopedia, focusing on the variables of empathy response, anonymity, customization, and interaction. This research is quantitative using descriptive and causal methods, involving 437 Tokopedia user respondents through structured questionnaires. Data analysis using the SEM-PLS model with SmartPLS. The results show that empathy response, anonymity, customization, and interaction have a positive and significant influence on customer trust. In addition, empathy response, anonymity, and customization also have a positive effect on interaction. The suggestion for Tokopedia is to improve the quality of chatbots in providing empathetic responses, such as greeting users, appreciating, and validating their problems. Innovation in the aspects of empathy response, anonymity, customization, and interaction is also important. Future research could explore additional factors that influence customer trust as well as the mediating or moderating effects of certain variables.

Perkembangan teknologi telah menggantikan banyak aktivitas manusia dengan mesin otomatis dan digital, termasuk dalam sektor perbankan Indonesia. Transformasi ini menciptakan ekonomi digital perbankan. Dalam survei Top Brand Index Mobile Banking, Livin' by Mandiri menempati posisi ketiga. Untuk meningkatkan layanannya, Livin' by Mandiri menerapkan fitur AI seperti pembukaan rekening online, login dengan face recognition dan fingerprint, serta catatan keuangan otomatis. Namun, beberapa pengguna masih mengalami kendala dengan AI tersebut. Tujuan dari penelitian ini untuk mengetahui pengaruh expectation confirmation theory dan artificial intelligence features seperti trendiness, visual attractiveness, problem solving, communication quality, dan customization terhadap user satisfaction. Selain itu pengaruh user satisfaction dan corporate reputation terhadap acceptance of AI enabled banking juga diteliti. Penelitian ini menggunakan metode kuantitatif dengan survei kuesioner. Sampel sebanyak 172 responden diperoleh menggunakan G*Power dan diperoleh jawaban valid sebanyak 320 responden. Analisis data dilakukan dengan PLS-SEM menggunakan software SmartPLS 4.0. Hasil dari penelitian menunjukkan expectation confirmation, perceived performance, customization, dan communication quality berpengaruh signifikan dan positif terhadap user satisfaction. User satisfaction dan corporate reputation juga berpengaruh signifikan dan positif terhadap acceptance of AI enabled Banking. Fitur trendiness, visual attractiveness, dan problem solving tidak berpengaruh terhadap user satisfaction.