Authors - Mainford Mutandavari, D. Hemavathi Abstract - Maize (Zea mays L.) is an essential staple produce for smallholder farmers in developing nations, yet Northern Corn Leaf Blight (NLB), Grey Leaf Spot (GLS), and Common Rust foliar diseases cause yield losses of 30–70%. Infection detection is done at advanced stages due to labor intensity resulting from the conventional disease monitoring methods. A Low-Cost Drone-Mounted Multispectral Imaging (LCDMI) framework for resource-constrained smallholder systems is presented in this paper, pairing a consumer-grade UAV with a five-band multispectral sensor. The vegetation-index features are fused with multispectral band data using a Spectral-Spatial Attention Vision Transformer (SSAViT) classifier and a Spectral-Constrained Synthetic Data Generation (SC-SDG) module addresses training-data scarcity. A hardware cost of USD1,940 is projected for field evaluations across twelve plots in Zimbabwe over two growing seasons yielding 95.8% detection accuracy, identifying diseases 7–12 days before visible symptom onset. A multi-label extension enables simultaneous classification of co-occurring infections. Georeferenced disease maps are delivered within 6.3 min/ha. With perhectare costs as low as USD2.10 on a scale, the economic analysis projects ROI within two seasons for cooperatives managing 50+ hectares.
Authors - IGN Oka Ariwangsa, Komang Widhya Sedana Putra P, Wayan Sri Maitri Abstract - The rapid adoption of artificial intelligence (AI) in higher education has transformed how students access information and engage in academic activities. While AI-powered technologies enhance efficiency and provide personalised support, their uncritical use may weaken independent reasoning and reduce meaningful social-academic participation. This raises concerns in digitally mediated environments where individuals must interpret complex information, evaluate uncertainty, and make informed judgments. Despite growing attention, most studies emphasise functional outcomes such as academic performance, overlooking the mechanisms through which AI-integrated teaching can foster deeper, more sustainable learning. This study examines how AI-aware pedagogy—defined as the intentional and reflective integration of AI in instructional design—enhances critical thinking through social-academic engagement. A quantitative approach was employed, involving 200 undergraduate students in Indonesia. Data were collected via structured questionnaires and analysed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings reveal that AI-aware pedagogy has no significant direct effect on critical thinking. However, it significantly influences critical thinking indirectly through social-academic engagement. This indicates that higher-order thinking develops not merely through technological integration, but through socially embedded learning processes that encourage interaction, reflection, and evaluation. Theoretically, this study links digital pedagogy with cognitive and social learning processes. Practically, it highlights the need for AIsupported environments that foster critical evaluation and responsible decisionmaking under conditions of uncertainty. Future research should explore its applicability across contexts and its long-term cognitive implications.
Authors - Mary Diana C. Yamzon, Janelli M. Mendez Abstract - This study provides a data-driven analysis of the academic performance of Bachelor of Science in Office Administration (BSOAD) students at Tagbilaran City College from Academic Year 2021 to 2024, employing data mining clustering techniques to ascertain the five most challenging subjects. The study specifically aimed to: (1) construct and preprocess a dataset of pertinent academic attributes; (2) employ K-Means, K-Medoids, and Agglomerative Hierarchical Clustering algorithms to discern groupings of subject difficulty; (3) validate clustering results utilizing the Davies-Bouldin Index (DBI); and (4) develop evidence-based recommendations for curriculum enhancement and academic assistance. The analysis involved a dataset of 26,965 valid student grade records across 68 subjects, all of which were processed using RapidMiner Studio. The research utilized the Cross-Industry Standard Process for Data Mining (CRISP-DM) framework within the context of Educational Data Mining (EDM). The DBI for K-Means (DBI = 0.461; Excellent) and K-Medoids (DBI = 0.9145) were used to check the clusters, and the visual dendrogram was used to check the Agglomerative Hierarchical Clustering. All three algorithms consistently recognized OA113 Advanced Shorthand and OA111 Foundations of Shorthand as the two most challenging subjects in the program. The results offer statistically substantiated, evidence-based insights to facilitate curriculum evaluation, instructional enhancement, and the formulation of specialized academic intervention programs for BSOAD students.
Authors - Hussein P. El Sayed Ahmed, Ardee Joy T. Ocampo Abstract - Youth participation in local governance remains a persistent challenge despite institutional mechanisms designed to promote engagement. In the Philippines, the Sangguniang Kabataan (SK) serves as a formal platform for youth involvement in local decisionmaking; however, many SK programs continue to experience low participation, limited feedback integration, and repetitive activity design. This study presents EmpowerSK, a data-driven framework that leverages data mining techniques to enhance youth engagement in SK programs. Using structured survey data from 1,055 youth respondents aged 18–25 across the nine barangays of Alilem, Ilocos Sur, the study applies the Knowledge Discovery in Databases (KDD) framework, K-Means clustering, and sentiment analysis to transform raw feedback into governance intelligence. K-Means clustering (k=3) identified three statistically validated engagement profiles: Highly Active (61.6%), Moderately Involved (17.0%), and Disengaged (21.3%). Sentiment analysis of open-ended responses revealed appreciation (77.8% positive), diagnosis (73.2% negative), and aspiration (85.5% neutral-aspirational) as a coherent three-phase youth governance narrative. An overall weighted mean of 3.75 ("Very Good") across eleven Likert-scale items confirmed a critical institutional gap: Digital Engagement (4.14) significantly outpaced SK Support Initiatives (3.52), with SK Training recording the lowest item score (3.44). A five-pillar data-driven action plan—Awareness and Inclusion, Program Diversification, Digital Transformation, Capacity Building, and Monitoring and Evaluation—was developed, validated by SK officials, and aligned with SDG 4, 11, and 16. The findings demonstrate that freely available data mining tools can transform rural youth governance into an annually replicable, evidence-based participatory system.
Authors - Reynaldo F. Agunod, Janelli M. Mendez Abstract - Higher education institutions collect large volumes of student data but these are underutilized for institutional planning. This study applies the CRISP-DM framework to enrolment records of a freshman cohort of 1,916 students across four academic years (2021-2025) across 28 academic programs from a private higher education institution in Central Visayas, Philippines, to forecast institutional progression metrics using predictive analytics. Descriptive analytics and three predictive models were applied based on their suitability for the dataset with 3-4 data points, namely: Linear Regression, Holt-Winters Exponential Smoothing, and ARIMA. Six institutional performance metrics were analyzed: enrolment, retention, persistence, attrition, program shifts, and graduation. Key findings reveal a continuous 29.6% enrolment decline within the cohort, an im-proving retention and persistence profile, a program-shift surge largely due to migrations from Accountancy to Finance, and a rapidly increasing graduation rate. Linear Regression (OLS) was identified as the most effective forecasting model for the study’s single-cohort dataset.
Authors - Edimar J. Rato, Janelli M. Mendez Abstract - Student dropout has remained a major problem in all higher education institutions globally, including in the Philippines, where the total college dropout rate in the country was recorded at about 35.15% in the Academic Year 2023–2024. This study aimed to develop a predictive analytics model that identifies dropout and retention patterns among students of Tagbilaran City College to support evidence-based intervention strategies. offered by the school from Academic Year 2021-2024. The algorithms implemented for the supervised learning process include Random Forest and Gradient Boosting, while the algorithm for the unsupervised learning process is K-Means Clustering implemented using the RapidMiner Studio tool. Results revealed that both supervised models had a poor performance due to class imbalance issues as well as a small feature set; the Random Forest model had an accuracy of 59.59%, while it had an AUC of 0.575. The Gradient Boosting model had an accuracy of 60.51%, while it had an AUC of 0.508. The K-Means Clustering model had a good performance since it resulted in three interpretable student risk clusters: a moderate-risk group with a dropout rate of 27.3%, a highest-risk group with a dropout rate of 44.7%, and a lower-risk but larger group with a dropout rate of 41.9%. The Davies-Bouldin Index of 0.967 confirmed adequate cluster separation. The K-Means model demonstrated the most practical utility as an early-warning risk stratification tool applicable at the start of each academic year, forming the foundation of an evidence-based intervention plan to improve student retention at Tagbilaran City College.
Authors - Ni Made Prasiwi Bestari, Jonathan Jacob Paul Latupeirissa, Suryanto Nugroho, Iwan Adinugroho, Melati Budi Srikandi, Ayu Made Bianca Juarez Abstract - The Fourth Industrial Revolution has been transforming the global tourism industry, shifting toward a dynamic Tourism 4.0 ecosystem. Given that the adoption of AI is expected to increase the revenue of the tourism industry, it is necessary to conduct a Systematic Literature Review to fill the gap in empirical research on the relationship between technological innovation and long-term sustainability. Most studies on smart tourism from different perspectives, including tourist behavior, tourist service quality, innovation, and sustainability, focus on the "hardware construction" at the macro level and its implementation based on related policies, ignoring the psychological mechanisms affecting tourists' experiences at the micro level. This study aims to identify the key technological drivers, including AI, IoT, and computer vision, and their influence on operational innovation and Sustainable Development Goals. A total of 23 core manuscripts from 2020 to 2025 gathered from Scopus database were synthesized and analyzed based on PRISMA guidelines. The results showed that smart tourism technologies can greatly improve efficiency and enhance hyper-personalization. However, most current applications of smart tourism technologies do not take adequate account of social and environmental metrics. Also, many digital tourism strategies prioritize revenue over social inclusion. For the future of smart tourism destinations, frameworks such as Society 5.0 that integrate high-tech with the human touch of hospitality and tourism are needed. Destinations should also seek governance models that ensure long-term resilience by moving the focus away from infrastructure and toward "Smart People" initiatives and the development of standardized real-time sustainability metrics.
