Analisis Ontologis, Epistemologis, dan Aksiologis dengan Studi Kasus Hutan Kota Mataram

Empiricism Journal
Vol. 7 No. 1: March 2026

Deteksi dini serangan hama merupakan langkah krusial dalam mempertahankan fungsi ekologis dan kenyamanan sosial hutan kota. Penelitian ini menggabungkan pendekatan filsafat sains—ontologis, epistemologis, dan aksiologis—dengan analisis empiris untuk merancang sistem deteksi dini serangan hama pada Hutan Kota Pagutan, Mataram. Metode yang digunakan meliputi tinjauan literatur terstruktur (Scopus, ScienceDirect, SpringerLink, IEEE Xplore, dan lainnya) terhadap studi 2023–2025, analisis model ontologi untuk representasi entitas (pohon, hama, gejala, parameter lingkungan), serta adaptasi temuan empiris penginderaan jauh (RDVI/NDVI dan UAV) menjadi skenario deteksi pada konteks lokal. Hasil studi literatur menunjukkan bahwa indikator spektral multitemporal mampu mendeteksi stres pra-visual dengan akurasi tinggi (> 90%) dan bahwa integrasi ontologi meningkatkan interoperabilitas data untuk dukungan keputusan. Simulasi adaptasi data menaksir sekitar 20–30% pohon berisiko mengalami infestasi musiman di Pagutan bila tanpa intervensi; penerapan sistem peringatan dini diperkirakan menurunkan tingkat kerusakan secara signifikan dan mempertahankan kenyamanan pengunjung. Implikasi penelitian ini adalah kerangka konseptual dan teknis yang memungkinkan pemantauan berkelanjutan, prioritas tindakan manajerial, serta penilaian nilai sosial dari pemeliharaan kesehatan pohon. Rekomendasi meliputi penerapan pemantauan multitemporal berbasis satelit/UAV, pengembangan ontologi domain lokal, dan partisipasi masyarakat sebagai bagian dari strategi aksiologis. Ontological, Epistemological, and Axiological Analysis with a Case Study of Mataram City Forest Abstract Early detection of pest attacks is essential for maintaining the ecological performance and social comfort of urban forests. This study integrates philosophical perspectives—ontology, epistemology, and axiology—with empirical evidence to design an early-warning framework for pest detection in the Pagutan Urban Forest, Mataram, Indonesia. A structured literature review was conducted using major academic databases (Scopus, ScienceDirect, SpringerLink, and IEEE Xplore) focusing on studies published between 2023 and 2025. An ontological model was developed to represent key entities such as tree species, pests, symptoms, and environmental parameters, while remote-sensing findings (RDVI/NDVI and UAV imaging) were adapted into a conceptual early-detection scenario applicable to local conditions. Results indicate that multispectral indicators are capable of identifying pre-visual stress with high accuracy (>90%). Ontology-based integration also enhances data interoperability and supports decision-making across monitoring systems. Simulated interpretation of empirical findings suggests that 20–30% of trees may face seasonal infestation risk in Pagutan without timely intervention. The proposed framework provides a foundation for continuous monitoring, management prioritization, and assessment of social values associated with maintaining healthy urban trees. Key recommendations include the adoption of multitemporal satellite/UAV monitoring, the development of localized domain ontologies, and community participation as an axiological component of urban-forest stewardship.