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All Journal Jurnal Penelitian Kehutanan Bonita JURNAL HUTAN TROPIS
Muh Daud
Universitas Muhammadiyah Makassar
Agriculture, Biological Sciences & Forestry Computer Science & IT Earth & Planetary Sciences Environmental Science Materials Science & Nanotechnology Social Sciences

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POTENSI OBJEK DAN DAYA TARIK WISATA ALAM PUNCAK TINAMBUNG DI DESA BISSOLORO KECAMATAN BUNGAYA KABUPATEN GOWA Hasanuddin Molo; Sultan Sultan; Husnah Latifah; Muh Daud; Asriani Asriani
Jurnal Penelitian Kehutanan BONITA Vol 2, No 1 (2020): JUli 2020
Publisher : Universitas Andi Djemma Palopo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55285/bonita.v2i1.432

Abstract

Objek wisata alam Puncak Tinambung Bissoloro ini merupakan objek wisata alam yang sangat potensial untuk dikembangkan karena masih banyak potensi lain didalamnya yang dapat mendukung perkembangan wisata alam, dan dapat menarik minat wisatawan domestik maupun mancanegara lebih banyak lagi. Penelitian ini dilaksanakan selama 2 bulan pada bulan November sampai bulan Desember 2018. Lokasi penelitian ini dilaksanakan di Kecamatan Bungaya Kabupaten Gowa Provinsi Sulawesi Selatan. Penelitian ini bertujuan untuk mengetahui potensi alam yang menjadi daya tarik objek wisata di Puncak Tinambung Bissoloro serta mengevaluasi tingkat kelayakan pengembangan objek  wisata alam tersebut. Pengambilan data dilakukan dengan menggunakan dua metode yaitu analisis kualitatif deskriptif dan metode penilaian kelayakan ekowisata. Berdasarkan hasil penelitian dapat diketahui bahwa Puncak Tinambung memiliki beragam potensi wisata baik flora dan fauna maupun panorama alam yang indah. Kawasan wisata alam Puncak Tinambung memenuhi kriteria  tingkat kelayakan diatas 66,6% yaitu sebesar 77,84% sehingga menjadikan kawasan wisata tersebut layak untuk dikembangkan.
HARNESSING UAV IMAGERY FOR EFFICIENT MONITORING AND EVALUATION OF FOREST REHABILITATION IN INDONESIA Naufal Naufal; Muhammad Tahnur; Hasanuddin Molo; Muh Daud; Fidaan Husein Azuz
Jurnal Hutan Tropis Vol 13, No 3 (2025): Jurnal Hutan Tropis Volume 13 Nomer 3 Edisi September 2025
Publisher : Lambung Mangkurat University-Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/jht.v13i3.23802

Abstract

The successful outcome of forest rehabilitation governance projects in Indonesia remains debatable due to a lack of convincing evidence, including the use of manipulable geotagging camera photos. Conversely, advancements in deep learning and machine learning for object detection have shown significant promise, yet their application often remains challenging for local communities and governments due to limited technical expertise. This research investigates the use of high-spatial-resolution UAV imagery integrated with deep learning techniques to identify planting holes and seedlings—key indicators of forest rehabilitation progress. Our study aimed to enhance accountability in forest rehabilitation governance projects by developing a user-friendly and government-aligned model that simplifies application for non-experts. By leveraging eCognition software, we customised learning templates and implemented parameter optimisations to address environmental complexities, such as shadows and dense grass, which often lead to misclassification. Applied to a 200-hectare rehabilitation site in Baringeng Village, Bone Regency, South Sulawesi, our method achieved an accuracy of 85.97% for the planting hole class and 82.15% for the planting seedlings class. This iterative refinement process revealed the potential for broad applicability in forest monitoring projects with tight timelines and the need for independent or third-party assessments. Our findings underscore the value of integrating UAV imagery and automated machine learning workflows for cost-effective and scalable forest rehabilitation monitoring while identifying limitations and opportunities for future research in larger and more diverse environments.
Co-Authors Asriani Asriani Fidaan Husein Azuz Hasanuddin Molo Husnah Latifah Muhammad Tahnur Naufal Naufal Sultan Sultan