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All Journal MARSEGU : Jurnal Sains dan Teknologi Maanu : Jurnal Pengabdian Kepada Masyarakat
Gun Mardiatmoko
Program Studi Kehutanan, Fakultas Pertanian, Universitas Pattimura, Ambon. Indonesia
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Engineering

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SOSIALISASI DAN EDUKASI URBAN FARMING DI RW 02 DAN 03, KELURAHAN BATU MEJA KOTA AMBON Zakarias Frans Mores Hukom; Gun Mardiatmoko; Pieter J. Kunu; Abraham Talahaturuson; Jollanda Effendy; Hermina N. Taihuttu
MAANU: Jurnal Pengabdian Kepada Masyarakat Vol 4 No 1 (2026): Maanu Jurnal Pengabdian Kepada Masyarakat
Publisher : Universitas Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/maanuv4i1p15-27

Abstract

Pekarangan adalah lahan sempit berpagar keliling atau pun tidak di sekitar rumah. Pemanfaatan lahan pekarangan di daerah kota (Urban Farming) merupakan program kegiatan pengelolaan dengan memanfaatkan inovasi teknologi yang menghasilkan produk pertanian hortikultura, perikanan dan ternak secara efektif. Kontribusi utama urban farming adalah suplai bahan pangan lokal sehat untuk kebutuhan keluarga dan nilai estetika untuk memenuhi kebutuhn sekunder keluarga. Oleh karena itu peningkatan produktivitas dan kualitas produk pekarangan perlu ditingkatkan melaui program sosialisasi pengembangan inovasi teknologi urban farming bagi masyarakat perkotaan. Paket teknolgi inovasi urban farming yang disosialisasikan dalam kegiatan PkM di Kelurahan Batu Meja merupakan salah satu realisasi program kerja dengan Pemerintah kota Ambon “keluar bercerita dengan masyarakat”. Materi sosialisasi meliputi pengembangan bentuk-bentuk inovasi teknologi urban farming, fungsi dan manfaat kegiatan urban farming sebagai potensi peningkatan bahan pangan lokal yang sehat, pola pengembangan urban farming ke depan secara berkelanjutan dan dampaknya. Diharapkan transfer teknologi dalam kegiatan sosialisasi ini dapat bermanfaat untuk memotivasi masyarakat kota kelurahan Batu Meja terhadap potensi pengembangan urban farming yang memiliki nilai ekonomi keluarga tambahan dan peluang bisnis ke depan.
MITIGASI BENCANA TSUNAMI BERBASIS EKOSISTEM MANGROVE DI DESA NANIA KOTA AMBON Juglans Howard Pietersz; Jan Willem Hatulesila; Gun Mardiatmoko; Simson Liubana
MAANU: Jurnal Pengabdian Kepada Masyarakat Vol 4 No 1 (2026): Maanu Jurnal Pengabdian Kepada Masyarakat
Publisher : Universitas Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/maanuv4i1p109-123

Abstract

Mangrove forests serve as natural green barriers that play a crucial role in disaster mitigation. However, their existence as ecological buffers for both terrestrial and marine environments is often overlooked due to development pressures. Nania Village represents an example of urban expansion that directly intersects with mangrove areas in the Inner Ambon Bay. Preliminary assessments indicate that the community is largely unaware of their daily interactions that may contribute to the degradation of the local mangrove ecosystem. This community engagement activity was designed to provide education to the residents of Nania, fostering greater awareness of the importance of mangrove forests as a key component of disaster mitigation. The program was carried out through public outreach sessions, collective clean-up activities within the mangrove area, and mangrove planting actions. As a result, the initiative successfully strengthened community awareness and commitment to protecting and conserving the surrounding mangrove forests, thereby reducing the potential risks of natural disasters, particularly tsunamis.
EFEKTIVITAS APLIKASI ALGORITMA MACHINE LEARNING DALAM KLASIFIKASI TUTUPAN LAHAN DI PULAU NUSALAUT Mark Chara Papilaya; Gun Mardiatmoko; Ronny Loppies
MARSEGU : Jurnal Sains dan Teknologi Vol. 2 No. 12 (2026): MARSEGU : Jurnal Sains dan Teknologi
Publisher : PT. BARRINGTONIA ASIATICA LESTARI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69840/marsegu/2.12.2026.851-864

Abstract

Monitoring and classification of forest land cover on small islands require accurate and efficient methods to support sustainable natural resource management. This study aims to evaluate the effectiveness of Machine Learning algorithms, namely Classification and Regression Tree (CART), Support Vector Machine (SVM), and Random Forest (RF), in classifying forest land cover on Nusalaut Island, Maluku Province, and to compare their performance in terms of accuracy, efficiency, and computational resource requirements. The study utilized Sentinel-2 Level-2A satellite imagery from 2025, processed using the Google Earth Engine platform. Supervised classification was applied to four land cover classes, namely water bodies, built-up areas, open land, and vegetation. Model performance was evaluated using a confusion matrix to obtain Overall Accuracy (OA) and the Kappa coefficient. The results indicate that all three algorithms produced high classification accuracy, with SVM and RF achieving the best performance, attaining an OA of 98% and a Kappa value of 0.97, while CART achieved an OA of 94% and a Kappa value of 0.90. SVM demonstrated superior class separation for land cover types with distinct spectral characteristics, whereas RF was more robust to data noise. These findings suggest that Machine Learning algorithms, particularly SVM and RF, are highly effective for forest land cover classification in small island environments such as Nusalaut Island.
Co-Authors Abraham Talahaturuson Hermina N. Taihuttu Jan Willem Hatulesila Jollanda Effendy Juglans Howard Pietersz Mark Chara Papilaya Pieter J. Kunu Ronny Loppies Simson Liubana Zakarias Frans Mores Hukom