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All Journal JSTT Ruang International Journal of Technology and Education Research CITAKARYA Jurnal Pengabdian Masyarakat
Agustina, Nadine Sandra
Prodi S1 Agroteknologi, Jurusan Budi Daya Pertanian, Fakultas Pertanian, Universitas Tadulako, Palu
Humanities Civil Engineering, Building, Construction & Architecture Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Languange, Linguistic, Communication & Media Social Sciences Other

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KAJIAN SINKRONISASI PERENCANAAN BLOK PEMBERDAYAAN MASYARAKAT PADA KPHP MODEL SINTUWU MAROSO KABUPATEN POSO Agustina, Nadine Sandra
JSTT Vol 3, No 1 (2014)
Publisher : JSTT

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (329.674 KB)

Abstract

The aim of the research was to synchronize the planning of community empowerment block using at PFMU of Sintuwu Maroso Model with factual condition in field and to analyze community responses on the planning of community empowerment block at PFMU of Sintuwu Maroso Model management. Community population were those who settle around the community empowerment block. Community forest at Kilo and Buyumpondoli villages were also sampled.  The respondent number was 50 people who lived around the area of HKm and village forest.  Data were analyzed using comparison analysis, land cover analysis, and the likert scale of 1, 3, and 5.  The research results showed that the community forests at both Kilo and Buyumpondoli villages are suitable for Hkm and village forest. The land of Kilo village Hkm and Buyumpondoli village forest were dominantly covered by dry land such as plantation and bushes.  Based on the likert scale, the criteria for the community empowerment block were that RKTN/RKTP and RKTK should be directed toward forest management at small scale 198 (79%); forest with low product 228 (91%); non forest area 236 (94%); HKm, village forest, and HTR  permit 142 (56%), area close to community 250 (100%); possibility of RKTN/RKTP/RKTK to be included in either rehabilitation region or  big/small scale forest region 144 (57%); and the community responses 220 (88%).  It can be concluded that the criteria and the community responses are classified high.
Pemanfaatan Pestisida Nabati dalam Pengendalian Hama Kakao Berbasis Ramah Lingkungan di Desa Bahomoleo Sandra Agustina, Nadine; Rimbun, Epriani; Hidayat, Rahmat; Iskandar
CITAKARYA Jurnal Pengabdian Masyarakat Vol. 3 No. 04 (2025): Nopember - Januari
Publisher : CITAKARYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63922/citakarya.v2i04.2629

Abstract

Kegiatan pengabdian ini bertujuan untuk meningkatkan kemampuan petani dalam memproduksi dan mengaplikasikan pestisida nabati sebagai alternatif pengendalian hama kakao yang ramah lingkungan. Metode yang digunakan meliputi penyuluhan, pelatihan teknis pembuatan pestisida nabati berbahan daun sirsak dan serai, demonstrasi plot, serta pendampingan evaluatif. Hasil kegiatan menunjukkan bahwa petani mampu memproduksi pestisida nabati secara mandiri dengan efektivitas tinggi dalam menekan serangan Penggerek Buah Kakao (PBK) dan penyakit Vascular Streak Dieback (VSD). Selain itu, kegiatan ini meningkatkan pemahaman petani terhadap pentingnya pertanian berkelanjutan dan mengurangi ketergantungan terhadap pestisida kimia. Temuan ini menunjukkan bahwa pestisida nabati merupakan teknologi tepat guna yang dapat diterapkan secara berkelanjutan dalam sistem usahatani kakao.
Identification of Soil Bacteria in Several Paddy Fields (Oryza sativa L.) in Wita Ponda District Novita Christanty Pasaungan; Nadine Sandra Agustina; Epriani Rimbun; Rahmat Hidayat
International Journal of Technology and Education Research Vol. 3 No. 04 (2025): October - December, International Journal of Technology and Education Research
Publisher : International journal of technology and education research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63922/ijeter.v3i04.2741

Abstract

Rice (Oryza sativa L.) is a major agricultural commodity in Wita Ponda District and serves as the primary livelihood and economic support for local communities. However, rice productivity in this area remains relatively low, partly due to limited farmer knowledge regarding crop management and insufficient information on soil conditions, particularly soil nutrient content. This study aimed to identify soil bacteria present in paddy fields (Oryza sativa L.) in Wita Ponda District. The research was conducted from March to May 2024. Soil samples were collected from five locations in Wita Ponda District at a depth of 0–20 cm. One composite soil sample from each location was used for bacterial identification as well as for soil pH and organic carbon (C-organic) analysis.The results showed that a total of 1,184 bacterial isolates were identified from paddy fields located in Lantula Jaya, Bumi Harapan, Puntari Makmur, Emea, and Solonsa Jaya villages. Among these, 362 distinct bacterial isolates were grouped into four genera: Azotobacter (119 isolates), Bacillus (45 isolates), Marinococcus (181 isolates), and Corynebacterium (17 isolates). Soil pH values ranged from slightly acidic to neutral, with values of 6.49, 6.44, 6.90, and 6.68. Soil organic carbon content ranged from low to moderate, with values of 1.18%, 1.37%, 2.44%, 1.16%, and 1.81%.
Penerapan GeoAI Berbasis Mask R-CNN untuk Deteksi Kendaraan pada Citra Orthophoto Kawasan Perkotaan Septyana, Dita; Andresi, Budi; Agustina, Nadine Sandra
JURNAL RUANG / ISSN : 2085-6962 Vol 20 No 1 (2026): JURNAL RUANG
Publisher : Jurusan Teknik Arsitektur, Fakultas Teknik Universitas Tadulako Kampus Bumi Tadulako Tondo Jl. Sukarno-Hatta Km.9, Palu 94118 e-mail :Jurusan Arsitektur, Fakultas Teknik Universitas Tadulako Kampus Bumi Tadulako Tondo Jl. Sukarno-Hatta Km.9, Palu 941

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22487/ruang.v20i1.333

Abstract

GeoAI technology, which integrates artificial intelligence with spatial analysis, offers a novel approach to extracting urban object information from high-resolution imagery. This study applies Mask R-CNN with a ResNet-50 backbone architecture to detect vehicle objects in orthophoto imagery derived from the processing of 100 UAV photographs over an urban area in Switzerland. A total of 80 vehicle objects were annotated and partitioned into training (70%), validation (15%), and testing (15%) datasets. Model evaluation was conducted using a multi-threshold Intersection over Union (IoU) approach at values of ≥0.5, ≥0.75, and ≥0.95, and analyzed through a confusion matrix alongside Precision, Recall, F1-score, and Mean Average Precision (mAP) metrics. The results demonstrate that the model achieved Precision and Recall scores of 1.00 at IoU ≥0.5; however, performance declined at stricter thresholds, with an aggregate mAP of 0.56, indicating moderate overall performance. These findings suggest that the model is effective for macro-spatial analytical needs such as vehicle count estimation and distribution mapping, yet remains insufficiently stable for applications requiring high geometric precision. Conceptually, this study underscores the importance of multi-threshold evaluation in the application of deep learning for urban spatial analysis, while demonstrating the potential of GeoAI integration in data-driven urban planning.
Co-Authors Budi Andresi Epriani Rimbun ISKANDAR Novita Christanty Pasaungan Rahmat Hidayat Rimbun, Epriani Septyana, Dita