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All Journal Agrotrop : Journal on Agriculture Science SOCA: Jurnal Sosial Ekonomi Pertanian E-Jurnal Agroekoteknologi Tropika (Journal of Tropical Agroecotechnology) Journal of Degraded and Mining Lands Management
MOH SAIFULLOH
Jurusan/Prodi Agroekoteknologi Fakultas Pertanian Universitas Udayana, Jl. PB. Sudirman Denpasar 80232 Bali
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Economics, Econometrics & Finance

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Pemetaan Kualitas Tanah pada Lahan Kebun Campuran dengan Geography Information System (GIS) di Kecamatan Tegallalang, Kabupaten Gianyar MOH SAIFULLOH; I KETUT SARDIANA; A.A. NYOMAN SUPADMA
Jurnal Agroekoteknologi Tropika (Journal of Tropical Agroecotechnology) Vol.6, No.3, Juli 2017
Publisher : Program Studi Agroekoteknologi, Fakultas Pertanian, Universitas Udayana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (314.91 KB)

Abstract

The Mapping of Soil Quality Under Mixed Dry Land Farming Using Geography Information System (GIS) at Tegallalang, Gianyar Regency Mapping of soil quality undermixed dry land farming area using GIS was held in Tegallalang, Gianyar Regency on October 2016 – January 2017. This research implementing exploratory method on the purposive land use followed by laboratory soil analysis. Soil samples were randomly taken on each homogeneous land units on the map developed by overlaying slope, soil type, and land use maps. The following soil quality indicators as the minimum data set (MDS) were measured: soil bulk density, porosity, field capacity water content, texture, pH, C-Organic, CEC, base saturation, nutrients (N, P and K), and C-biomass. The values of soil quality were mapping using QGIS 2.18.0 and refer to land management direction. The results showed that the soil quality in the research area considered being good and medium level. The good soil quality present on land units laid down on the wavy slope had different land cover vegetation, different land management systems (fertilizer, without fertilizer, soil tillage and without soil tillage). The medium soil quality was including land units that present on steep slope, had different land cover vegetation without land managements. The limiting factors of soil quality were texture, C-Organic, CEC, base saturation, N and C-biomass. It was recommended to tillage the soil using hoe and applying organic fertilizer, Urea, and dolomite on the farming area.
Aplikasi Sistem Informasi Geografis untuk Identifikasi Potensi dan Kerentanan Longsor di Kabupaten Bondowoso Provinsi Jawa Timur AHMADI FAUZAN NUR RAHMAN; NI MADE TRIGUNASIH; I DEWA MADE ARTHAGAMA; MOH SAIFULLOH
Agrotrop : Journal on Agriculture Science Vol 13 No 2 (2023)
Publisher : Fakultas Pertanian Universitas Udayana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/AJoAS.2023.v13.i02.p11

Abstract

Application of Geographic Information System for Identification of Landslide Potential and Susceptibility in Bondowoso Regency. The study was to determine the potential and susceptibility of landslides, as well as to determine the distribution of landslide points in Bondowoso Regency. This research uses a survey and scoring method which refers to PSBA UGM (2001) and BNPB (2012). The parameters used in this study were five, namely soil movement, presipitation, soil type, slope, and land use. The landslide susceptibility level is obtained by overlapping the landslide potential map and the vital land use map. The class of landslide potential in Bondowoso Regency is categorized into areas with no potential to high landslide potential. The class with no potential spread in the lowlands is 27,906.57 ha or 18.27%, the low potential class is 60,391.34 ha or 39.53%, the medium potential class is 43,803.54 ha or 28.67% and the high potential class covering an area of 20,685.32 ha or 13.54% of the total area of Bondowoso Regency. Areas with high landslide susceptibility are Grujugan, Klabang, Maesan, Pakem, and Sempol sub-districts with 50 landslide points found in the sub-district.
Spatio-temporal of landslide potential in upstream areas, Bali tourism destinations: remote sensing and geographic information approach I Wayan Diara; I Ketut Agus Wahyu Wiradharma; R Suyarto; W Wiyanti; Moh Saifulloh
Journal of Degraded and Mining Lands Management Vol 10, No 4 (2023)
Publisher : Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15243/jdmlm.2023.104.4769

Abstract

Upstream Bali has tourist destinations with beautiful natural panoramas such as mountains, forest areas, and lakes. Characteristics of the area with steep slopes, high rainfall, and altitude above 1,500 masl. The area is inseparable from the threat of disasters, such as landslides, especially in the Baturiti District. This area often experiences landslides but has not been mapped spatially. Mitigation efforts are needed to minimize the impact of landslides. This study aimed to determine the potential for landslides and their distribution in different periods, namely 2000, 2010, and 2020. The scoring method considers four parameters: rainfall, slope, soil type, and vegetation density, using ArcGIS 10.8 Apps. Parameters extracted from remote sensing data include Landsat with ETM+ and OLI sensors, rainfall from the CHIRPS satellite, and slopes from DEMNAS. Geographic Information System (GIS) data includes soil types. Another role of GIS is to quantify raster data to build a landslide potential prediction model. Baturiti Subdistrict has a low to high potential for landslides, which are administratively distributed in Candikuning, Baturiti, Antapan, Batunya, and Bangli villages. The landslide potential in the high category in 2000, 2010, and 2020 respectively, is 70.12 ha (1%), 597.05 ha (5%), and 39.12 ha (1%). Based on the findings of this study, the leading cause of landslides is high rainfall followed by reduced vegetation density. Other factors include steep slopes (>45%) and soil types of Andosol and Regosol.
Mapping eruption affected area using Sentinel-2A imagery and machine learning techniques Ni Made Trigunasih; I Wayan Narka; Moh Saifulloh
Journal of Degraded and Mining Lands Management Vol. 11 No. 1 (2023)
Publisher : Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15243/jdmlm.2023.111.5073

Abstract

Volcanic eruptions are natural disasters with significant environmental and societal impacts. Timely detection and monitoring of volcanic eruptions are crucial for effective hazard assessment, mitigation strategies, and emergency response planning. Remote sensing technology has emerged as a valuable tool for detecting and assessing the effects of volcanic eruptions. One of the challenges in remote sensing image processing is handling large data dimensions that are difficult to address using traditional methods. Machine learning approaches offer a suitable solution to tackle these challenges. Machine learning demonstrates increasing computational capabilities, the ability to handle big data and automation. This study aimed to compare different machine learning classification algorithms, including Random Forest (RF), Support Vector Machine (SVM), Gaussian Mixture Model (GMM), and K-Nearest Neighbors (KNN). The data utilized in this study was derived from Sentinel-2A Multi-Spectral Instrument (MSI) imagery, which was tested in areas affected by the eruption of Mount Agung, Bali Province, in 2017. The results indicated that the GMM algorithm performed the best among the machine learning classifiers, achieving an Overall Accuracy (OA) value of 82.04%. It was followed by RF (78.86%) and KNN (77.55%). The areas affected by volcanic eruptions were determined by overlaying disaster-prone regions with areas mapped using the machine learning approach. The total affected area was measured as 29.89 km2, with an additional 3.31 km2 outside the designated zone. The findings of this study serve as a guideline for governmental entities, stakeholders, and communities to implement effective mitigation efforts for disaster risk reduction.
Laying Hen Farming as Agribusiness Potential Anak Agung Istri Agung Peradnya Dewi; Moh Saifulloh
SOCA: Jurnal Sosial Ekonomi Pertanian Vol 18 No 2 (2024): Vol 18 No 2 (2024)
Publisher : Program Studi Agribisnis, Fakultas Pertanian, Universitas Udayana Jalan PB.Sudirman Denpasar, Bali, Indonesia. Telp: (0361) 223544 Email: soca@unud.ac.id

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/SOCA.2024.v18.i02.p06

Abstract

This study examined the potential of Quantum GIS (QGIS) as a cutting-edge tool for analyzing and comprehending the spatial dynamics of the livestock industry, particularly in identifying ideal locations for layer chicken farms in Tabanan Regency’s Penebel District. The objective of this study was to identify the characteristics of entrepreneurs who own layer-breeder chicken farms in the Penebel District and analyze the agribusiness prospects of such farms in the same district. Employing a descriptive qualitative analysis approach with an exploratory survey using the QGIS 3.34.5 application. The respondents were selected through purposive sampling. The results indicated that the majority of layer chicken farm owners in Penebel District are middle-aged men (56-65 years old) with high school education and have gathered substantial cash amounting to hundreds of millions. They typically own between 3,001 and 10,000 laying hens. Potential farm owners are concentrated in the villages of Babahan, Senganan, and Jatiluwih. This study aimed to facilitate collaboration between the Tabanan District Government and relevant companies to assist layer farmers in expanding their market share and identifying suitable financial options. Furthermore, this study has the potential to extend the application of GIS technology in the livestock business to other regions undergoing comparable development
Co-Authors A.A.NYOMAN SUPADMA AHMADI FAUZAN NUR RAHMAN Anak Agung Istri Agung Peradnya Dewi I DEWA MADE ARTHAGAMA I Ketut Agus Wahyu Wiradharma I Ketut Sardiana I Wayan Diara I Wayan Narka NI MADE TRIGUNASIH R Suyarto W Wiyanti