Studi Optimalisasi Pemanfaatan Lahan Di Kampus Universitas Diponegoro Tembalang Berdasarkan Analisis Citra Multi Temporal

UNDIP is one of the biggest university in Central Java Province. UNDIP has 1.352.054 m2 area located in Tembalang. Since 1996 Tembalang campus has been used, until 2015 land use in Tembalang campus and the surrounding area has many changes especially wasteland that become educational activities building. If we see today, Tembalang campus still has many wasteland spreaded inside the campus area and not yet functioned. Because of that, a research that studying land use changes multitemporally and studying optimalization of campus land is needed.This research use 2006 and 2011 Quickbird imagery data and 2015 UAV aerial photo. These data are interpreted and digitalized so land use in Tembalang campus multitemporally can be analized. Land use in Tembalang campus is classified into five classes: administration building, public facility, learning facility, wasteland and student activity center.From land use each year, its change from area, function and spatial distribution pattern is analized. On period 2006 – 2011, there is no change of land use as much as 90.93% and wasteland decreased 21.94%. The biggest development is in administration building class as much as 52.77%. Spatial distribution pattern on 2006 – 2011 period is clustered. While on 2011 – 2015, land use change is about 24.18%. The biggest change is from wasteland that become public facility with spatial distribution pattern is clustered.Land optimalization studies with 2015 land use data using masterplan compatibility parameter, appropriateness of land use need and capability as campus land. The result is 71.76% of Tembalang campus is categorized very optimal, 11.66% is optimal, 0.56 is less optimal, and 16.02% is not optimal

Analisis Identifikasi Kawasan Karst Menggunakan Metode Polarimetrik Sar (Synthetic Aperture Radar) dan Klasifikasi Supervised

Indonesia mempunyai bentang alam yang sangat beragam, salah satunya adalah bentang alam karst.Di Kecamatan Cipatat terdapat bentang alam karst yang berada pada batu gamping formasi Rajamandala. Topografi kawasan karst Cipatat berbentuk bukit dantelah mengalami Perubahan akibat penambangan karst. Dengan memanfaatkan pengindraan jauh aktif atau sistem RADAR dilakukan identifikasi kawasan karst. Saat ini masih sedikit yang mengembangkan metode klasifikasi citra berbasis RADAR untuk identifikasi geologi, khususnya di negara Indonesia yang beriklim tropis.Penelitian ini menggunakan citra ALOS PALSAR 1.1 dual polarisasi (HH, HV) tahun 2007-2008. Identifikasi karst menggunakan tiga parameter (Anisotropy, Entropy dan Alpha) dari metode dekomposisi polarimetrik H/α/A dimana masing-masing parameter merepresentasikan sifat fisik objek. Karst biasanya berada di bawah penutup lahan maka dilakukan klasifikasi dengan algoritma supervised wishart untuk klasifikasi tutupan lahan (non karst). Tutupan lahan ini digunakan untuk melihat keterkaitan hasil identifikasi kawasan karst dengan penutup lahan. Validasi dilakukan dengan membandingkan koordinat geografis citra hasil identifikasi karst dengan koordinat citra pada Google Earth dan dibantu data geologi karst.Kawasan karst teridentifikasi seluas 533 Ha (2007) dan 1165 Ha (2008) dengan Perubahan luas 632 Ha. Diketahui karst termasuk tipe surface scattering, keacakan hamburan sedang dan kekasaran permukaan sedang. Kemudian kawasan karst paling banyak teridentifikasi pada tutupan lahan vegetasi jarang. Untuk kawasan non karst (tutupan lahan) diperoleh nilai overall accuracy 53,83% dan kappa 46,13% (2007). Kemudian overlay accuracy 53,41% dan kappa 45,65%(2008). Hal ini mengindikasikan nilai akurasi kelas tutupan lahan tidak sesuai dengan kondisi lapangan yang sebenarnya. Namun hasil klasifikasi tersebut sudah bisa membedakan dengan baik antara lahan terbangun, perairan, dan vegetasi.Validasi spasial hasil identifikasi karst tidak menunjukan hasil yang baik. Hal ini dikarenakan banyak objek lain yang memiliki tipe scatter yang sama dengan karst.Kata Kunci : ALOS PALSAR, Dekomposisi H/α/A, Karst, Polarimetrik SAR, Wishart Supervised.ABSTRACTIndonesia has a very diverse landscape, one of them is karst landscape. In sub Cipatat karst landscapes are located on limestone formations Rajamandala. Cipatat shaped karst topography of the region and the hill has undergone changes due to mining karst. By utilizing remote sensing active or RADAR system to identify karst region. Currently there are still a few who develop RADAR based image classification method for the identification of geology, particularly in countries tropical Indonesia.This study uses ALOS PALSAR 1.1 dual polarization (HH, HV) in 2007-2008. for the identification of the karst region. Identification of karst uses three parameters (Anisotropy, Entropy and Alpha) of the decomposition method polarimetric H/α A which each parameters represents the physical properties of the object. Karst usually exist below land cover so classification is carried out by wishart supervised algorithm for land cover classification (non karst). Land cover is used to see how the results of the identification of karst area related with land cover. Validation is done by comparing the geographical coordinates of the image of the karst identification with the imagery in Google Earth coordinates and assisted karst geological data.Karst areas identified an area of 533 Ha (2007) and 1165 Ha (2008) with 632 ha area changes. Known karst include the type of surface scattering, the randomness of the medium scattering and moderate surface roughness. Then most of karst areas identified in land cover sparse vegetation. For non-karst area (land cover) values obtained overall accuracy 53.83% and kappa 46.13% (2007). Then overlay accuracy 53.41% and kappa 45.65% for 2008. This indicates the value of the accuracy of land cover classes do not correspond to actual field conditions. But the results of these classifications have been able to distinguish well between developed and undeveloped land, water, and vegetation. Validation karst spatial identification results did not show good results. This is because many other objects that have the same type of scatter with karst. Keywords: ALOS PALSAR, Karst, H/α/A Decomposition, SAR Polarimetric, Wishart SupervisedABSTRAKIndonesia mempunyai bentang alam yang sangat beragam, salah satunya adalah bentang alam karst.Di Kecamatan Cipatat terdapat bentang alam karst yang berada pada batu gamping formasi Rajamandala. Topografi kawasan karst Cipatat berbentuk bukit dantelah mengalami Perubahan akibat penambangan karst. Dengan memanfaatkan pengindraan jauh aktif atau sistem RADAR dilakukan identifikasi kawasan karst. Saat ini masih sedikit yang mengembangkan metode klasifikasi citra berbasis RADAR untuk identifikasi geologi, khususnya di negara Indonesia yang beriklim tropis.Penelitian ini menggunakan citra ALOS PALSAR 1.1 dual polarisasi (HH, HV) tahun 2007-2008. Identifikasi karst menggunakan tiga parameter (Anisotropy, Entropy dan Alpha) dari metode dekomposisi polarimetrik H/α/A dimana masing-masing parameter merepresentasikan sifat fisik objek. Karst biasanya berada di bawah penutup lahan maka dilakukan klasifikasi dengan algoritma supervised wishart untuk klasifikasi tutupan lahan (non karst). Tutupan lahan ini digunakan untuk melihat keterkaitan hasil identifikasi kawasan karst dengan penutup lahan. Validasi dilakukan dengan membandingkan koordinat geografis citra hasil identifikasi karst dengan koordinat citra pada Google Earth dan dibantu data geologi karst.Kawasan karst teridentifikasi seluas 533 Ha (2007) dan 1165 Ha (2008) dengan Perubahan luas 632 Ha. Diketahui karst termasuk tipe surface scattering, keacakan hamburan sedang dan kekasaran permukaan sedang. Kemudian kawasan karst paling banyak teridentifikasi pada tutupan lahan vegetasi jarang. Untuk kawasan non karst (tutupan lahan) diperoleh nilai overall accuracy 53,83% dan kappa 46,13% (2007). Kemudian overlay accuracy 53,41% dan kappa 45,65%(2008). Hal ini mengindikasikan nilai akurasi kelas tutupan lahan tidak sesuai dengan kondisi lapangan yang sebenarnya. Namun hasil klasifikasi tersebut sudah bisa membedakan dengan baik antara lahan terbangun, perairan, dan vegetasi.Validasi spasial hasil identifikasi karst tidak menunjukan hasil yang baik. Hal ini dikarenakan banyak objek lain yang memiliki tipe scatter yang sama dengan karst.Kata Kunci : ALOS PALSAR, Dekomposisi H/α/A, Karst, Polarimetrik SAR, Wishart Supervised.ABSTRACTIndonesia has a very diverse landscape, one of them is karst landscape. In sub Cipatat karst landscapes are located on limestone formations Rajamandala. Cipatat shaped karst topography of the region and the hill has undergone changes due to mining karst. By utilizing remote sensing active or RADAR system to identify karst region. Currently there are still a few who develop RADAR based image classification method for the identification of geology, particularly in countries tropical Indonesia.This study uses ALOS PALSAR 1.1 dual polarization (HH, HV) in 2007-2008. for the identification of the karst region. Identification of karst uses three parameters (Anisotropy, Entropy and Alpha) of the decomposition method polarimetric H/α A which each parameters represents the physical properties of the object. Karst usually exist below land cover so classification is carried out by wishart supervised algorithm for land cover classification (non karst). Land cover is used to see how the results of the identification of karst area related with land cover. Validation is done by comparing the geographical coordinates of the image of the karst identification with the imagery in Google Earth coordinates and assisted karst geological data

Model Peramalan Inflow Waduk Plta Koto Panjang Menggunakan Pendekatan Adaptive Neuro Fuzzy Inference System

Many studies has shown that the ANFIS model has good prediction performance results for forecasting hydrological phenomena such as reservoir inflow. The existence of the success, it is necessary to research the reliability of the ANFIS models when applied to existing reservoirs in Riau Province, namely Koto Panjang Hydropower Reservoir. The results of the ANFIS models in the development phase of this study indicate that the scheme 4 (4 inputs) with the ROI value of 0.02 is the best scheme so that the scheme is used to build the model of ANFIS. The cross validation on ANFIS models provide the best performance prediction with correlation coefficient (R) between the prediction values and the observational value is 0.90

Analisis Penggunaan Ndvi Dan Bsi Untuk Identifikasi Tutupan Lahan Pada Citra Landsat 8 (Studi Kasus : Wilayah Kota Semarang, Jawa Tengah)

Land is one of the important natural resources that are needed by living things both animals, plants and humans to stand, as a place of life and activities of life as well as to meet their needs. Land and human beings have a very complex relationship and closely with each other which can not be separated. So that people can meet their needs as optimally as possible, the natural resources requires the processing, preservation and protection. In this study, carried out the identification of land cover in the Landsat 8 May 29, 2015 acquisition of the city of Semarang. The method used is the analysis of NDVI and combination of NDVI BSI which later developed land cover classes consist of five classes including water, barren, settlements, rice fields and vegetation classification results are then compared with reference Maximum Likelihood classification.Results from this study to the level of accuracy obtained NDVI classification results amounted to 49.43% with the user\u27s accuracy for the class of water by 76.15%, barren by 12.60%, settlements by 85.37%, rice fields by 25.44% and vegetation by 65.55%. As for the combination of NDVI BSI classification results obtained by 60.14% accuracy level with the user\u27s accuracy for the class of water by 77.03%, barren by 8.07%, settlements by 82.47%, rice fields by 39.48% and vegetation by 65, 88%

Identifikasi Kesesuaian Lahan untuk Relokasi Permukiman Menggunakan Sistem Informasi Geografis (Studi Kasus: Kabupaten Banjarnegara)

Kabupaten Banjarnegara terletak antara 7o12'–7 o 31' Lintang Selatan dan 109 o 29'- 109 o 45'50” Bujur Timur. Berada pada jalur pegunungan di bagian tengah Provinsi Jawa Tengah sebelah barat yang membujur dari arah barat ke timur. Banjarnegara adalah kabupaten yang memiliki kawasan pegunungan dengan kerawanan tanah bergerak maupun longsor cukup tinggi. Salah satu bencana yang ada adalah tanah begerak. Tanah bergerak yang terjadi di Kabupaten Banjarnegara menyebabkan lumpuhnya perekonomian, kerusakan bangunan, korban jiwa serta kehilangan harta benda. Oleh karena itu, diperlukan diperlukan upaya-upaya yang komprehensif untuk mengurangi risiko bencana alam, antara lain yaitu dengan melakukan kegiatan mitigasi berupa relokasi.Permukiman yang akan direlokasi adalah permukiman yang terletak pada daerah sangat rentan tanah bergerak dan memiliki daerah yang luas serta tingkat kepadatan yang tinggi. Sedangkan penentuan posisi relokasi yang tepat melibatkan enam parameter kesesuaian lahan permukiman yaitu kerawanan longsor, kelerengan, jenis tanah, penggunaan lahan, hidrogeologi dan aksesibilitas. Penelitian ini menggunakan metode Analitycal Hierarchy Process dalam penentuan nilai bobot tiap parameter yang kemudian dilakukan klasifikasi nilai kesesuaian lahan dengan interval 0-30 sebagai lahan tidak sesuai, 30-70 sebagai lahan kurang sesuai dan >70 adalah lahan sesuai untuk relokasi. Permukiman terdampak bencana tanah bergerak teridentifikasi sejumlah 88 titik dengan total luas sebesar 196 Ha atau 0,114 % dari total luas permukiman di Kabupaten Banjarnegara yang tersebar di bagian utara wilayah Kabupaten Banjarnegara. Sedangkan hasil pengolahan kesesuaian lahan permukiman didapatkan luas lahan dari tiap klasifikasi yaitu tidak sesuai relokasi 8,72% atau 10.019,274 Ha, kurang sesuai relokasi 59,26% atau 68.123,307 Ha dan lahan sesuai relokasi 32,03 % atau 36.816,024 Ha. Lahan pada kelas sesuai merupakan daerah yang akan dijadikan lahan relokasi.Pemilihan posisi relokasi terhadap permukiman terdampak bencana tanah bergerak yaitu dengan melakukan analisis kedekatan antar keduanya yang menghasilkan jarak rata rata perpindahan adalah 1,5 KM, dengan jarak terpendek yaitu 92 meter yang terdapat pada Kecamatan Kalibening dan jarak perpindahan terpanjang adalah titik di Kecamatan Pandanarum dengan sebesar 6,21 KM

Estimasi Nilai Dan Korelasi Biomassa Terhadap Nilai Ndvi Berbasis Metode Polarimetrik Sar Pada Citra Quad-pol Alos Palsar Tahun 2007

One way to hold the temperature rise of the earth\u27s surface is to reduce greenhouse effect emissions. Through photosynthesis, the CO2 is absorbed and converted by plants into organic carbon in the form of biomass. Absolute carbon content in the biomass at a certain time is known as the carbon stock. The existence of the REDD+ program enables develop countries to have the incentive of carbon absorption. Therefore it is necessary for calculating the biomass which is efficient and effective so it is able to determine the carbon stock in a large area.One way to estimate the value of the biomass is by remote sensing method. The remote sensing method can estimate the value of the biomass without having to pitch directly to savings, energy and time. The Remote sensing method which was used in this study is the polarimetric SAR method using ALOS PALSAR at Subang in 2007.The aim of this study was to determine the amount of biomass with the polarimetric SAR method, analyzing the comparative the biomass value in Subang with previous studies, analyzing the relationship between the biomass value with the NDVI value and analyzing the distribution maps the biomass value and land cover in Subang in 2007.The results from this study showed the value of the biomass of each land cover like these. They are forest cover is 466.061 tons/ha, sparse woods cover is 244.122 tons/ha, plantation cover is 183.587 tons/ha, residential cover is 108.949 tons/ha and waters cover is 7.137 tons/ha. The result of linear regression between NDVI values with biomass value is y = 240.99x + 26.668 and the value of R2 = 0.7181. The Result of land cover classification ALOS PALSAR by using Scattering Model-Based Unsupervised Classification method have given overall accuracy value from confusion matrix is 49% and kappa coefficient value is 40%

Evaluasi Dan Implementasi Sistem Surveilans Demam Berdarah Dengue (Dbd) Di Kota Singkawang, Kalimantan Barat, 2010

Introduction: Dengue Haemorrhagic Fever (DHF) is still a public health problem in Singkawang Municipalitywhich was an endemic area. DHF surveillance is expected to inform endemicity of an area, season of transmission anddisease progression that can be use to make the system more effective and efficient.Methods: Observational study by using a structured questionnaire. Interview was conducted to all DHF surveillanceofficers. Evaluated had been done to the variable of input, process, and output of the surveillance system. We conducted anon the job training to all DHF surveillance officers after the evaluation.Results: 66.7% officers never got any trainings of surveillance, 83.3% had double duty, budgeting limited to physicalneeds, facilities and infrastructures. Process variable, data collection was late; analysis and recommendation had notbeen directed to the distribution of cases, the relationship between risk factors and the mortality of DHF incidence, andenvironment changing, feedback; data distribution had not been implemented optimally. Output variable was still weak,no surveillance epidemiology profile. Attribute surveillance such as simplicity, flexibility, and positive predictive valuewere good, but still weak in acceptability, sensitivity, representativeness, and timeliness. Short-term evaluation resultedthat there was an increasing knowledge of surveillance officers (p value <0.05). Mid-term evaluation resulted that therewas an increasing of completeness and accuracy of DHF report from 80% to 100%, active case finding, epidemiologyinvestigation conducted to all DHF cases.Discussion and Conclusions : DHF surveillance system in Singkawang needs to be improved, there were many attributesof surveillance system that had not done well. Training of surveillance system is needed to improve capability and capacityof the surveillance officers

Analysis of QTL for high grain protein content in Canadian durum wheat

Non-Peer ReviewedDurum wheat (Triticum turgidum L. var. durum) varieties with high grain protein concentration (GPC) produce pasta products with greater cooking firmness and increased tolerance to overcooking. However, the large environmental effect on expression of GPC and the negative correlation between GPC and grain yield slow breeding progress of durum wheat varieties with elevated GPC. Identification of molecular markers associated with high GPC would aid durum wheat breeders to select for this important trait earlier. The objectives of this study were to identify molecular markers associated with quantitative trait loci (QTL) for elevated GPC in durum wheat. A preliminary genetic map was constructed by screening polymorphic microsatellite markers on a set of 95 double haploid lines derived from the cross Strongfield (high GPC) X DT695 (low GPC). QTL analysis using single marker regression was performed on GPC data collected at Swift Current and Regina in 2002 and Swift Current, Regina and Saskatoon in 2003. To date, we have identified two QTL for GPC flanked by Xgwm448 and Xgwm558 on chromosome 2AS, and on chromosome 2BL at wmc332. No QTL for high GPC could be detected on chromosome 6BS, the location of a high GPC gene isolated previously from durum, wheat suggesting that Strongfield contains novel QTL for high GPC not previously reported in the literature. The molecular markers flanking the QTL identified in this study can be used by durum wheat breeders to enhance selection of high GPC in durum wheat