Abstrak. Masalah kuantitatif dalam metode potensial diri terkadang masih ditemukan. Beberapa asumsi dan metode inversi digunakan dalam proses pembuatan modelnya agar dicapai error yang sekecil mungkin sehingga makin mendekati kondisi sebenarnya. Telah dilakukan "Pemodelan Numerik Data Potensial Diri (Self Potential)" untuk menginterpretasi secara kuantitatif anomali dan parameter geometri dari anomalinya. Asumsi model yang digunakan adalah geometri sederhana dari data potensial diri. Program untuk pemodelan numerik ini dibuat di perangkat lunak MATLAB yang meliputi pemodelan kedepan dan pemodelan kebelakang. Metode inversi yang dipakai yaitu metode yang dikembangkan oleh El-Araby (2003). Program yang dibuat selanjutnya diuji pada data sintetik (3 data) dan data sekunder (3 data). Berdasarkan hasil penelitian yang sudah dilakukan, untuk data sintetik, hasil RMS error-nya sebagian besar dibawah 10%. Di samping itu, untuk data sekunder, hasilnya sesuai dengan referensi yang dipakai, dimana data sekunder 1 dan 2 saling memvalidasi karena berada pada daerah yang sama namun beda lintasan pengukuran yang jenis sumber anomalinya adalah bola karena berkaitan dengan mineral tembaga. Sedangkan untuk data sekunder 3, sumber anomalinya adalah vertical cylinder, yaitu rembesan yang berhubungan dengan potensial streaming yang terjadi di daerah bendungan. Abstract. Quantitative problems in self-potential method sometimes still be found. Some of the assumptions and methods used in the model inversion in order to get the smallest possible error so perilously close to actual conditions. It has been done "Numerical Modeling of Self Potential Data" to quantitatively interpret anomalies and geometry parameters of the anomaly. Assumptions model that are used is the simple geometry of the self potential data. Program for numerical modeling is created in the MATLAB software that includes forward modeling and inverse modeling. The inversion method that is used is the method developed by El-Araby (2003). The program that created subsequently tested on synthetic data (3 data) and secondary data (3 data). Based on the research that has been done, for synthetic data, the results of all RMS error mostly is below 10%. In addition, secondary data, the results are in accordance with the reference that was used, in which secondary data 1 and secondary data 2 validating each other because they are in the same area but different line measurement that type of source anomaly was associated with the sphere for a copper mineral. As for the secondary data 3, the source of the anomaly was a vertical cylinder, that is seepage associated with streaming potential that occurs in the area of the dam.

This research analyzes the metaheuristic methods, that is, ant colony optimization (ACO), genetic algorithm (GA), and particle swarm optimization (PSO), in the selection of path pairs on multicriteria ad hoc network. Multicriteria used are signal-to-noise ratio (SNR), load variance, and power consumption. Analysis of the simulation result is done as follows: first, in terms of computing time, the ACO method takes the most time compared with GA and PSO methods. Second, in terms of multicriteria performance, i.e., the performance of SNR, load variance, and power consumption, the GA method shows the same value in each repetition. It is different from ACO and PSO that show varying values. Finally, the selection of the path pairs by the GA method indicates the pairs of the path that are always the same as by the ACO and PSO methods indicate those that vary.

Human activity is the most contributor of carbon dioxide gas (CO2) to the air. The oceans have an important role in the carbon cycle, about 93% of the Earth's CO2 is stored in the oceans. Seagrass is one of sea plants that has a role as carbon sinks in ocean. Seagrass beds are able to absorb carbon by an average 0.21 tons/ha and the important species are Enhalus acoroide. The aim of this study is determine the carbon storage in seagrass at aboveground (leaf), belowground (roots and rhizomes) and carbon storage on each species of seagrass obtained at Mengiat coastal area. Determination of sampling point refer to seagrass density that used by purposive sampling. This method was assumed to represent or describe the condition of this area. This research used dry dyeing method which components sample was destruction with 500oC inside the furnace. The results showed that carbon storage of seagrass at belowground (root and rhizoma) is 25.70 gC/m2, and aboveground (leaf) is 17.18 gC/m2. Carbon storage at belowground is higher than aboveground because carbon will accumulate in the sediment. The type of seagrass that is obtained at Mengiat coastal area is Thalassodendron ciliatum, Thalassia hemprichii, Cymodocea serrulata, Halodule uninervis, Cymodocea rotundata, and Syringodium isoetifolium, the highest carbon storage are 62.46 gC/m2 is owned by Thalassodendron ciliatum, and the lowest carbon storages are 17.25 gC/m2 is owned by Syringodium isoetifolium.

Plankton is one of the biological resources that have an important role in the marine ecosystem. Plankton life is strongly influenced by the water quality parameters, one of which is the content of nutrients (nitrates and phosphates). Increased nutrient content caused by the increased load input from human activities. Serangan Island waters including the coastal ecosystem is widely used for a variety of human activities, such as tourism, aquaculture, residential, and transportation. All human activity will affect water quality will lead to an increase in nutrients and organic matter which in turn can lead to changes in water quality chemical physics and structure of plankton. The purpose of this study was to determine the relationship of the abundance and diversity of phytoplankton abundance and diversity of zooplankton and to know the physical parameters -chemical effect on the abundance of plankton. The method used is the Pearson correlation analysis to determine the relationship between abundance and diversity of phytoplankton abundance and diversity of zooplankton, and principal component analysis to look at the parameters of the water the most influence on the abundance of plankton. Results of Principal Component Analysis showed that the waters of the parameters that influence the abundance of plankton varies at each observation station. Pearson correlation analysis showed a strong relationship between the abundance of phytoplankton to zooplankton abundance with a correlation value of 0.64. AbstrakPlankton merupakan salah satu sumberdaya hayati yang mempunyai peranan penting dalam ekosistem laut. Kehidupan plankton sangat dipengaruhi oleh parameter kualitas perairan, salah satunya yaitu kandungan unsur hara (nitrat dan fosfat). Meningkatnya kandungan unsur hara diakibatkan oleh peningkatan beban masukan dari aktifitas manusia. Perairan Pulau Serangan termasuk pada ekosistem pesisir yang banyak dimanfaatkan untuk berbagai aktifitas manusia, antara lain kegiatan pariwisata, perikanan budidaya, pemukiman penduduk, dan jalur transportasi. Semua aktivitas manusia tersebut akan mempengaruhi kualitas perairan yang akan mengakibatkan peningkatan unsur hara dan bahan organik yang selanjutnya dapat menyebabkan perubahan kualitas fisika kimia perairan dan struktur plankton. Tujuan dari penelitian ini adalah untuk mengetahui hubungan kelimpahan dan keanekaragaman fitoplankton dengan kelimpahan dan keanekaragaman zooplankton serta mengetahui parameter fisika -kimia yang berpengaruh terhadap kelimpahan plankton. Metode yang digunakan adalah analisis korelasi pearson untuk mengetahui hubungan antara kelimpahan dan keanekaragaman fitoplankton dengan kelimpahan dan keanekaragaman zooplankton, dan analisis komponen utama untuk melihat parameter perairan yang paling berpengaruh terhadap kelimpahan plankton. Hasil Analisis Komponen Utama menunjukkan bahwa parameter perairan yang berpengaruh terhadap kelimpahan plankton berbeda-beda di masing-masing stasiun pengamatan. Analisis korelasi pearson menunjukkan hubungan yang kuat ant...

One of fisheries potential in Bali Strait is tuna fish (Euthynnus sp). Tuna fish (Euthynnus sp) resources is highly influenced by waters productivity which indicated by the chlorophyll-a concentration distribution. The aims of this study are: to find out the concentration spatial of chlorophyll-a distribution in Bali strait, to find out temporal variability of chlorophyll-a and tuna fish (Euthynnus sp) in Bali strait, and to find out the influence of chlorophyll-a concentration distribution to the catch of tuna fish (Euthynnus sp) in Bali strait. The analysis of the influence of chlorophyll-a concentration distribution to the catch of tuna fish (Euthynnus sp) in Bali strait uses regression polynomial order 2, correlation, and cross correlation. The influence of chlorophyll-a concentration distribution to the catch of tuna fish (Euthynnus sp) in Bali strait yearly time series climatology amounted to R2 = 0,1624 or 16,24%, the correlation coefficient values obtained by r = 0,1889. Seasonal time series climatology in west season (December - February) R2 = 0,0707 or 7,07%, the correlation coefficient values obtained by r = 0,0749. The transitional season 1 (March - May) R2 = 0,0095 or 0,95%, the correlation coefficient values obtained by r = - 0,0092. The east season (June - August) R2 = 0,086 or 8,6%, the correlation coefficient values obtained by r = - 0,2155. The transitional season 2 (September - November) R2 = 0,0482 or 4,82%. The correlation coefficient values obtained by r = - 0,1805

Calculation of Gross Primary Production that utilize remote sensing data is can be done on commercial remote sensing software by manual method. The commercial remote sensing software does not provides a specific feature that allow the user to do the Gross Primary Production calculation. This research is aimed to to build a remote sensing software that can be specifically used to do the Gross Primary Production calculation for Denpasar area. This software accepts remote sensing data as an input, such as satellite image from Landsat 8 OLI and TIRS and metadata file. The formulas and supporting data that required on the Gross Primary Production calculation are implemented on software in order to make an automatic image processing software. There also some additional feature on this software such as automatic data parsing from metadata file, cropping, masking and zoom that could help user to do the Gross Primary Production calculation. The developed software is able to produce information such as Gross Primary Production  value that depicted by a figure with color segmentation, area of the segments and mean, minimum and maximum value of the Gross Primary Production.

The content of secondary metabolite compounds in marine plants, especially mangrove has a certain bioactivity. Therefore, this study was aims to determine the content of secondary metabolites in the mangrove leaf extract of Rhizophora apiculata and Rhizophora mucronata. This study was conducted on January-April 2020. Sampling locations were carried out in the Mangrove Conservation Area of Tuban Village, Bali. In this study, samples were collected in the form of old leaves. The samples were taken in three sites and in each sites, 500 gr of leaves were collected randomly for each species. Therefore, in total we have 1.5 kg of leaves for each species. Samples are then extracted and tested to determine the content of secondary metabolites. The results showed the content of secondary metabolites in the leaves of R. apiculata mangrove were phenols, alkaloids, flavonoids, tannins, saponins, steroids while R. mucronata contained phenols, flavonoids, tannins, saponins, terpenoids.

Tidal flood is a water inundation phenomenon happened on the coast of the mainland or the coast which is caused by the tides of the sea. Tidal flood causes inundation on the certain parts of the mainland due to land altitude is lower than sea level at high tides. Some beaches around Gianyar, Bali, potentially experience a tidal flood. There is no research about tidal flood in Gianyar regency yet gives impact to the information about areas that potentially experience a tidal flood. This study aimed to determine the distribution of flood-prone areas in Gianyar Regency. Remote sensing and other spatial data by using scoring methods were utilized to determine prone areas of tidal flood. The parameters used included land elevation, distance area from shoreline, distance area from river, slope, land cover, and soil types. Result of the study shows that the 1104 ha of the total research area 66,37 ha or 6,02% are vulnerable areas, 684,51 ha or 62,00% are quite vulnerable area, and 353,12 ha or 31,98% are classified as non-prone areas. Spatial distribution of tidal flood potential in Gianyar Regency includes Rangkan Beach, around Purnama Beach, Saba Beach, Keramas Beach, Pering Beach, Lebih Beach and the west side of Siyut Beach . Observations and interviews toward vulnerable areas were conducted as the validation of the result of the study.