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Abstrak -Saat ini, limbah cair dari kegiatan manusia pada umumnya langsung dibuang atau dialirkan ke sungai. Hal ini akan berdampak buruk kepada kualitas air sungai dan air sumur. Penelitian ini dilakukan untuk menentukan parameterparameter kualitas air, diantaranya adalah parameter fisik berupa suhu, parameter kimia berupa pH dan DO (Dissolved Oxigen), dan parameter biologi, yaitu bakteri E.coli pada beberapa sampel air sumur di sekitar aliran sungai Gajah Wong. Hasil analisis kehadiran golongan bakteri coli dilakukan melalui uji laboratorium di Laboratorium Hidrologi Fakultas Geografi UGM. Kandungan golongan bakteri coli di sepanjang Kali Gajah Wong sangat tinggi, sebanyak 46 % penyebaran E.coli berada diatas ambang batas yaitu 2400 mg/l pada titik 1, Abstract -Currently, wastewater from human activities generally discarded into the river. This will have a negative impact on the quality of river water and well water. This research was done to determine the water quality parameters, physical parameters such as the temperature, the chemical parameters such as pH and DO (Dissolved Oxygen), and biological parameters such as E. coli bacteria in some samples of well water along Gajah Wong river. Analysis coli bacteria was conducted by laboratory testing at the Hydrologic Laboratory, Gadjah Mada University. Along Gajah Wong river, Coli bacteria content is very high, as much as 46% from the distribution of E.coli were 2400 mg / l at point 1,

Lithofacies classification is a process to identify rock lithology by indirect measurements. Usually, the classification is processed manually by an experienced geoscientist. This research presents an automated lithofacies classification using a machine learning method to increase computational power in shortening the lithofacies classification process's time consumption. The support vector machine (SVM) algorithm has been applied successfully to the Damar field, Indonesia. The machine learning input is various well-log data sets, e.g., gamma-ray, density, resistivity, neutron porosity, and effective porosity. Machine learning can classify seven lithofacies and depositional environments, including channel, bar sand, beach sand, carbonate, volcanic, and shale. The classification accuracy in the verification phase with trained lithofacies class data reached more than 90%, while the accuracy in the validation phase with beyond trained data reached 65%. The classified lithofacies then can be used as the input for describing lateral and vertical rock distribution patterns.

ABSTRAKRasio elektrifikasi di Indonesia belum mencapai 100%, penyebabnya antara lain masalah lokasi di daerah terpencil atau kepulauan dan mahalnya biaya operasi PLTD. Salah satu solusi adalah membangkitkan listrik berbasis energi terbarukan setempat. Tahap awal pemanfaatan energi terbarukan perlu dihitung faktor kapasitas (CF). Tujuan penelitian ini menganalisis CF untuk PLTB dengan metode perhitungan analitik berbasis potensi energi angin, spesifikasi teknologi PLTB dan PLTD, profil beban dan energi listrik yang dapat diproduksi untuk pengembangan sistem hibrida dengan mengambil kasus di Elat Pulau Serau Maluku. Hasil perhitungan CF untuk 5 teknologi PLTB yang berbeda dengan variasi ketinggian di Elat telah diverifikasi dengan simulasi menggunakan perangkat lunak HOMER dengan nilai rerata galat -0,030. Semakin tinggi PLTB, nilai CF semakin besar dengan konstanta 0,0030.Kata kunci: elektrifikasi, faktor kapasitas, PLTB, PLTD, sistem hibrida ABSTRACTThe electrification ratio in Indonesia has not reached 100%, the causes include problems with the location in remote areas or islands and the high operating costs of diesel power plant (DPP). One solution is to generate electricity based on local renewable energy. The initial stage of utilizing renewable energy needs to calculate the capacity factor (CF). The purpose of this research is to analyze CF for wind turbine generator (WTG) with analytical calculation methods based on wind energy potential, technology specifications of WTG and DPP, load profiles and electrical energy that can be produced for hybrid system development by taking the case in Elat Serau Island, Maluku. The results of CF calculations for 5 different WTG technologies with altitude variations in Elat have been verified by simulation using HOMER software with a mean error value of -0.030. The higher the WTG, the greater the CF value with a constant of 0.0030.Keywords: electrification, capacity factor, diesel power plant, wind turbine generator, hybrid system

In this study, Fast Fourier Transform (FFT) was used in order to detect bore hole in a structure. FFT is a common method in digital signal processing (DSP) to characterize the frequency emitted by some structure. This method is widely used because of its simplicity. Computational time needed for FFT is relatively lower than another method. The use of FFT to analyze defect in structure is not commonly used since FFT has some weakness for example spatial frequency cannot be extracted to point out the defect location. In this paper, defect was designated as a hole in a strip iron plate with 20 mm in diameter. The strip iron plate was 1 meter long, 38 mm wide and 3 mm thick. This strip iron plate was clamped at one of its ends while the other side is left free. In order to produce vibration signal, impact hammer Bruel Kjaer Type 8202 was used with plastic tip to limit the vibration frequency in to the range of 0 - 1000 Hz. The trigger point was 30 mm from its free end. Three accelerometers were placed series in one line with the trigger point with 300 mm distance of each accelerometer. The position of the hole was varied in three different position. The first position was between trigger point and first accelerometer, between first and second accelerometer and between the second and third accelerometer. The raw signal obtained from the accelerometer was processed by using FFT to understand the mode shape changes in the strip iron plate due to the bore hole. Furthermore, the FFT result was analyzed as function of receiver position to determine the position of hole. The result shows that the frequency characters were different in each case and further analysis by using magnitude-squared coherence function need to be used in order to quantitatively find the difference between FFT result.