Judul: Restorasi Inflow Area Danau Situgede dengan Pendekatan Ekohidrolika
Abstrak: Restorasi inflow area danau dengan pendekatan ekohidrolik adalah upaya mengembalikan fungsi awal inflow area danau, untuk memperbaiki dan menyehatkan seluruh komponen ekologi (flora-fauna) dan hidrologi. Tujuan penelitian ini adalah mengidentifikasi perubahan kondisi inflow area, dan mendesain restorasi danau dengan menggunakan pendekatan ekohidrolika. Identifikasi perubahan inflow area dilakukan dengan membandingkan sebaran tutupan lahan dan koefisien limpasan kondisi eksisting, dengan kondisi ideal. Topografi dasar danau dan profil bantaran danau menjadi acuan dalam melakukan perencanaan desain restorasi inflow area sesuai dengan peraturan yang berlaku. Perubahan lahan menjadi pemukiman pada DTA Danau Situgede antara 1% - 42,37%, dan semua DTA Danau Situgede mengalami perubahan nilai koefisien limpasan (c). Peningkatan nilai C tertinggi terjadi pada DTA 1 yaitu sebesar 0,15. Bantaran danau dan kali dibagi menjadi 4 zona, yaitu zona banjir, zona longsor, zona ekologi, dan zona pengaman. Zona banjir Danau Situgede berada pada elevasi 176,24 mdpl dan untuk Kali Cibenda pada elevasi 179,99 mdpl. Direncanakan 7 buah gorong-gorong dengan lebar 4 m, dan tinggi 1,5 m serta setiap gorong-gorong mampu mengalirkan debit sebesar 6,34 m3/det.
Keyword: bantaran, daerah tangkapan air, ekohidrolika, inflow area, restorasi

Judul: The Potential of SDGs Lake as an Irrigation Water  Source
Abstrak: Danau SDGs memiliki potensi dan dapat dimanfaatkan sebagai sumber air irigasi. Pemanfaatan air Danau dilakukan dengan konstruksi bangunan pengambilan (intake) sesuai dengan satuan kebutuhan air irigasi. Tujuan dari penelitian ini adalah mengetahui kapasitas tampungan dan kisaran volume Danau, menentukan kapasitas intake Danau untuk memenuhi kebutuhan air irigasi, dan merencanakan desain pintu intake. Metode yang digunakan yaitu pengukuran langsung dengan Echo Sounder untuk memperoleh kedalaman, analisis debit dengan neraca air FJ Mock, dan analisis kurva lengkung kapasitas Danau, dan penentuan dimensi pintu air sesuai dengan Kriteria Perencanaan Irigasi-09 tentang spesifikasi teknis pintu air. Volume kapasitas tampungan optimal Danau SDGs adalah ±1218.07 m3 dengan luas ±6000 m2 berada pada elevasi ±163.5 mdpl. Kapasitas intake yang diperoleh untuk debit pengambilan yaitu 0.002 samapi 0.011 m3/det dengan luas lahan yang dapat dipenuhi kebutuhan irigasi adalah 6.36 ha sampai 20.58 ha. Dimensi pintu intake yang digunakan yaitu tinggi 80 cm dan lebar 60 cm dengan pola operasi bukaan pintu adalah 2.7 cm sampai 16.83 cm untuk memenuhi kebutuhan irigasi yang optimal., SDGs Lake has the potential and can be utilized as a source of irrigation water. The utilization of the lake water is carried out through the construction of an intake structure according to the irrigation water requirement unit. The objectives of this research are to determine the storage capacity and volume range of the reservoir, to determine the intake capacity of the reservoir to meet the irrigation water needs, and to plan the design of the intake gate. The methods used include direct measurement with an Echo Sounder to obtain the depth, discharge analysis using the FJ Mock water balance, and analysis of the reservoir capacity curve, as well as determining the dimensions of the water gate according to planning irrigation criteria (09) about gate technical specification. The optimal storage capacity volume of the SDGs Reservoir is approximately 1218.07 m3 with an area of approximately 6000 m2 at an elevation of approximately 163.5 m above sea level. The obtained intake capacity for withdrawal discharge ranges from 0.002 to 0.011 m3/sec, with a land area that can meet the irrigation needs ranging from 6.36 ha to 20.58 ha. The dimensions of the intake gate used are a height of 80 cm and a width of 60 cm, with a gate opening operation pattern ranging from 2.7 cm to 16.83 cm to meet the optimal irrigation requirements.
Keyword: Discharge, FJ Mock, Gate, Intake, Reservoir

Judul: Application of Mel Frequency Cepstrum Coefficients (MFCC) as Feature Extraction on Phoneme Recognition with Probabilistic Neural Network (PNN) as Classifier
Abstrak: Voice recognition (speech recognition) is one field of study in voice processing. This technology can convert voice signals into a form of written information (text). With this technology, people can interact with a computer. MFCC feature extraction computes the cepstral coefficients by considering the human hearing. This research is a phoneme recognition using feature extraction MFCC and PNN as feature matching model. This study compares the coefficient, overlap, and the test data without noise and with noise. MFCC is used with 13, 20, 26 coefficients and 0%, 25%, 50% overlap. Noise is added by 30 dB, 20 dB and 10 dB. From the comparison of the three overlaps, produced the best accuracy at 50% overlap with an accuracy of 94,71%. From comparing the three coefficients, better accuracy resulting in coefficients of 20 and 26 with an accuracy of 97,12% at 50% overlap. After comparing between the coefficients of 20 and 26 with overlap of 25% then the coefficient 26 is obtained that better accuracy of 94,23%. This shows that in this study the coefficient 26 is the best. In this research, there are three noise variables. The variables are 10 dB, 20 dB, and 30 dB. The best accuracy reached when the noise variable is 30 dB rather than 10 dB or 20 dB because the accuracy has the closest accuracy compared by the accuracy when noise was not added into the data. When the noise variable is 30 dB, the percentage of accuracy is 85,3%.
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