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Speech coder is one of the most important part of communication systems. Speech signal can be represented as a combination of many sinusoidal signals.....

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ABSTRAK
Pada kondisi saat ini, jika beban puncak dibandingkan dengan daya mampu pembangkit pada sistem kelistrikan wilayah Sumatera dengan menerapkan kriteria cadangan 35%, maka diperkirakan terjadi kekurangan sekitar 2.000 MW. Sumber gas bumi di wilayah Jambi dapat dipertimbangkan karena tersedia cadangan gas dan dapat digunakan sebagai energi untuk memenuhi kebutuhan listrik. Gas tidak mudah untuk disimpan dibandingkan dengan Bahan Bakar Minyak (BBM) yang banyak digunakan sebagai bahan bakar pemenuhan beban puncak saat ini. Compressed Natural Gas (CNG) dapat menjadi salah satu alternatif metode penyimpanan gas. Kajian CNG Plant meliputi kajian keekonomian berupa NPV, IRR dan Payback Period serta analisis sensitivitas yang menggambarkan sensitivitas proyek terhadap faktor-faktor yang berpengaruh. Analisis teknik dari CNG Plant juga dikaji untuk mendapatkan tekanan optimum pada CNG Plant serta analisis perbandingan keekonomian antara CNG dan BBM jenis HSD. Dari hasil perhitungan keekonomian didapatkan harga jual gas dari CNG Plant sebesar US$ 10,4/MMBTU dengan tekanan optimum CNG sebesar 3215 psia. Berdasarkan perhitungan didapatkan biaya pembangkit listrik tenaga gas dari CNG plant yaitu sebesar Rp. 1.735,34/kWh, sedangkan biaya pembangkit listrik tenaga diesel sebesar Rp. 2.765,55/kWh sehingga ada penghematan sebesar Rp. 1.030/kWh apabila digunakan gas CNG pada beban puncak. Potensi penghematan dari sisi PLN apabila menggunakan gas CNG pada saat beban puncak adalah sebesar Rp. 530 Juta/hari

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ABSTRACT
In the current conditions, when compared the peak load with capable power generator in Sumatera area electricity system, when applying the 35% reserved criteria, it is predicted that there is a shortage of around 2,000 MW. Sources of natural gas in Jambi region can be considered as available gas reserves and can be utilized as energy to meet the electricity needs. Gas is not easy to be stored compared with fuel oil which is widely used as fuel for the fulfillment of the peak load now. Compressed Natural Gas (CNG) can be an alternative method of gas storage. Study of CNG Plant was included the study of economics in the form of NPV, IRR and Payback Period as well as a sensitivity analysis that illustrates the sensitivity of the project on the factors that influence. Technical Analysis of CNG also examined to obtain optimum pressure on the CNG Plant as well as the economics of comparative analysis between CNG and fuel oil types HSD. From the calculation results obtained economical gas price of CNG Plant amounted to US $ 10.4/MMBTU with CNG optimum pressure of 3215 psia. Based on the calculation, the cost of gas power plant of CNG plant is Rp. 1735.34/kWh, while the cost of diesel power plant is Rp. 2765.55/kWh so that there is a savings of Rp. 1.030/kWh when used CNG gas at peak loads. Potential savings of PLN side when using CNG gas during peak load is Rp. 530 Million/day

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Kebutuhan masyarakat Indonesia saat ini akan energi sangat tinggi. Jumlah cadangan energi primer yang semakin menurun serta terbatasnya sumber daya terbarukan mengharuskan adanya solusi untuk masalah tersebut. Jam Bumi merupakan salah satu kegiatan penghematan energi yang bertujuan untuk menghemat sumber energi non-renewable seperti batubara dan minyak bumi. Salah satu bentuk energi yang mudah untuk diamati pengaruh dari Jam Bumi adalah energi listrik. Besar penghematan yang diperoleh dari Jam Bumi dapat dilihat dari besar penurunan nilai beban puncak, beban puncak siang, beban rata-rata tahunan dan beban rata-rata per jam dalam satu tahun. Pada skripsi ini, dilakukan analisis terhadap penurunan konsumsi energi listrik yang dihasilkan oleh Jam Bumi. Penurunan konsumsi energi listrik yang terjadi berkisar dari 500-2000 MW dari pelaksanaan tahun 2009-2014 dengan durasi waktu sekitar dua jam.

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Indonesian people's need for energy nowadays is very high. The decreasing number of primary energy back-up and limited renewable energy require a solution for these problems. Earth Hour is one way for energy saving which aims to save non-renewable energy sources, such as coal and oil. An energy form which is easy to be observed in Earth Hour is electric power. The amount of energy saved from Earth Hour can be seen from the decreasing rate of peak load, daylight peak load, annual average load, and average load per hour in one year. In this paper, an analysis of electric power decrease by Earth Hour is conducted. From Earth Hour events which were held for about two hours during 2009 ? 2014, the electric power consumption decrease was about 500?2000 MW.

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This book examines the major social and economic impacts of the peak of the oil Age. It explains everything you need to know to understand peak oil and its world-changing consequences from an insider's perspective.

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Kabupaten Pandeglang memiliki kedekatan wilayah dengan zona subduksi dan wilayah pertemuan Lempeng Indo-Australia dan Lempeng Eurasia di Selat Sunda. Akibatnya Kabupaten Pandeglang memiliki tingkat kerawanan dan kerentanan gempa bumi, dan untuk itu wilayah rawan gempa bumi dan kerentanan terhadap gempa bumi perlu ditentukan sebagai upaya mitigasi bencana gempa bumi. Faktor - faktor seperti litologi, struktur geologi, lereng, dan nilai PGA (Peak Ground Acceleration) dapat digunakan untuk menentukan wilayah rawan gempa bumi dengan metode skoring. Kerentanan wilayah terhadap gempa bumi ditentukan dengan metode weighted overlay dengan pembobotan dalam aspek lingkungan, sosial, ekonomi, dan fisik. Kerawanan merupakan aspek lingkungan dalam penentuan kerentanan, sedangkan kepadatan penduduk, jumlah penduduk wanita, ratio ketergantungan, dan penyandang disabilitas digunakan dalam penentuan kerentanan aspek sosial. Kerentanan aspek ekonomi menggunakan indikator penduduk miskin dan kerentanan fisik menggunakan kepadatan bangunan. Hasil penelitian menunjukkan wilayah rawan gempa bumi sedang mendominasi Kabupaten Pandeglang dengan luas 64,99% dan mayoritas tersebar pada bagian timur dan selatan Kabupaten Pandeglang. Dalam kerentanan, wilayah kerentanan tinggi terdapat di Kecamatan Labuan dengan luas sebesar 36,07 % dari luas Kecamatan Labuan, sedangkan Kecamatan Sindangresmi dan Kecamatan Munjul merupakan kecamatan dengan kerentanan rendah dengan luas 73.93 % dari luas Kecamatan Sindangresmi dan 61.52 % dari luas Kecamatan Munjul.

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Pandeglang Regency has a proximity to the subduction zone and the meeting area of ​​the Indo-Australian Plate and the Eurasian Plate in the Sunda Strait. So that Pandeglang District has an earthquake level of vulnerability and vulnerability. Areas prone to earthquakes and vulnerability to earthquakes need to be determined as an effort to mitigate earthquakes. Factors such as lithology, geological structure, slope, and PGA (Peak Ground Acceleration) values ​​can be used to determine earthquake prone areas by the scoring method. Regional vulnerability to the earth's herds is determined by the weighted overlay method by weighting in environmental, social, economic and physical aspects. Vulnerability is an environmental aspect in determining vulnerability, while population density, female population, dependency ratio, and people with disabilities are used in determining the vulnerability of social aspects. Vulnerability in economic aspects uses indicators of poor population and physical vulnerability using building density. The results showed that earthquake-prone areas were dominating Pandeglang Regency with an area of ​​64.99% and the majority was spread in the eastern and southern parts of the Pandeglang Regency. In susceptibility, the high vulnerability area is in Labuan Subdistrict with an area of ​​36.07% of the area of ​​Labuan Subdistrict, while the Sindangresmi Subdistrict and Munjul Subdistrict are sub-district with low vulnerability with an area of ​​73.93% of the area of ​​Sindangresmi Subdistrict and 61.52% of the total area of ​​Munjul Subdistrict.

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It has been studied the forecasting of electric power peak load in the Indonesian electric system by using Artificial Neural Network (ANAU) Back Propagation method with the study period is 2000 - 2025. The long-range forecasting of electric peak load is influenced by economic factors. in this study, it?s selected the economic data which is estimated very influence to forecasting, which in this case become input ofAN1\L i. e.: Gross of Domestic Product (GDP) per-capita, Population, Amount of Households, Electrification Ratio, Amount of CO, Pollution, Crude Oil Price, Coal Price, Usage of Final Energy, Usage Qf Final Energy on Industrial Sector; and Average Electric Charges. Data used for study are actual data, start year 1990 up to 2000. Result of the peak load forecasting in the end of study (2025) by using ANN is 85,504 MHC meanwhile the load forecasting in the National Electricity General lan (NEGP) is 79,920 MW (the difference of both is about 6. 6%). Based on ANN approach is obtained results that the peak load forecasting in Indonesia in the year 2005, 2010, 2015, 2020 and 2025 are 16,516 MHC 24,402 MHC 36, 15 7 MIK 56,060 MW and85,584 MW respectively.

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Di Kalimantan Tengah, pembangunan jaringan saluran secara besar-besaran dan sangat kompleks menyebabkan kerusakan dan degradasi yang intensif terhadap kondisi gambut dan rawa gambut yang ditandai dengan perubahan tutupan lahan, penurunan muka air tanah, kebakaran, beberapa bencana kekeringan, penurunan tanah, dan lain sebagainya. Salah satu langkah untuk mengatasi kondisi tersebut adalah dengan mengatur kondisi hidrologi di area tersebut. Dujlow digunakan untuk mensimulasi tinggi muka air di saluran dan Modjlow digunakan untuk mencari distribusi dan karakteristik dari muka air tanah di lapangan. Berdasarkan simulasi dari 2 model tersebut, kondisi eksisting (musim kerinq], area studi mengalami kekeringan karena muka air tanah turun lebih dari 1 m dibawah permukaan tanah. Hasil yang diperoleh, usaha untuk merehabilitasi rawa gambut dengan mengatur muka air tanah mendekati elevasi permukaan tanah melalui 2 skenario (canal blocking and pemasangan bendung bertingkat) di musim kering tidak sepenuhnya menyelesaikan semua masalah di area itu. Meskipun hasilnya akan berbeda, jika itu disimulasikan di musim basah ketika pengaruh air hujan dimasukkan ke dalam perhitunqan, beberapa langkah lain seharusnya di diterapkan seperti pendekatan manajemen air lainnya dan program silvikultur. Dalam pendekatan silvikultur, beberapa teknik dan prosedur seharusnya dilakukan untuk meningkatkan kualitas dan kuantitas tutupan lahan guna mendukung fungsi hidrologi secara alamiah. Penanaman kembali secara intensif semestinya dilakukan di lapangan melalui sistem jalur. jadi, dengan 2 pendekatan yang terintegrasi tersebut; tingkat kesuksesan dalam rehabilitasi rawa gambut akan dapat dioptimalkan.

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A study has been done on the potential integration of Demand Side Management (DSM) to the reduction of electric power peak load forecasting in the Indonesian electric system by using the indonesia Energy Outlook by .System Dynamic (INOSYD) model and Artificial Neural Network (ANN) method, within the study period of 2005-202l. DSM is the process of managing the consumption of energy. generally to optimize availability and development plan of energy resources. DSM application in this research refers to actions taken on the customer 's side of the matter to change the amount or timing of energy consumption, therefore it influences the reduction of the long-range forecasting of electricity peak load In this paper, the lang-term load forecasting is studied by using INOSYD model, JS T method and Model for Analysis of Energy Demand (MAED) as comparison, where the calculation results of average annual load growth rate are around 4.60% (DVOSYD) ; 7.l6% (ISD and 6.87% (MAED) respectively. Afterwards, the influence of DSM by an effort to reduce energy consumption of residential sector by an amount of 5% and l0% respectively, with the respect to the lang-term load forecasting by using INOSYD model and ANN method is performed The study results show that DSM application at residential (household sector at an amount of 5% and l 0% by using lNOS}'D model will reduce the average long-term load forecasting by about 4.95% and 9.90% respectively, meanwhile ANN method will reduce the average long-term load forecasting by about 2.74 and 5.36% respectively.