In the last two decades there has been significant leap on the spatial resolution of the satellite digital images which may be very useful for estimating stand parameter required for forest as well as environment management. This paper describes development of stand volume estimator models using SPOT 6 panchromatic and multispectral images with an object-based digital image analysis (OBIA) and conventional pixel-based approaches. The data used include panchromatic band with1.5m spatial resolution, and multispectral band with6m spatial resolution. The proposed OBIA technique with mean-shift algorithm was functioned to derive a canopy cover variable from the fusion of the panchromatic and multispectral, while the pixel-based vegetation index was used to develop model with an original pixel-size of 6 m. The estimator models were established based on 65 sample plots both measured in the field and images. The study found that the OBIA provides more accurate identification with Kappa Accuracy (KA) of 71% and Overall Accuracy (OA) of 86%. The study concluded that the best stand volume estimation model is the model that developed from the canopy cover (C) derived from OBIA i.e., v = 13.47e<sup>0.032C</sup> with mean deviation of only 0.92%, better than the model derived from conventional pixel-based approach, i.e., v = 0.0000067e<sup>16.48TNDVI</sup> with a mean deviation of 5.37%.
<p><em>Abstrak</em><strong> </strong>- <strong>Pelajaran utama dalam membaca Al Qur'an adalah mengenali dan </strong><strong>melafalkan</strong><strong> huruf-huruf Hijaiyah. Beberapa fakta menunjukkan bahwa pengucapan yang salah dapat memengaruhi makna secara har</strong><strong>a</strong><strong>fiah. <em>Speech Recognition</em>, sebagai teknologi saat ini, dapat digunakan untuk memeriksa kesalahan dalam melafalkan surat Hijaiyah melalui pengenalan suara atau ucapan. Itu dapat dikonversi menjadi data yang dapat dipahami oleh sistem. Tujuan dari penelitian ini adalah untuk menerapkan <em>Speech Recognition</em> dengan <em>Hidden Markov Model</em> untuk pelafalan huruf Hijaiyah ketika belajar membaca Alquran. Pengenalan ucapan dan Model Hidden Markov dilakukan untuk mengembangkan sistem antar</strong><strong> </strong><strong>muka mesin berbasis suara. Dalam penelitian ini juga menggunakan metode <em>Fast Fourier Transform</em> (FFT) untuk mengekstraksi sifat. <em>Hidden Markov Model</em> (HMM) yang digunakan dalam proses pelatihan. Juga, menghasilkan karakteristik khusus untuk setiap huruf Hijaiyah. Dan kemudian, <em>Euclidean Distance</em> (ED) untuk klasifikasi akhir dalam mendeteksi pelafalan huruf Hijaiyah. Hasil penelitian menunjukkan bahwa hasil tes huruf Hijaiyah pada tingkat akurasi yang sama adalah 100%, sedangkan pengujian huruf yang berbeda adalah 54,6%. Dengan demikian, penelitian ini akan memberikan kontribusi kepada siswa yang sedang belajar membaca Al-Qur'an untuk dapat mengenali dan me</strong><strong>lafalkan</strong><strong> huruf-huruf Hijaiyah</strong><strong><em>.</em></strong></p><p><em>Abstract</em> – <strong>The main lesson in reading the Al Qur'an is recognizing and reciting the letters Hijaiyah. Some facts show that incorrect pronunciation can affect meaning literally. Speech Recognition, as the current technology, can be used to check the mistakes in pronouncing the Hijaiyah's letter through recognizing the voice or speech. It can convert into data that can be understood by the system. The purpose of this study is to implement Speech Recognition with Hidden Markov Model for Hijaiyah letter pronunciation when learning to read the Qur'an. Speech recognition and Hidden Markov Models were carried out to develop a sound-based machine interface system. In this study also used the Fast Fourier Transform (FFT) method to extract traits. Hidden Markov Model (HMM) used in the training process. Also, produced the especially characteristics for each letter of Hijaiyah. And then, Euclidean Distance (ED) for the final classification in detecting Hijaiyah letter pronunciation. The results of the study show that the results of the Hijaiyah letter test on the same level of accuracy are 100%, while the testing of different letters is 54.6%. Thus, this study will contribute to students who are learning to read Al-Qur'an to be able to recognize and recite the Hijaiyah letters</strong><strong><em>.</em></strong></p><p><strong><em>Keywords</em></strong> - <em>Speech Recognition, Hidden Markov Model, Recognizing, Reciting, Letter Hijaiyah </em></p>
Forest has a potency to support food security and overcome poverty. This study was expected to measure the contribution of private forest and design a strategy to increase the role of private forests in food security and proverty alleviation in Nanggung.The respondent consisted of 60 private forest farmers. The qualitative and quantitative approach of this study revealed that private forest contributed about 23 food plants species as household daily food support and about 35.68% as means to better income for proverty alleviation. The strategy to increase the role of private forest were: 1) to strengthen the institutional of farmer groups in the private forest for food security; 2) to provide subsidies for private forest development for food; 3) to utilize the abandoned land as private forest for food; 4) to establish business partnership in terms of seeding, planting, harvesting, and marketing; and 5) to conduct efficient forest product marketing.Key words: Private forest, food security, proverty
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