Judul: Analisis Biaya Produksi Usaha Persuteraan Alam: Studi Kasus di Kabupaten Polewali Mandar Sulawesi Barat dan Kabupaten Enrekang Sulawesi Selatan
Abstrak: Sericulture is a part of social forestry programmes to improve the welfare of society, which live mainly around forest areas. In order to compete and overcome the obstacles that exist in Sericulture business today, production cost analysis should be done, as an important component in decision-making. The objective of this study is to determine production cost including break even point (BEP), and profitability rate. This study was conducted at Polewali Mandar Regency West Sulawesi and Enrekang Regency South Sulawesi in November untill December 2011. The results showed that: (a) the largest cost in Polewali Mandar Regency was at sericulture process that is 36,8% while in Regency Enrekang was at weaving process 53,8%; (b) production costs of cocoon in Enrekang Regency Rp 24.310/kg, the production cost of yarn Rp 393.940/kg, production cost of kain ikat Rp 197.340/sheet, and production cost of kain sarung Rp 303.440/sheet. While the cost of producing kain sarung in Polewali Mandar Regency Rp 143.940/sheet; (C) Break-even point of farmers group in Polewali Mandar Regency was 119 sheet of kain sarung in a year. Break even point of KUB Sinar Buntu Kurung was 2647,15 kg cocoons in a year. Break even point of UPT Tekstil was 481,5 kg silk yarn in a year. Break even point of Pertenunan Nenek Mallomo was 68 sheet of kain ikat in a year and 826 sheet of kain sarung in a year; (d) Profitability rate was obtained by Farmers Group Pallis is 25,1% from production costs, KUB Sinar Buntu Kurung was 43,9%, Pertenunan Nenek Mallomo was 26,7% for kain ikat, While UPT Tekstil suffer loss; (e) An integrated sericulture process in Polewali Mandar Regency have ROI point at 3,3%. Sericulture process in Regency Enrekang which done separately, reached ROI point at 15,7% on cocoons production stage. At fabric production, Pertenunan Nenek Mallomo have ROI point at 6,7%. While ROI point at spinning process in UPT Tekstil showed negative value.
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Judul: Analisis nilai tambah pada persuteraan alam (studi kasus pada koperasi petani pengrajin ulat sutera sabilulungan III, Kecamatan Sukaresik, Tasikmalaya, Jawa Barat)
Abstrak: Sericulture development has a promising prospect because it support several business unit : cocoon producing, spinning and weaving The objective of this research was to analyse the level of added value from silk activity in business unit h m silkworm to cocoon, cocoon to silk yam, and the silkworm to silk cloth at KOPPUS Sabilulungan III, Tasikmalaya This research was designed as a case study research and conducted from March until April 2006. The data consisted of primary and secondary data This research used descriptive analysis and hayami method for added value analysis. The result indicated that activity in KOPPUS Sabilulungan III consisted of sericulture, spinning and weaving. Added value in each business unit wexe low : 2333% in sericulture, 25,51% in spinning unit and 39,68% in weaving unit To generate the . added value there has to be improvement in maintenance system in sericulture, optimalization of utility in spinning unit and diversification product in weaving unit Keywords : added value, silk
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Judul: Quantum Convolutional Neural Network (QCNN) for Classification Brain Tumor MRI Image
Abstrak: Penelitian ini bertujuan untuk mengembangkan model deteksi tumor otak menggunakan citra MRI dengan metode Quantum Convolutional Neural Network. Data yang digunakan adalah citra MRI otak yang terdiri dari citra tumor otak glioma, meningioma, hipofisis, dan negatif tumor yang dibagi kedalam data latih dan uji dengan rasio 80:20. Selanjutnya data latih dilakukan penyeimbangan dengan teknik oversampling. Dilakukan pengembangan model untuk mendeteksi tumor otak (biner), dan juga model untuk mengklasifikasikan jenis tumor otak (multiclass). Parameter yang digunakan dalam pengembangan model adalah jumlah ukuran filer = 2 dan kedalaman = 4, dengan qubit 4, dan dilatih dengan jumlah epoch sebanyak 10. Data akan diproses dengan metode Quantum Convolutional Neural Network yang terdiri dari lapisan quantum convolution, lapisan flatten, dan lapisan dense. Model multiclass mendapatkan akurasi 52%, setelah data latih diseimbangkan akurasi model meningkat menjadi 62%, sedangkan model biner mendapatkan akurasi 88%, setelah data latih diseimbangkan akurasi model meningkat menjadi 89%.
Keyword: Citra MRI, Klasifikasi Citra, QCNN, Tumor Otak