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: Strategi pengembangan persuteraan alam (studi kasus pada unit-unit usaha koperasi petani pengrajin ulat sutera Sabilulungan III Kabupaten Tasikmalaya)
Abstrak: Natural silk is one of the agriculWvestock commodity which could be developed to be prime commodity in Indonesia Silkworm farmer cooperation (KOPPUS) Sabilulungan Ill was one of natural silk center in Tasikmalaya The objective of this research was to analyzed internal and external factors, and formula~ed the development strategies of Sabilulungan III. This research used three phases of megy formulation analysies. Internal Factor Evaluation (IFE) and Fxterd Factor Evaluation (EFE) matrix as input phase. Internal-Eksternal matrix and SWOT (Strengths, Weaknesses, Opportunities, and Threats) matrix as guiding phase. Quantitative Strategic Planning Matrix (QSPM) as decision phase.
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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