Judul: Serangga Pengunjung Bunga Betina dan Polen yang Terbawa Kumbang Elaeidobius kamerunicus pada Kelapa Sawit
Abstrak: Receptive female flowers of oil palm emit volatile compounds that attract insect to visit the flowers. This research aimed to study female flowers visiting insect and pollen loaded by weevil E. kamerunicus of oil palm. Observations of insects were done by using fix sample method during 10 minutes on receptive female flowers. The weather condition which were measured during observation of insects, i.e. temperature, humidity, light intensity, and wind speed. Insect samples were collected for identification and weevil E. kamerunicus were taken for pollen load measurement. Result showed that the female flowers visiting insect of oil palm were E. kamerunicus (Coleoptera), Chelisoches sp., Forficula sp. (Dermaptera), Scaptodrosophila sp. (Diptera), Camponotus sp., Oecophylla sp., Plagiolepis sp., Polyrhachis sp., Leptanilla sp., Odontoponera sp. (Hymenoptera), and Thrips hawaiiensis (Thysanoptera). Weevil E. kamerunicus was dominant visiting insect. The diversity and evenness of visiting insect on oil palm was considerably low (H ’=0,85; E=0,35). Pollen loaded by male weevil E. kamerunicus (1604 pollens) was higher than that of the female (719 pollens).
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Judul: Keragaman Serangga Pengunjung Bunga Jantan Kelapa Sawit .. (Elaeis guineensis Jacq.)
Abstrak: Oil palm (Elaeis guineensis Jacq.) is one of the plantation crops that became the source of non-oil foreign exchange earner in Indonesia. Oil palm is monoecious plant, in which the same tree bears male and female flowers. Generally a male and female flowers do not bloom simultaneously. Consequently, cross-pollination with weevils help is needed. A weevil species, Elaeidobius kamerunicus is an effective pollinating insect of oil palm because it is specific and well adapted either in wet or dry seasons. The objective of this research was to study the diversity of insects that visit male flowers of oil palm besides E. kamerunicus. The observations were conducted during three days in each month, ie May, June and July 2011 for 10 minutes using fix sample method. During the observation, the name and the number of individual species of insects visiting the male flowers of oil palm were recorded. Performance of weather elements, ie temperature, humidity, and light intensity were measured. Further, insects identification were conducted in the laboratory. Results showed that, besides E. kamerunicus, in PTPN VIII, AFD IV Toge, Garden Cikasungka Bogor were found Rhynchomyia, Scaptodrosophila (Diptera), Diplatys, Forficula (Dermaptera), Camponotus, Dolichoderus, Cerapachys, Crematogaster, and Heteroponera (Hymenoptera). Scaptodrosophila was the dominant visitor insect. Dermaptera and Hymenoptera were predatory insects. The diversity and evennes of insects visiting male flowers of oil palm were low (H’ = 0,59 and E = 0,27).
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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