Judul: Aktivitas Terbang dan Produktivitas Lebah Trigona laeviceps pada Bahan Stup Kayu Sengon dan Kayu Lapis (Plywood)
Abstrak: Trigona laeviceps is one of stingless bees species which produce propolis and its productivity influenced by flight activity of the workers. This study aimed to obtain stup material place Trigona hive which may improve the weight of bees colony and increase production of propolis. The weight gain of colony was counted every week while propolis+bee pollen and honey was measured at the end of the study. Temperature, internal temperature, relative humidity, light intensity and wind speed were also measured. Daily flight activity of Trigona for obtain pollen/resin and nectar/water on sengon stup was very significantly higher (p<0.01) compared to the bees kept in plywood stup (80.40±41.40 vs 42.33±21.90 bees/day). The light intensity affect the daily flight activity (R2=78.3% in sengon stup and R2= 85.6% in stup plywood). Colony weight gain on sengon stup was significantly higher (p<0.05) as compared to those on plywood stup (23.70 vs -−8.44 g/week). Whereas the production of propolis+bee pollen and honey were not significantly different (24.77 and 19.66 vs 19.92 and 17.61 g/8 weeks).
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Judul: Aktivitas Terbang dan Pertambahan Bobot Koloni Trigona laeviceps laeviceps pada Bahan Stup Berbeda Sebelum dan Setelah Introduksi Introduksi Koloni Baru
Abstrak: This study aimed to obtain the information about the flight activity, determinant environmental factors and weight gain of Trigona laeviceps colonies which were placed in different materials of cages (sengon wood/ KS and plywood/ KL) after introduction of new ten colonies. Completely Randomized Design in a factorial experiment 2x2x13 was used. The first, second and third factors were materials, introduction of new colonies and time of observation respectively. Flight activity data were analyzed using Analysis of variance (ANOVA) and colony weight gain data were analyzed by T-test of 95% confidence interval. Observations on flight activity were conducted for every 10 minutes within an hour, from 06.00-18.00. Temperature, relative humidity, light intensity and wind speed were measured. The colonies average initial weight was 30-59 grams and then were weighed once a week. The range of daily flight activity was 0-20. Temperature, relative humidity and wind speed were not significantly influenced flight activity. Light intensity (R2=0.688) especially after introduction of new colonies was significantly influenced (P<0.05) flight activity. Flight activity and colony weight gain were not significantly influenced by stup material, but after introduction new colonies (P<0.05), flight activity was lower. Flight activity was significantly influenced (P<0.05) by time which form the specific pattern at peak time between 07.00-16.00
Keyword: colony weight gain, environment factor, flight activity, stup, Trigona.

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