Judul: Keanekaragaman Ordo Hymenoptera di Perkebunan Kelapa Sawit, Perkebunan Karet dan Hutan Karet di Jambi
Abstrak: There were much deforestations from primary forest to rubber and oil palm plantation in Jambi. Some taxa could be bioindicator, for example is Hymenopteran insects. This research purposed to study the diversity of Hymenoptera in oil palm plantation, rubber plantation and jungle rubber in Jambi. Insect collected by using insect net at 7 to 11 am. The environment parameters measured were temperature, humidity, and light intensity. In three vegetation habitats and villages adjacent Hutan Harapan, Jambi were found six families of Hymenoptera, i.e. Apidae, Halictidae, Megachilidae, Pompilidae, Sphecidae and Vespidae. Highest abundant found were Apis and Trigona. Endemic species found was Trigona minangkabau. The highest diversity of Hymenoptera was found in oil palm plantation (16 species), and the less diversity was in rubber plantation and village (9 species). Generally, number of species of Hymenoptera didn’t correlated significantly with temperature, light intensity and humidity.
Keyword: diversity, Hymenoptera, oil palm plantation, rubber plantation, jungle rubber

Judul: Diversity and Abundance of Insect Pollinators in Different Land Use Types in Jambi, Sumatra
Abstrak: Indonesia is one of the tropical countries with high biodiversity. Recently, the biodiversity of plants and animals is declining due to forest converting to settlements, mines, agricultures, and plantations. In Central Sumatra including Jambi, annual deforestation rate reaches 3.2%-5.9%. Converting natural landscapes to agricultural land affects insects biodiversity, including insect pollinators. The research aimed to study diversity and abundance of insect pollinators in different land use types, i.e. oil palm plantation, rubber plantation, and jungle-rubber in Jambi, Sumatra. Observation and collection of insect pollinators were conducted from November until December 2012 at Bejubang, Batanghari regency, Jambi province. Insect observations were conducted in understory plants at three land use types, i.e. oil palm plantation, rubber plantation, and jungle-rubber. Scan sampling method was employed to explore the diversity and abundance of insect pollinators at 08.00-10.00 and 14.00-16.00 in sunny days. This study consist of found 1308 individuals of insect pollinators, belonging to 54 species,7 families, and 3 orders (Hymenoptera, Diptera, and Lepidoptera). Number of species and individual of insect pollinators found in oil palm plantation (43 species, 561 individuals) and rubber plantation (40 species, 650 individuals) were higher than jungle-rubber (7 species, 97 individiuals). Giant honey bee (Apis dorsata) and stingless bee (Trigona sp. (=aff. T. planifrons)) were abundant in oil palm plantation, while stingless bee (T. laeviceps) and small carpenter bee (Ceratina lieftincki) were abundant in rubber plantation. Whereas, hoverfly (Syrphidae sp5) was abundant in the jungle-rubber. The abundance and species richness of insect pollinators in the morning were higher than in the afternoon. Diversity and evenness of insect pollinators in oil palm and rubber plantations were higher than jungle-rubber. The abundant of flower of understory plants in oil palm and rubber plantations indicated insect pollinators forage in those locations. Meanwhile, jungle-rubber and rubber plantations were prefered by insect pollinators for nest building, because there were many large trees, branches, and broken twigs. The insect pollinators community in oil palm plantation and rubber plantation were more similar than in oil palm plantation and jungle-rubber, or in rubber plantation and jungle-rubber.
Keyword: Apidae, Halictidae, insect pollinators, Megachilidae, Syrphidae

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