Judul: Shelf life and quality of sapota cultivar sukatali st1 coated by wax
Abstrak: Waxing is one way that can be used to extend the shelf life of horticultural products with a relatively low cost. By doing waxing on sapota, it was expected to increase the shelf life of the fruit, so in marketing, sapota quality could be improved. The material used to coat the sapota was beeswax, Sapota cultivar Sukatali ST1 was obtained from the farmers at Sumedang, Jawa Barat. Several concentrations of wax were applied in the preliminary experiment, i.e, 0%, 2%, 4%, 6%, 8%, 10%, and 12% at room temperature. The results indicated that sapota coated with a wax concentration of 10% had longer shelf life of 5 days. In the next stage, the wax applied in the treatments were 9%, 10% , 11%, and without wax coating as control. In addition to room temperature, 15°C storage temperature was also applied as treatment. Observations were made on the respiration rate, weight losses, total soluble solids, hardness, and organoleptic for every 2 days. The results of this experiment saw that sapota coated with a wax concentration of 10% and 11% at room temperature achieved the longest shelf life for 5 days. At 15 ° C storage temperature, longer shelf life of sapota was performed by sapota coated with 9% wax with a shelf life of 11 days.
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Judul: Penggunaan Kitosan dan Lilin Lebah sebagai Bahan Pelapis untuk Meningkatkan Masa Simpan dan Mempertahankan Kualitas Buah Pepaya
Abstrak: Papaya is a climacteric fruit with increased respiration rate during ripening process. Respiration rate papaya can be inhibited by providing a coating on the surface of the fruit. Coating materials that can be used include chitosan and beeswax. The purpose of this research is to study the effect of chitosan and beeswax coating on the shelf life and quality of Callina papaya fruit during storage. Experiment was carried from November 2014 until March 2015 at the Experimental Field of PKHT, Tajur and Postharvest Laboratory, Department of Agronomy and Horticulture, Faculty of Agriculture, Bogor Agricultural University. The experimental design used was completely randomized design with 3 treatments and 3 replications, there are chitosan 0.75%, beeswax 6%, and the control (without coating). Data were analyzed by F test and treatments that affect were analyzed by Duncan Multiple Range Test (DMRT). Parameters measured were shelf life, the index of fruit skin color scale, respiration rate, weight loss, skin firmness, pulp firmness, total soluble solids, total titratable acids, and the content of vitamin C. Results of the experiments showed that chitosan and beeswax treatments can extend the shelf life of papaya Callina 4–5 days compared to control because it inhibits respiration rate papaya during storage. The use of chitosan and beeswax can maintain the physical and chemical quality of papaya fruit Callina.
Keyword: Callina, chemical quality, physical quality, postharvest ripening, respiration rate

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