Judul: The Development of a Front-End Module as an  Implementation of the Melon Ripeness Detection Model Based on a Mobile  Application
Abstrak: Buah melon disukai di Indonesia karena rasanya yang lezat dan manfaat kesehatannya, sehingga penting bagi para petani untuk mengetahui masa panen yang tepat demi mempertahankan nilai jual dan kualitas buah melon. Karena menentukan masa panen secara manual lebih sulit dan beresiko, penelitian sebelumnya berhasil membangun model machine learning yang dapat mendeteksi kematangan buah melon berjala C. melo var. reticulatus. Untuk mengimplementasikan model tersebut, penelitian ini akan menghasilkan sebuah modul front-end berbasis aplikasi mobile yang dapat menerima input foto melon baik dari kamera maupun galeri dan mengirimkannya ke server dengan mengintegrasikan API back-end sehingga dapat memberikan hasil klasifikasi kemantangan buah melon yaitu "siap panen" atau “belum matang”. Modul dibangun menggunakan IDE Android Studio dengan bahasa kotlin dan mengikuti metode SDLC yaitu metode prototyping dengan melakukan tiga iterasi. Berdasarkan hasil pengujian black-box 17 dari total 19 skenario berhasil dijalankan sesuai yang diharapkan, sedangkan hasil pengujian kompatibilitas menunjukkan bahwa level komputasi paling tua dimana aplikasi dapat berfungsi dengan maksimal adalah pada Android versi 10 dan RAM sebesar 4 GB.
Keyword: detection, deteksi, melon, Foto, front-end, front-end, melon, mobile application, photo

Judul: Decision Support System for In Situ Melon’s
Fruit Harvesting Time Based on Fuzzy Logic and Single Shot Detector (DSS).
Abstrak: Decision Support Systems (DSS) are wildly used in agriculture, industry, and healthcare, due to their efficient information management and decision-making activities. Melon has a considerably large genetic variability and is widely used in scientific research such as biology and genetics. In this study, we aimed to develop a system to detect the right harvesting time for melon fruits because the quality cannot be maintained after harvesting. Hence, it would be best if the harvesting is carried out at the right time, which will not affect the fruit harvested earlier. Secondly, melon fruit harvesting is done every day and not on the same day for all fruits due to environmental factors. The harvest DSS will classify melon’s categories on the plant regarding melon’s skin color by the guidance of the expert. The essential sensory factor for freshness and maturity is color, as mentioned in many studies. We use the skin color to classify melon fruit while it is on the plant into three categories: Ripe, About to Ripe, and Under Ripe, using a melon image. Firstly, localizing the melon on the plant using SSD and extracting its skin color. The input of fuzzy logic is the extracted color channels means values. The extraction of color is a challenging job because some fruits blocked with plant’s stem, root, or leaves, so, we divided the process of reaching the purest melon skin into several steps: detecting the melon fruit on the plant, segmenting the detected melon and removing the unwanted part from the skin, and finally, applying image normalization and calculate mean values for melon’s skin color. In this study, we achieved 91-100% accuracy in classifying melon ripeness levels. All images source were captured using iPhone rear camera.
Keyword: SSD-Mobilenet

Judul: Buprenorphine and Fischer-344 Rats Post-operative Stress Levels: Impact on Serum Corticosterone Levels.
Abstrak: The use of laboratory animals in experiment often requires surgical operations. Post-operative condition often associated with pain and stress. Preemptive analgesia and post operative treatment with buprenorphine can reduce post operative pain and stress and prevent prolonged pain. Therefore, the aim of the present study was to test the hypothesis that pre operative treatment of buprenorphine reduces stress and suppresses the pre-operative serum corticosterone levels in Fischer 344 rats. A total of 14 adult male Fischer 344 (F344/Sca) rats permanently catheterized in artery carotis communis were used. During 94 hour post-operatively the blood sample was collected at specific time point using Accusampler®, an automated blood sampling. Corticosterone levels were quantified with ELISA method using sheep polyclonal antibody to corticosterone. Buprenorphine is effective as pre-emptive analgesia in F344 strain. However the dose is higher than what has been reported in Sprague Dawley strain. Buprenorphine 1.0 mg/kg significantly suppressed serum corticosterone levels immediately after rats regain consciousness. Rats in buprenorphine 1.0 mg/kg group were protected from post operative body weight loss and reduced water intake compare to control, nutella, and buprenorphine 0.4 mg/kg group. Quantification of fecal corticosteroid metabolites failed to detect stress in present study.
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