Judul: Backend Development for Implementing the Netted Melon Maturity Detection Model
Abstrak: Melon merupakan salah satu buah yang diminati oleh masyarakat Indonesia, tetapi budidaya melon sering mengalami kendala dalam menentukan waktu panen yang tepat. Penelitian sebelumnya telah membuat model machine learning menggunakan ekstraksi ciri GLCM (Gray Level Co-Occured Matrix), fitur jala, dan classifier RF (Random Forest) dan SVM (Support Vector Machine) untuk mendeteksi kematangan melon berjala (Cucumis melo L). Namun, penggunaannya masih terbatas karena membutuhkan pemahaman teknis. Oleh karena itu, penelitian ini dibuat untuk memudahkan pengguna dengan mengimplementasikan model ke dalam aplikasi. Penelitian ini berfokus pada pengembangan back-end untuk mendukung aplikasi. Metode yang digunakan adalah tahapan SDLC (Software Development Life Cycle) model waterfall. Hasil penelitian menunjukkan bahwa program ekstraksi fitur hingga normalisasi berhasil diimplementasikan dan dua endpoint dibuat serta diuji dengan blackbox testing. Endpoint ini memudahkan pengguna mengunggah gambar melon dan menerima prediksi kematangan tanpa memerlukan pemahaman teknis mendalam. Pengujian kinerja menunjukkan total waktu rata-rata untuk mengunggah gambar hingga prediksi selesai adalah 445.2 ms, dengan waktu unggah 208.1 ms, dan waktu prediksi 200 ms., Melon is one of the fruits favored by the Indonesian community, but melon cultivation often encounters challenges in determining the right harvest time. Previous research has developed a machine learning model using GLCM (Gray Level Co-Occurred Matrix) feature extraction, net features and RF (Random Forest) and SVM (Support Vector Machine) classifiers to detect the ripeness of netted melons (Cucumis melo L). However, its usage is still limited as it requires technical understanding. Therefore, this research aims to facilitate penggunas by implementing the model into an application. This research focuses on Back-end development to support the application. The method used is the waterfall model of the SDLC (Software Development Life Cycle) stages. The results show that the feature extraction and normalization program was successfully implemented, and two endpoints were created and tested with blackbox testing. These endpoints enable penggunas to upload melon images and receive ripeness predictions without requiring deep technical knowledge. Performance testing shows that the total average time for uploading an image to completing the prediction is 445.2 ms, with an upload time of 208.1 ms, and a prediction time of 200 ms.
Keyword: Backend, melon, REST API, Waterfall, glcm

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: Hydroxyapatite-Chitosan Composite Coating on Stainless Steel 316 to Improve Corrosion Resistance
Abstrak: Metals that were used in bone implantation should have good corrosion resistance that were not easily corroded when implanted into the body. Metal corrosion resistance of stainless steel 316 can be enhanced through the metal coating by hydroxyapatite-chitosan composite with composition ratio of hydroxyapatite (gram) and chitosan 3% (mL) 0.2:0, 0.2:0.1, 0.2:0.5, 0.2:1.0, and 0.2:1.5. Besides improving the corrosion resistance of metal, the presence of chitosan also maintain the position of hydroxyapatite on metal surfaces. Corrosion resistance increases with increasing amount of chitosan added and the best composition of hydroxyapatite-chitosan composite was 0.2:1.0.
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