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: Wire electroplating sludge sebagai katalis pembakaran batu bara
Abstrak: A fuel additive (combustion catalyst) can improve the combustion efficiency by promoting complete oxidation of fuel. Combustion catalyst in this research was composed of wire electroplating sludge dispersed in a mixture of vegetable oil, water, and surfactant. Four types of catalyst suspensions were prepared, namely A (2% sludge); B (3% sludge); C (2% sludge, 3% oxalyc acid); and D (3% sludge, 3% oxalyc acid). Addition of oxalyc acid in catalyst suspension C and D showed significant effects on combustion efficiency. Each suspension was applied in coal having caloric value of 5235.09 cal/g. The concentration of catalyst suspension in coal was about ±0.1% w/w. The results of activity test by using differential thermal analyzer showed that the energy efficiency from catalyst A was 62.21%, B: 97.19%, C: 77.96%, and D: 97.54%. Catalyst D (3% sludge, 3% oxalyc acid) is the best catalyst suspension that can be used as coal combustion catalyst.
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