Judul: Pengembangan Model Probabilistic Neural Networks untuk Penentuan Kematangan Belimbing Manis
Abstrak: The objectives of this research were to develop PNN model in determining starfruit ripeness. Experiments done by involving 354 image data of starfruits that classified into 5 classes. Feature extraction from those hundreds images was yield an image representation among others: amount of red pixels (R), amount of green pixels (G), substraction between R and G (R-G), and average value of Hue. Those features then became PNN inputs. After enjoining the last two classes, the best result could reach accuracy untill 90.86%.
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Judul: Design of Averrhoa carambola L. Fruit Maturity Detection using Convolutional Neural Network and Integrated with Raspberry Pi
Abstrak: Belimbing merupakan hasil pertanian yang memiliki tingkat produksi yang tinggi, budidaya yang mudah, dan waktu kematangan yang relatif singkat. Petani umumnya mengklasifikasi tingkat kematangan buah biasanya masih dilakukan secara manual. Penelitian ini telah dibuat alat yang dapat menentukan tingkat kematangan buah belimbing secara presisi dan realtime. Convolutional Neural Network (CNN) digunakan untuk metode penentuan tingkat kematangan buah berdasarkan citra yang didapatkan. Tiap citra dilatih menggunakan tensorflow dengan model CNN yang berbeda, yaitu mobilenetV1, mobilenetV2, dan inceptionV3 dan diimplementasikan pada Raspberry Pi. Hasil pengujian alat menunjukkan model CNN mobilenetV1 memiliki akurasi sebesar 73.07% dengan kecepatan inferensi objek dalam waktu ±1150 milisekon. inceptionV3 sebesar 76.92% dengan kecepatan ±7000 milisekon, dan mobilenetV2 sebesar 61.54% dengan kecepatan ±900 milisekon., Starfruit is an agricultural product that has a high production rate, easy cultivation, and a relatively short maturity time. Farmers generally classify the level of fruit maturity manually. This research were produced a device that can determine the level of star fruit ripeness with precision and realtime. Convolutional Neural Network (CNN) is method that were used of determining the level of fruit maturity based on the image obtained. Each image was trained using tensorflow with a different CNN model, that is mobilenetV1, mobilenetV2, and inceptionV3 and implemented on the Raspberry Pi. The test results show that the CNN mobilenetV1 model has accuracy at 73.07% with the object inference velocity in ± 1150 milliseconds. InceptionV3 at 76.92% with inference in ±900 milliseconds , and mobilenetV2 at 61.54% with inference in ±7000 milliseconds.
Keyword: Raspberry Pi, Convolutional Neural Network, Belimbing

Judul: Characterizations of low alloy steel sae 4422  plated with chromic acid (Cro3)
Abstrak: Characterizations of low alloy steel SAE 4422 plated with chromic acid (CrOrO3) was use Optical Microscopy (OM), Scanning Electron Microscopy (SEM), X-Ray Diffraction (XRD), RD),Energy Dispersive Analysis of X-Ray (EDAX), Hardness Test and Roughness Test. Microstructures analysis exhibits cracks and grain boundaries microcracks as a characteristics of chrome metal found at plating metal. Perlite and Ferrite as grains of steel found at base metal with grain boundaries around it. Phase analysis of plating metal appropriate to chrome metal with lattice constant (a) is 2.559 Å and phase analysis of base metal appropriate to iron ferrite with lattice constant (a) is 3.318 Å. Both plating and base metal have cubic system. Chemical analysis of plating metal at surface section exhibits that cracks area have highest value of carbon element compare with grains and grains boundaries area. While chemical analysis of base metal at cross section exhibits that perlite have highest value of carbon element compare with ferrite and grain boundaries. The hardness value of plating metal at surface section and cross section are 753 and 637 VHN. While, the hardness value of base metal and interface area at cross section are 205 and 373 VHN. The roughness value at measurement points of chrome surface for as received sample are 0.445 μm. Observation used Optical Microscopy and Scanning Electron Microscopy exhibits surface irregularities combine to form surface texture of plating metal at surface section.
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