Judul: Pengembangan sistem prediksi kemajuan belajar mahasiswa berbasis jaringan saraf tiruan: kasus universitas terbuka
Abstrak: Data akademik, demografi dan data latar belakang sosial dikumpulkan pada saat mahasiswa memasuki jenjang perguruan tinggi. Meskipun demikian perguruan tinggi masih berjuang atas rendahnya tingkat kelulusan. Penelitian ini memperkenalkan penggunaan Jaringan Saraf Tiruan (JST) Multi Layer Perceptron untuk prediksi kemajuan belajar mahasiswa dengan menggunakan beberapa parameter yaitu parameter individu, lingkungan dan akademik pada Universitas Terbuka. Kami juga menggunakan analisa sensitivitas untuk mengukur pengaruh tiap input terhadap output JST. Hasil penelitian dan simulasi mengindikasikan bahwa analisa sensitivitas merupakan metode yang potensial untuk mereduksi kompleksitas JST dan meningkatkan tingkat generalisasi. Prototipe yang dihasilkan dalam penelitian ini mempunyai tingkat generalisasi 96.63%.
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Judul: Jaringan saraf tiruan untuk prediksi tingkat kelulusan mahasiswa diploma Program Studi Manajemen Informatika Universitas Negeri Gorontalo
Abstrak: In this research is made a system of student graduation rate prediction using artificial neural network and back propagation method. Predicted graduation rate is length of study and grade point average. The Input variables is the value of 16 subjects in the first year program. Output variables are 2 categories for the length of study and 3 categories for grade point average. The aims of this research are to get the best parameters and architecture of artificial neural network, to predict student graduation rate and to measure the input variable sensitivity. To obtain a convergent learning outcomes, has done some trial and error with several variations the number of hidden node, learning rate and training function, to generate the high level of data. The result shows, graduation rate prediction using artificial naural network with back propagation method has good result, which 100.00% validation data generalization and 93.94% testing data generalization for lenght of study prediction model, and 96.67% validation data generalization and 100% testing data generalization for grade point average prediction model. The best parameters and architecture from the both of prediction models generated by training in second data group and levenbarg-marquardt training functions. Length of study prediction model, optimal on learning rate 0.1 and number of hidden node 10, and grade point average prediction models optimal on learning rate 0.05 and number of hidden node 15. The sensitivity analysis results that subject as input variables, which have the most impact is Aljabar Vector and Matriks.
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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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