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: Insecticidal Activity of Essential Oils Leaves of Cinnamomum spp. (Lauraceae) against Crocidolomia pavonana and Their Phytotoxicity on Broccoli Seedlings
Abstrak: Essential oils from leaves of 13 species of Cinnamomum were tested for their insecticidal activity on the cabbage head caterpillar Crocidolomia pavonana with leaf immerse method. The results showed that the treatment with 1% (w/v) essential oils leaves of C. celebicum, C. multiflorum, C. sintoc, and C. verum caused 21.45, 93.65, 60.30, and 76.10% mortality, respectively. Based on the results of phytotoxicity and insecticidal activity tests, C. multiflorum oil was further tested at 6 concentration levels. In all treatments, larval mortality has occurred at 24 hours after treatment (HAT) and increased at 48 HAT. LC50 of C. multiflorum oil at 24 and 48 HAT was 0.504 and 0.396%, respectively. The treatment with C. multiflorum oil at ≤0.31% did not inhibit the development of C. pavonana larvae. Determination of the chemical constituents of Cinnamomum oils were carried out by gas chromatography-mass spectrometry (GCMS). Totally, 345 compounds were identified. Methyleugenol constituted the main component (49.4%) of leaf essential oil of C. multiflorum and was assumed to be responsible for the insecticidal activity of the oil against C. pavonana larvae. LC50 of pure methyleugenol obtained at 0.365% (48 HAT) and 0.363% (72 HAT). The results showed that C. multiflorum was more effective with LC50 value equivalent to methyleugenol content of 0.32% (24 HAT) and 0.24% (48 HAT).
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