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PEMODELAN MATEMATIKA

PEMODELAN MATEMATIKA. Kudang B. Seminar. KARAKTERISTIK BUAH JERUK KEPROK GARUT MELALUI PEMODELAN RANGKAIAN LISTRIK YANG DIDASARKAN PADA SIFAT RESISTIF DAN KAPASITIFNYA (J. Juansah , W. Budiastra , K. Dahlan , K.B. Seminar 2013).

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PEMODELAN MATEMATIKA

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  1. PEMODELAN MATEMATIKA Kudang B. Seminar

  2. KARAKTERISTIK BUAH JERUK KEPROK GARUT MELALUI PEMODELAN RANGKAIAN LISTRIK YANG DIDASARKAN PADA SIFAT RESISTIF DAN KAPASITIFNYA (J. Juansah, W. Budiastra, K. Dahlan, K.B. Seminar 2013)

  3. Diagram alirprinsipkarakteristikpemodelanspektroskopiimpedansi (Macdonald 1987)

  4. Analisis Model RangkaianListrik

  5. PenurunanRumus Dari Model

  6. Pengukuran parameter impedansinyapadabagian-bagianbuah INTERPRETASI: Hal itujugamenjadipertimbanganbahwabijidominanresistif, sementarakulit, SACS, danbuahutuhmemilikikomponenkapasitif

  7. PerbandinganKesesuaian Model-Model Yang TelahDikembangkan

  8. Coefficient of Determination The coefficient of determination, denoted R2 and pronounced R squared, indicates how well data points fit a line or curve.

  9. PengukuranAkurasi Model The mean absolute percentage error (MAPE), also known as mean absolute percentage deviation (MAPD), is a measure of accuracy of a method for constructing fitted time series values in statistics, specifically in trend estimation. It usually expresses accuracy as a percentage, and is defined by the formula: At :The actual value Ft :The forecast value The root-mean-square deviation (RMSD) or root-mean-square error (RMSE) is a frequently used measure of the differences between values predicted by a model or an estimator and the values actually observed. The RMSD serves to aggregate the magnitudes of the errors in predictions for various times into a single measure of predictive power. RMSD is a good measure of accuracy, but only to compare forecasting errors of different models for a particular variable and not between variables, as it is scale-dependent. : predicted values for times t : regression dependent variable

  10. Koefisendeterministik (a), MAPE (b), dan RMSE (c) padahasilsimulasiuntuk model barupadabeberapatingkatkeasaman (pH). Nilai parameter impedansi (Z/m), reaktansi (X/m), danresistansi (R/m) dalamorde

  11. HubunganantaraResistensidan Tingkat KeasamanJerukKeprok INTERPRETASI: Makin tinggitingkatkeasamanmakinrendahnilairesistensinya.

  12. HubunganantaraResistensidan Tingkat KekerasanJerukKeprok INTERPRETASI: Makin tinggitingkatkekerasanmakintingginilairesistensinya.

  13. HubunganantaraKapasitansidan Tingkat KeasamanJerukKeprok INTERPRETASI: Makin tinggitingkatkeasamanmakintingginilaikapasitansinya.

  14. HubunganantaraKapasitansidan Tingkat KeasamanJerukKeprok INTERPRETASI: Makin tinggitingkatkekerasanmakinrendahnilaikapasitansinya.

  15. NilaiKomponen Internal BerdasarkanHasilKomputasi Model Yang Dikembangkan

  16. KesimpulanPengamatan • InterpretasisifatlistrikmemberipeluangdankesempatanuntukmeninjauperilakukematanganJerukKeprokGarut. • Taksatu pun dari model listrikmampumemprediksisemuaperubahanperilakusecarasempurna. • Model yang dikembangkanJuansahet almemilikikinerjaakurasi yang lebihbaikdibandingkanmodel Hayden danZhang • Pembentukan model listriktelahmembantupemahamankitatentangkarakteristikbuahJerukKeprokGarut. • Perubahankekerasandankeasamandalambuah-buahandiikutidenganperubahankapasitansimembrandanresistansikomponenjaringanpenyusunbuah. • Perubahanresistansijaringandankapasitansimembranmenunjukkanadanyaperubahanmobilitas ion dalamseldanperubahanfisiologisbuahselamapematangan.

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