Pemodelan matematika
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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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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)


Diagram alirprinsipkarakteristikpemodelanspektroskopiimpedansi (Macdonald 1987)


Analisis Model RangkaianListrik


PenurunanRumus Dari Model


Pengukuran parameter impedansinyapadabagian-bagianbuah

INTERPRETASI: Hal itujugamenjadipertimbanganbahwabijidominanresistif, sementarakulit, SACS, danbuahutuhmemilikikomponenkapasitif


PerbandinganKesesuaian Model-Model Yang TelahDikembangkan


Coefficient of Determination

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


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


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


HubunganantaraResistensidan Tingkat KeasamanJerukKeprok

INTERPRETASI: Makin tinggitingkatkeasamanmakinrendahnilairesistensinya.


HubunganantaraResistensidan Tingkat KekerasanJerukKeprok

INTERPRETASI: Makin tinggitingkatkekerasanmakintingginilairesistensinya.


HubunganantaraKapasitansidan Tingkat KeasamanJerukKeprok

INTERPRETASI: Makin tinggitingkatkeasamanmakintingginilaikapasitansinya.


HubunganantaraKapasitansidan Tingkat KeasamanJerukKeprok

INTERPRETASI: Makin tinggitingkatkekerasanmakinrendahnilaikapasitansinya.


NilaiKomponen Internal BerdasarkanHasilKomputasi Model Yang Dikembangkan


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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