2021
DOI: 10.1088/1757-899x/1071/1/012022
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Utilization of the Batch Training Method for Predicting Natural Disasters and Their Impacts

Abstract: Indonesia is one of the countries that often experiences natural disasters, including earthquakes, floods, tsunamis, etc. All of this causes losses, both casualties, Broken, and Anguishing for the population. Based on this, this paper is proposed, which aims to predict natural disasters in the coming years in Indonesia, casualties, Broken, and their consequences. This paper is an extension of previous research, which is still an architectural model to predict Indonesia’s natural disasters and their impacts. Mo… Show more

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Cited by 7 publications
(4 citation statements)
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“…Beberapa fungsi pelatihan dapat mempengaruhi keluaran hasil optimasi misalnya trainscg (gradien konjugasi berskala), pelatihan trainr (urutan acak), trainbr (regulasi bayesian) [23], pelatihan trainru (bobot/bias tidak terawasi), trainlm (Levenberg-Marguardt) [24], [25], trainoss (OSS) [26], pelatihan trains (incremental fungsi pembelajaran) [27], pelatihan trainbu (bobot tanpa pengawasan) [28], pelatihan trainb (aturan pembelajaran bobot) [29], traingcgp, traincgb, traincgf (pelatihan konjugasi gradien) [30], trainrp (resilient) [31], BFGS trainbfg (quasi-newton) [32], trainc (urutan nilai bobot/bias) serta BFGS trainbfgc (referensi quasi-newton) [33].…”
Section: Pendahuluanunclassified
“…Beberapa fungsi pelatihan dapat mempengaruhi keluaran hasil optimasi misalnya trainscg (gradien konjugasi berskala), pelatihan trainr (urutan acak), trainbr (regulasi bayesian) [23], pelatihan trainru (bobot/bias tidak terawasi), trainlm (Levenberg-Marguardt) [24], [25], trainoss (OSS) [26], pelatihan trains (incremental fungsi pembelajaran) [27], pelatihan trainbu (bobot tanpa pengawasan) [28], pelatihan trainb (aturan pembelajaran bobot) [29], traingcgp, traincgb, traincgf (pelatihan konjugasi gradien) [30], trainrp (resilient) [31], BFGS trainbfg (quasi-newton) [32], trainc (urutan nilai bobot/bias) serta BFGS trainbfgc (referensi quasi-newton) [33].…”
Section: Pendahuluanunclassified
“…Penggunaan fungsi transfer pada Machine Learning berbasis jaringan saraf, khususnya backpropagation, yaitu fungsi transfer sigmoid tangen hiperbolik (tansig) [20], fungsi transfer log-sigmoid (logsig) [21], dan fungsi transfer linear (purelin) [22], sedangkan penggunaan fungsi perlatihan pada metode backpropagation standar, diantaranya gradient descent (traingd, traingdx, traingda dan traingdm) [23]. Fungsi perlatihan lain dapat digunakan untuk optimasi dan berpengaruh terhadap hasil komputasi, seperti Levenberg-Marquardt (trainlm) [24,25], perlatihan batch dengan aturan pembelajaran bobot dan bias (trainb) [26], BFGS quasi-Newton (trainbfg) [27], BFGS quasi-Newton dengan referensi adaptif control (trainbfgc) [28], regulasi bayesian (trainbr) [29], perlatihan batch bobot/bias tanpa pengawasan (trainbu) [30], urutan siklus bobot/bias (trainc) [31], perlatihan konjugasi gradien (traincgf, traingcgp, traincgb) [32], One-Step Secant (garis potong satu langkah, trainoss) [33], perlatihan tambahan urutan acak dengan fungsi pembelajaran (trainr) [34], resilient (trainrp) [35], perlatihan bobot/bias perintah acak tidak diawasi (trainru) [36], perlatihan urutan inkremental berurutan dengan fungsi pembelajaran (trains) [37], dan gradien konjugasi berskala (trainscg) [38].…”
Section: Pendahuluanunclassified
“…Spatial data is geographic information that describes objects on Earth with geographic references [5] [6]. This spatial data is usually based on maps that contain interpretations and projections of all phenomena on Earth, including natural and artificial phenomena [7] [8]. Initially, all data and information on maps represented objects on the Earth's surface.…”
Section: Introductionmentioning
confidence: 99%