?url_ver=Z39.88-2004&rft_id=1917031085+&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rft.title=PERAMALAN+DATA+TIME+SERIES+PADA+HARGA+KOMODITAS+PANGAN+MENGGUNAKAN+METODE+LONG+SHORT+TERM+MEMORY+(LSTM)+(Studi+Kasus%3A+Harga+Penutupan+Kedelai+Amerika+Serikat)&rft.creator=BERLIAN+SASYA+DEVI+PRADANA+%2C+1917031085+&rft.subject=510+Matematika&rft.description=ABSTRACT%0D%0A%0D%0A%0D%0A%0D%0AForecasting+the+closing+price+of+US+soybeans+plays+a+crucial+role+in+preventing+price+fluctuations+at+a+specific+time+and+can+be+used+as+a+reference+for+tomorrow's+opening+price.++Therefore%2C+we+need+a+method+that+can+be+used+for+forecasting+the+closing+price+of+US+soybeans.++This+study+discusses+the+analysis+of+forecasting+time+series+data+on+food+commodity+prices%2C+namely+the+closing+price+of+US+soybeans%2C+using+the+Long+Short+Term+Memory+(LSTM)+method+with+several+parameters+needed%2C+namely+hidden+layer%2C+epoch%2C+hidden+neurons%2C+batch+size+and+activation+function.+This+study+identified+the+optimal+LSTM+model+from+several+models+formed+based+on+the+results+of+a+combination+of+hidden+layer+parameters%2C+number+of+epochs%2C+number+of+hidden+neurons%2C+and+number+of+batch+sizes.+The+model+chosen+is+the+one+that+predicts+the+closing+price+of+US+soybeans+with+the+smallest+Root+Mean+Square+Error+(RMSE)+and+Mean+Absolute+Percentage+Error+(MAPE).++The+best+model+is+given+by+the+composition+of+hidden+layer+parameter+values+of+1%2C+epoch+of+100%2C+hidden+neurons+of+64%2C+and+batch+size+of+16%2C+which+can+predict+the+closing+price+of+US+soybeans+with+an+RMSE+value+of+23.6674+and+a+MAPE+value+of+1.2152%25.%0D%0A%0D%0A%0D%0AKeywords%3A+Soybean+Closing+Price%2C+Forecasting%2C+Prediction%2C+Time+Series%2C+LSTM%2C+RMSE+and+MAPE.+%0D%0A%0D%0AABSTRAK%0D%0A%0D%0APeramalan+harga+penutupan+kedelai+Amerika+Serikat+berperan+sangat+penting+dalam+mencegah+fluktuasi+harga+pada+waktu+tertentu+dan+dapat+menjadi+acuan+untuk+pembukaan+harga+esok+hari.+Oleh+karena+itu%2C+diperlukan+metode+yang+dapat+digunakan+untuk+peramalan+harga+penutupan+kedelai+Amerika+Serikat.++Penelitian+ini+membahas+tentang+analisis+peramalan+data+time+series+pada+harga+komoditas+pangan+yaitu+harga+penutupan+kedelai+Amerika+Serikat+menggunakan+metode+Long+Short+Term+Memory+(LSTM)+dengan+sejumlah+parameter+yang+dibutuhkan+yakni+hidden+layer%2C+max+epoch%2C+neuron+hidden%2C+batch+size+dan+fungsi+aktivasi.+Penelitian+ini+mengidentifikasi+model+LSTM+yang+optimal+dari+sejumlah+model+yang+terbentuk++berdasarkan++hasil++kombinasi++parameter++hidden+layer%2C++jumlah+epoch%2C+jumlah+neuron+hidden%2C+dan+jumlah+batch+size.++Model+yang+dipilih+adalah+model+yang+memberikan+hasil+prediksi+harga+penutupan+kedelai+Amerika+Serikat+dengan+dengan+nilai+Root+Mean+Square+Error+(RMSE)+dan+Mean+Absolute+Percentage+Error+(MAPE)+paling+kecil.+++Model+terbaik+diberikan+oleh+komposisi+nilai+parameter+hidden+layer+sebanyak+1%2C+epoch+sebanyak+100%2C+neuron+hidden+sebanyak%0D%0A64%2C+batch+size+sebanyak+16+yang+mampu+meramalkan+harga+penutupan+kedelai%0D%0AAmerika+Serikat+dengan+nilai+RMSE+sebesar+23%2C6674+dan+nilai+MAPE+sebesar%0D%0A1%2C2152%25.%0D%0A%0D%0A%0D%0AKata+kunci+%3A+Harga+Penutupan+Kedelai%2C+Peramalan%2C+Prediksi%2C+Time+Series%2C+LSTM%2C+RMSE+dan+MAPE.%0D%0A&rft.publisher=MATEMATIKA+DAN+ILMU+PENGETAHUAN+ALAM&rft.date=2023-02-28&rft.type=Skripsi&rft.type=NonPeerReviewed&rft.format=text&rft.identifier=http%3A%2F%2Fdigilib.unila.ac.id%2F70303%2F1%2FABSTRAK.pdf&rft.format=text&rft.identifier=http%3A%2F%2Fdigilib.unila.ac.id%2F70303%2F2%2FSKRIPSI%2520FULL.pdf&rft.format=text&rft.identifier=http%3A%2F%2Fdigilib.unila.ac.id%2F70303%2F3%2FSKRIPSI%2520TANPA%2520BAB%2520PEMBAHASAN.pdf&rft.identifier=++BERLIAN+SASYA+DEVI+PRADANA+%2C+1917031085+++(2023)+PERAMALAN+DATA+TIME+SERIES+PADA+HARGA+KOMODITAS+PANGAN+MENGGUNAKAN+METODE+LONG+SHORT+TERM+MEMORY+(LSTM)+(Studi+Kasus%3A+Harga+Penutupan+Kedelai+Amerika+Serikat).++MATEMATIKA+DAN+ILMU+PENGETAHUAN+ALAM%2C+UNIVERSITAS+LAMPUNG.+++++&rft.relation=http%3A%2F%2Fdigilib.unila.ac.id%2F70303%2F