PERAMALAN HARGA SAHAM PT. INDOFOOD SUKSES MAKMUR TBK DENGAN METODE AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA) DAN LONG SHORT-TERM MEMORY (LSTM)- submit jurnal

Amadea, Jesselyn Nixie (2026) PERAMALAN HARGA SAHAM PT. INDOFOOD SUKSES MAKMUR TBK DENGAN METODE AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA) DAN LONG SHORT-TERM MEMORY (LSTM)- submit jurnal. Bachelor thesis, Institut Teknologi kalimantan.

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Abstract

Pergerakan harga saham yang fluktuatif menyebabkan ketidakpastian dalam pengambilan keputusan investasi, sehingga diperlukan metode peramalan yang mampu menghasilkan prediksi secara akurat. Penelitian ini bertujuan untuk mengimplementasikan dan membandingkan metode Autoregressive Integrated Moving Average (ARIMA) dan Long Short-Term Memory (LSTM) dalam meramalkan harga saham PT. Indofood Sukses Makmur Tbk. Data yang digunakan berupa data historis penutupan harga saham periode 2021-2025 sebanyak 1.205 observasi yang diperoleh dari situs Investing.com. Data dibagi menjadi 80% data pelatihan dan 20% data pengujian untuk proses pelatihan dan pengujian model. Pada metode ARIMA dilakukan transformasi Box-Cox, pengujian stasioneritas, differencing, identifikasi model menggunakan plot ACF dan PACF, serta pengujian diagnostik residual. Sementara itu, metode LSTM dilakukan melalui tahap normalisasi data menggunakan MinMaxScaler, pembentukan sequence windowing, dan pelatihan model menggunakan kombinasi hyperparameter (baseline) yang ditentukan berdasarkan studi terdahulu. Evaluasi kinerja model dilakukan menggunakan Mean Absolute Percentage Error (MAPE). Hasil penelitian menunjukkan bahwa model ARIMA terbaik adalah ARIMA(0,1,1) dengan nilai MAPE sebesar 5,2256%, sedangkan model LSTM menghasilkan nilai MAPE sebesar 2,2080%. Berdasarkan hasil tersebut, model LSTM dipilih untuk melakukan peramalan harga saham selama satu tahun ke depan yang dilengkapi dengan Bootstrap Prediction Interval 95%.

Item Type:	Thesis (Bachelor)
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:	Jurusan Matematika dan Teknologi Informasi > Ilmu Aktuaria
Depositing User:	Jesselyn Nixie Amadea
Date Deposited:	14 Jul 2026 06:01
Last Modified:	14 Jul 2026 06:01
URI:	http://repository.itk.ac.id/id/eprint/26582

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