Judul: A Comparative Study of GARCH, LSTM, and Hybrid GARCH-LSTM Models for Analyzing the Dynamical Pattern of Stock Price Volatility
Abstrak: Most of the statistical methods used in time series analytics assume that the data properties be stationary. In real cases, this assumption may be violated. The stock price is one of the highly volatile data or has a time-dependent variance. GARCH model is a model that overcomes the limitation of the stationary assumption on most statistical methods. In addition, artificial intelligence methods are widely used and have promising performance. This study aims to examine the performance of the GARCH, LSTM, and hybrid GARCH–LSTM models in analyzing the dynamical pattern of volatility data on simulation and empirical data. This study uses two kinds of data, simulation and empirical data. The simulation data are obtained from the simulation process by generating data from the GARCH model with ten scenarios. The scenarios are the combination of two factors: (1) variance with two levels, high and low; (2) dataset with five levels, GARCH (1,1), GARCH (1,3), GARCH (3,1), GARCH (3,3), and Contaminated GARCH. The design for the simulation study is the randomized complete block factorial design with three factors (i.e., variance, dataset, and model), and the response is the MAPE value. The effects of the factors on the MAPE values are analyzed using ANOVA and Tukey's multiple comparisons. Meanwhile, the stock closing prices of PT Bumi Resources Minerals Tbk. in 2021 are used as the empirical study. For the modeling process in empirical study, data are transformed into returns and then modeled using three proposed models. Seven days ahead, predictions are carried out after the model validation process. The ANOVA results show that there are different effects of variance on MAPE values, but these variance effects depend on the kind of dataset. For high variance, the MAPE values of Contaminated GARCH are low, while for the other datasets, the MAPE values are high. According to Tukey's multiple comparisons, the Contaminated GARCH dataset from high variance has different effects on the MAPE value than other datasets from low variance. In addition, the mean MAPE values of GARCH (3,1) and GARCH (3,3) datasets from high variance are higher than those of the GARCH (1,1) dataset from low variance. Another result shows that the GARCH (1,3) and GARCH (3,3) datasets from high variance have different effects on the MAPE value than the Contaminated GARCH dataset from low variance. Furthermore, all comparisons show no significant effect on the MAPE value for the same datasets but with different variances. Another significant factor that affects the MAPE value is the model. The effect of the model does not depend on the variance or the dataset type. Tukey's multiple comparisons show that the Hybrid GARCH–LSTM model outperforms all the other models, followed by the LSTM and GARCH models. For the empirical study, the results show that all models can capture the volatility pattern of the BRMS stock closing prices, but there are some different performances. The GARCH and LSTM models provide the MAPE values of 3.18% and 3.06%, respectively. Meanwhile, for the Hybrid GARCH–LSTM model, the MAPE value is 0.93%. The Hybrid GARCH–LSTM is the best model among others. Based on the three
Keyword: GARCH, Hybrid, LSTM, Stock price, Volatility

Judul: Study of Hybrid ARIMA-GARCH and LSTM Methods for Price Modeling Approach of Some Staple Foods
Abstrak: Model deret waktu telah banyak digunakan untuk tujuan peramalan. Salah satu yang umum digunakan adalah model autoregressive integrated moving average (ARIMA). Namun apabila volatilitas dari data tinggi, maka ragam bersyarat menjadi tidak konstan (bersifat heteroskedastik). Kombinasi model ARIMA dan model generalized autoregressive conditional heteroscedasticity (GARCH) menjadi model Hybrid ARIMA-GARCH dapat digunakan untuk peramalan data deret waktu dengan mempertimbangkan sifat heteroskedastik dalam ragam bersyarat. Selain itu, metode long short term memory (LSTM) yang secara spesifik dirancang untuk memungkinkan jaringan memahami ketergantungan data dalam selang yang lebih panjang juga dapat digunakan untuk prediksi data deret waktu. Data harga komoditas pangan adalah salah satu contoh data deret waktu yang memiliki volatilitas tinggi. Beberapa komoditas pangan tersebut antara lain beras, bawang merah, cabai merah, dan cabai rawit. Keempat komoditas tersebut merupakan komoditas pangan yang paling banyak dikonsumsi masyarakat Indonesia dan merupakan komoditas yang strategis. Pada penelitian ini, model hybrid ARIMA-GARCH dan LSTM digunakan untuk memprediksi harga keempat komoditas pangan tersebut. Kedua metode dibandingkan berdasarkan nilai akurasi model yaitu root mean square error (RMSE), mean absolute percentage error (MAPE), dan mean absolute deviation (MAD) pada masing-masing komoditas. Hasil penelitian menunjukkan bahwa metode hybrid ARIMA-GARCH memiliki nilai RMSE, MAPE, dan MAD yang lebih kecil dibandingkan metode LSTM. Hal ini menunjukkan bahwa metode hybrid ARIMA-GARCH lebih akurat dibandingkan metode LSTM dalam pemodelan prediksi harga beras, bawang merah, cabai merah, dan cabai rawit di DKI Jakarta. Adapun model hybrid ARIMA-GARCH terbaik untuk harga beras di DKI Jakarta adalah ARIMA(1,1,0)-GARCH(1,0). Sementara untuk harga bawang merah yaitu ARIMA(2,1,1)-GARCH(1,1), untuk harga cabai merah yaitu ARIMA(0,1,1)-GARCH(1,1), dan untuk cabai merah yaitu ARIMA(0,1,5)-GARCH(1,1).
Keyword: hybrid ARIMA-GARCH, LSTM, volatility

Judul: Kebiasaan makanan ikan endemik bonti-bonti (Paratherina striata) di danau Towuti, Sulawesi Selatan
Abstrak: Ikan bonti-bonti (Paratherina striata) merupakan salah satu jenis ikan endemik di perairan Danau Towuti. Populasi ikan tersebut cenderung terbatas karena hanya terdapat di perairan Danau Towuti dan Danau Mahalona, ditambah dengan kegiatan penangkapan yang tinggi oleg nelayan setempat. sehingga perlu diupayakan kegiatan pengelolaan dengan cara konservasi agar kelestarian dan keberadaan ikan tersebut tetap terjaga.
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