Judul: Penerapan Long  Short-Term Memory dalam Peramalan Return Indeks Harga Saham Gabungan  untuk Pendeteksian Krisis Pasar Modal di Indonesia
Abstrak: Pemodelan data deret waktu multivariate telah banyak dikembangkan dengan menggunakan algoritma deep learning. Salah satu metode deep learning yang dapat digunakan untuk pemodelan data sekuensial adalah Long Short-Term Memory (LSTM). LSTM dapat mempelajari dependensi jangka panjang karena memiliki cell state dan gate untuk mengatur informasi yang akan dipertahankan atau dihapus sehingga banyak diterapkan untuk menganalisis data perdagangan pasar modal. Salah satu produk yang diperdagangkan di pasar modal adalah saham. Pasar saham Indonesia merupakan emerging market yang cenderung rentan terhadap fluktuasi pasar internasional. Dalam sejarah Indeks Harga Saham Gabungan (IHSG) selama 20 tahun terakhir, telah terjadi beberapa kejadian market crash yang perlu diantisipasi dengan baik agar kerugian dapat diminimalkan. Penelitian ini bertujuan untuk membangun model untuk memprediksi return IHSG yang berguna dalam deteksi dini kejadian krisis pasar modal di Indonesia. Metode yang digunakan adalah LSTM dengan strategi Multiple Input-Multiple Output (MIMO). Data yang digunakan adalah data return harian tahun 2002-2023 yang mencakup peubah IHSG, indeks saham pasar global, emas, minyak mentah, indeks VIX, serta nilai tukar rupiah. Model terbaik dihasilkan menggunakan input 3 periode; output 1 periode; serta hyperparameter berikut: optimizer Adam, learning rate 0,0001, neurons 16, batch size 32, dropout 0,1, serta epoch 150. Model tersebut menghasilkan nilai RMSE 0,0108 dan MAE 0,0078., Multivariate time series modeling has been widely developed using deep learning algorithms. One of the deep learning methods that is useful for sequential data modeling is Long Short-Term Memory (LSTM). LSTM has cell states and gates that regulate the information to be retained or removed to learn long-term dependencies. LSTM can be used for forecasting long-term periods, such as capital market data (stocks). Indonesia's stock market is an emerging market that tends to be vulnerable to international market fluctuations. In the history of the IDX Composite over the past 20 years, there have been several market crashes. This study aims to build a model to predict IDX Composite returns that are useful in early detection of capital market crisis events in Indonesia. The method used is LSTM with Multiple Input-Multiple Output (MIMO) strategy. The data used is daily return data from 2002-2023 which includes IDX Composite, global market stock index, gold prices, crude oil prices, VIX index, and exchange rate. The best model is generated using 3 periods of input; 1 period of output; and the following hyperparameter: Adam optimizer, learning rate 0.0001, neurons 16, batch size 32, dropout 0,1, and epoch 150. The model produces an RMSE value of 0,0108 and MAE 0,0078.
Keyword: LSTM, crisis, early warning, idx composite, mimo strategy

Judul: Technical Analysis of the Indonesian Stock Market for Crisis
Detection using Deep Learning Approach.
Abstrak: Big data dan deep learning memainkan peran penting dalam era industri 4.0, terutama dalam pengambilan keputusan di sektor keuangan. Penelitian ini berfokus pada pasar modal Indonesia melalui Jakarta Stock Exchange (JKSE), dengan tujuan mengidentifikasi leading indicator krisis JKSE dan menerapkan serta membandingkan model deep learning Gated Recurrent Unit (GRU) dan Temporal Convolutional Network (TCN) dalam menangkap pola JKSE. Krisis pasar saham Indonesia diidentifikasi melalui penurunan harian atau return JKSE yang mencapai -5% atau lebih. Korelasi silang dan pergerakan return digunakan untuk memilih leading indicator yang mempengaruhi krisis JKSE. Peubah prediktor yang dipilih yaitu SET.BK, SGDIDR, JPYIDR, HSI, GDAXI, KS11, RUT, dan PSEI.PS. Model terbaik dari masing-masing algoritma deep learning GRU dan TCN mampu memprediksi JKSE dengan cukup akurat. GRU memberikan kesalahan prediksi lebih kecil pada data pelatihan berdasarkan rata-rata kesalahan dan uji Duncan sedangkan TCN unggul dalam memprediksi data validasi berdasarkan ukuran kesalahan dan plot visualisasi. Namun, GRU lebih baik dalam menangkap pola JKSE dan peubah prediktor lainnya pada data validasi dengan waktu pelatihan lebih cepat dibandingkan TCN sehingga dipilih sebagai model terbaik., Big data and deep learning play an important role in the industrial era 4.0, especially in decision making in the financial sector. This research focuses on the Indonesian capital market through the Jakarta Stock Exchange (JKSE), with the aim of identifying leading indicators of the JKSE crisis and applying and comparing Gated Recurrent Unit (GRU) and Temporal Convolutional Network (TCN) deep learning models in capturing JKSE patterns. The Indonesian stock market crisis is identified through a daily decline or JKSE return that reaches -5% or more. Cross correlation and return movements are used to select leading indicators that affect the JKSE crisis. The selected predictor variables are SET.BK, SGDIDR, JPYIDR, HSI, GDAXI, KS11, RUT, and PSEI.PS. The best model from each deep learning algorithm GRU and TCN can predict JKSE quite accurately. GRU provides smaller prediction error on training data based on average error and Duncan test while TCN excels in predicting validation data based on error measure and visualization plot. However, GRU is better at capturing the pattern of JKSE and other predictor variables in the validation data with a faster training time than TCN, so it is chosen as the best model.
Keyword: gru, jkse, stock market crisis, tcn

Judul: Sintesis Flavon dari Fenol dan Benzoil Klorida
Abstrak: Flavone is a class of flavonoid, commonly found in plants with wide bioactivity spectrum. A widely used flavone synthesis route is through acidcatalyzed oxidative cyclization of β-diketone. In this study, unsubstituted flavone had been synthesized via 5-step from phenol, with 22% yield and successfully increased the yield of previous study on Fries rearrangement of phenyl acetate and benzoylation of o-HAP steps. Phenol was acetylated resulting 93% phenyl acetate. AlCl3-catalyzed Fries rearrangement in 1.5 hour and at 120 °C converted phenyl acetate into o-hydroxyacetophenone (o-HAP) with 40% yield. AlCl3 as catalyst produced o-HAP with higher and reproducible yield compared with BF3·Et2O and ZnCl2. Benzoylation of o-HAP followed by Baker-Venkataraman rearrangement of the ester product using KOH in dried pyridine produced β-diketone 1-(2- hydroxyphenyl)-3-phenylpropane-1,3-dione. The yield of ester and β-diketone were 71% and 85%, respectively. Finally, oxidative cyclization of β-diketone with sulphuric acid as catalyst produced 99% of flavone. The flavone product had been characterized spectroscopically and its melting point as well.
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