Judul: Pemodelan Dinamika Harga Saham Perbankan Menggunakan Recurrent Neural Network dengan Optimasi Momentum
Abstrak: Semakin pesatnya perkembangan pasar modal di Indonesia membuat semakin banyak investor bergabung di Bursa Efek Indonesia. Salah satu sektor industri yang banyak diminati investor adalah perbankan, karena memiliki kapitalisasi pasar yang tinggi dan merupakan industri yang penting untuk suatu negara. Oleh karena itu perlu dilakukan penelitian mengenai dinamika harga saham perbankan. Penelitian ini bertujuan menggunakan model machine learning yang dioptimalkan dengan ilmu fisika untuk melakukan pemodelan dan prediksi harga saham empat perusahaan perbankan terbesar di Indonesia berdasarkan kapitalisasi pasar tertinggi. Prediksi harga saham akan menggunakan model Recurrent Neural Network (RNN) menggunakan sel Long Short Term Memory yang dioptimalkan menggunakan momentum. Hasil prediksi pada penelitian ini dievaluasi dengan data aktual menggunakan Mean Absolute Percentage Error (MAPE). Model RNN dengan optimasi momentum memiliki nilai MAPE 1,32% untuk satu data target dan 1,65% model yang melibatkan 15 data target., The rapid development of the stock market in Indonesia has encouraged more investors to join the Indonesia Stock Exchange. The banking industry garners investor interest due to its substantial market capitalization and its significance as a pivotal industry for any nation. Hence, it is necessary to do research on the dynamics of banking stock prices. The objective of this study is to utilize physics- optimized machine learning models for the purpose of modeling and forecasting the stock prices of the top four banking companies in Indonesia based on their highest market capitalization. Stock price prediction will use the Recurrent Neural Network (RNN) using Long Short Term Memory cells which are optimized using momentum. The assessment of prediction outcomes in this research was conducted by comparing it to real-world data using the Mean Absolute Percentage Error (MAPE) as the evaluation metric. The RNN model, enhanced with momentum optimization, yielded a MAPE of 1.32% for a single target data point and 1.65% for a dataset involving 15 target data points.
Keyword: Long Short Term Memory, Momentum, Neural Network, Stock Price Prediction

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: Sintesis Flavonol melalui 2’-Hidroksikalkon
Abstrak: Flavonol is a class of flavonoids commonly found in fruits and vegetables. Flavonol is very useful, especially in food sector and health. In this study, flavonol unsubstituted was successfully synthesized from an intermediate material 2’-hydroxychalcone. The 2’-hydroxychalcone was synthesized by Claisen-Schmidt condensation method between o-hydroxyacetophenone and benzaldehyde using 4 different catalysts, namely KOH 60%, solid pellet of KOH-grinding technique, BF3-Et2O, and NaH in DMF. The KOH 60% was the best catalyst, giving the highest yield (57%). Afterward, the flavonol was synthesized in 1-stage oxidation cyclization of o-hydroxyacetophenone using Algar-Flynn-Oyamada (AFO) reaction, giving 76%. The flavonol total yield from 2’-hydroxychalcone was 43%. All synthesized products were characterized by their melting points and their spectra. The success of the synthesis flavonol opens the way to synthesis of useful flavonol derivatives such as quercetin and kaempferol
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