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: Prediction of The Closing Price of Stock using Backpropagation Neural Network Method
Abstrak: Saham adalah tanda penyertaan modal seseorang atau badan usaha dalam suatu perusahaan atau perseroan terbatas. Sebelum melakukan investasi, para investor perlu mempelajari prospek harga saham di masa mendatang. Oleh karena itu, peramalan harga saham sangat diperlukan. Pada karya ilmiah ini, dilakukan prediksi harga saham menggunakan metode jaringan saraf tiruan perambatan balik. Metode ini memperhitungkan input, jumlah neuron, dan fungsi pembelajaran yang harus ditentukan. Berdasarkan penghitungan Mean Absolute Error (MAE) dan Mean Absolute Percentage Error (MAPE), jaringan yang paling optimal adalah jaringan yang menggunakan input harga pembukaan saham dan tingkat suku bunga dengan satu neuron dan fungsi pembelajaran traincgb. MAE yang diperoleh sebesar 237.7063 rupiah dan MAPE sebesar 2.934777%., Stocks are sign of a person’s or business entity’s capital participation in a company or limited liability company. Before making an investment, an investor needs to study the prospect of future stock price. Therefore, making a forecast of future stock is indispensable. In this paper, backpropagation neural network method is used to forecast stock closing price. This method takes into accounts the input variables, the number of neurons, and it requires a learning function which must be defined. Based on Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE), the most optimal network is achieved by using stock opening price and interest rate as input variables with one neuron and traincgb learning function. The obtained MAE is IDR 237.7063 and MAPE is 2.934777% which is relatively small.
Keyword: stock closing price, backpropagation neural network, forecasting

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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