Judul: The Implementation of Viterbi Algorithm to Predict Stock Market Conditions
Abstrak: Kondisi pasar saham selalu mengalami perubahan setiap waktu. Hal ini ditunjukkan salah satunya oleh pergerakan nilai penutupan suatu indeks saham baik meningkat maupun menurun. Kondisi pasar saham memengaruhi sentimen pasar dalam memutuskan suatu strategi perdagangan, oleh karena itu penting untuk diketahui. Jika kondisi pasar saham yang menyebabkan terjadinya nilai penutupan suatu indeks saham diasumsikan tidak teramati secara langsung dan membentuk rantai Markov, maka pasangan kondisi pasar saham dan nilai penutupan indeks saham dapat dimodelkan sebagai hidden Markov model. Terdapat tiga permasalahan dasar yang harus diselesaikan dalam hidden Markov model yaitu evaluation problem, decoding problem, dan learning problem. Beberapa algoritma yang dapat digunakan untuk menyelesaikan permasalahan tersebut adalah algoritma ¬forward-backward, algoritma Viterbi, dan algoritma Baum-Welch. Saat decoding problem menggunakan algoritma Viterbi inilah kondisi pasar saham yang terjadi dapat diduga. Pada tugas akhir ini akan diduga kondisi pasar saham dari nilai penutupan harian indeks FTSE 100. Dari hasil perhitungan, selama periode pengamatan, nilai penutupan harian indeks FTSE 100 disebabkan oleh tiga state hidden Markov yang dapat dikategorikan sebagai bullish, sideways, dan bearish., Stock market conditions change over time. It is indicated by the movement of the closing values of a stock index either increasing or decreasing. It affects market sentiment in deciding a trading strategy, therefore it is important to know. If the stock market conditions that cause the closing values of a stock index are assumed not to be observed directly and form a Markov chain, then the pair of stock market conditions and the closing values of a stock index can be modelled as a hidden Markov model. Three basic problems must be solved in hidden Markov model are evaluation problem, decoding problem, and learning problem. Several algorithms that can be used to solve these problems are forward-backward algorithm, Viterbi algorithm, and Baum-Welch algorithm. When the decoding problem using Viterbi algorithm, the stock market conditions that occur can be predicted. In this final project, the stock market conditions will be estimated from the daily closing values of FTSE 100 index. From the calculation results, during the observation period, the daily closing values of FTSE 100 index are caused by three hidden Markov states which can be categorized as bullish, sideways, and bearish.
Keyword: hidden Markov model, stock market conditions, Viterbi algorithm

Judul: Random Forest and XGBoost method: A Case Study of Predicting the Direction of Closing Stock Prices
Abstrak: Kemampuan dalam memprediksi tren pergerakan harga pasar saham dengan baik dan akurat sangat penting bagi investor karena akan mempengaruhi keputusan untuk membeli atau menjual asset yang dapat menghasilkan keuntungan yang signifikan. Namun, karena harga saham dinilai tidak tetap dan bersifat non-parametrik, maka ada ketidakpastian yang harus diperhitungkan. Dalam beberapa tahun terakhir, algoritma Machine Learning khususnya algoritma pembelajaran gabungan telah digunakan untuk memprediksi arah harga saham. Tujuan dari penelitian ini adalah menerapkan metode Random Forest dan XGBoost dengan algoritma grid search untuk membangun model prediksi arah harga saham BBRI. Hasil penelitian menunjukkan model prediksi metode Random Forest menghasilkan nilai akurasi sebesar 90,01% dengan AUC sebesar 0,95 dan model prediksi metode XGBoost menghasilkan nilai akurasi sebesar 89,45% dengan AUC sebesar 0,94., The ability to predict trends in stock market price movements well and accurately is very important for investors because it will affect the decision to buy or sell assets that can generate significant profits. However, since stock prices are considered non-fixed and non-parametric, there is uncertainty that must be accounted for. In recent years, Machine Learning algorithms, especially ensemble learning algorithms, have been successful in producing good accuracy in predicting the direction of stock prices. The purpose of this research is to apply Random Forest and XGBoost methods with a grid search algorithm to build a prediction model for BBRI stock price direction. The result shows that the Random Forest method prediction model produces an accuracy value of 90,01% with an AUC of 0,95 and the XGBoost method prediction model produces an accuracy value of 89,45% with an AUC of 0,94.
Keyword: Machine Learning, Random Forest, stock prices, XGBoost

Judul: Synthesis of Apatite - Chitosan Composite Using Duck Eggshell and Chitosan with Temperature and pH Control
Abstrak: Composite of apatite-chitosan was prepared by using chitosan as polymer matrix. Calcium phosphate was prepared from CaO and (NH4)2HPO4, which the ratio Ca : P had to be 1.67. Chitosan is produced by deacetilase from biopolymer chitin. Duck eggshell (95% CaCO3) as starting material was converted to be calcium oxide (CaO) through calcinations with temperature 1000oC for 5 hours. In-situ and ex-situ methods were prepared from apatite and chitosan (dissolved with acetate acid 2%). Calcium phosphate prepared for bone implantation, but it is too fragile and brittle to be implant into the body. So addition of chitosan can decrease crystalinity and increase mechanic characteristic (tougher). Composite apatite-chitosan was resulted by fast precipitation. Fast precipitation was held in a breaker glass with nitrogen atmosphere, under controlled temperature at 37oC and pH 7, in order to increase amount of hydroxyapatite. Precipitation with CaO which was dropped by (NH4)2HPO4. Hydroxyapatite and chitosan were appearing at samples with in-situ and ex-situ methods which indicating by peak matching of XRD (closed lattice parameter, crystal size), overlapping at FTIR spectra and supporting by SEM picture (homogenous and agglomerated particle). However, it still needs further research to examine biocompatibility osteoconductive degree of sample by in-vivo and in-vitro test.
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