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: Pemodelan Hidden Markov Kontinu pada Indeks Harga Saham FTSE100
Abstrak: Harga penutupan indeks pasar saham selalu mengalami perubahan setiap waktu. Hal ini dipengaruhi salah satunya oleh perubahan kondisi pasar saham. Kondisi pasar saham yang menyebabkan terjadinya nilai penutupan suatu indeks saham diasumsikan tidak teramati secara langsung dan membentuk rantai Markov, sehingga dapat dimodelkan oleh hidden Markov kontinu. Dengan menggunakan barisan tersembunyi pada hidden Markov diskret, parameter sebaran pada hidden Markov kontinu dapat diperkirakan. Pada tugas akhir ini akan digunakan model hidden Markov kontinu untuk memprediksi nilai penutupan harian indeks FTSE 100 periode November 2020 – Desember 2020 dengan data latih pada periode Januari 2018 – Oktober 2020. Dari hasil perhitungan, nilai penutupan harian indeks FTSE 100 yang disebabkan oleh tiga state hidden Markov kontinu memiliki sebaran masing masing state yaitu Weibull(25, 6001, 104), KhiKuadrat(7284), dan campuran(0.45, 0.55)[seragam (6581, 7877), lognormal (9, 0.01)]. Hasil simulasi dengan Mathematica 12.2 mendapatkan nilai MAPE latih 3.54% dan nilai MAPE uji 5.84%., The closing prices of stock market indices always experience changes over time. This is influenced by changes in market conditions. The market conditions that cause the closing value of a stock index are assumed to be unobservable directly and form a Markov chain, hence, it can be modeled as a continuous hidden Markov. By using the hidden sequence on the discrete hidden Markov, the distribution parameters of continuous hidden Markov can be estimated. In this final project, the hidden Markov model will be used to predict daily closing values of the FTSE 100 index on November 2020 – December 2020, with training data on January 2018 – October 2020. From the calculation results, period, the daily closing values of the FTSE 100 index, which are caused by the three states of the continuous hidden Markov, have the following distributions for each state: Weibull(25, 6001, 104), Chi-Square(7284), and mixture(0.45, 0.55)[uniform(6581, 7877), lognormal(9, 0.01)]. The simulation results using Mathematica 12.2 obtained a training MAPE of 3.54% and a testing MAPE of 5.84%.
Keyword: continuous HMM, simulation, stock price index

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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