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: Penerapan Algoritme Genetika dalam Penentuan Indikator Krisis  Keuangan Hasil Pemodelan Machine Learning
Abstrak: Krisis keuangan adalah situasi ketika terdapat tanda-tanda kesulitan dan kerugian keuangan yang memengaruhi sebagian besar sistem keuangan. Besar dampak yang akan diterima suatu negara dipengaruhi oleh seberapa siap sistem keuangan yang ada dalam menghadapi syok atau tekanan yang terjadi. Diperlukan sebuah deteksi dini dalam mempersiapkan jika terjadi tekanan pada sistem keuangan. Pemodelan machine learning menggunakan data historis krisis keuangan dapat dimanfaatkan untuk mengantisipasi kejadian krisis tersebut. Penelitian ini menggunakan data dari macrohistory.net/database yang terdiri dari systemic financial crises sebagai peubah respon dan 18 indikator makroekonomi dari 17 negara selama 151 tahun. Data tersebut kemudian diolah dengan metode machine learning yang berasal dari tiga pendekatan berbeda (regresi logistik, random forest, dan XGBoost). Tingkat kepentingan peubah dalam mendeteksi krisis keuangan dapat diukur dengan menghitung permutation feature importance. Selanjutnya, penerapan algoritme genetika dimanfaatkan untuk mendapatkan solusi optimal untuk urutan tingkat kepentingan peubah berdasarkan ketiga metode tersebut. Optimasi urutan pemeringkatan menunjukkan bahwa peubah X14 (equity total return), X10 (leverage) dan X4 (short-term interest rate) merupakan tiga peubah paling penting berdasarkan algoritme genetika. Hasil pemeringkatan kepentingan peubah tersebut memiliki kesetujuan dengan ketiga model machine learning, sehingga dapat digunakan sebagai peubah paling signifikan untuk menjadi indikator krisis keuangan., A financial crisis is characterized by signs of financial distress and losses affecting much of the financial system. The magnitude of the impact a country receives is influenced by its financial system's preparedness to face the occurring shock or pressure. Early detection is needed to prepare for potential pressure on the financial system. Machine learning modeling using historical financial crisis data can be utilized to anticipate crisis events. Data from macrohistory.net/database, consisting of systemic financial crises as a response variable and 18 macroeconomic indicators from 17 countries over 151 years, is used in this research. The data is then processed using machine learning methods derived from three approaches (logistic regression, random forest, and XGBoost). The level of importance of variables in detecting a financial crisis is measured by calculating the permutation feature importance. Subsequently, a genetic algorithm is applied to determine the optimal solution for the order of variable importance levels based on the three methods. Optimization of the ranking order showed that the variables X14 (equity total return), X10 (leverage), and X4 (short-term interest rate) were the three most important variables based on the genetic algorithm. These variable’s importance rankings matched the three machine learning models, making them the most critical financial crisis indicators.
Keyword: Financial crisis, Genetic algorithm, Machine learning, Permutation feature  importance, Variable importance

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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