Judul: Fuzzy Time Series Markov Chain and Its Application in Jakarta  Islamic Index (JKII) Stock Price Prediction
Abstrak: Saham atau stock merupakan instrumen investasi yang populer dan banyak dipilih oleh para investor karena mampu memberikan tingkat keuntungan yang menarik. Pergerakan harga penutupan saham yang flukluatif membuat para investor sulit untuk mengikuti naik atau turunnya harga saham. Oleh sebab itu dibutuhkan suatu model matematis untuk meramalkan harga saham. Salah satu metode yang dapat digunakan untuk peramalan harga saham adalah metode fuzzy time series Markov chain. Penelitian ini menggunakan data harga penutupan saham Jakarta Islamic Index (JKII) dari Januari 2016 sampai Maret 2022. Penelitian ini menggunakan 7 himpunan, dan hasil yang didapatkan yaitu ramalan harga penutupan saham Jakarta Islamic Index (JKII) untuk bulan April 2022 sebesar 580.5576 dengan tingkat penyimpangan sebesar 2.2996%., Stocks are popular investment instruments and are widely chosen by investors because they are able to provide attractive profit levels. The fluctuating movement of closing stock prices makes it is difficult for investors to follow rising or falling stock prices. Therefore it is needed a mathematical model to forecast stock prices. One method that can be used to forecast stock price is the fuzzy time series Markov chain method. This study uses Jakarta Islamic Index (JKII) closing price data from January 2016 to March 2022. This study uses 7 sets, and the results obtained are the Jakarta Islamic Index (JKII) closing stock price forecast for April 2022 of 580.5576 with a level deviation of 2.2996%.
Keyword: fuzzy time series Markov chain, forecasting, stock price

Judul: Implementasi High Order Fuzzy Time Series Berdasarkan Pi-Sigma Neural Network (Studi Kasus: Estimasi Harga Penutupan IHSG)
Abstrak: Indeks harga saham gabungan (IHSG) merupakan salah satu barometer penting dalam investasi, sehingga estimasi IHSG menjadi pertimbangan krusial dalam pengambilan keputusan investasi. Fuzzy time series (FTS) merupakan model berbasis machine learning dengan pendekatan data-driven yang dapat digunakan untuk estimasi data saham. Pi-Sigma neural network (PSNN) dikombinasikan pada pembentukan model FTS untuk meningkatkan performa dengan kompleksitas rendah, yang memungkinkan penyederhanaan dalam pengaturan parameter model. PSNN membutuhkan metode pembelajaran untuk melatih model, metode yang digunakan adalah particle swarm optimization. Pengaturan parameter model dilakukan melalui penyesuaian pada orde FTS dari 2 sampai 5 dengan kenaikan sebesar 1, orde PSNN dari 2 sampai 5 dengan kenaikan sebesar 1, serta panjang interval partisi (l) pada himpunan fuzzy dari 10 sampai 50 dengan kenaikan sebesar 10 untuk mendapatkan model terbaik selama proses pelatihan. Hasil pelatihan dan pengujian menunjukkan konsistensi yang baik pada model, tanpa adanya penurunan performa yang signifikan. Performa terbaik berdasarkan nilai RMSE dan MAPE didapatkan ketika orde FTS adalah 4, orde PSNN adalah 3, panjang interval (l) adalah 10, dengan nilai RMSE sebesar 55.3083 dan MAPE sebesar 0.6242 persen., The Jakarta Composite Index (JCI) serves as a significant barometer in investments such that the estimation of JCI is a crucial consideration in investment decision-making. Fuzzy time series (FTS) is a data-driven machine learning model used for stock data estimation. To enhance performance of the model with low complexity and simplify parameter adjustments, Pi-Sigma neural network (PSNN) is integrated into the FTS model formation. PSNN requires a learning method to train the model, which employs particle swarm optimization. The parameter tuning of the model involves adjusting the FTS order from 2 to 5 with an increment of 1, the PSNN order from 2 to 5 with an increment of 1, and the length of fuzzy set partition (l) from 10 to 50 with an increment of 10 to obtain the optimal model during the training process. The training and testing result exhibits good consistency in the model, with no significant performance degradation. The best performance, based on RMSE and MAPE values, is achieved when the FTS order is 4, the PSNN order is 3, and the interval length (l) is 10, resulting in an RMSE value of 55.3083 and a MAPE value of 0.6242 percent.
Keyword: estimation, high order fuzzy time series, JCI close price, particle swarm  optimization, pi-sigma neural network

Judul: Growth, Concentration Chlorophyll-a, and Structure of Macroalgae Gracilaria edulis in the Culture Media Containing Copper
Abstrak: The physiological responses (specific growth rate, concentration of chlorophyll-a, morphological and anatomical structure of the vegetative cells) of the marine macroalga Gracilaria edulis at the different concentrations of copper (0.01 as control, 0.04, 0.06 and 0,5 ppm) delivered continuously for 28 days under controlled laboratory conditions were investigated. Result showed that the degree of copper toxicity depends on both factor the concentration and exposure time. Copper concentration and day of exposure decreased significantly the specific growth rate (SGR) of the Gracilaria edulis but only 0,5 ppm Cu decreased the chlorophyll-a content when compared to control. Of the various physiological end points measured, growth proved to be the most sensitive response than chlorophyll-a with reductions in specific growth rate (SGR). The reason for this could be related to a greater sensitivity of the enzymes involved in growth than those related to chlorophyll-a or a greater energy needs to maintain cellular integrity and metabolic processes under sublethal Cu stress. In all cases, the main effect on vegetative cells were structure changes in cortex and subcortex cells, compared to control, macroalgae exposed to copper showed depigmentation and blending in the lateral branches, heavy staining of the cell wall and severe damage of cell membrane, while the mucilage and the cell walls was densely fibrillar, thick and heavily stained, the cortex cell was clearly vacuolated, the presence of endophytic on the cell wall and endophytic spores in the mucilage. The presence vacuoles should be related to a metal immobilization mechanism, minimizing the Cu toxicity for cells. The epiphytic bacteria can act as a biofilter diminishing the availability of Cu for macroalgae accumulation.
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