Judul: Application of ARIMA Model and Average-Based Fuzzy Time Series  Method on Shares Forecasting (Case Study: Close Price of PT. United Tractors Tbk Shares)
Abstrak: Terdapat beragam instrumen yang dapat digunakan untuk berinvestasi, salah satunya adalah saham. Berinvestasi dengan membeli sejumlah saham cukup berisiko karena adanya perubahan harga saham yang fluktuatif tiap waktu. Oleh karena itu, dibutuhkan metode peramalan data deret waktu sehingga pengambilan keputusan di kemudian waktu dapat dilakukan lebih akurat. Penelitian ini menerapkan model Autoregressive Integrated Moving Average (ARIMA) dan metode Fuzzy Time Series (FTS) berbasis rerata. Data yang digunakan dalam penelitian ini merupakan data harian dari harga penutupan saham PT. United Tractors Tbk periode 2 Januari 2017 – 26 Juni 2020. Peramalan pada data latih dengan metode FTS berbasis rerata menghasilkan MAPE sebesar 1,67% dan RMSE sebesar 590,528 sedangkan model ARIMA menghasilkan nilai MAPE sebesar 2,74% dan RMSE sebesar 978,491. Nilai MAPE dan RMSE pada metode FTS berbasis rerata lebih kecil dibandingkan dengan model ARIMA, sehingga dapat disimpulkan bahwa metode FTS berbasis rerata bekerja lebih baik dalam meramalkan nilai harga penutupan saham PT. United Tractors Tbk periode 2017 – 2020., There are several instruments that can be used for investment, one of them is shares. Investing by buying shares are pretty risky due to fluctuating price changes overtimes. Therefore, a time series data forecasting is needed so that future decision making can be done more accurately. This research applies Autoregressive Integrated Moving Average (ARIMA) model and average-based Fuzzy Time Series (FTS) method. The data used in this research is daily data of close price of PT. United Tractors Tbk shares start from 2 January 2017 – 26 June 2020. Forecasting on testing data with average-based FTS produces MAPE of 1,67% and RMSE of 590,528 whilst ARIMA model produces MAPE of 2,74% and RMSE of 978,491. MAPE and RMSE values in average-based FTS methods are smaller than ARIMA model, so it can be concluded that average-based FTS works better on forecasting close price of PT. United Tractors Tbk shares for period 2017 – 2020
Keyword: ARIMA, fluctuating, forecasting, Fuzzy Time Series, shares

Judul: Comparative Analysis of ARIMA and Fuzzy Time Series Methods for Stock Price Forecasting in the Energy Sector (Case Study: PT XYZ)
Abstrak: Peramalan merupakan hal penting untuk dapat memaksimalkan keuntungan dengan memprediksi di masa mendatang. Metode peramalan terus dikembangkan hingga dibagi menjadi metode konvensional dan soft-computing yang bertujuan untuk mendapatkan galat terkecil. Penelitian ini bertujuan untuk membandingkan kinerja prediksi metode ARIMA dan Fuzzy Time Series menggunakan model Saxena-Easo. Data yang digunakan adalah data harian dari PT XYZ sejak 1 Januari 2022 hingga 31 Desember 2022. Evaluasi kinerja model dilakukan dengan membandingkan nilai Mean Absolute Percentage Error (MAPE) untuk menentukan model yang lebih unggul. Hasilnya, baik model ARIMA(0,1,1) maupun Fuzzy Time Series merupakan model yang baik. MAPE yang dihasilkan oleh model ARIMA(0,1,1) sebesar 4,78% sementara Fuzzy Time Series dengan model Saxena-Easo sebesar 1,72%. Secara visual model ARIMA(0,1,1) menghasilkan peramalan garis lurus yang terus meningkat, sementara metode Fuzzy Time Series dengan model Saxena-Easo cenderung mengikuti pola data aktual. Sehingga didapatkan kesimpulan bahwa metode Fuzzy Time Series dengan model Saxena-Easo memiliki kinerja yang lebih baik daripada model ARIMA dalam meramal data non-linear., Forecasting is crucial for maximizing profits by predicting future prices. Forecasting methods have evolved, braching into conventional and soft-computing techniques aimed at minimizing prediction errors. The objective is to compare the predictive performance of ARIMA and Fuzzy Time Series models using the Saxena-Easo framework. Daily data from PT XYZ spanning from January 1, 2022, to December 31, 2022, is employed in this study. Model performance is assessed by comparing the Mean Absolute Percentage Error (MAPE) values to determine the superior model. Both the ARIMA(0,1,1) and Fuzzy Time Series models demonstrate strong predictive capabilities. ARIMA(0,1,1) yields a MAPE of 4.78%, while Fuzzy Time Series with the Saxena-Easo model achieves an even lower MAPE of 1.72%. Visually, ARIMA(0,1,1) generates a steadily increasing linear forecast, whereas the Fuzzy Time Series method with the Saxena-Easo model closely tracks the actual data pattern. Consequently, it can be concluded that the Fuzzy Time Series method with the Saxena-Easo model outperforms the ARIMA model in forecasting non-linear data.
Keyword: ARIMA, forecasting, fuzzy time series, saxena-easo model

Judul: Pengembangan Bionanokomposit Film Berbasis Tapioka/Nanopartikel Perak dan Tapioka/Nanopartikel Seng Oksida dengan Plasticizer Gliserol.
Abstrak: Cassava starch can be formed into bioplastic film which is transparance as synthetic plastic films. However, pure cassava starch film has some drawbacks such as brittle, poor barrier to water vapour, sensitive to humidity and not stable during the application. The addition of nanoparticles in film was intended to increase the elongation and tensile strength, moisture stability and to provide antimicrobial activity. Meanwhile, the addition of glycerol was intended to increase the plasticity of films so they will not be easily cracked. The main purpose of this research is to elaborate the functional packaging characteristics of bionanocomposites films made from cassava starch with or without silver nanoparticles (Ag-Np) of zinc oxide nanoparticles (ZnO-Np) with glycerol as plasticizer. The addition of nanoparticles was conducted at the amount of 0%, 0,5%, and 1% by weight of the starch, whereas glycerol was used at the amount of 0% and 20% by weight of the solids (starch only or starch+nanoparticles). The value of water vapor transmission decreased significantly with the addition of Ag-Np and ZnO-Np at the concentration of 1% (from 18,0569 g/hm2 to 12,0574 g/hm2) so it enhance the water barrier of the films as packaging materials. The addition of Ag-Np and ZnO-Np at the amount of 1% with 20% of glycerol was also able to inreased the value of elongation (from 1,2% to 88,8%), but decreased the value of tensile strength (from 22,93 MPa to 4,03 MPa). The addition of glycerol gave contrary effect to the tensile strength because it produces low tensile strength values. Moreover, the results show that films made with ZnO-Np had a stronger antimicrobial activity than those made with Ag-Np for both gram positive and gram negative bacteria. In overall, it seems that the formulation of 1% ZnO-Np is the best formulation for developing bionanocomposite film based on cassava starch.
Keyword: Bionanocomposite Films, Silver Nanoparticle (Ag-Np), Zinc Oxide Nanoparticle (ZnO-Np), Cassava Starch, Antibacterial Films