Judul: Peramalan Harga Saham TLKM Menggunakan Model Hybrid Regresi Deret Waktu dan ARIMA
Abstrak: Investor saham di Indonesia meningkat pasca pandemik Covid-19. Saham diminati masyarakat Indonesia karena memberikan return yang cukup besar. Namun return yang tinggi sebanding dengan risiko tinggi yang harus dihadapi oleh investor. Untuk mengurangi risiko yang terjadi, maka diperlukan peramalan harga saham. Harga saham merupakan data deret waktu maka harga saham dapat dimodelkan dengan regresi deret waktu. Namun hasil yang diperoleh untuk harga saham TLKM melanggar asumsi autokorelasi sehingga penelitian dilanjutkan dengan memodelkan sisaan menggunakan model ARIMA. Model ARIMA(2,1,1) merupakan model terbaik untuk memodelkan sisaan karena memenuhi semua asumsi dan memiliki nilai AIC terkecil. Jadi, harga saham TLKM dimodelkan dengan menggabungkan regresi deret waktu dan model ARIMA (ARIMA Hybrid). Hasil peramalan harga saham TLKM untuk sembilan periode berikutnya menghasilkan nilai MAPE 2.97%., Investor in Indonesia increased after the Covid-19 pandemic. Stocks are in demand by the Indonesian because they provide big return. However, the high return is proportional to the high risk that must be faced by investors. To reduce the risk that occurs, it is necessary to forecast stock prices. Stock prices are time series data, so stock price can be modeled with time series regression. However, the results obtained for TLKM stock price violated the assumption of autocorrelation so that the research is continued by modeling the residuals using the ARIMA model. ARIMA(2,1,1) model is the best model to the residuals because it fulfills all assumptions and has the smallest AIC value. Therefore, the stock price of TLKM is modeled by combining the time series regression and ARIMA model (ARIMA Hybrid). Forecasting results for the next nine periods of the TLKM stock price produce a MAPE value of 2.97%.
Keyword: ARIMA Hybrid, Harga Saham, Peramalan, Regresi Deret Waktu

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: Duodenal Histopathology of Pediatric Rat (Rattus norvergicus)&#160;Fed with Orange Juice (Citrus sinensis)
Abstrak: This research investigates the impact of orange juice consumption on the histological structure of the duodenum, a critical site for nutrient absorption and digestion. Focusing on pediatric animals, orange juice (with a pH of 3) was administered to post-weaning rats aged 4 weeks to 6 weeks at dosages ranging from 0g/kg BW to 20g/kg BW over two weeks. Duodenal histopathology was assessed, focusing on parameters such as the quantity of crypts of Lieberkuhn, as well as the length, height, width, and depth of villi (measured in µm). Anatomical pathology examination included measurements of the length and width of the duodenum (also in µm), as well as the color mean (OD). The research findings indicate no significant changes in histopathology or anatomy pathology of the duodenum. This suggests that young individuals can adapt to acidic challenges without compromising gastrointestinal health. These findings offer reassurance regarding the short-term consumption of acidic orange juice (with a pH of 3) with minimal risks to intestinal integrity, both during growth and development.
Keyword: acidic changes