Judul: Agricultural Production Modeling using seasonal ARIMA
Abstrak: Pertanian menjadi sektor yang sangat penting dalam perekonomian banyak negara, termasuk Thailand. Karena itu, perlu ada suatu asuransi pertanian. Salah satu hal yang perlu diperhatikan dalam asuransi pertanian adalah meramalkan produksi pertanian agar perusahaan asuransi pertanian dapat menentukan premi, dan manfaat di masa yang akan datang. Pemodelan data deret waktu harus sesuai dengan karakteristik data yang dimiliki. Karakteristik data di Thailand tahun 2010-2018 memiliki pola musiman yang berulang setiap 12 bulan, sehingga tidak bisa dimodelkan dengan ARIMA biasa. Seasonal ARIMA adalah pemodelan data deret waktu yang dapat mengatasi pengaruh musiman pada data tersebut. Untuk itu dilakukan pemodelan seasonal ARIMA pada data tersebut dengan langkah-langkah yaitu cek asumsi, identifikasi model, pendugaan parameter, evalusi model, dan terakhir melakukan peramalan beberapa bulan ke depan. Model seasonal ARIMA (2,0,0)(1,1,1)12 adalah model terbaik untuk data indeks produk di Thailand karena telah memenuhi semua asumsi. Setelah dilakukan peramalan didapat MAPE sekitar 10%., Agriculture is a very important sector in the economy of many countries, including Thailand. Therefore, an agricultural insurance is needed. One of the things that need to be considered in agricultural insurance is agricultural production forecasting so that agricultural insurance companies can determine premiums and benefits in the future. Time series modeling must be in accordance with the characteristics of the data owned. The characteristics of Thailand's agricultural production data in 2010-2018 have a seasonal pattern that repeats every 12 months, so it can't be modeled with ordinary ARIMA. Seasonal ARIMA is a time series model that can overcome seasonal effects on the data. For this reason, seasonal ARIMA modeling is carried out on the data with steps namely checking assumptions, identifying models, estimating parameters, evaluating models, and finally forecasting the next few months. ARIMA seasonal Model (2,0,0)(1,1,1)12 is the best model for product index data in Thailand because it meets all assumptions. After forecasting, MAPE is obtained at around 10%.
Keyword: agriculture insurance, agriculture production, forecasting, seasonal ARIMA

Judul: Analisis Intervensi Fungsi Step terhadap Produksi Padi di Indonesia
Abstrak: Salah satu komoditas pertanian terbesar di Indonesia yaitu padi. Keberadaan padi sebagai makanan pokok harus terjaga sehingga dilakukan peramalan terhadap produksi padi di Indonesia. Peramalan terhadap produksi padi di Indonesia menggunakan analisis intervensi karena adanya perubahan metode penghitungan luas panen padi yang digunakan oleh BPS menyebabkan perbedaan hasil produksi padi di Indonesia cukup signifikan. Model digunakan dalam peramalan ini yaitu model Autoregressive Integrated Moving Average (ARIMA). Data yang digunakan dalam penelitian ini merupakan data deret waktu tahunan produksi padi di Indonesia dari tahun 2000 hingga tahun 2021. Tujuan dari penelitian ini yaitu mengetahui peramalan produksi padi di Indonesia tahun 2022 dan 2023 menggunakan model regresi dengan ARIMA error dan mengetahui pengaruh perubahan metode penghitungan luas panen padi terhadap produksi padi di Indonesia. Model ARIMA pre-intervensi terbaik pada penelitian ini yaitu ARIMA (1,1,0). Model intervensi dibentuk melalui model regresi dengan ARIMA (1,1,0) error dengan peubah penjelas regressor yaitu peubah boneka Kerangka Sampel Area (KSA). Peramalan menggunakan model regresi dengan ARIMA (1,1,0) error pada produksi padi di Indonesia pada tahun 2022 dan 2023 berturut-turut sebesar 54.303.880 ton dan 54.250.800 ton. Uji validasi model regresi dengan ARIMA (1,1,0) error menggunakan nilai MAPE. Hasil menunjukkan nilai MAPE pada data latih dan data uji berturut-turut sebesar 2,684 dan 5,980. Hal tersebut menunjukkan model regresi dengan ARIMA (1,1,0) error sangat baik digunakan untuk peramalan. Produksi padi di Indonesia menggunakan metode KSA memiliki perbedaan nilai sebesar 207,477% lebih rendah dibandingkan dengan produksi padi di Indonesia menggunakan metode eye estimate., One of the largest agricultural commodities in Indonesia is rice. The existence of rice as a staple food must be maintained so that forecasting of rice production in Indonesia is carried out. Forecasting of rice production in Indonesia used intervention analysis because there was a change in the method of calculating rice harvest area used by BPS causing significant differences in rice production results in Indonesia. The model used in this forecasting was the Autoregressive Integrated Moving Average (ARIMA) model. The data used in this research was annual time series data for rice production in Indonesia from 2000 to 2021. The purpose of this research were to determine the forecast for rice production in Indonesia in 2022 and 2023 using a regression model with ARIMA errors and to determine the effect of changes in the method of calculating area rice harvest on rice production in Indonesia. The best pre-intervention ARIMA model in this research was ARIMA (1,1,0). The intervention model was formed through the regression model with ARIMA (1,1,0) error with the regressor exogenous variable, namely the Area Sampling Frame (ASF) dummy variable. Forecasting used a regression model with an ARIMA (1,1,0) error for rice production in Indonesia in 2022 and 2023 of 54.303.880 tonnes and 54.250.800 tonnes respectively. Regression model validation test with ARIMA (1,1,0) error used the MAPE value. The results showed that the MAPE values in the training data and test data were 2,684 and 5,980 respectively. This showed that the regression model with ARIMA (1,1,0) error was very good for forecasting. Rice production in Indonesia used the ASF method had a value difference of 207,477% lower
Keyword: Analisis intervensi fungsi step, ARIMA, Produksi padi, ARIMA, rice production, step function intervention

Judul: Sintesis dan uji aktivitas katalitik Fe3O4 dari campuran limbah wire plating sludge dan FeSO4 pada efisiensi pembakaran batu bara
Abstrak: Combustion catalyst is an additive added to the fuel to increase the heat energy released. Fe3O4 was the combustion catalyst used in this study, synthesized from PT Sumiden Cibinong wire plating sludge waste by coprecipitation between FeCl3 from wire plating sludge and FeSO4. Synthetic Fe3O4 was characterized by using Faurier transform infrared spectrophotometer, X-ray diffractometer, and atomic absorption spectrophotometer. The Fe2O3, Fe2O3+Fe3O4, and Fe3O4 catalysts were dispersed separately with oil, surfactant, and water. The catalyst mixed into coal having 4596 cal/g calorific value, with overall concentration of catalyst mixture in the coal was 0.01%(w/w). The combustion activity resulted by differential thermal analyzer showed an increase of combustion fficiency of 91.0% with Fe2O3 catalist, whereas addition of Fe3O4 catalyst decreased the efficiency of 55.6%.
Keyword: catalyst, coal, Fe3O4, wire plating sludge.