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: Forecasting Garlic Production in Indonesia Using Intervention Analysis
Abstrak: Bawang putih (Allium sativum) merupakan komoditas pertanian yang memiliki nilai ekonomi tinggi. Indonesia merupakan negara importir bawang putih tertinggi di dunia. Kebijakan pemerintah mengenai impor bawang putih menyebabkan penurunan hasil produksi bawang putih di Indonesia pada tahun 2021. Hasil produksi bawang putih mengalami penurunan dan perubahan pola rataan data deret waktu atau yang disebut dengan intervensi. Metode intervensi digunakan untuk menganalisis data deret waktu yang mengalami intervensi. Peramalan produksi bawang putih di Indonesia dilakukan menggunakan analisis intervensi berbasis ARIMA dengan fungsi step. SARIMA merupakan perluasan dari ARIMA. SARIMA (Seasonal Autoregressive Integrated Moving Average) merupakan model yang digunakan dalam peramalan data deret waktu yang memiliki pola musiman. Model intervensi terbaik yang didapatkan pada penelitian ini adalah model intervensi berbasis ARIMA(1,0,0)(1,0,1)12 . Model tersebut layak digunakan karena memiliki performa peramalan MAPE sebesar 36,234%. Hasil peramalan menunjukkan rataan produksi bawang putih lebih kecil dibandingkan tahun sebelumnya dan tahun sebelum intervensi dan mengikuti pola intervensi., Garlic (Allium sativum) is an agricultural commodity that has high economic value. Indonesia is the highest garlic importing country in the world. The government's policy regarding garlic imports causes a decrease in garlic production in Indonesia in 2021. Garlic production has decreased and changes in the average pattern of time series data or what is called intervention. The intervention method was used to analyze the time series data that experienced the intervention. Forecasting of garlic production in Indonesia is carried out using an ARIMA-based intervention analysis with a step function. SARIMA is an extension of ARIMA. SARIMA (Seasonal Autoregressive Integrated Moving Average) is a model used in forecasting time series data that has a seasonal pattern. The best intervention model obtained in this study is an intervention model based on ARIMA ARIMA(1,0,0)(1,0,1)12. This model is feasible to use because it has a MAPE forecasting performance of 36.234%. Forecasting results show that the average garlic production is smaller than the previous year and the year before the intervention and has intervention pattern.
Keyword: ARIMA, bawang putih, deret waktu, intervensi, step

Judul: Penggunaan Teorema Homeomorphy 2-Manifold dan Teorema Euler Poincare pada Torus 𝑇�� dan Simplicial Complex 𝐾��
Abstrak: Two dimensional topological spaces are said to be homeomorphic if they have the same topological invariant, where one of topological invariant used is an Euler characteristic. Homeomorphy 2-Manifold’s Theorem and Euler Poincare’s Theorem are used to distinguish two topological spaces. Homeomorphy 2-Manifold’s Theorem uses Euler characteristic to identify two dimensional topological spaces. Euler Poincare’s Theorem is an alternative way to find Euler characteristic with Betti number, which is topological invariant as well. The objective of this paper is to investigate the homeomorphism of torus 𝑇����� and simplicial complex 𝐾�����. Topological space torus and simplicial complex 𝐾����� have the same Euler characteristic, which is two. Based on Homeomorphy 2-Manifold’s Theorem topological space torus 𝑇����� and simplicial complex 𝐾����� is homeomorphic.
Keyword: simplicial, torus, Euler, homeomorphism, topology