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All Journal Science Map Journal Pattimura International Journal of Mathematics (PIJMath)
Bustan, Ariestha W
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Chemistry Education Mathematics Physics Social Sciences

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PERAMALAN VOLUME IMPOR MIGAS DI INDONESIA MENGGUNAKAN METODE AUTOREGRESSIVE INTEGRATED MOVING AVERAGE Latupeirissa, Indri Kezia; Laamena, Novita Serly; Bustan, Ariestha W; Talib, Taufan
Science Map Journal Vol 6 No 2 (2024): Science Map Journal
Publisher : Jurusan Pendidikan MIPA FKIP Universitas Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/jmsvol6issue2pp44-55

Abstract

Indonesia is one of the countries with abundant natural resources. Mining is one of the most important factors that need to be maintained to improve the welfare of its people and also contributes to the majority of state revenues in the non-tax sector, such as oil and gas. The development of Indonesia's oil and gas sector is very dynamic. Indonesia and countries in the world must adjust production, consumption, domestic and foreign policies from time to time due to changes in world oil prices in order to achieve people's welfare. In addition, our oil and gas production and reserves will continue to decline over time, so we have to import oil and gas. The increase in oil and gas imports also has an impact on the strengthening of the Rupiah exchange rate, so that demand for domestic currency also increases. Therefore, it is necessary to have a forecast to determine the volume of oil and gas imports in Indonesia for the next year. This study aims to predict the volume of oil and gas imports in Indonesia using one of the time series forecasting methods, namely the ARIMA method. The data used is oil and gas import data from January 2019 to December 2023 sourced from the Central Statistics Agency. The results of the study show that the right method for oil and gas import data is the ARIMA Model (0,1,1). The forecast results from January to June 2024 are 4557.45 tons, 4582.71 tons, 4608.04 tons, 4633.44 tons, 4658.91 tons and 4684.45 tons. The MAPE value of 9.31% indicates that the forecast results are very accurate
Ordinal Logistic Regression Analysis of Factors that Affecting the Blood Sugar Levels Diabetes Mellitus Patients Mayawi, Mayawi; Nurhayati, Nurhayati; Talib, Taufan; Bustan, Ariestha W; Laamena, Novita S
Pattimura International Journal of Mathematics (PIJMath) Vol 2 No 1 (2023): Pattimura International Journal of Mathematics (PIJMath)
Publisher : Pattimura University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/pijmathvol2iss1pp33-42

Abstract

Penelitian ini bertujuan untuk menganalisis pengaruh faktor-faktor risiko terhadap kadar gula darah pada penderita diabetes mellitus menggunakan analisis regresi logistik ordinal. Faktor-faktor risiko yang dijadikan variabel bebas adalah usia, jenis kelamin, Indeks Massa Tubuh (IMT), tekanan darah, Tingkat Kolesterol (TC), Low Density Lipoprotein (LDL), High Density Lipoprotein (HDL), Thyrocalcitonin Hormone (TCH) dan Loss Trigliserida(LTG). Data yang digunakan dalam penelitian ini diperoleh dari https://hastie.su.domains/Papers/LARS/diabetes.data. Jumlah sampel yang diambil sebanyak 100 responden yang telah terdiagnosis diabetes mellitus. Hasil penelitian menunjukkan bahwa faktor-faktor risiko seperti usia, Indeks Massa Tubuh (IMT), Tingkat Kolesterol (TC), Low Density Lipoprotein (LDL), High Density Lipoprotein (HDL) dan jenis serum Thyrocalcitonin Hormone (TCH) berpengaruh signifikan terhadap kadar gula darah pada penderita diabetes mellitus. Model logit terbaik untuk regresi logistic ordinal adalah Logit 1 yaitu g(x_1 )= -2.721-0.079 X_1+2.813〖 X〗_3+〖0.100 X〗_5-0.099 X_6-0.119 X_7-0.989 X_8 dan Logit 2 yaitu g(x_2 )= -8.571-0.079 X_1+2.813〖 X〗_3+〖0.100 X〗_5-0.099 X_6-0.119 X_7-0.989 X_8. Disimpulkan bahwa analisis regresi logistik ordinal dapat digunakan untuk mengidentifikasi faktor-faktor yang mempengaruhi kadar gula darah pada penderita diabetes mellitus dan membantu pengembangan strategi pengelolaan dan intervensi yang lebih efektif
Co-Authors Laamena, Novita S Laamena, Novita Serly Latupeirissa, Indri Kezia Mayawi, Mayawi Nurhayati Nurhayati Taufan Talib