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All Journal JTAM (Jurnal Teori dan Aplikasi Matematika) MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer
Sari, Silvia Kartika
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Computer Science & IT Mathematics

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Modeling the Farmer Exchange Rate in Indonesia Using the Vector Error Correction Model Method Farida, Yuniar; Hamidah, Afanin; Sari, Silvia Kartika; Hakim, Lutfi
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 23 No. 2 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v23i2.3407

Abstract

The agricultural sector plays a crucial role in the Indonesian economy. However, the farm sector still has serious problems, including agricultural product prices, which often fall when the harvest supply is abundant. So often, the income obtained is not proportional to the price spent by farmers, which has an impact on decreasing the welfare of farmers. An indicator to observe changes in the interest of Indonesian farmers is the Farmer Exchange Rate Index (NTP). This study aims to form a model and project the welfare level of farmers in Indonesia, focusing on NTP indicators, which are caused by the influence of variables such as inflation, Gross Domestic Product (GDP), interest rates, and the rupiah exchange rate. The method used is the Vector Error Correction Model (VECM), used when there are indications that the research variables do not show stability at the initial level and there is a cointegration relationship. The results of this study show that in the long run, significant factors affecting NTP are inflation, interest rates, and the rupiah exchange rate. Meanwhile, in the short term, the variables that have an impact are GDP and the rupiah exchange rate. The resulting VECM model shows a MAPE error rate of 1.79%, indicating excellent performance, as the MAPE error rate is below 10%. The implication of this research is provides information related to NTP projection that can be used to formulate strategies to strengthen Indonesia's agricultural sector.
Classification of Hypertension in Pregnant Women Using Multinomial Logistic Regression Farida, Yuniar; Tiasti, Roro Niken Enggar; Sari, Silvia Kartika
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 7, No 4 (2023): October
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v7i4.16481

Abstract

Maternal Mortality Rate (MMR) is still a crucial problem in Indonesia and other developing countries; one of the causes is Hypertension in Pregnancy (HDK). This study aims to classify hypertension in pregnant women based on the factors that influence it, with a case study of patients from the Obstetrics and Gynecology Specialist Clinic at the Regional General Hospital (RSUD) Dr. R. Sosodoro Djatikoesoemo Bojonegoro. The variables used were age, gravidity, gestational age, obesity, history of abortion, hypertension, and diabetes mellitus. The research method used in this study is multinomial logistic regression because it uses four categories of dependent variables, namely pregnant women without hypertension, pregnant women with chronic hypertension, pregnant women with gestational hypertension, and pregnant women with preeclampsia. The results obtained in this study were from 3 categories of hypertension in pregnant women; the influencing factors were obesity, gestational age > 36 weeks, having a history of hypertension, and diabetes mellitus, with the resulting model classification accuracy value of 79.6%, which means the classification is classified as good. This research contributes to applying statistical methods in the health sector and as a mitigation effort to help minimize the number (prevalence) of maternal deaths, especially those caused by hypertension. 
Co-Authors Hamidah, Afanin Lutfi Hakim Tiasti, Roro Niken Enggar Yuniar Farida, Yuniar