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All Journal Imajiner: Jurnal Matematika dan Pendidikan Matematika Pelita Eksakta GAUSS: Jurnal Pendidikan Matematika Leibniz: Jurnal Matematika Jurnal Riset Statistika Rangkiang Mathematics Journal UNP Journal of Statistics and Data Science Indonesian Journal of Statistics and Its Applications
Salma, Admi
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Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Education Languange, Linguistic, Communication & Media Mathematics Physics Social Sciences Other

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Journal : UNP Journal of Statistics and Data Science

 1 
Pengelompokan Wilayah Potensi Kebakaran Hutan dan Lahan di Pulau Sumatera Berdasarkan Titik Panas Menggunakan Metode CLARA Safitri, Melda; Salma, Admi; Amalita, Nonong; Fitri, Fadhilah
UNP Journal of Statistics and Data Science Vol. 2 No. 3 (2024): UNP Journal of Statistics and Data Science
Publisher : Departemen Statistika Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/ujsds/vol2-iss3/180

Abstract

Sumatera Island is one of the areas with the potential for forest and land fires in Indonesia. Sumatra Island has the largest oil palm plantation in Indonesia. The vast land area of oil palm plantations in Indonesia can increase the risk of fires due to land expansion by burning. In addition, the burning of peatlands in Sumatra can exacerbate the impact of forest and land fires. Forest and land fires on the island of Sumatra that occur every year can cause various negative impacts, indicating the need for countermeasures and prevention efforts to minimize the impact of forest and land fires. Hotspots can be used to detect fires in a region and help with prevention and countermeasures to reduce the impact of land and forest fires. Clustering the hotspot data allows one to obtain information on the presence of a fire in a given area as well as its potential status high, medium, or low. The clustering method used is the CLARA method. The CLARA method is a clustering method that breaks the dataset into groups. The advantages of the CLARA method are robust to outliers and effective for large data sets. The results of this research show that the CLARA method can be used for hotspot clustering with a silhouette coefficient of 0.53 in the use of 2 clusters. The analysis of the clustering results shows that cluster 1 is a cluster with low fire potential while cluster 2 is a cluster with high fire potential.
Vector Error Correction Model to Analyze the Impact of Exchange Rates and Money Supply on Inflation in Indonesia Faulina; Fitri, Fadhilah; Amalita, Nonong; Salma, Admi
UNP Journal of Statistics and Data Science Vol. 2 No. 3 (2024): UNP Journal of Statistics and Data Science
Publisher : Departemen Statistika Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/ujsds/vol2-iss3/188

Abstract

This study analyzes inflation in Indonesia in relation to the influence of exchange rates and the money supply (M2), which pose challenges in controlling inflation amidst rapid economic growth. Data from the Ministry of Trade of the Republic of Indonesia (Kemendag) were used to investigate the relationship between exchange rates and the money supply (M2) on inflation using the Vector Error Correction Model (VECM). The results indicate that in the short term, inflation tends to decrease towards stability, with a strong exchange rate capable of reducing inflation, while an increase in the money supply slightly raises inflation. However, in the long term, inflation demonstrates a strong self-correction mechanism, with the influence of exchange rates and the money supply becoming limited. This model proves effective in forecasting inflation for the period from March to August 2024, with a Mean Absolute Percentage Error (MAPE) of 19.59%.
Penerapan Rantai Markov pada Data Curah Hujan Harian di Kota Semarang Tsani, Nahda Maesya; Permana, Dony; Kurniawati, Yenni; Salma, Admi
UNP Journal of Statistics and Data Science Vol. 2 No. 3 (2024): UNP Journal of Statistics and Data Science
Publisher : Departemen Statistika Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/ujsds/vol2-iss3/189

Abstract

Rainfall is a measure of the amount of water that falls on the earth's surface in a given period of time. High rainfall can cause flooding in certain areas, while low rainfall can leave areas vulnerable to drought. Semarang City is one of the largest cities in Java Island that is often hit by floods. Efforts can be made to anticipate the risk of flooding, one of which is by studying the pattern of rainfall. This study will determine the chances of rainfall transition in Semarang City in steady state conditions using Markov chains. The results are expected to be used to anticipate the risk of flooding in Semarang City. The probability of daily rainfall transition in Semarang City in each state for the next period of time is 90.5% chance of staying in the light rain state, 7.97% chance of staying in the medium rain state and 1.50% chance of staying in the heavy rain state.
Penanganan Ketidakseimbangan Multikelas pada Dataset Survei Kerangka Sampel Area menggunakan Metode SCUT Sondriva, Wilia; Kurniawati, Yenni; Amalita, Nonong; Salma, Admi
UNP Journal of Statistics and Data Science Vol. 2 No. 2 (2024): UNP Journal of Statistics and Data Science
Publisher : Departemen Statistika Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/ujsds/vol2-iss2/163

Abstract

Area Sampling Frame (ASF) is a survey used by the Indonesian government to measure rice productivity in Indonesia. ASF survey is important data because accurate and high-quality rice productivity data is highly needed. There is extreme imbalance in the ASF survey data, thus requiring handling of this imbalance. SMOTE and Cluster-based Undersampling Technique (SCUT) is a method that can be used to address the dataset imbalance. SCUT combines oversampling using SMOTE and undersampling using CUT. The results from SCUT show that the number of data points in each class becomes balanced. Subsequently, a two-sample mean test is conducted to observe the mean differences between the original dataset and the dataset after handling. The results show that in the early vegetative, late vegetative, and harvest phases, the means are significantly similar between the original dataset and the dataset after handling, but in the generative phase, the means are not significantly similar. Therefore, synthetically generated data using the SCUT method generally exhibit similar mean characteristics.
Co-Authors Aldwi Riandhoko Amalia, Nonong Aulia, Yuke Dina Fitria, Dina Dodi Vionanda Dwi Sulistiowati, Dwi Faulina Fitri, Fadhilah Lubis, Riskiani Mawaddah, Nurul Mukhti, Tessy Octavia Nonong Amalita Permana, Dony Safitri, Melda Sondriva, Wilia Suliswati, Yeni Syafriandi Syafriandi Syafriandi Tsani, Nahda Maesya Winartha, Mardia Wirdiastuti, Chairina Yenni Kurniawati Zamahsary Martha Zilrahmi, Zilrahmi