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All Journal Jurnal Varian ARRUS Journal of Mathematics and Applied Science VARIANSI: Journal of Statistics and Its Application on Teaching and Research Journal of Research Applications in Community Services Eksponensial IJASDS: Indonesian Journal of Applied Statistics and Data Science
Siringoringo, Meiliyani
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Penerapan Algoritma Divisive Analysis dalam Pengelompokan Provinsi di Indonesia Berdasarkan Prevalensi Stunting Suyono, Ari Krisna; Hayati, Memi Nor; Siringoringo, Meiliyani; Prangga, Surya; Fathurahman, M.
EKSPONENSIAL Vol. 15 No. 2 (2024): Jurnal Eksponensial
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/eksponensial.v15i2.1341

Abstract

Cluster analysis is an analysis that aims to group data (objects) based only on the information contained in the data that describes objects and the relationships between the objects. Divisive analysis is a clustering method using a top-down approach which starts by placing all objects into one cluster or what is called a hierarchical root and then dividing the cluster root into several smaller clusters. This research aimed to group 34 provinces in Indonesia into 2,3, and 4 clusters based on stunting prevalence data and factors causing stunting in 2022 using a divisive analysis algorithm. The results showed that for 2 clusters, cluster 1 consisted of 32 provinces with low stunting prevalence, and cluster 2 consisted of 2 provinces with high stunting prevalence. For 3 clusters, cluster 1 consisted of 26 provinces with moderate stunting prevalence, cluster 2 consisted of 6 provinces with low stunting prevalence, and cluster 3 consisted of 2 provinces with high stunting prevalence. For 4 clusters, cluster 1 consisted of 21 provinces with moderate stunting prevalence, cluster 2 consisted of 5 provinces with low stunting prevalence, cluster 3 consisted of 6 provinces with high stunting prevalence, and cluster 4 consisted of 2 provinces with very high stunting prevalence.
Peramalan Curah Hujan Di Kota Samarinda Menggunakan Vector Error Correction Model Astafira, Ilyas; Siringoringo, Meiliyani; Fathurahman, M.
EKSPONENSIAL Vol. 15 No. 2 (2024): Jurnal Eksponensial
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/eksponensial.v15i2.1377

Abstract

The Vector Error Correction Model (VECM) was one of the multivariate time series models that was a development of the Vector Autoregressive (VAR). VECM could be used to forecast non-stationary time series variables that had cointegration relationships. This study used monthly data of rainfall, minimum air temperature, and maximum air humidity variables from January 2015 to December 2023 to form the VECM model. The purpose of this study was to obtain a VECM model for rainfall in the city of Samarinda and to forecast rainfall in the city of Samarinda using VECM. The results of the study showed that the VECM model that formed was VECM(1) with two cointegration relationships. The rainfall forecasted results with VECM(1) indicated a downward trend until April 2024 and a horizontal pattern from May to December, with the highest rainfall in January at 214 mm and the lowest rainfall in April at 182.5 mm. The forecasted results ranged between 180-300 mm, which was categorized as moderate, with forecasting accuracy using a MAPE value of 32.369%, which was considered quite good.
Forecasting the Total of Paddy Production in Indonesia Using Time Series Regression Model Kesuma, Ahmad Rizky; Siringoringo, Meiliyani; Mahmuda, Siti
ARRUS Journal of Mathematics and Applied Science Vol. 4 No. 2 (2024)
Publisher : PT ARRUS Intelektual Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/mathscience3175

Abstract

Time series regression (TSR) is a forecasting model that can be used when there are trend and seasonal patterns. Paddy is a source of rice, which is a staple food for the Indonesian people. Paddy has a seasonal pattern because its cultivation depends on the rainy season. This study forecasts the amount of paddy production in Indonesia based on monthly data of paddy production in Indonesia to determine the production of paddy in 2023 and to assess the ability of the TSR model to forecast paddy production. The results showed that paddy production in 2023 is forecasted to have the same seasonal pattern as in previous years and reaches its peak production in April at 8.699 million tons. The TSR model of paddy production has a mean absolute percentage error (MAPE) of 8.654% indicating a very good forecasting accuracy.
Pendampingan Desain Infografis dengan Statistika dan Sains Data Bagi Siswa/Siswi MAN 1 Kota Samarinda Muhammad Fathurahman; Dani, Andrea Tri Rian; Fauziyah, Meirinda; Darnah; Goenjatoro, Rito; Hayati, Memi Nor; Prangga, Surya; Siringoringo, Meiliyani; Oroh, Chiko Zet
Journal of Research Applications in Community Service Vol. 4 No. 3 (2025): Journal of Research Applications in Community Service
Publisher : Universitas Nahdlatul Ulama Sunan Giri Bojonegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32665/jarcoms.v4i3.5158

Abstract

Kegiatan pengabdian masyarakat ini bertujuan untuk memberikan pendampingan desain infografis yang mengintegrasikan ilmu statistika dan sains data serta meingkatkan literasi data bagi siswa dan siswi MAN 1 Kota Samarinda. Dalam era digital yang ditandai dengan kemudahan akses informasi, masih terdapat kekurangan pemahaman di kalangan siswa mengenai pemanfaatan teknologi, khususnya dalam desain infografis berbasis statistika dan sains data. Infografis merupakan alat yang efektif untuk menyajikan informasi secara visual yang membantu mempercepat pemahaman data kompleks menjadi lebih mudah dipahami. Aplikasi Canva dipilih sebagai platform dalam pendampingan ini karena kemudahan penggunaannya, yang memungkinkan siswa untuk berkreasi secara mandiri. Berdasarkan hasil tes awal, siswa belum memanfaatkan dengan optimal pengembangan ilmu data sains dalam pembuatan desain infografis. Oleh karena itu, kegiatan ini dirancang untuk memberikan pemahaman dan keterampilan praktis kepada peserta agar mereka dapat menggunakan teknologi visual dalam mengelola dan menyampaikan informasi berbasis data dengan lebih efektif dan inovatif. Melalui metode pengabdian ini, diharapkan terjadi peningkatan pemahaman dan keterampilan dalam penggunaan desain infografis serta pemanfaatan sains data literasi siswa yang dapat diterapkan dalam kegiatan belajar mengajar, terutama dalam pengolahan dan penyajian data statistik.
Peramalan Jumlah Produksi Kelapa Sawit Provinsi Kalimantan Timur Menggunakan Metode Singular Spectrum Analysis Siringoringo, Meiliyani; Wahyuningsih, Sri; Purnamasari, Ika; Arumsari, Melisa
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 4 No. 3 (2022)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm46

Abstract

Singular spectrum analysis (SSA) is a nonparametric method that does not rely on assumptions such as stationary nature or residual normality. SSA separates time series data into its components, which are trend, seasonality, and error (noise). This study aimed to obtain forecasting results for the amount of oil palm production in East Kalimantan Province for the period January 2021 to December 2021 using SSA. Based on the results of the data analysis, in the process of forming the forecasting model with in-sample data, the parameter window length (L) was 24, which produced a MAPE value of 0.464%, and while the forecasting model validation process used out-sample data, it produced a MAPE value of 41.172%.
PENGELOMPOKAN PROVINSI DI INDONESIA BERDASARKAN DATA JUMLAH KEJADIAN DAN DAMPAK BENCANA BANJIR MENGGUNAKAN METODE FUZZY C-MEANS Hayati, Memi Nor; Goejantoro, Rito; Siringoringo, Meiliyani; Purnamasari , Ika; Yuniarti, Desi; Nida, Khairun; Messakh, Gerald Claudio
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 6 No. 01 (2024)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm167

Abstract

Cluster analysis is a technique used to find groups of similar data objects. The Fuzzy C-Means (FCM) method is a data grouping method where the existence of each data in a cluster is determined by the degree of membership. This study aims to determine the optimal number of clusters based on the Modified Partition Coefficient (MPC) validity index and to determine the optimal grouping results of 34 provinces in Indonesia based on data on the number of events and the impact of floods in 2017-2021. The optimal number of clusters using the FCM method is based on MPC value consists of 2 clusters, namely the first cluster consisting of 27 provinces in Indonesia and the second cluster consisting of 7 provinces in Indonesia.
Peramalan Jumlah Kedatangan Penumpang Domestik di Bandara APT Pranoto Samarinda Menggunakan Maximal Overlap Discrete Wavelet Transform dengan Model Multiresolution Autoregressive Octavianto, Thifan; Siringoringo, Meiliyani; Purnamasari, Ika
Indonesian Journal of Applied Statistics and Data Science Vol. 2 No. 1 (2025): Mei
Publisher : Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/ijasds.v2i1.5796

Abstract

The problem of forecasting domestic passenger arrivals has become increasingly important due to frequent fluctuations and seasonal patterns, as observed at APT Pranoto Airport in Samarinda. Such data requires an approach capable of capturing both long-term trends and rapid changes. This study employs the Maximal Overlap Discrete Wavelet Transform (MODWT), a modified version of the Discrete Wavelet Transform (DWT), which can be applied to data of any size. MODWT decomposes the data into wavelet coefficients and scaling coefficients, which are then used to construct a Multiresolution Autoregressive (MAR) model at each level of Daubechies wavelets. This method is used as a preprocessing step to improve forecasting accuracy. The best model is selected based on the smallest Mean Absolute Percentage Error (MAPE). The analysis results show that the best forecasting model is the one using Daubechies 6 wavelets, with an in-sample MAPE of 13.758% and an out-of-sample MAPE of 9.525%. The forecast of domestic passenger arrivals at APT Pranoto Airport for the period from October 2024 to December 2024 follows a trending pattern.
The Weibull Regression Model Analysis of Mahakam River Water Pollution Potential Pradipa, Zalva; Suyitno, Suyitno; Siringoringo, Meiliyani
Jurnal Varian Vol. 8 No. 1 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v8i1.3699

Abstract

Mahakam River has a vital role in the lives of the people of the East Kalimantan province, includingproviding a raw source of clean water. The multi-activity of the Mahakam River watershed, as a watertraffic lane, mining, fisheries, hotels, restaurants, and resident houses, has the potential to produce wasteinto the water. Increasing waste in the water flow can increase the pollution potential of river water,threatening people’s health. Therefore, precaution is necessary. In this research, statistical preventionwas proposed, providing information to the East Kalimantan people regarding the factors affecting thepollution potential of the Mahakam River through Weibull regression (WR) modeling on dissolved oxygen (DO) data 2022. Research data was secondary data provided by the Life Environmental Departmentof East Kalimantan province. The WR model is a Weibull distribution that is directly influenced by covariates. WR model consists of Weibull survival regression, cumulative distribution regression, hazardregression, and Weibull mean regression. This research aims to obtain the factors affecting the pollution potential and to provide the pollution potential information of Mahakam River 2022. The researchconcluded that factors influencing the pollution potential of the Mahakam River were watercolor degreeand nitrate concentration. Applying the WR model to DO data 2022 was able to provide the pollutionpotential information of Mahakam River, namely the probability of river water isn’t polluted is 0.6555,or the probability of the polluted river water is 0.3445, the pollution rate is 6 locations are polluted forevery 10 mg/L DO, and the DO average of river water is 5.7450 mg/L. Increasing water color degreeand nitrate concentration will decrease the probability of the Mahakam River being polluted, increasethe probability of the Mahakam River being polluted, increase the pollution rate, and reduce the DO ofMahakam River water.
Co-Authors Alfian Hidayat Andini, Putri Dwiayu Aulia Arumsari, Melisa Astafira, Ilyas Cahyani, Era Tri Chairina, Puspa Dani, Andrea Tri Rian Darnah Fauziyah, Meirinda Goenjatoro, Rito Ika Purnamasari Inayah, Ulfah Resti Kesuma, Ahmad Rizky Krisna Rendi Awalludin Lestari, Vivi Dwi M. Fathurahman Mahmuda, Siti Memi Nor Hayati Messakh, Gerald Claudio Mochammad Imron Awalludin Nida, Khairun Octavianto, Thifan Oroh, Chiko Zet Pradipa, Zalva Pratiwi, Nadya Primadigna, Ullimaz Sam Rahmah, Siti Mahmudatur Ramadhani, Adelia Rito Goejantoro, Rito Saputra, Ahmad Ronaldy Sari, Hafivah Rosvita Sergio, Andrean Sinaga, Yetty Veronica Lestari Sri Wahyuningsih Sudarman, Alfiannur Rizki Surya Prangga Suyitno Suyitno Suyono, Ari Krisna Syawal, Al Fitri Wahyuni, Risa Kristia Yani, Tika Anggre Ria Yuniarti, Desi Yustiani, Iis