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All Journal MAGISTRA Electro Luceat Jurnal Ilmiah Matematika dan Pendidikan Matematika (JMP) JURNAL PENGABDIAN PENDIDIKAN MASYARAKAT (JPPM) Jurnal Sistem Informasi dan Ilmu Komputer International Journal of Technology and Education Research Ulil Albab PESHUM
Agustini Tripena
Unknown Affiliation
Religion Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Library & Information Science Mathematics Public Health Social Sciences Other

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Journal : PESHUM

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Poverty Mapping of District/City in Central Java Province in 2023 Using Biplot Analysis Abdiyansah, Muhamad Nur; Tripena, Agustini; Estri, Mutia Nur
PESHUM : Jurnal Pendidikan, Sosial dan Humaniora Vol. 4 No. 3: April 2025
Publisher : CV. Ulil Albab Corp

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56799/peshum.v4i3.8739

Abstract

Kemiskinan merupakan salah satu permasalahan yang dihadapi Indonesia. Kemiskinan di Indonesia harus diatasi untuk mencapai salah satu tujuan bangsa, yaitu memajukan kesejahteraan umum sebagaimana tercantum dalam pembukaan Undang-Undang Dasar 1945. Jawa Tengah merupakan salah satu provinsi di Indonesia dengan jumlah penduduk miskin per Maret 2023 sebanyak 3.791.500 jiwa. Kabupaten Brebes memiliki jumlah penduduk miskin tertinggi yaitu 286.140 jiwa, sedangkan jumlah penduduk miskin terendah terdapat di Kota Magelang yaitu 7.450 jiwa. Hal ini menunjukkan adanya perbedaan distribusi penduduk yang cukup signifikan di Provinsi Jawa Tengah, sehingga diperlukan pemetaan kemiskinan pada 35 kabupaten/kota di Jawa Tengah berdasarkan variabel kemiskinan. Salah satu metode yang dapat digunakan adalah analisis biplot. Variabel yang digunakan meliputi garis kemiskinan, jumlah penduduk miskin, indeks kedalaman kemiskinan, indeks keparahan kemiskinan, tingkat pengangguran terbuka, kepadatan penduduk, dan indeks pembangunan manusia. Grafik biplot menghasilkan 5 kelompok pemetaan dengan nilai kelayakan biplot sebesar 79,17%, artinya grafik biplot menjelaskan 79,17% informasi yang terkandung dalam data.
Rainfall Forecasting Using Seasonal Autoregressive Integrated Moving Average Method And Decomposition Method in Pangkalan Bun Grisella Estefania R; Agustini Tripena; Triyani, Triyani
PESHUM : Jurnal Pendidikan, Sosial dan Humaniora Vol. 4 No. 4: Juni 2025
Publisher : CV. Ulil Albab Corp

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56799/peshum.v4i4.9431

Abstract

Flash floods and droughts that often occur in Pangkalan Bun, Central Kalimantan are clear evidence of the impact of climate change. Global climate change has caused an increase in the frequency and intensity of extreme weather, including significant fluctuations in rainfall. The purpose of this study is to create the best model for predicting rainfall using the SARIMA method and the Decomposition method in the case of data in Pangkalan Bun. The data used are rainfall data in Pangkalan Bun as many as 180 observations (January 2009 - December 2023). Rainfall data is divided into training data and testing data. Training data is used to determine the forecasting model using the SARIMA method and the Decomposition method. The results, the SARIMA (1,0,1) (1,0,1)12 model gives the lowest MAPE value compared to other decomposition models, namely a MAPE value of 26.05%. The MAPE value indicates that the SARIMA (1,0,1) (1,0,1)12 model is suitable for use in predicting future data. The forecast results show that the highest rainfall will occur in November 2024 at 263.97 mm and the lowest in August 2024 at 106.26 mm.
Co-Authors Abdiyansah, Muhamad Nur Agus Sugandha Alifah Alifah, Alifah Amru, Diwyacitta Ari Wardayani Budi Pratikno, Budi Esa Anugrah Salsabila Grisella Estefania R Majdina, Nadhilah Idzni Marini, Yulia Mutia Nur Estri, Mutia Nur Niken Larasati Putri Raffikah Setyawan, Antonius Ary Slamet Riyadi Triyani Triyani Triyani Triyani Yosita Lianawati