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All Journal Indonesian Journal of Artificial Intelligence and Data Mining Jurnal Ilmiah Ilmu Komputer Fakultas Ilmu Komputer Universitas Al Asyariah Mandar Pelita Eksakta ILKOMNIKA: Journal of Computer Science and Applied Informatics GAUSS: Jurnal Pendidikan Matematika Suluah Bendang: Jurnal Ilmiah Pengabdian Kepada Masyarakat Rangkiang Mathematics Journal UNP Journal of Statistics and Data Science Mathematical Journal of Modelling and Forecasting Indonesian Journal of Statistics and Its Applications
Zilrahmi, Zilrahmi
Universitas Negeri Padang
Humanities Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Mathematics Physics Social Sciences Other

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Forecasting Analysis of Total Coconut Production in Padang Pariaman Using the Double Exponential Smoothing Holt Della Amelia; Zilrahmi; Fitri Mudia Sari
UNP Journal of Statistics and Data Science Vol. 3 No. 2 (2025): 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/vol3-iss2/367

Abstract

Kelapa merupakan buah khas daerah tropis yang memiliki banyak manfaat. Kelapa memiliki arti penting yang strategis bagi Indonesia. Sumatera Barat merupakan salah satu provinsi penghasil kelapa di Indonesia dengan total produksi sebesar 88 ribu ton pada tahun 2023. Dimana Kabupaten Padang Pariaman merupakan kabupaten penghasil kelapa terbesar di Provinsi Sumatera Barat dengan total produksi sebesar 38.794 ton pada tahun 2022. Kelapa merupakan salah satu komoditas utama dan sumber perekonomian di Kabupaten Padang Pariaman. Melihat pentingnya peranan kelapa di Kabupaten Padang Pariaman, maka perlu dilakukan peramalan produksi kelapa untuk mengetahui kondisi hasil perkebunan tersebut. Double Exponential Smoothing merupakan metode yang sesuai digunakan dalam peramalan jumlah produksi kelapa di Kabupaten Padang Pariaman. Hal ini dikarenakan metode ini sesuai dengan data yang memiliki pola trend. Hasil peramalan menunjukkan bahwa produksi kelapa pada tahun 2024 sampai dengan tahun 2028 adalah sebesar 39.506,16 ton, 39.943,43 ton, 40.380,7 ton, 40.817,97 ton, dan 41.255,24 ton. Dimana hasil tersebut menunjukkan bahwa produksi kelapa mengalami peningkatan setiap tahunnya sekitar 1% dengan nilai MAPE sebesar 16,19% yang menunjukkan bahwa hasil peramalan tersebut termasuk dalam kriteria akurat.
Forecasting Inflation Rate in Indonesia Using Autoregressive Integrated Moving Average Method Lathifa Putri; Zilrahmi
UNP Journal of Statistics and Data Science Vol. 3 No. 3 (2025): 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/vol3-iss3/377

Abstract

Inflasi merupakan salah satu indikator penting untuk menilai stabilitas ekonomi suatu negara. Peningkatan inflasi yang terus menerus akan memperlambat pertumbuhan ekonomi. Oleh karena itu, prakiraan tingkat inflasi yang akurat penting untuk perencanaan ekonomi jangka menengah hingga panjang. Penelitian ini dilakukan untuk meramalkan tingkat inflasi di Indonesia selama 12 periode mendatang, yaitu dari Januari 2025 hingga Desember 2025. Penelitian ini menggunakan metode ARIMA, karena model ARIMA bersifat fleksibel terhadap semua jenis pola data deret waktu, meskipun data tersebut bersifat non-stasioner. Hasil penelitian menunjukkan bahwa ARIMA (2,0,2) merupakan model terbaik dengan nilai akurasi MAPE sebesar 25,21%. Model ini dapat memprediksi tingkat inflasi yang stabil di Indonesia selama 12 periode mendatang, dengan rata-rata sebesar 1,861%. Hasil ini menunjukkan bahwa kenaikan harga umum barang dan jasa di Indonesia selama periode tersebut akan stabil tanpa fluktuasi, yang merupakan tanda positif bagi stabilitas makroekonomi dan daya beli masyarakat.
Applications of Panel Data Analysis on Human Development Index Indicators in Districts/Cities of Lampung 2022 – 2024 Rahmad Wanizal Pastha; Zilrahmi; Zamahsary Martha
UNP Journal of Statistics and Data Science Vol. 3 No. 3 (2025): 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/vol3-iss3/411

Abstract

This paper aims to identify the determinants affecting the Human Development Index (HDI) in Lampung Province, Indonesia, during the periode 2022-2024 using panel data regression. Lampung consistenly ranks among the provinces with the lowest HDI scores in Sumatera, indicating developmental disparties across regions. The research employs secondary data from 15 districts/cities and includes variables such as life expectancy, expected years of schoolingm mean years of schooling, and expenditure per capita. Panel data regression models fixed effect, random effect, and common effect were evaluated using chow, hausman, and lagrang multiplier tests to select the most approriate model. The random effect model was chosen, supported by a high R-Squared value of 92,71% indicating strong explanatory power. The analysis found that life expectancy and mean years of schooling significantly influence HDI, while expected years of schooling and expenditure per capita were not statistically significant in this model. The analysis shows that ensuring equal opportunities in health and education significantly contributies to better human development. Future research is recomended to incorporate qualitative approaches and more recent variables to enrich the analysis.
Forecasting the Consumer Price Index of Padang City in 2024 using the Autoregressive Integrated Moving Average Method Suci; Devi Yopita Sipayung; Dila Sari; Fajri Juli Rahman Nur Zendrato; Hadid Habiburrahman; Dwi Sulistiowati; Zilrahmi
UNP Journal of Statistics and Data Science Vol. 4 No. 1 (2026): 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/vol4-iss1/437

Abstract

The Consumer Price Index (CPI), which changes, is influenced by fluctuations in the prices of goods and services in Padang City every year. This is triggered by various factors that are of primary concern to the government. This study uses the Autoregressive Integrated Moving Average (ARIMA) forecasting method to forecast CPI in 2024 by relying on monthly data on the Padang City CPI for the period 2020 to 2023 obtained from BPS. This analysis identifies the ARIMA model (0,2,1) as the best and most optimal model based on the AIC and BIC values, does not show any autocorrelation, and is normally distributed. The forecasting model used shows a smooth and stable increase in the CPI in the period from January to December 2024. This model provides a positive signal for people's purchasing power and economic stability in Padang City in 2024. The results obtained are expected to be used as a strategic tool for preparing future goods and services price planning with more precision.
Classification of Tuberculosis in Rumah Sakit Paru Sumatera Barat Using the C5.0 Algorithm Meliani Maya Sari; Zilrahmi; Dony Permana; Dwi Sulistiowati
UNP Journal of Statistics and Data Science Vol. 4 No. 1 (2026): 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/vol4-iss1/444

Abstract

Tuberculosis (TB) remains a serious public health problem, including in West Sumatra Province, where the number of reported cases has continued to increase in recent years. Consequently, effective methods are required to support early detection and accurate classification of TB patients. This study aims to classify the tuberculosis status of patients at Rumah Sakit Paru Sumatera Barat by applying the C5.0 algorithm. The data used in this study consists of secondary data extracted from patient medical records collected from october to december 2024 with a total of 150 patient medical records. The dataset included eight predictor variables representing clinical symptoms and one target variable, namely sputum smear (BTA) examination results. The research process involved data preprocessing, after which the dataset was divided into training and testing subsets using a 70:30 ratio, a classification model was developed using the C5.0 algorithm, and its performance was evaluated using a confusion matrix. The findings indicate that the C5.0 algorithm achieved an accuracy of 91.11%, with a precision of 95.83%, sensitivity of 88.46%, and specificity of 94.74%. Night sweats were identified as the most influential variable in the construction of the decision tree. These findings indicate that the C5.0 algorithm demonstrates excellent performance and can be applied as a decision support method for classifying tuberculosis based on patients’ clinical symptoms
Co-Authors Abilya Amanda Adinda Dwi Putri Afendi, Farit M Afifa Lufti Insani Amelia Fadila Rahman Atus Amadi Putra Chairina Wirdiastuti Devi Yopita Sipayung Dila Sari Dina Fitria Dina Fitria Dina Fitria, Dina Dinda Fitriza Diva Aliyah Dodi Vionanda Dodi Vionanda Dony Permana Dwi Sulistiowati Fadhilah Fitri Fadhilah Fitri Fadhillah Fitri Fajri Juli Rahman Nur Zendrato Fajrin Putra Hanifi Farit M Afendi FAZHIRA ANISHA Febri Ramayanti Fedisha Elfiri Fedisha Fitri Mudia Sari Fitri, Fadhilah Gilang Ibnul farizi Hadid Habiburrahman Hamida, Zilfa Hanifah Nazhiroh Hari Wijayanto Hari Wijayanto Hendrawan, Muhammad Ichlas Djuazva Ihsanul Fikri Khasanah, Nurviqotun Khoirun Nisa Lathifa Putri Manja Danova Putri Martia Rosada Meliani Maya Sari Meliani Putri Melin Wanike Ketrin Moh. Erkamim Muhammad Alif Yustin Muhammad Fadhil Aditya Aditya Muhammad Fadlan Rafly Muhammad Faisal Muslimah, Nailul Amani Mutiara Amazona Sosiawati Nilda Yanti Nonong Amalita Nurdalia Nurwijayanti Permana, Dony Putri, Fadhira Vitasha Rahmad Wanizal Pastha Rahmadani Iswat Rahmanesta, Frandito Rizal Bakri Rizqa Fajriaty Fitri MY Said Thaufik Rizaldi Salma, Admi Sepriano Sepriano silfia wisa fitri Sindy Amelia Putri Sri Wahyu suci Sulhatun Sulhatun Syafriandi Syafriandi Syafriandi Syifa Azahra Syifa Miftahurrahmi Syifa Nabilah Wandira Tessy Octavia Mukhti Tessy Octavia Mukhti Ully Martha martha Ulya Syafitri.J Velya Rahma Putri Widia Handa Riska Winalia Agwil Yarman Yarman, Yarman Yenni Kurniawati Yurivo Rianda Saputra Zamahsary Martha