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All Journal Pelita Eksakta UNP Journal of Statistics and Data Science Journal of Multidisciplinary Science: MIKAILALSYS
Fadhilah Fitri
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Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Environmental Science Mathematics Physics Social Sciences Other

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Peramalan Harga Bawang Merah di Kota Padang Menggunakan Metode SARIMA Dwika Larissa; Fadhilah Fitri; Dina Fitria
UNP Journal of Statistics and Data Science Vol. 3 No. 1 (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-iss1/330

Abstract

The fluctuation of shallot prices in Padang City has become a major concern for consumers, producers, and the government. This study applies the Seasonal Autoregressive Integrated Moving Average (SARIMA) method to forecast shallot prices from January 2020 to August 2024, using monthly time-series data. The analysis identifies ARIMA(1,1,2)(0,1,1)12 as the optimal model for predicting shallot prices in Padang City, effectively capturing seasonal and non-seasonal patterns. Predictions for the period from September 2024 to August 2025 indicate a price increase trend, peaking in May 2025 before declining. The findings are expected to serve as a reference for planning production, distribution, and price control of shallots.
Analysis of the Determinants of the Gender Empowerment Index in West Sumatra 2024 Using the Group Lasso Method Devi Yopita Sipayung; Fadhilah Fitri
Journal of Multidisciplinary Science: MIKAILALSYS Vol 4 No 3 (2026): Journal of Multidisciplinary Science: MIKAILALSYS
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/mikailalsys.v4i3.11886

Abstract

Gender empowerment remains a multidimensional development challenge in West Sumatra, where regional disparities reflect interconnected educational, economic, political, health, demographic, and infrastructural factors. This study aims to model the Gender Empowerment Index (GEI) across 19 regencies and municipalities in West Sumatra in 2024, identify its relevant determinants, and determine the dominant predictor using Group LASSO. A quantitative research design was employed using secondary data from Statistics Indonesia, comprising 22 predictors classified into six dimensions. All 19 regions were included through census sampling. The data were standardised, assessed for multicollinearity, and analysed using Group LASSO, with K-fold cross-validation applied to determine the optimal penalty parameter. The optimal λ value of 0.2392388 retained 14 predictors and produced a mean squared error of 1.190105 and an R² of 0.985264. The proportion of women serving in Regional People’s Representative Councils emerged as the dominant predictor, with the largest coefficient of 0.892008. These findings demonstrate the utility of Group LASSO for selecting relevant predictors in a multidimensional regional dataset and provide empirical evidence for establishing gender-empowerment policy priorities. The results particularly underscore the importance of women’s political representation in efforts to strengthen gender empowerment across West Sumatra.
Modeling Human Development Index Based on Socio-Economic Factors in West Sumatra Using Local Polynomial Regression Alya Zafirah; Fadhilah Fitri
Journal of Multidisciplinary Science: MIKAILALSYS Vol 4 No 3 (2026): Journal of Multidisciplinary Science: MIKAILALSYS
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/mikailalsys.v4i3.11902

Abstract

Human development is a central concern in regional policy because economic advancement does not necessarily translate into improved quality of life. However, research modelling nonlinear relationships between the Human Development Index (HDI) and socioeconomic factors across the regencies and municipalities of West Sumatra using Local Polynomial Regression (LPR) remains limited. This study aims to model HDI based on the senior high school net enrolment rate (NER), gross regional domestic product (GRDP), and population density. A quantitative, nonexperimental, cross-sectional design was employed using 2023 secondary data from Statistics Indonesia covering 19 regencies and municipalities. The data were analysed using nonparametric LPR with a local-linear estimator and optimal bandwidth selection. The findings indicate that all three predictors are positively associated with HDI, with senior high school NER exhibiting the strongest correlation. The model achieved an R² of 0.9147, a root mean squared error of 1.3458, and a mean absolute error of 1.0292, demonstrating its ability to capture nonlinear patterns and explain regional variation in HDI. This study contributes to regional development modelling by demonstrating the applicability of LPR to multidimensional human development data. The findings provide an empirical basis for more context-sensitive development policies that integrate educational participation, economic capacity, and population distribution.
Implementation of XGBoost Algorithm for Sentiment Classification of Public Opinions on the Rupiah Redenomination Policy Andinie Rachmah Basri; Fadhilah Fitri; Dodi Vionanda
Journal of Multidisciplinary Science: MIKAILALSYS Vol 4 No 3 (2026): Journal of Multidisciplinary Science: MIKAILALSYS
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/mikailalsys.v4i3.11949

Abstract

Although rupiah redenomination has long featured in Bank Indonesia’s monetary policy discourse as a means of simplifying currency denominations without altering the exchange rate or real purchasing power, public concerns about declining purchasing power and price rounding underscore the need for effective policy communication. This study analyzes Indonesian public sentiment toward rupiah redenomination using the Extreme Gradient Boosting (XGBoost) algorithm to classify YouTube comments. Data were collected through the YouTube Data API v3 from 1,763 comments posted on the Tribunnews video titled Purbaya Targets Rupiah Redenomination Bill to Be Completed in 2027. Following text cleaning and tokenization, 1,169 comments were retained and transformed using Term Frequency–Inverse Document Frequency (TF-IDF). Manual labeling classified 657 comments as positive and 512 as negative. The XGBoost model, trained using optimized hyperparameters, achieved an accuracy of 73.39%, with F1-scores of 0.772 for positive sentiment and 0.680 for negative sentiment. These results indicate that the model classified positive sentiment more effectively, although ambiguous comments remained challenging. The findings reveal the distribution of public responses to the proposed policy and emphasize the need for intensive public outreach to mitigate potential resistance and misconceptions. This study contributes a data-driven basis for developing more effective monetary policy communication and anticipating the social dynamics associated with rupiah redenomination.
Co-Authors Afdhal Rezeki Afdhal AL Rezki Ivansyah Alya Zafirah Andini Yulianti Andinie Rachmah Basri Anggi Adrian Danis Aprilla Suhada Atus Amadi Putra Aurumnisva Faturrahmi Devi Yopita Sipayung Devni Prima Sari dhea afrila harelvi DHEA PUTRI RIZKIA Dina Fitria Dina Fitria Dodi Vionanda Dony Permana Dwika Larissa Fanni Rahma Sari Fashihullisan Fauziah Taslim Fayza Annisa Febrianti Findri Wara Putri Fitri Mudia Sari Hanifah Nazhiroh Huriati Khaira Indonesia Isra Miraltamirus Kayla Faradina Melin Wanike Ketrin Muhammad Alif Yustin Natasya Dwi Ovalingga Nazifatul Azizah Nonong Amalita Puti Utari Maharani Rahmadani Iswat rama novialdi Rizki Akbar Rizqia Salsabila Sabina Chairun Najwa Salma, Admi Silvia Agustina Sri Wahyuni Syafriandi Syafriandi Syafriandi Syifa Azahra Syifa Nabilah Wandira Tessy Octavia Mukhti Tessy Octavia Mukhti Vinka Haura Nabilla Viola Yuniza Yenni Kurniawati Zamahsary Martha Zilrahmi, Zilrahmi