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All Journal Seminar Nasional Variansi (Venue Artikulasi-Riset, Inovasi, Resonansi-Teori, dan Aplikasi Statistika) JINAV: Journal of Information and Visualization Quantitative Economics and Management Studies ARRUS Journal of Mathematics and Applied Science ARRUS Journal of Engineering and Technology Seminar Nasional Pengabdian Kepada Masyarakat Jurnal Pengabdian Masyarakat Bhinneka VARIANSI: Journal of Statistics and Its Application on Teaching and Research Journal of Mathematics, Computation and Statistics (JMATHCOS)
Aidid, Muhammad Kasim
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Library & Information Science Mathematics Physics Public Health

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IMPLEMENTASI ANALISIS REGRESI LOGISTIK DENGAN METODE MACHINE LEARNING UNTUK MENGKLASIFIKASI BERITA DI INDONESIA Fahmuddin S, Muhammad; Aidid, Muhammad Kasim; Nurliah, Muhammad Jabbar Taslim
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 5 No. 03 (2023)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm116

Abstract

Perkembangan internet sangat pesat, internet menjadi sumber informasi yang mudah untuk diakses seperti halnya berita. Perkembangan ini selain membawa dampak yang positif tentu juga dampak yang negatif di dalamnya. Penelitian ini bertujuan untuk mengetahui hasil evaluasi dan tingkat akurasi klasifikasi berita di Indonesia dengan menggunakan analisis regresi logistik beserta metode supervised learning. Data yang digunakan diperoleh dari data.mendeley.com diantaranya berita dengan total berita 600. Setelah dilakukan preprocessing data, diperoleh jumlah kata dalam dataset sebanyak 104.020 kata. Setelah membagi dataset menjadi data latih sebanyak 80% atau 480 data dan data uji sebanyak 20% atau 120 data, diperoleh hasil akurasi dalam mengklasifikasi berita menggunakan analisis regresi logistik dengan metode supervised learning sebesar 78,3%.
Pendekatan Geographically Weighted Regression (GWR) untuk Menganalisis Hubungan PDRB Sektor Pertanian, Kehutanan, dan Perikanan dengan Faktor Pencemaran Lingkungan di Jawa Timur Bakri, Nurul Aulya; Annas, Suwardi; Aidid, Muhammad Kasim
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/variansiunm194

Abstract

The Geographically Weighted Regression (GWR) method is a method used to analyze spatial heterogeneity, where the same independent variable gives unequal responses at different locations in a research area. The purpose of this study was to determine the environmental pollution factors that affect GRDP in the agricultural, forestry and fisheries sectors in East Java. The data used in this study are the GRDP of the Agriculture, Forestry and Fisheries sectors in East Java in 2020 along with the environmental pollution factors that are thought to influence it. The results of this study obtained a different model for each district/city. The GWR model shows better results than the multiple linear regression model, as seen from the smallest AIC value and the largest R2
TSA App by R Shiny : Time Series Analysis Application for Univariate Series Data Tri Utomo, Agung; Ahmar, Ansari Saleh; Aidid, Muhammad Kasim; Rais, Zulkifli; Alfairus, Muh. Qodri
ARRUS Journal of Engineering and Technology Vol. 5 No. 1 (2025)
Publisher : PT ARRUS Intelektual Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/jetech4398

Abstract

Time series analysis is a statistical method used to model and forecast sequential data over time. This modeling is typically performed using software, but most analytical tools require paid licenses. To address this issue, the TSA App by R Shiny is developed as an open-source application that is easily accessible. The application features a dashboard-based interface designed to help users perform univariate time series analysis without requiring programming skills. This study compares the analysis results of the TSA App with other software such as R Studio, Minitab, and Python. The results show that the TSA App produces comparable outputs in terms of visualization, ARIMA modeling, and forecasting accuracy. Therefore, the TSA App provides a practical and legal solution for time series analysis, especially for users who are unfamiliar with coding.
Penerapan Metode Kuadratik untuk Peramalan Banyaknya Penduduk Miskin di Sulawesi Selatan Tahun 2008-2025 Mangiri, Nalto Batty; Aidid, Muhammad Kasim; Ikhwana, Nur
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 7 No. 2 (2025)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm436

Abstract

Masalah kemiskinan adalah masalah yang kompleks dan bersifat multidimensional atau saling berkaitan antara berbagai aspek diantaranya yaitu aspek sosial, ekonomi, dan budaya, serta aspek lainnya. Banyaknya penduduk miskin di Indonesia adalah 23,85 juta pada Maret 2025. Provinsi Sulawesi Selatan pada Maret 2025, terdapat kurang lebih 698,13 ribu penduduk miskin. Sebagai langkah pencegahan meningkatnya angka kemiskinan perlu dilakukan peramalan banyaknya penduduk miskin sehingga pemerintah dapat melakukan perencanaan kebijakan. Data yang digunakan pada penelitian ini adalah data tahun 2008-2025 yang bersumber dari Badan Pusat Statistik Provinsi Sulawesi Selatan. Penelitian ini menggunakan Analisis Trend Nonlinear khususnya Metode Kuadratik untuk melakukan peramalan banyaknya penduduk miskin. Metode ini cocok digunakan untuk data 10 periode atau lebih. Metode Kuadratik memiliki nilai R-Square sebesar 80,24% dan MAPE sebesar 3,28%. Hasil Peramalan selama 6 tahun menunjukkan banyaknya penduduk miskin di Provinsi Sulawesi Selatan mengalami peningkatan.
Implementation of Support Vector Regression (SVR) and Double Exponential Smoothing (DES) for Forecasting BRI Stock Prices Meliyana, Sitti Masyitah; Aidid, Muhammad Kasim; Rahmadhani, Amaliyah
ARRUS Journal of Mathematics and Applied Science Vol. 5 No. 2 (2025)
Publisher : PT ARRUS Intelektual Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/mathscience4282

Abstract

This study aims to forecast the closing stock prices of BRI using Support Vector Regression (SVR) and Double Exponential Smoothing (DES) methods. The data used in this research is secondary data obtained from the Yahoo Finance website, covering the period from January 2020 to November 2023. The analytical steps using the SVR method involve selecting the optimal model by applying Grid Search Optimization to various kernels (linear, polynomial, radial, and sigmoid). The best-performing model was found to be the radial kernel with parameters ε = 0.1, C = 100, and γ = 10, yielding a Mean Absolute Percentage Error (MAPE) of 0.2431%, which was then used for forecasting. For the DES method, the steps involved parameter determination and minimizing the MAPE value, followed by smoothing calculations and forecasting. The optimal parameters obtained were α = 0.89 and β = 0.01, resulting in a MAPE value of 1.4832%. Based on the comparison of MAPE values, it can be concluded that the SVR method with a radial kernel (ε = 0.1, C = 100, γ = 10) provides the most accurate forecasts for BRI closing stock prices, with the lowest MAPE of 0.2431%.
Co-Authors Adiyanty, Tri Amelia Ahmar, Ansari Saleh Alfairus, Muh. Qodri Amira, Husnul Annisa, Nurawalia Aswi, Aswi Azrahwati Bakri, Nurul Aulya Elisa, Nur Fahmuddin S, Muhammad Ikhwana, Nur Mangiri, Nalto Batty Mar'ah, Zakiyah Muhammad Arif Tiro, Muhammad Arif Muthahharah, Isma Nurliah, Muhammad Jabbar Taslim Nusrang, Muhammad Rahmadhani, Amaliyah Rais, Zulkifli Ruliana Ruliana, Ruliana Sitti Masyitah Meliyana R. Suwardi Annas Syalsabila, Annisa Utomo, Agung Tri Wea, Maria Dominggo Zakiyah Mar'ah