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All Journal Teknosains: Media Informasi Sains dan Teknologi Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi
Fadila, Risfa
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Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Mathematics Physics

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ANALISIS HARGA SAHAM BANK MANDIRI MENGGUNAKAN REGRESI NONPARAMETRIK: PERBANDINGAN SPLINE TRUNCATED DAN DERET FOURIER Fadila, Risfa; Sriliana, Idhia; Hayadi, Ilham; Fhaeza, Veronnica Noer; Novianti, Pepi
Teknosains Vol 19 No 1 (2025): Januari-April
Publisher : Fakultas Sains dan Teknologi Universitas Islam Negeri Alauddin Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/teknosains.v19i1.57387

Abstract

Saham Bank Mandiri merupakan salah satu bank terbesar di Indonesia yang masuk ke dalam Big four Bank. Harga saham Bank Mandiri tidak terhindar dari fluktuasi yang disebabkan oleh berbagai faktor ekonomi dan kebijakan pasar. Penelitian ini bertujuan untuk memahami pola pergerakan saham Bank Mandiri, menggunakan metode regresi nonparametrik dengan membandingkan metode Spline truncated dan Deret Fourier dalam memodelkan dan memprediksi harga saham Bank Mandiri. Metode Spline truncated menangkap perubahan lokal pada data dengan membaginya menjadi beberapa segmen, sedangkan Deret Fourier menggunakan fungsi sinus dan cosinus untuk mendeteksi pola periodik. Data yang digunakan pada penelitian ini meliputi harga penutupan saham BMRI bulanan, inflasi Indonesia dan BI Rate dari Januari 2021 hingga Desember 2024. Hasil penelitian menunjukkan bahwa kedua metode memiliki performa yang hampir sama. Namun, Deret Fourier sedikit lebih unggul dengan nilai R^2 sebesar 92,27% memiliki 5 titik osilasi. Penelitian ini menegaskan pentingnya model nonparametrik untuk menangkap sifat non-linier harga saham, mendorong pengembangan model yang lebih adaptif.
Pendekatan Data Mining dalam Optimalisasi Margin Penjualan Adidas: Studi Klasterisasi dengan K-Means dan Fuzzy C-Means Firlian, Lutfiah; Fadila, Risfa; Ridho, Muhammad Kevyn; Sunandi, Etis; Aflah, Ukasyah
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Volume 13 Issue 2 August 2025
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/euler.v13i2.32417

Abstract

Adidas is trying to stay competitive, especially when it comes to selling in the U.S. market. This study aims to cluster Adidas sales data in the US by comparing K-Means and Fuzzy C-Means clustering methods to achieve effective market segmentation. Data management is performed by first standardizing the data from the variables of units sold, total sales, operating profit, and profit margin. Cluster analysis is performed by determining the optimal cluster with the elbow plot, and the most optimal result is four clusters. Cluster performance evaluation was performed by comparing the Davies-Bouldin Index (DBI), Sum of Squares Within (SSW), and Sum of Squares Between (SSB) values of each method. The analysis results show that K-Means has a DBI of 0.98 and an SSW of 8645.74, while Fuzzy C-Means has a DBI of 0.983 and an SSW of 8678.49. Based on these results, K-Means is considered more optimal because it produces clusters that are more compact and separated. This finding can be the basis for developing a more targeted and efficient sales strategy according to the characteristics of each market segment.
Co-Authors Aflah, Ukasyah Fhaeza, Veronnica Noer Firlian, Lutfiah Hayadi, Ilham Idhia Sriliana Pepi Novianti Ridho, Muhammad Kevyn Sunandi, Etis