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All Journal Jurnal Gaussian Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi)
Mega Susilowati
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ANALISIS KINERJA PORTOFOLIO OPTIMAL DENGAN METODE MEAN-GINI Mega Susilowati; Rita Rahmawati; Alan Prahutama
Jurnal Gaussian Vol 5, No 3 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (543.705 KB) | DOI: 10.14710/j.gauss.v5i3.14705

Abstract

Investments in financial assets has a special attraction that investors can form a portfolio. Portfolio is investment which comprised of various stocks from different companies. A common issue is the uncertainty when investors are faced with the need to choose stocks to be formed into a portfolio of his choice. A rational investor, would choose the optimal portfolio. Determination of the optimal portfolio can be performed by various methods, one of which is a method of Mean-Gini. Mean-Gini is the expected value of the portfolio return is the weight density function while the random variable is the average of the shares. Mean-Gini methods used to generate the greatest value of portfolio return expectations with the smallest risk. Mean-Gini does not require the assumption of normality on stock returns. Mean-Gini was first introduced by Shalit and Yitzhaki in 1984. This research uses data of closing prices stocks from January 2008 to December 2015. Measurement of portfolio performance with Mean-Gini performed using the Sharpe index. Based on Sharpe index, the optimal portfolio is second portfolio with three stocks portfolio and the proportion investments are 25.043% for SMGR, 68.148% for UNVR and 6.809% for BMRI. Keywords:   Stock, Portfolio, Mean-Gini, Sharpe index.
Analisis Segmentasi Pelanggan Mall Menggunakan Algoritma K-Means untuk Optimalisasi Strategi Pemasaran Dede Kurnia Putri; Mega Susilowati; Tria Nissa Nurhayati; Wina Nurfadilah
Prosiding SISFOTEK Vol 9 No 1 (2025): SISFOTEK IX 2025
Publisher : Ikatan Ahli Informatika Indonesia

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Abstract

In today’s competitive retail environment, understanding customer behavior is essential for shopping malls to maintain loyalty and increase sales. Many mall managers still face challenges in identifying customer spending patterns because available data is underutilized. Based on field observations and related studies, marketing strategies often miss their targets due to limited analysis of customer characteristics, leading to wasted budgets and low campaign effectiveness. The root of the problem lies in the lack of data analytics implementation to objectively map customer behavior. To address this, the K-Means Clustering algorithm is applied to segment mall customers based on annual income and spending score. The research process involves collecting secondary data from public sources, performing data cleaning and normalization using the Min–Max method, and evaluating cluster quality using the Davies-Bouldin Index (DBI) to determine the optimal number of clusters. The results divide customers into five distinct groups with varying income and spending patterns. The purpose of this study is to help mall management create more targeted and efficient marketing strategies aligned with each segment’s behavior. The findings show that K-Means Clustering provides valuable insights into customer shopping patterns and can serve as a foundation for improving promotional effectiveness and customer satisfaction through data-driven decision-making.
Co-Authors Alan Prahutama Dede Kurnia Putri Rita Rahmawati Tria Nissa Nurhayati Wina Nurfadilah