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All Journal Jurnal Indonesia Sosial Sains IKRAM: Jurnal Ilmu Komputer Al Muslim ALMUISY: Journal of Al Muslim Information System
Mayang, Putri
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Humanities Astronomy Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Energy Environmental Science Immunology & microbiology Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Neuroscience Physics Social Sciences

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The Influence Factors on Stock Returns of Real Estate and Property Companies Listed in Bei in 2010 - 2014 Mayang, Putri
Jurnal Indonesia Sosial Sains Vol. 5 No. 12 (2024): Jurnal Indonesia Sosial Sains
Publisher : CV. Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59141/jiss.v5i12.1555

Abstract

This research seeks to examine and offer insights into the factors that impact stock returns of real estate and property companies listed on the Indonesia Stock Exchange. The examined factors include DER, EPS, NPM, PBV, inflation, exchange rate, SBI, and GDP. The research focuses on real estate and property companies listed on the Indonesia Stock Exchange during the 2010-2014 period, involving a total of 38 companies. The study analyzes the impact of these variables DER, EPS, NPM, PBV, inflation, exchange rate, SBI, and GDP on the stock returns of these companies. Data were collected from sources such as Bank Indonesia, the Central Bureau of Statistics, and the Indonesia Stock Exchange. The findings reveal that DER, EPS, NPM, and PBV significantly influence stock returns, whereas inflation, exchange rate, SBI, and GDP do not have a significant impact on the stock returns of real estate and property companies.
Analisis Segmentasi Pelanggan E-Commerce Menggunakan Metode Clustering Berbasis RFM Mayang, Putri; Yana, Adelia Alvi; Uripto, Casto
ALMUISY: Journal of Al Muslim Information System Vol. 5 No. 1 (2026): ALMUISY: Journal of Al Muslim Information System
Publisher : STMIK Al Muslim

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Abstract

Pertumbuhan pesat industri e-commerce menghasilkan volume data transaksi pelanggan yang besar dan kompleks. Pemanfaatan data tersebut secara optimal menjadi tantangan bagi perusahaan dalam menyusun strategi pemasaran berbasis perilaku pelanggan. Penelitian ini bertujuan untuk melakukan segmentasi pelanggan e-commerce menggunakan pendekatan Recency, Frequency, Monetary (RFM) yang dikombinasikan dengan algoritma K-Means clustering. Dataset yang digunakan adalah Brazilian E-Commerce Public Dataset (Olist) yang diperoleh dari Kaggle. Tahapan penelitian meliputi preprocessing data, perhitungan nilai RFM, normalisasi menggunakan Min-Max Scaling, penentuan jumlah cluster menggunakan metode Elbow, serta evaluasi model menggunakan Silhouette Score. Hasil penelitian menunjukkan bahwa segmentasi berbasis RFM mampu mengelompokkan pelanggan ke dalam beberapa cluster dengan karakteristik berbeda. Evaluasi model menghasilkan nilai Silhouette Score sebesar 0,278 yang menunjukkan kualitas cluster cukup baik. Segmentasi yang dihasilkan dapat digunakan sebagai dasar penyusunan strategi retensi pelanggan, reaktivasi pelanggan berisiko churn, serta optimalisasi pemasaran berbasis data.
Analisis Pola Pembelian Produk Digital Menggunakan Metode FP-Growth untuk Optimalisasi Strategi Bundling pada Marketplace Online Uripto, Casto; Mayang, Putri
IKRAM: Jurnal Ilmu Komputer Al Muslim Vol. 5 No. 1 (2026): IKRAM: Jurnal Ilmu Komputer Al Muslim
Publisher : IKRAM: Jurnal Ilmu Komputer Al Muslim

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Abstract

The rapid growth of online marketplaces has generated massive transaction data, which is often underutilized in supporting marketing strategies, particularly in product bundling. This study aims to analyze digital product purchasing patterns using the FP-Growth algorithm to optimize bundling strategies in online marketplaces. The dataset used is the Online Retail dataset from the UCI Machine Learning Repository, which has undergone preprocessing, transformation, and analysis stages. The FP-Growth algorithm is applied to extract frequent itemsets and generate association rules based on support, confidence, and lift ratio metrics. The results indicate that FP-Growth effectively identifies relationships between frequently co-purchased products in an efficient manner. The generated association rules can serve as a foundation for developing bundling strategies and product recommendations. Therefore, the application of FP-Growth proves to be effective in enhancing the utilization of transaction data for business decision-making in online marketplaces.
Co-Authors - STMIK Nusa Mandiri, Adelia Alvi Yana Uripto, Casto