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All Journal In Search (Informatic, Science, Entrepreneur, Applied Art, Research, Humanism) Bulletin of Intelligent Machines and Algorithms
Fikri Muhamad Fahmi
Universitas Informatika dan Bisnis Indonesia
Arts Computer Science & IT Economics, Econometrics & Finance Other

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Perencanaan Strategi Sistem Informasi Menggunakan Ward and Peppard pada PT. Priadi Sidik Jari Psikologi Imannudin Akbar; Fikri Muhamad Fahmi
In Search (Informatic, Science, Entrepreneur, Applied Art, Research, Humanism) Vol 21 No 2 (2022): In Search
Publisher : LPPM UNIBI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37278/insearch.v21i2.885

Abstract

In this era, technological advances force companies and organizations to adapt in order to compete with their competitors. Strategic planning of information systems has a major impact on the continuity of business competition. PT. Priadi Sidik Jari Psikologi is a psychological assessment institution that has used information systems to support its business activities, but the implementation has not been maximized. Therefore, to maximize the existing information system at PT. Priadi Sidik Jari Psikologi, it is necessary to strategically plan information systems to achieve the company's vision, mission and goals. In this research, the Ward and Peppard framework is used with several analysis techniques including: PESTLE, Porter's Five Competitive Forces, and Value Chain analysis to analyze the internal and external environment of the business, SWOT to analyze the internal and external environment of SI and IT, and McFarlan Strategic Grid for application portfolio mapping. The results of this research are expected to improve the company's achievements and can also be used as a reference for the development of information systems in the company.
Stacking Ensemble Revenue Predictions in Digital Marketing: A SHAP-Based Analysis of Price and Quantity as Key Predictors Hana Maulid; Tiara Permata Hati; Fikri Muhamad Fahmi
Bulletin of Intelligent Machines and Algorithms Vol. 1 No. 1 (2025): BIMA November 2025 Issue
Publisher : Maheswari Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65780/bima.v1i1.1

Abstract

This study focuses on developing a digital marketing conversion prediction model using an ensemble stacking approach combined with explainable artificial intelligence (XAI) methods to improve model transparency. The primary objective of this study is to investigate the impact of price and product quantity on revenue predictions, as well as to gain a clearer understanding of the factors that influence customer purchasing behaviour in the context of digital sales. The methodology used includes data collection from a Kaggle dataset containing 3,000 records and 15 features related to customer demographics, product information, and marketing channels. The preprocessing stage ensures data quality, followed by feature engineering and model development using an ensemble stacking model consisting of Logistic Regression, Gaussian Naïve Bayes, and Support Vector Classification. Model evaluation was conducted using precision, recall, F1-score, and ROC-AUC metrics, with performance improvements achieved through cross-validation and probabilistic calibration. The study results showed that model accuracy reached 0.97, with significant contributions from price and product quantity features, as seen in the SHAP analysis. The ensemble stacking model provided stable and reliable predictions. These findings underscore the importance of effective pricing strategies and product volume optimisation in driving revenue growth. The use of SHAP enhances interpretability, enabling businesses to make more informed decisions. This research contributes to the development of transparent and practical machine learning applications in digital marketing, providing valuable implications for business strategy optimization.
Co-Authors Akbar, Imannudin Hana Maulid Tiara Permata Hati