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All Journal Sistemasi: Jurnal Sistem Informasi Journal of Computer Networks, Architecture and High Performance Computing
Wibowo, Suluh Arif
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Computer Science & IT Education Electrical & Electronics Engineering

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Implementing Histogram of Oriented Gradients to Recognize Crypto Price Graphic Patterns with Artificial Neural Network Wibowo, Suluh Arif; Rachmat, Nur
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 2 (2024): Articles Research Volume 6 Issue 2, April 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i2.3975

Abstract

Technical analysis stands as a pivotal strategy in analyzing graphic patterns to forecast future movements in crypto asset prices. However, comprehending numerous patterns poses a significant challenge for novice investors venturing into the investment realm. This study aims to facilitate investors in recognizing crypto price graph forms by classifying cryptographic price chart patterns. The dataset comprises images of seven types of crypto price graphic patterns obtained from the Kagle website, totaling 210 data points. A 70:30 training and testing data split is employed to ensure robust model evaluation. The study explores nine different Histogram of Oriented Gradients (HOG) parameter combinations for graphic pattern extraction. Leveraging the artificial neural network (ANN) classification method with parameter hyper tuning, the study assesses various HOG parameter configurations to optimize classification performance. The most optimal results are achieved with parameters Bin = 9, Cell Size = 16x16, and Block Size = 1x1, boasting an accuracy rate of 95.23%, precision of 95.55%, and recall of 95.23%. This classification approach streamlines the process for investors, enabling them to discern crypto price graph patterns effectively, thereby enhancing their investment decision-making capabilities in the dynamic cryptocurrency market landscape. By providing a structured method for pattern recognition, this study contributes to democratizing access to technical analysis tools, particularly benefiting novice investors seeking to navigate the complexities of cryptocurrency investment.
Implementing Histogram of Oriented Gradients to Recognize Crypto Price Graphic Patterns with Artificial Neural Network Wibowo, Suluh Arif; Rachmat, Nur
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 2 (2024): Articles Research Volume 6 Issue 2, April 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i2.3975

Abstract

Technical analysis stands as a pivotal strategy in analyzing graphic patterns to forecast future movements in crypto asset prices. However, comprehending numerous patterns poses a significant challenge for novice investors venturing into the investment realm. This study aims to facilitate investors in recognizing crypto price graph forms by classifying cryptographic price chart patterns. The dataset comprises images of seven types of crypto price graphic patterns obtained from the Kagle website, totaling 210 data points. A 70:30 training and testing data split is employed to ensure robust model evaluation. The study explores nine different Histogram of Oriented Gradients (HOG) parameter combinations for graphic pattern extraction. Leveraging the artificial neural network (ANN) classification method with parameter hyper tuning, the study assesses various HOG parameter configurations to optimize classification performance. The most optimal results are achieved with parameters Bin = 9, Cell Size = 16x16, and Block Size = 1x1, boasting an accuracy rate of 95.23%, precision of 95.55%, and recall of 95.23%. This classification approach streamlines the process for investors, enabling them to discern crypto price graph patterns effectively, thereby enhancing their investment decision-making capabilities in the dynamic cryptocurrency market landscape. By providing a structured method for pattern recognition, this study contributes to democratizing access to technical analysis tools, particularly benefiting novice investors seeking to navigate the complexities of cryptocurrency investment.
Development of a Flask-based Application for Bank Customer Churn Prediction as a Decision Support Tool Wibowo, Suluh Arif; Rezky, Muhammad; Ibrahim, Ali; Afrina, Mira; Fathoni, Fathoni
Sistemasi: Jurnal Sistem Informasi Vol 15, No 4 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i4.6257

Abstract

Customer churn prediction is a crucial aspect of the banking industry for maintaining customer loyalty and reducing the cost of acquiring new customers. This study aims to develop a web-based decision support system capable of predicting potential customer churn using the Gradient Boosting Machine (GBM) algorithm. The dataset used is the Bank Customer Churn Dataset, consisting of 10,000 customer records with 14 attributes. The research stages include exploratory data analysis and preprocessing, which involves data cleaning, categorical feature encoding, feature engineering (BalanceSalaryRatio, TenureByAge, CreditScoreGivenAge), and data balancing using SMOTE to address class imbalance. The GBM model was trained on the balanced dataset and evaluated using accuracy, precision, recall, and F1-score metrics. The evaluation results show that the model achieved an accuracy of 83.95%, with a recall of 67.32% for the churn class, indicating a strong capability in identifying customers at risk of churn. Feature importance analysis reveals that Age and NumOfProducts are the most dominant features, contributing approximately 77% to the prediction. The model was then implemented in a Flask-based web application with an HTML and CSS interface, enabling non-technical users to perform real-time churn predictions. This system is expected to assist banking institutions in designing more targeted and data-driven customer retention strategies.
Co-Authors Ali Ibrahim Fathoni Fathoni Mira Afrina Nur Rachmat Nur Rachmat Rezky, Muhammad