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All Journal Indonesian Journal of Applied Informatics Journal of Dinda : Data Science, Information Technology, and Data Analytics Nuansa Informatika
Syafrial Fachri Pane
Universitas Logistik dan Bisnis Internasional
Computer Science & IT

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Optimizing Search Efficiency in Ordered Data: A Hybrid Approach Using Jump Binary Search Gabriella Youzanna Rorong; Syafrial Fachri Pane; M Amran Hakim Siregar
Indonesian Journal of Data Science, IoT, Machine Learning and Informatics Vol 5 No 1 (2025): February
Publisher : Research Group of Data Engineering, Faculty of Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/dinda.v5i1.1764

Abstract

This research presents the development of a hybrid algorithm called Jump Binary Search (JBS), which integrates jump search and binary search techniques to improve search efficiency in sorted data distributions. JBS is designed to accelerate the search process using a jump technique to find the target block, after the block is identified, it is followed by a binary search to narrow down the search space. The results of this study show that the performance of JBS is superior compared to Jump Linear Search (JLS) when applied to non-uniform and ordered categorical data distributions. JBS only requires an execution time ranging from 0-15ms and 0-10ms, demonstrating efficiency and speed on elements consisting of 400 elements. The execution time of JBS demonstrates its efficiency compared to JLS. By minimizing unnecessary data access, JBS becomes the right solution for finding target elements in sorted data distribution.
Pengaruh Metode Seleksi Fitur terhadap Akurasi Model SVM dalam Klasifikasi Customer Churn pada Perusahaan Telekomunikasi Mayke Andani Rohmaniar; Roni Habibi; Syafrial Fachri Pane
IJAI (Indonesian Journal of Applied Informatics) Vol 9, No 1 (2024)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/ijai.v9i1.92983

Abstract

Abstrak:Penelitian ini menganalisis pengaruh metode seleksi fitur terhadap akurasi model Support Vector Machine dalam memprediksi pelanggan di industri telekomunikasi. Empat metode seleksi fitur (Correlation Matrix, PCA, dan GA) dan empat kernel (Linear, Polynomial, RBF, dan Sigmoid) dibandingkan menggunakan dataset pelanggan telekomunikasi dari Kaggle dengan 7043 entri dan 33 fitur. Metodologi CRISP-DM digunakan, meliputi Pemahaman Bisnis, Pemahaman Data, Persiapan Data, Pemodelan, Evaluasi, dan Implementasi. Hasil penelitian menunjukkan bahwa metode seleksi fitur menggunakan Correlation Matrix dengan kernel Linear memberikan kinerja terbaik. Model ini mencapai akurasi tertinggi sebesar 92,48%, dengan precision 0,93, recall 0,97, dan f1-score 0,95. Metode seleksi fitur lainnya, seperti PCA dan GA, memberikan hasil yang lebih rendah dibandingkan dengan Correlation Matrix. Implementasi model prediksi yang akurat diharapkan dapat membantu perusahaan telekomunikasi mengembangkan strategi retensi pelanggan yang lebih efektif.=================================================Abstract:This study examines the impact of various feature selection methods on the accuracy of the Support Vector Machine (SVM) model in predicting customer behavior within the telecommunications sector. Specifically, the research compares four feature selection techniques: Correlation Matrix, Principal Component Analysis (PCA), and Genetic Algorithm (GA). Additionally, it evaluates the performance of four SVM kernels: Linear, Polynomial, Radial Basis Function (RBF), and Sigmoid. Utilizing a telecom customer dataset from Kaggle, which comprises 7043 entries and 33 features, the study adheres to the CRISP-DM methodology. This methodology includes phases such as Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation, and Implementation. The findings indicate that the Correlation Matrix feature selection method, when paired with the Linear kernel, provides the best performance. This particular configuration achieves the highest accuracy rate of 92.48%, along with a precision score of 0.93, a recall score of 0.97, and an F1-score of 0.95. In contrast, other feature selection methods, such as PCA and GA, result in lower performance metrics. These findings underscore the effectiveness of the Correlation Matrix and Linear kernel combination in enhancing the predictive accuracy of SVM models.
Design and Implementation of a RESTful API-Based Point of Sale System Fulandi Hudza Grahitama; Waskitho Cito Adiwiguno; Syafrial Fachri Pane
NUANSA INFORMATIKA Vol. 19 No. 1 (2025): Nuansa Informatika 19.1 Januari 2025
Publisher : FKOM UNIKU

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25134/ilkom.v19i1.343

Abstract

Point of Sale (POS) systems are essential for modern businesses, streamlining transactions, inventory management, and customer interactions. However, traditional POS systems face challenges such as limited real-time data processing, scalability issues, and restricted integration capabilities. This study proposes a RESTful API-based POS system using Supabase and Express.js to overcome these limitations.The system is developed using a hybrid waterfall methodology, combining structured phases with iterative refinement, and employs a relational database normalized to the third normal form (3NF) for data integrity and scalability. Supabase, as a backend-as-a-service platform, simplifies backend operations with its robust features for database management, authentication, and real-time APIs. Meanwhile, Express.js provides a lightweight and efficient framework for developing RESTful APIs, ensuring seamless integration and efficient data handling. Comprehensive testing, including black box testing, confirms the system’s reliability, ensuring its readiness for real-world implementation. The results highlight the system’s ability to enhance operational efficiency and adapt to dynamic business requirements. This study demonstrates how integrating RESTful APIs, Supabase, and Express.js can modernize POS systems, providing scalable, secure, and efficient solutions tailored to the demands of a data-driven marketplace.
Predicting the Happiness Index Based on the HDI Indicator in Indonesia Using the Ensemble Learning Approach: Prediksi Indeks Kebahagiaan Berdasarkan Indikator IPM di Indonesia Menggunakan Pendekatan Ensemble Learning Syafrial Fachri Pane; Rofi Nafiis Zain; Iwan Setiawan; Virdiandry Putratama
NUANSA INFORMATIKA Vol. 19 No. 2 (2025): Nuansa Informatika 19.2 Juli 2025
Publisher : FKOM UNIKU

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25134/ilkom.v19i2.410

Abstract

Machine Learning is used to analyze complex data in various fields of research. In this study, we applied an ensemble learning approach consisting of Random Forest Regression (RF), XGBoost Regression (XGB), Decision Tree Regression (DT) and Pearson correlation analysis as well as Shapley Additive Explanations (SHAP) to analyze the relationship between the HDI and Happiness indicators in Indonesia. Second, building a prediction model with an ensemble learning approach, namely stacking, which consists of several algorithms including RF, XGB, DT. The results of this study, one, based on the results of Pearson correlation analysis, Permutation Importance (PI), and SHAP, show that the happiness score of Indonesian people has a strong correlation with the Human Development Index variable. The Pearson correlation result shows a value of 0.88, which indicates a very strong positive relationship between HDI and happiness. In addition, the Permutation Importance and SHAP analysis also confirms that HDI is one of the most influential variables in predicting happiness scores in Indonesia. Second, the performance model for predicting happiness using stacking regressors with an R-Squared value of 97.68\%, MAE 0.002900, MSE 0.000021, and RMSE 0.004604.
Co-Authors Fulandi Hudza Grahitama Gabriella Youzanna Rorong Iwan Setiawan M Amran Hakim Siregar Mayke Andani Rohmaniar Rofi Nafiis Zain Roni Habibi Virdiandry Putratama Waskitho Cito Adiwiguno