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All Journal International Journal of Advances in Intelligent Informatics EMITTER International Journal of Engineering Technology Jurnal Informatika Upgris JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Eksplora Informatika Technomedia Journal Jurnal Tekno Insentif Jurnal Telematika Jurnal Teknologi Terapan Mobile and Forensics Jurnal Teknik Industri Terintegrasi (JUTIN) Journal of Applied Data Sciences Journal of Dinda : Data Science, Information Technology, and Data Analytics Nuansa Informatika
Syafrial Fachri Pane, Syafrial Fachri
Politeknik Pos Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mechanical Engineering Neuroscience

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Journal : Nuansa Informatika

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Design and Implementation of a RESTful API-Based Point of Sale System Grahitama, Fulandi Hudza; Adiwiguno, Waskitho Cito; Pane, Syafrial Fachri
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 Pane, Syafrial Fachri; Zain, Rofi Nafiis; Setiawan, Iwan; Putratama, Virdiandry
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 Adiwiguno, Waskitho Cito Adiwijaya Alfian Akbar Gozali Ekra Sanggala Faisal Abdullah Fajri, Ravi Rahmatul Fatonah, Nuraini Siti Grahitama, Fulandi Hudza Habibi, Roni Haq, Dzikri Izzatul Harani, Nisa Hanum Iwan Setiawan Khaera Tunnisa, Khaera Kurniawan, Alit Fajar Kurniawan, Cahya Mahmud Dwi Sulistiyo Poetra, Chandra Kirana Prianto, Cahyo Putratama, Virdiandry Ramadhan, Bachtiar Rolly Maulana Awangga, Rolly Maulana Siregar, M. Amran Hakim Vegita, Yola Zain, Rofi Nafiis