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All Journal JTERA (Jurnal Teknologi Rekayasa) Journal of Applied Computer Science and Technology (JACOST) Journal of Dinda : Data Science, Information Technology, and Data Analytics Nuansa Informatika
Syafrial Fachri Pane
Universitas Logistik dan Bisnis Internasional
Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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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.
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.
Paradoks Keamanan Autentikasi Dua Faktor (2FA): Systematic Literature Review terhadap Kesenjangan Protokol Teoretis dan Kegagalan Implementasi Praktis Dzikri Izzatul Haq; Syafrial Fachri Pane
Journal of Applied Computer Science and Technology Vol. 7 No. 1 (2026): Juni 2026 (In progress)
Publisher : Indonesian Society of Applied Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52158/h9qv3j56

Abstract

Two-Factor Authentication (2FA) has been widely adopted as a fundamental security standard, yet sophisticated cyberattacks continue to exploit security loopholes that often lie not in the protocol itself, but in its implementation. This study aims to systematically synthesize current scientific literature to uncover the root causes of the gap between the theoretical security of 2FA protocols and practical exploitation risks in the field. Using the Systematic Literature Review (SLR) method with PRISMA guidelines, 43 high-quality articles (Q1-Q4) from the Scopus database published between 2020 and 2025 were analyzed using thematic synthesis. The findings reveal a central paradox where, although 2FA protocols are becoming mathematically stronger, 88% of failure points have shifted to implementation fundamentals; the most critical weaknesses identified are the storage of secret keys in plaintext format on client applications and the effectiveness of social engineering attacks against users. This study concludes that real-world 2FA security is determined more by the quality of implementation code and user awareness than by the cryptographic strength of the protocol alone, implying that industry priorities must shift from developing new protocols to enforcing secure implementation audits and continuous user education.
Model Prediktif Indeks Kebahagiaan Berbasis Gradient Boosting Regressor dengan Optimalisasi Seleksi Fitur dan Implementasi Web Dani Ferdinan; Nisa Hanum Harani; Syafrial Fachri Pane
JTERA (Jurnal Teknologi Rekayasa) Vol 10 No 2: December 2025
Publisher : Politeknik Sukabumi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31544/jtera.v10.i2.2025.59-68

Abstract

Penelitian ini menghadapi tantangan dalam memodelkan Indeks Kebahagiaan 2021 dari Badan Pusat Statistik (BPS) yang memiliki dimensi fitur sangat tinggi dan potensi redundansi, yang dapat menurunkan akurasi dan interpretabilitas model. Tujuan utama penelitian ini adalah untuk mengidentifikasi fitur-fitur paling berpengaruh dalam data tersebut untuk meningkatkan akurasi, efisiensi komputasi, dan transparansi model prediksi berbasis pohon keputusan. Metodologi mencakup pra-pemrosesan data dengan imputasi modus, transformasi Yeo-Johnson, dan Robust Scaler. Tiga algoritma regresi diuji: Decision Tree, Random Forest, dan Gradient Boosting Regressor, yang dioptimalkan menggunakan Particle Swarm Optimization (PSO). Model terbaik dievaluasi menggunakan metrik R², MSE, RMSE, dan MAE serta dianalisis lebih lanjut menggunakan SHAP untuk interpretasi. Hasil menunjukkan bahwa Gradient Boosting Regressor adalah model paling unggul dengan nilai R² sebesar 0,696 saat menggunakan 20 fitur terseleksi. Selain itu, sebagai bentuk implementasi praktis, model diimplementasikan ke dalam sebuah aplikasi web interaktif berbasis Flask yang memungkinkan pengguna memasukkan data melalui antarmuka kuisioner dan menerima prediksi indeks kebahagiaan secara real-time. Integrasi ini menjembatani hasil riset dengan pemanfaatan nyata oleh pengguna akhir.
Co-Authors Dani Ferdinan Dzikri Izzatul Haq Fulandi Hudza Grahitama Gabriella Youzanna Rorong Iwan Setiawan Nisa Hanum Harani Rofi Nafiis Zain Siregar, M. Amran Hakim Virdiandry Putratama Waskitho Cito Adiwiguno