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All Journal Telematika JTERA (Jurnal Teknologi Rekayasa) Indonesian Journal of Applied Informatics Journal of Applied Computer Science and Technology (JACOST) Journal of Dinda : Data Science, Information Technology, and Data Analytics JEECS (Journal of Electrical Engineering and Computer Sciences) Nuansa Informatika
Syafrial Fachri Pane
Universitas Logistik dan Bisnis Internasional
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Education 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
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.
Enhancing OCR Accuracy on Indonesian ID Cards Using Dual-Pipeline Tesseract and Post-Processing Rendy Dwi Reksiyano; Syafrial Fachri Pane; Rolly Maulana Awangga
JEECS (Journal of Electrical Engineering and Computer Sciences) Vol. 10 No. 2 (2025): December
Publisher : Fakultas Teknik Universitas Bhayangkara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54732/jeecs.v10i2.3

Abstract

Manual transcription of data from Indonesian identity cards (KTP) remains prevalent in public institutions, often resulting in inefficiencies and human errors that compromise data accuracy. While Optical Character Recognition (OCR) technologies such as Tesseract have been widely adopted. However, the performance on KTP images is still inconsistent due to non-uniform layouts, low contrast, and background noise. This study proposes a dual-pipeline OCR framework designed to enhance the recognition accuracy of Indonesian KTPs under real-world conditions. First, the pipeline performs static region segmentation based on predefined Regions of Interest (ROI), then uses dynamic keyword heuristics to locate text adaptively across varying layouts. The outputs of both pipelines are merged through a voting and regex-based post-processing mechanism, which includes character normalization and field validation using predefined dictionaries. Experiments were conducted on 78 annotated KTP samples with diverse resolutions and quality of images. Evaluation using Character Error Rate (CER), Word Error Rate (WER), and field-level accuracy metrics resulted in an average CER of 69.82%, WER of 80.20%, and character-level accuracy of 30.18%. Despite moderate performance in free-text areas such as address or occupation, structured fields achieved higher accuracy above 60%. The method runs efficiently in a CPU-only environment without requiring large annotated datasets, demonstrating its suitability for low-resource OCR deployment. Compared to conventional single-pipeline approaches, the proposed framework improves robustness across heterogeneous document layouts and illumination conditions. These findings highlight the potential of lightweight, rule-based OCR systems for practical e-KYC digitization and form a foundation for integrating deep-learning-based layout detection in future research.
Predictive Modeling for ETA and Delivery Delay Prediction in Logistics and Transportation: A Systematic Literature Review Syafrial Fachri Pane; Muhammad Qinthar Sabilla Almaliki
Telematika Vol 19, No 2: August (2026)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v19i2.3307

Abstract

This study presents a Systematic Literature Review (SLR) of data-driven predictive models for delivery delay prediction in logistics and transportation systems. A total of 508 articles were initially retrieved from the Scopus database (IEEE, Elsevier, MDPI, Springer), which served as the primary source of literature, and were systematically evaluated using the PRISMA framework through the identification, screening, eligibility, and inclusion stages, resulting in 55 selected studies published between 2016 and 2026. This review addresses two research questions: (RQ1) how data-driven predictive models, including machine learning and modern statistical approaches, are developed and applied to predict delivery delays; and (RQ2) how these models perform under varying operational conditions. The findings reveal the dominance of machine learning, deep learning, and hybrid models, which leverage heterogeneous data sources such as GPS, AIS, traffic, weather, and IoT data to capture complex spatio-temporal dependencies. Hybrid and deep learning approaches generally demonstrate superior predictive performance in dynamic and nonlinear environments, whereas conventional methods offer advantages in interpretability and computational efficiency. Model performance is strongly influenced by operational conditions, including congestion, weather variability, prediction horizon, and data quality. Inconsistent evaluation metrics limit cross-study comparability, while context-specific datasets reduce model generalizability. Dependence on historical data further constrains adaptability in real-time and disruption-prone environments. This study provides a structured synthesis of predictive modeling approaches, performance trends, and research gaps, offering guidance for researchers and practitioners in selecting and developing delivery delay prediction models. Future research should focus on integrating underutilized contextual and human-related variables, real-time multi-source data, and multi-objective optimization techniques to improve model robustness, scalability, and real-world applicability in intelligent logistics systems.
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.
Co-Authors Dani Ferdinan Dzikri Izzatul Haq Fulandi Hudza Grahitama Gabriella Youzanna Rorong Iwan Setiawan Mayke Andani Rohmaniar Muhammad Qinthar Sabilla Almaliki Nisa Hanum Harani Rendy Dwi Reksiyano Rofi Nafiis Zain Rolly Maulana Awangga Roni Habibi Siregar, M. Amran Hakim Virdiandry Putratama Waskitho Cito Adiwiguno