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All Journal Techno.Com: Jurnal Teknologi Informasi
Derry Setiawan
Universitas Selamat Sri, Kendal, Jawa Tengah, Indonesia
Computer Science & IT Engineering

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An XGBoost-Driven Intelligent Classification Model for Textile Product Quality Eligibility: A Case Study at PT ABC Textile Yuni Handayani; Derry Setiawan; Taufik Hidayat; Tri Muji Waluyo
Techno.Com Vol. 25 No. 1 (2026): February 2026
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/tc.v25i1.15447

Abstract

Product quality is a critical aspect of the textile industry because it determines whether a product meets the company’s quality standards. This study develops a product eligibility classification model using the XGBoost algorithm to support the Quality Control (QC) process at PT ABC Textile. The novelty of this research lies in positioning XGBoost as an interpretability-driven decision-support tool by integrating real QC inspection data, feature importance and SHAP-based interpretability analysis, and stratified 5-fold cross-validation to support practical QC decision-making. The dataset consists of 500 samples manually labeled based on the company’s quality criteria and includes four technical features: Yarn Strength, Knitting Density, Color, and Defect Level. Data preprocessing involved data cleaning, label transformation, and MinMaxScaler normalization. Model performance was evaluated using stratified 5-fold cross-validation to ensure robust and unbiased assessment. The experimental results demonstrate stable and high classification performance across all folds, with strong class-wise precision, recall, and F1-score values. Confusion matrix analysis indicates that the model performs particularly well in identifying Non-Eligible products, which is critical for minimizing quality risks in industrial applications. Overall, the proposed approach demonstrates that XGBoost can effectively support textile quality control as an interpretable and reliable decision-support system. Future work may explore dataset expansion and cost-sensitive learning to further enhance industrial applicability. Keywords – XGBoost; Classification; Textile Products, Quality Control, Data Mining
Enhancing System Security Using Cryptographic Algorithms for Student Attendance Management Derry Setiawan; Yuni Handayani
Techno.Com Vol. 25 No. 3 (2026): August 2026
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/tc.v25i3.15778

Abstract

Student attendance records are essential for academic administration, performance monitoring, and institutional decision-making. However, conventional digital attendance systems remain vulnerable to unauthorized access, data tampering, replay attacks, and information leakage, which may compromise the confidentiality and integrity of attendance data. This study proposes a hybrid cryptographic framework for securing student attendance management systems by integrating AES-256 for data encryption, RSA-2048 for secure session key exchange and authentication, and SHA-256 for integrity verification. The proposed framework protects attendance records throughout the entire data lifecycle, including data collection, transmission, storage, and retrieval. Experimental evaluation was conducted using simulated attendance datasets ranging from 100 to 10,000 records to assess encryption time, decryption time, computational overhead, and security effectiveness. The results indicate that the proposed framework maintains efficient computational performance while significantly enhancing confidentiality, integrity, authentication, and resistance to unauthorized access, replay attacks, data tampering, and man-in-the-middle attacks compared with conventional single-algorithm approaches. These findings demonstrate that the proposed hybrid cryptographic framework provides a reliable, scalable, and practical security solution for modern student attendance management systems. Keywords- Student Attendance Management, AES-256, RSA-2048, SHA-256, Hybrid Cryptography 
Toward Quantum-Resilient and Privacy-Preserving Student Attendance Systems Using Hybrid Post-Quantum Cryptography Derry Setiawan
Techno.Com Vol. 25 No. 3 (2026): August 2026
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/tc.v25i3.16034

Abstract

The rapid digital transformation of academic record and student attendance systems demands security mechanisms that simultaneously provide confidentiality, integrity, privacy-preserving data analytics, and long-term resilience against emerging quantum threats. While conventional cryptographic algorithms such as RSA, ECC, and AES remain computationally efficient, public-key schemes based on RSA and ECC are vulnerable to quantum attacks, limiting their suitability for protecting sensitive educational information in the long term. Existing studies generally focus either on post-quantum secure communication or on privacy-preserving computation, resulting in fragmented security architectures that increase implementation complexity in resource-constrained academic environments. To address this challenge, this paper proposes a Lightweight Hybrid Post-Quantum Cryptographic Algorithm (LHPQCA) that integrates the CRYSTALS-Kyber Key Encapsulation Mechanism (KEM) for quantum-resistant session key establishment, AES-GCM for authenticated encryption of attendance records, and the Paillier additive homomorphic cryptosystem for privacy-preserving aggregation of attendance statistics over encrypted data. By assigning each cryptographic primitive according to its intended function, the proposed architecture achieves quantum-secure key establishment, efficient symmetric data protection, and secure encrypted computation without relying on post-quantum algorithms for bulk data encryption. Experimental evaluation demonstrates that incorporating the Paillier cryptosystem introduces only modest computational overhead while preserving practical encryption latency, reducing key establishment latency by 28–35%, decreasing CPU utilization by 22%, and maintaining bandwidth overhead below 12% compared with conventional hybrid deployment architectures that perform secure communication without privacy-preserving aggregation. These results demonstrate that the proposed integration of CRYSTALS-Kyber, AES-GCM, and the Paillier cryptosystem provides an effective balance between quantum resilience, computational efficiency, and privacy-preserving analytics, making LHPQCA suitable for next-generation academic attendance systems and other resource-constrained educational applications.   Keywords - Post-Quantum Cryptography, Hybrid Cryptographic Algorithm, CRYSTALS-Kyber, Key Encapsulation Mechanism (KEM), AES-GCM, Paillier Cryptosystem, Privacy-Preserving Analytics.
Co-Authors Taufik Hidayat Tri Muji Waluyo Yuni Handayani