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All Journal Techno.Com: Jurnal Teknologi Informasi
Yuni Handayani
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 
Analisis Perbandingan Kinerja Algoritma Random Forest dan XGBoost dalam Klasifikasi Preferensi Wisatawan Kabupaten Kendal Yuni Handayani; Taufik Hidayat; Muhammad Khozin; Tri Muji Waluyo
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.v25i3.16214

Abstract

Penelitian ini bertujuan untuk menganalisis dan membandingkan kinerja algoritma Random Forest dan XGBoost dalam mengklasifikasikan preferensi pengunjung destinasi wisata di Kabupaten Kendal. Data diperoleh melalui kuesioner yang mencakup atribut usia, jenis kelamin, asal, transportasi, harga tiket, fasilitas, dan akses. Tahapan penelitian meliputi pembagian data menggunakan train-test split dengan proporsi 80:20 serta penanganan ketidakseimbangan data menggunakan teknik SMOTE pada data pelatihan. Proses pemodelan dilakukan dengan optimasi parameter menggunakan GridSearchCV untuk memperoleh model terbaik dari masing-masing algoritma. Evaluasi model dilakukan menggunakan metrik akurasi, presisi, recall, dan F1-score pada data uji, serta diperkuat dengan Stratified K-Fold Cross Validation (k=5) untuk memastikan stabilitas dan kemampuan generalisasi model. Hasil penelitian menunjukkan bahwa Random Forest memperoleh akurasi sebesar 89,55%, sedangkan XGBoost menghasilkan akurasi yang lebih tinggi yaitu 90,30%. Hasil cross-validation juga menunjukkan bahwa XGBoost memiliki performa yang lebih stabil dibandingkan Random Forest. Analisis feature importance menunjukkan bahwa atribut usia menjadi faktor paling berpengaruh, diikuti oleh akses, asal, dan harga tiket. Hasil penelitian ini diharapkan dapat menjadi dasar dalam perumusan strategi pengembangan dan promosi pariwisata berbasis data.   Kata kunci – Random Forest, XGBoost, Klasifikasi, Preferensi Wisata
Co-Authors Derry Setiawan Muhammad Khozin Taufik Hidayat Tri Muji Waluyo