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All Journal IT JOURNAL RESEARCH AND DEVELOPMENT Jurnal Manajemen Informatika dan Sistem Informasi JATI (Jurnal Mahasiswa Teknik Informatika)
Adjie Eryadi, Ridha
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Computer Science & IT Control & Systems Engineering Engineering

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PENGEMBANGAN LAWKEEPER : PLATFORM WEB DINAMIS UNTUK PERSURATAN INTERNAL Isnan, Zakfa; Adjie Eryadi, Ridha
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 8 No. 6 (2024): JATI Vol. 8 No. 6
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v8i6.11550

Abstract

Lembaga Hukum Graha Yustisia Law and Mediation Office menghadapi tantangan dalam pengelolaan dan pengarsipan dokumen persuratan yang masih dilakukan secara manual, menyebabkan inefisiensi dan risiko kehilangan arsip penting. Oleh karena itu, diperlukan solusi yang terintegrasi untuk meningkatkan efisiensi dan akurasi dalam pengelolaan dokumen. Penelitian ini bertujuan untuk mengembangkan Lawkeeper, sebuah aplikasi web yang dirancang untuk menyederhanakan manajemen dokumen, mempercepat proses pencarian arsip, dan memastikan akses yang lebih mudah terhadap dokumen penting. Pengembangan aplikasi dilakukan menggunakan metode waterfall dengan bahasa pemrograman PHP dan basis data MySQL. Hasil pengujian menunjukkan bahwa Lawkeeper berhasil meningkatkan efisiensi pengelolaan dokumen dan meminimalkan kesalahan pengarsipan, serta memberikan alat yang efektif untuk memantau proses surat-menyurat secara komprehensif. Dengan implementasi Lawkeeper, Lembaga Hukum Graha Yustisia Law and Mediation Office mampu mengoptimalkan alur kerja persuratan secara lebih terstruktur dan efisien.
Comparative Analysis of SVM and XGBoost Classifiers with HOG Features for Concrete Crack Detection Adjie Eryadi, Ridha
IT Journal Research and Development Vol. 10 No. 2 (2025)
Publisher : UIR PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25299/itjrd.2025.20560

Abstract

This study offers a comparative assessment of the Support Vector Machine with Radial Basis Function Kernel and Extreme Gradient Boosting for automated concrete crack detection based on Histogram of Oriented Gradients feature extraction. Data comprised 40,000 RGB concrete images from an open-source Mendeley dataset; half were cracked and half were non-cracked. They processed through a preprocessing pipeline that includes the Poisson noise reduction and bilateral filtering techniques. Two approaches, holdout validation over several training/testing configurations (50:50, 60:40, 70:30, and 80:20) and systematic 5-fold cross-validation, were adopted for evaluation of the Wilcoxon signed-rank test for statistical significance and inference time for computational efficiency assessment. The experimental results indicate that SVM achieved a better holdout accuracy of 98.94% with the 80:20 configuration, while XGBoost achieved a cross-validation mean accuracy of 98.83% ± 0.0015. However, no statistically significant performance difference was revealed between the models according to the Wilcoxon analysis. Results indicated SVM excels at minimising false positives on undamaged surfaces, whereas XGBoost is better for identifying cracks, meaning that the choice of models used should depend on the application requirements, where applications require either the minimisation of false alarms or maximum sensitivity for detection in the case of structural health monitoring.
SEQUENTIAL DATA PREPROCESSING APPROACH FOR ENHANCED MATERNAL HEALTH RISK CLASSIFICATION PERFORMANCE Adjie Eryadi, Ridha; Wildan Hanif Hafidudin
Jurnal Manajemen Informatika dan Sistem Informasi Vol. 9 No. 1 (2026): MISI Januari 2026
Publisher : LPPM STMIK Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36595/misi.v9i1.1928

Abstract

Maternal mortality is still a major health issue worldwide, and it, along with other reasons, has been leading to predictions that need risk-assessing systems to be improved. The current study performed the sequential outlier detection combining Interquartile Range followed by Local Outlier Factor methods on six machine learning algorithms using the UCI Maternal Health Risk dataset. The comprehensive preprocessing pipeline included the removal of duplicates, application of SMOTE for balancing, followed by Min-Max normalization and detection of outliers in a sequence. The performance of the model was evaluated through holdout validation and 10-fold cross-validation with statistical validation through Wilcoxon signed-rank tests and Cohen's d effect sizes. The Extra Trees Classifier resulted in a 98.34% accuracy rate, which is higher than that in previous studies. The distance-based methods showed the highest sensitivity, with KNN gaining 8.35% while tree-based ensembles were consistent with the accuracy gains. The statistical validation proved that there was a great extent of practical significance with a large effect size of more than 1.0 for the top performers, thereby establishing evidence-based guidelines for the application of sequential preprocessing in maternal health risk prediction systems.
Co-Authors Isnan, Zakfa Wildan Hanif Hafidudin