Article Per Year (5 Year)
p-Index From 2021 - 2026
5.26
P-Index
This Author published in this journals
All Journal Dinamik Techno.Com: Jurnal Teknologi Informasi Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik Prosiding SNATIF JAIS (Journal of Applied Intelligent System) Proceeding SENDI_U Jurnal Informatika Upgris Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control CCIT (Creative Communication and Innovative Technology) Journal Jurnal SITECH : Sistem Informasi dan Teknologi Building of Informatics, Technology and Science Jurnal Mantik Jurnal Informatika dan Rekayasa Perangkat Lunak Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Pengabdian, Pendidikan dan Teknologi Jurnal Teknik Informatika (JUTIF) Jurnal Program Kemitraan dan Pengabdian Kepada Masyarakat Jurnal Pendidikan dan Teknologi Indonesia Jurnal Citrakara Prosiding Seminar Nasional Hasil-hasil Penelitian dan Pengabdian Pada Masyarakat Seminar Nasional Teknologi dan Multidisiplin Ilmu Scientific Journal of Informatics INOVTEK Polbeng - Seri Informatika Palawa Jurnal Pengabdian Kepada Masyarakat
Elkaf Rahmawan Pramudya
Universitas Dian Nuswantoro
Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mechanical Engineering Physics Public Health Social Sciences Other

Published : 37 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 4 Documents
Search
Journal : Building of Informatics, Technology and Science

 1 
Prediksi Periode Fosil Trilobita Menggunakan XGBoost dengan Seleksi Fitur Geologi–Geospasial dan Hyperparameter Tuning Ramadhan, Naufal Rizky; Pramudya, Elkaf Rahmawan
Building of Informatics, Technology and Science (BITS) Vol 7 No 4 (2026): March 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i4.8862

Abstract

This study investigates the application of the Extreme Gradient Boosting (XGBoost) algorithm to predict the age period of trilobite fossils based on geological and geospatial data. The challenges addressed in this research include the high complexity of paleontological data, the presence of missing values, and class imbalance in the target variable time_period, which can negatively affect predictive performance. The objective of this study is to develop an accurate and robust fossil age prediction model through systematic data preprocessing, feature selection, and model optimization. The dataset used in this research was obtained from Kaggle and consists of the attributes longitude, latitude, lithology, environment, and collection_type as the main features. The research workflow includes data cleaning, missing value imputation, categorical feature encoding, data splitting using stratified train–test split, and class imbalance handling through a class weight adjustment approach. The XGBoost model was trained on the training dataset and further optimized using RandomizedSearchCV to obtain the optimal hyperparameter configuration. Evaluation results on the testing dataset show that the tuned XGBoost model achieved an accuracy of 95%, precision of 90%, recall of 93%, and an F1-score of 91%, outperforming the model without hyperparameter tuning. These results demonstrate that the integration of geological–geospatial feature selection and hyperparameter tuning in XGBoost is effective in improving the performance of trilobite fossil age period prediction. The results of this study are expected to serve as a computational support approach in paleontology to assist fossil period determination in a more objective, efficient, and data-driven manner.
Reversible Data Hiding Citra MRI T1-Weighted Menggunakan Spatial Fuzzy C-Means dan Selective Histogram Shifting Suharyoto, Aufa Fadholi; Pramudya, Elkaf Rahmawan
Building of Informatics, Technology and Science (BITS) Vol 7 No 4 (2026): March 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i4.8863

Abstract

The transmission of medical images over telemedicine networks increases the risk of data leakage and manipulation of sensitive information. This study develops a Reversible Data Hiding framework that integrates Spatial Fuzzy C-Means, Selective Histogram Shifting, and a measurable Distortion Control Mechanism for securing T1-weighted brain MRI images. The proposed method prioritizes the preservation of Region of Interest intensity characteristics and full reversibility over embedding capacity. SFCM is employed to generate Region of Interest and Non-Region of Interest mappings based on intensity distribution, with adaptive parameter adjustment for each slice. Data embedding is performed selectively on NROI using histogram shifting, while ROI areas remain unmodified. An Adaptive Feedback Control mechanism monitors image quality metrics SNR, CNR, GLCM with conservative thresholds (ΔSNR ≤ 2.0%, ΔCNR ≤ 1.0%) to ensure ROI stability. Experimental evaluation on the OASIS-1 dataset shows that the proposed method achieves an average PSNR of 54.13 dB, SSIM of 0.9996, and NCC of 0.9999, with an embedding capacity of 630 bits per slice (BPP 0.007-0.013 within NROI). Reversibility verification confirms perfect recovery (maximum difference = 0) for all samples. Batch testing on five slices demonstrates consistent performance across varying intensity characteristics, with ΔSNR and ΔCNR remaining at 0.0%. These results indicate that the method is capable of maintaining ROI technical integrity and pixel-perfect reversibility, although with a limited capacity suitable for lightweight metadata such as integrity hashes and patient identifiers. Limitations of the study include the technical-only evaluation without radiologist clinical validation and testing restricted to T1-weighted MRI modality.
Deteksi Malware Android Berbasis Ensemble Soft Voting LightGBM, Logistic Regression dan CatBoost Danendra, Ardian; Pramudya, Elkaf Rahmawan
Building of Informatics, Technology and Science (BITS) Vol 7 No 4 (2026): March 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i4.8865

Abstract

The Android operating system faces serious challenges with increasingly complex and diverse malware evolution. This research proposes an Android malware detection system based on soft voting ensemble that integrates three algorithms (LightGBM, Logistic Regression, and CatBoost) to improve detection accuracy while maintaining computational efficiency. The dataset used is CCCS-CIC-AndMal-2020, which is highly imbalanced with over 400,000 Android application samples. The proposed model leverages hybrid features that combine static information (permissions, intents, API calls from the AndroidManifest) with dynamic behavior (memory activities, runtime API calls, logcat, and network traffic in an emulated environment), balancing low extraction cost with improved robustness against obfuscation. The methodology includes multi-stage preprocessing (IQR capping 40×, StandardScaler, RFE 150 features, SMOTE 30%) to improve data quality and reduce dimensionality by 56% without losing important information. The ensemble model is trained with F1-Macro-based weights (33.46% LightGBM, 30.99% Logistic Regression, 35.55% CatBoost) approximating 1:1:1 proportion. Evaluation results on the testing set demonstrate very high performance: Accuracy 95.58%, Balanced Accuracy 92.21%, F1-Macro 0.9208, True Positive Rate 100%, and False Alarm Rate 0.00%. The combination of these metrics indicates that the model can detect all malware samples without false positives on benign applications, making it suitable for production deployment. This research contributes by demonstrating the effectiveness of an efficient soft voting ensemble (only 3 models) for Android malware detection with multi-dimensional evaluation metrics representative of imbalanced data.
Implementasi Deep Learning Berbasis MobileNetV2 untuk Deteksi Real-Time Bacterial Spot dengan Pendekatan Arsitektur Lightweight Nabilul As'ad, Ahmad; Pramudya, Elkaf Rahmawan
Building of Informatics, Technology and Science (BITS) Vol 7 No 4 (2026): March 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i4.9252

Abstract

Bacterial spot caused by Xanthomonas campestris pv. vesicatoria is a critical disease in bell peppers that can reduce productivity by up to 50%. This study implements MobileNetV2 with two-stage transfer learning for real-time bacterial spot detection using lightweight architecture approach, with ResNet50 as baseline comparison. PlantVillage dataset (2,475 images) was used for training and in-domain evaluation, while India dataset (132 images) for domain shift assessment. Results demonstrate MobileNetV2 achieves 98.66% accuracy on PlantVillage test set, outperforming ResNet50 (89.78%) by 8.88 percentage points despite being 9.2× lighter (2.7 MB vs 24.3 MB TFLite) and 2.0× faster (22.4 ms vs 45.8 ms inference time). MobileNetV2 efficiency advantage is also evident in its inference memory footprint of only 107 MB RAM, significantly 2.3x lower than ResNet50(242 MB RAM), making it highly suitable for deployment on mid-range smartphones with limited RAM. External dataset evaluation reveals MobileNetV2 maintains superior robustness with 65.3% retention rate versus ResNet50's 52.3%. Trade-off analysis positions MobileNetV2 on the Pareto frontier, achieving optimal accuracy-efficiency sweet spot for plant disease detection applications. This research contributes empirical evidence for lightweight architecture superiority, comprehensive efficiency-oriented evaluation framework, ULTRA-LIGHT training strategy for addressing inverse overfitting, and realistic generalization assessment using tropical external dataset.
Co-Authors ., Muslih Abdussalam Abdussalam Abdussalam Abdussalam Abdussalam Abdussalam, Abdussalam Abi Senoprabowo Ajib Susanto Akhmad Rizaldy ALI MUQODDAS Ali Muqoddas Annahdli, Zacky Fahd Ari Yahya A Arief Soeleman Arry Maulana Syarif Briliantino Abhista Prabandanu Budi Harjo Cahaya Jatmoko Christy Atika Sari Danendra, Ardian De Rosal Ignatius Moses Setiadi Doheir, Mohamed Dwi Puji Prabowo, Dwi Puji Edi Faisal Edi Sugiarto Eko Hari Rachmawanto Erna Zuni Astuti Erwin Yudi Hidayat Farrel Athaillah Putra Fikri Budiman Gilang Raharjito Guruh Fajar Shidik Henry Bastian, Henry Ibnu Utomo Wahyu Mulyono Khafiidz Hastuti Khafiizh Hastuti L. Budi Handoko Luqman Abdul Malik Mega Bintang Hatmi Moch Arief Soeleman Muslih - Muslih - Muslih Muslih Muslih Muslih Muslih Muslih Nabilul As'ad, Ahmad Noor Hasyim Noor Hasyim Nugraha, Arifinza Eska Nugraini, Siti Hadiati Oki Setiono Pratama, Zudha Pulung Nurtantio Andono Ramadhan Rakhmat Sani Ramadhan, Naufal Rizky Rico Rian Alvian S Nurhendratno Sarker, Md. Kamruzzaman Sarwadi Sarwadi Sinaga, Daurat Soeleman, Arief Soeleman, M. Arief Suharyoto, Aufa Fadholi Supriyono Asfawi Viky Luffiandi Rismawan Widyatmoko Karis Wildanil Ghozi