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Essential Gene Classification in Drosophila melanogaster Using Genomic Signal Processing and Boosting Putri, Gendis Ananda; Lumbanraja, Favorisen Rosyking; Junaidi, Akmal; Aristoteles; Tristiyanto
International Journal of Electronics and Communications Systems Vol. 6 No. 1 (2026): International Journal of Electronics and Communications System
Publisher : Universitas Islam Negeri Raden Intan Lampung, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/ijecs.v6i1.31239

Abstract

This study evaluates the efficacy of AdaBoost and XGBoost in classifying Cellular Essential Genes (CEG) and Organismal Essential Genes (OEG) of Drosophila melanogaster using a hybrid feature set of DNA sequences, protein sequences, and network topology 185 features comprising Tri-Nucleotide Composition (TNC, from DNA) and Fourier Transform (FT, from DNA only), Amino Acid Composition (AAC, from protein sequences), and Protein-Protein Interaction (PPI) degree (from network topology) retrieved from the CLEARER database, with Random Forest Gini feature selection and SMOTETomek balancing nested within a leakage-free stratified 5×10-fold cross-validation pipeline, demonstrating that XGBoost consistently outperforms AdaBoost by achieving 96.88% accuracy, 0.864 F1-score, and 0.845 MCC on the CEG hold-out test set, while sequence-derived features (TNC and AAC) emerge as the dominant predictors. Sequence-based features (TNC and AAC) dominated the selected feature set, with FT features accounting for 18 of the 45 selected features, confirming the value of genomic spectral signal processing as a complement to compositional representation. Overall, this study demonstrates the value of integrating genomic signal processing with boosting-based learning and provides a reproducible, leakage-controlled framework for essential gene classification that can inform future cross-organism prediction studies.
Comparison of FA-SSD: Performance of Feature Extractors VGG-19, MobileNetV3, and ResNet-152 for Human Body Temperature Prediction Ridho Sholehurrohman; Akmal Junaidi; Hamzah Hanif; Favorisen Rosyking Lumbanraja; Muhammad Reza Habibi
Jurnal Nasional Teknologi dan Sistem Informasi Vol 12 No 2 (2026): Agustus 2026
Publisher : Departemen Sistem Informasi, Fakultas Teknologi Informasi, Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/TEKNOSI.v12i2.2026.300-311

Abstract

Body temperature is a critical health indicator, and non-contact thermal imaging-based prediction systems are essential for early detection of infectious diseases. This study compares the performance of three FA-SSD (Feature Fusion and Spatial Attention-Based Single Shot Detector) models with different feature extractors—VGG-19, MobileNetV3, and ResNet-152—for face detection and body temperature prediction using thermal images. A subset of 520 thermal facial images from the Comprehensive Facial Thermal Dataset was used, with 80% for training, 10% for validation, and 10% for testing. Model performance was evaluated using Generalized Intersection over Union (GIoU) for detection accuracy, and Mean Absolute Error (MAE) with Mean Absolute Percentage Error (MAPE) for temperature prediction accuracy. The results showed that no single model excelled in all metrics. ResNet-152 achieved the highest average GIoU, indicating superior object detection performance. VGG-19 delivered the lowest average MAE of 0.451°C and MAPE of 1.278%, making it the best for temperature prediction. MobileNetV3 achieved the lowest minimum MAE of 0.000050°C but showed higher average errors and validation fluctuations. In conclusion, VGG-19 is recommended for clinical temperature accuracy, ResNet-152 for robust detection, and MobileNetV3 for edge deployment.
Co-Authors - Damayanti . Wamiliana Adawiyah, Laila Adinda Aulia Sari Admi Syarif Admi Syarif Admi Syarif Aflaha Asri Ahyarudin AKBAR RISMAWAN TANJUNG Akbar, Mohammed Raihan Akmal Junaidi Akmal Junaidi Akmal Junaidi Amelia Jasmine Andrian, Rico Annisa Rizqiana Apri Candra Ardiansyah Ardiansyah Aristoteles Aristoteles, Aristoteles Asmiati Asmiati Aulia Putri Ariqa Ayu Amalia Bambang Hermanto Damayanti Damayanti Danu Sasmita Desti Fatmalasari Destian ade anggi Sukma Dian Kurniasari Didik Kurniawan Dwi Kartini, Dwi Dwi Sakethi Dwi Sakethi, Dwi Edi Arif Effendi Eliza Fitri Elly Lestari Rusitati Erdi Suroso Fanni Lufiana Fanni Lufiana Farida Ariyani Febi Eka Febriansyah Febi Eka Febriansyah Fitriyana, Silfia Hadi, Normi Abdul Hamim Sudarsono . Hamzah Hanif Hdiana, Yazid Zinedine Hendra Kurniawan Heningtyas, Yunda Hijriani, Astria Igit Sabda Ilman Ika Rahma Alia Indah Pasaribu Ira Hariati Br Sitepu Irawati, Anie Rose Irwan Adi Pribadi Jasmine, Amelia Jihan Aferiansyah Junaidi Junaidi Junaidi Junaidi Khairun Nisa Kristina Ademariana Kurnia Muludi Kurnia Muludi Kurnia Muludi Kurnia Muludi Lilies Handayani M. Iqbal Parabi M. Juandhika Rizky Machudor Yusman Manurung, Yunita Rosalina Megawaty, Dyah Ayu Meria Nensi Muhammad Reza Faisal, Muhammad Reza Muhammad Reza Habibi Muhammad Rizki Muhaqiqin Muhaqiqin Muliadi Mustofa Usman Nadila Rizqi Muttaqina Naurah Nazhifah Nirwana Hendrastuty Nova Ayu Lestari Siahaan Nugroho Susanto, Gregorius Nuning Nurcahyani Nurdin, Muhaymi Nurhasanah Nurhasanah Nurjoko Nurjoko Parjito Parjito Prabowo, Rizky Pratama, Rinaldo Adi Priyambodo Priyambodo Priyambodo Priyambodo Putri, Gendis Ananda Qory Aprilarita Rahmat Safe'i Raka Akbar Hartolo Rangga Agustiantino Reza Aji Saputra Rico Andrian Ridho Sholehurrohman Ridho Sholehurrohman RM Sulaiman Sani Rosdiana, Siti Rudy Herteno Rudy Herteno Rusitati, Elly Lestari Saragih, Triando Hamonangan Shofiana, Dewi Asiah Sintiya Paramitha Siti Aisyah Solechah Siti Rosdiana Su'admaji, Arif Susanto, Gregorius Nugroho Sutyarso Sutyarso Sutyarso, - Syangap Diningrat Sitompul TANJUNG, AKBAR RISMAWAN Tiyara Saghira Tristiyanto Tristiyanto Tristiyanto Wamiliana Wamiliana Wamiliana Warsono Warsono Warsono Warsono Warsono Wartariyus Wartariyus YOHANA TRI UTAMI, YOHANA TRI Zaenal Abidin Zuliana Nurfadlilah