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All Journal Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) Transmisi: Jurnal Ilmiah Teknik Elektro Techno.Com: Jurnal Teknologi Informasi Jurnal Simetris Proceeding SENDI_U Jurnal Informatika Upgris JOIV : International Journal on Informatics Visualization Sinkron : Jurnal dan Penelitian Teknik Informatika Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Building of Informatics, Technology and Science Infotekmesin Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Ilmiah Intech : Information Technology Journal of UMUS Jurnal Teknik Informatika (JUTIF) JAMU : Jurnal Abdi Masyarakat UMUS Moneter : Jurnal Keuangan dan Perbankan
Megantara, Rama Aria
Dian Nuswantoro University
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Public Health Social Sciences Transportation Other

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Decision Tree Classification for Reducing Alert Fatigue in Patient Monitoring Systems Herfiani, Kheisya Talitha; Nurhindarto, Aris; Alzami, Farrikh; Budi, Setyo; Megantara, Rama Aria; Soeleman, M Arief; Handoko, L Budi; Rofiani, Rofiani
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8414

Abstract

The development of information technology in healthcare opens new opportunities to improve continuous patient monitoring. A major challenge is alert fatigue, where medical personnel are overwhelmed by excessive notifications, reducing concentration, work efficiency, and potentially compromising patient safety. This study presents a proof-of-concept application of the Decision Tree algorithm to analyze alert triggering factors in patient monitoring systems. The dataset is a synthetic health monitoring dataset from Kaggle, containing 10,000 entries with vital parameters including blood pressure, heart rate, oxygen saturation, and glucose levels, designed with deterministic logical relationships between threshold indicators and alert outcomes. The imbalanced dataset (73.67% alert triggered, 26.33% no alert) was intentionally not processed using imbalanced learning techniques to demonstrate Decision Tree's capability in processing structured health data and producing interpretable classifications. The research methodology included data preprocessing, exploratory data analysis, data splitting (90% training, 10% testing), GridSearchCV optimization, and performance evaluation. Results showed perfect metrics (100% accuracy, precision, recall, F1-score), reflecting the deterministic nature of the synthetic dataset rather than real-world clinical complexity. Feature importance analysis identified blood pressure as the most dominant variable, followed by heart rate and glucose levels. This study demonstrates Decision Tree's interpretability and feature importance analysis capabilities in health data contexts, establishing a methodological framework that requires validation on real clinical Electronic Health Record (EHR) data for practical application in reducing alert fatigue and supporting informed clinical decisions.
Data-Driven K-Means Clustering Analysis for Stunting Risk Profiling of Pregnant Women Nazella, Desvita Dian; Hadi, Heru Pramono; Al Zami, Farrikh; Ashari, Ayu; Kusumawati, Yupie; Suharnawi, Suharnawi; Megantara, Rama Aria; Naufal, Muhammad
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8415

Abstract

Stunting in children is influenced by maternal health conditions during pregnancy. This study aims to classify pregnant women to prevent stunting based on clinical, demographic, and environmental factors using the K-Means Clustering algorithm. A total of 229 data from the Primadona application (Disdalduk KB Kota Semarang) were analyzed using 14 normalized variables. The optimal number of clusters was determined using the Elbow Method and validated using the Silhouette Score, Davies-Bouldin Index, and Calinski-Harabasz Index. The Kruskal-Wallis test was performed to verify differences between clusters. This study resulted in seven clusters with different profiles, with a Silhouette Score of 0.134, Davies-Bouldin Index of 1.509, and Calinski-Harabasz Index of 29.54. These values ​​indicate that the cluster structure is formed and reflects the variation in risk for pregnant women, although there is overlap due to differences in characteristics between individuals. The clustering successfully differentiated pregnant women with low to high risk, influenced by health and environmental factors. This study proves the effectiveness of K-Means in identifying stunting risk patterns in pregnant women and supports more targeted interventions, such as nutritional counseling, disease risk monitoring, education on cigarette smoke exposure, and referrals. Limitations of this study include the unbalanced distribution of data between and the use of cross-sectional data. Future research is recommended to improve pre-processing and compare other clustering methods such as K-Medoids or DBSCAN for more precise stunting risk analysis.
Multi-Disease Retinal Classification Using EfficientNet-B3 and Targeted Albumentations: A Benchmark on Kaggle Retinal Fundus Images Dataset Saputra, Kurniawan Aji; Alzami, Farrikh; Kurniawan, Defri; Naufal, Muhammad; Muslih, Muslih; Megantara, Rama Aria; Pramunendar, Ricardus Anggi
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 1 (2026): Article Research January 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i1.15530

Abstract

Retinal diseases remain one of the leading causes of blindness worldwide. This study develops a deep learning pipeline for multiclass retinal disease classification using EfficientNet-B3 combined with Albumentations to improve generalization. We target four classes: cataract, diabetic retinopathy, glaucoma, and normal. We use the Kaggle Retinal Disease dataset (4,217 fundus images) divided into 70% training, 10% validation, and 20% testing. Images are resized to 224×224 and augmented with horizontal flip, random brightness contrast, CLAHE, shiftscale rotate, crop, gamma correction, and elastic transformation. The EfficientNet-B3 backbone is refined after head training with warm-up and learning rate regularization (batch normalization, dropout). After 50 epochs, the best validation performance reaches 0.9526, and on the hold-out test set, the model achieves 95.38% overall accuracy. The F1 scores per class were 1.0000 (diabetic retinopathy), 0.9685 (cataract), 0.9255 (normal), and 0.9184 (glaucoma). Confusion analysis showed that most errors involved glaucoma being misclassified as normal, likely due to optic disc similarities. These results demonstrate that EfficientNet-B3 with targeted augmentation provides accurate and reliable multi-disease screening of fundus images, with the potential to support faster and more consistent triage in clinical workflows. Future research should expand clinical validation and explore attention mechanisms or multimodal input to reduce glaucoma-normal ambiguity.
Hybrid Multilayer Architecture Integrating Suricata, Wazuh, and Cyber Threat Intelligence for Drive-by-Download Malvertising Detection Adrian, Aurell Zulfa Angger; Megantara, Rama Aria; Al Zami, Farrikh
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 1 (2026): Article Research January 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i1.15616

Abstract

Malvertising has emerged as a serious cybersecurity threat, leveraging legitimate advertising networks to deliver malware through drive-by-download techniques without requiring user interaction. Existing standalone network- or host-based detection solutions provide limited protection because they lack integrated visibility and contextual validation across detection layers. However, no existing research has specifically evaluated the integration of Suricata, Wazuh, and VirusTotal for endpoint-focused malvertising detection, creating a critical gap in multi-layer defense strategies. This study proposes a hybrid multilayer architecture combining Suricata as a Network Intrusion Detection System, Wazuh as a Host-based Intrusion Detection and Prevention System, and VirusTotal as an external Cyber Threat Intelligence source to provide correlated threat detection and automated mitigation. The system was evaluated in a controlled virtual laboratory consisting of attacker, victim, and SIEM environments replicating real malvertising scenarios. The results show that the proposed architecture successfully detected malicious payloads and completed an end-to-end detection-to-mitigation cycle in approximately 5-7 seconds while maintaining zero false positives under non-malicious conditions. This research contributes a practical and reproducible architecture for endpoint-based malvertising detection, demonstrating effective multi-layer correlation and rapid autonomous response. The limitation of this study lies in its reliance on signature-based detection and external API communication, which may reduce effectiveness against zero-day threats or offline deployments.
Comprehensive Benchmark of Yolov11n, SSD MobileNet, CenterFace, Yunet, FastMtCnn, HaarCascade, and LBP for Face Detection in Video Based Driver Drowsiness Go, Agnestia Agustine Djoenaidi; Alzami, Farrikh; Naufal, Muhammad; Azies, Harun Al; Winarno, Sri; Pramunendar, Ricardus Anggi; Megantara, Rama Aria; Maulana, Isa Iant; Arif, Mohammad
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8678

Abstract

Face detection is a critical foundation of video-based drowsiness monitoring systems because all downstream tasks such as eye-closure estimation, yawning detection, and head movement analysis depend entirely on correctly identifying the face region. Many previous studies rely on detector-generated outputs as ground truth, which can introduce bias and inflate model performance . To avoid this limitation, I manually constructed a ground truth dataset using 1,229 frames extracted from 129 yawning and microsleep videos in the NITYMED dataset. Ten representative frames were sampled from each video using a face-guided extraction script, and all frames were manually annotated in Roboflow following the COCO format to ensure accurate bounding box labeling under varying lighting, head poses, and facial deformation. Using this manually annotated dataset, I conducted a comprehensive benchmark of seven face-detection algorithms: YOLOv11n, SSD MobileNet, CenterFace, YuNet, FastMtCnn, HaarCascade, and LBP. The evaluation focused on localization quality using Intersection over Union (IoU ≥ 0.5) and Dice Similarity, allowing each algorithm’s predicted bounding box to be directly compared against human defined ground truth. The results show that HaarCascade achieved the highest IoU and Dice scores, particularly in frontal and well-lit frames. FastMtCnn also produced strong alignment with a high number of correctly matched frames. CenterFace and SSD MobileNet demonstrated smooth bounding box fitting with competitive Dice scores, while YOLOv11n and YuNet delivered moderate but stable performance across most samples. LBP showed the weakest results, mainly due to its sensitivity to lighting variations and soft-texture regions. Overall, this benchmark provides an unbiased and comprehensive comparison of modern and classical face-detection algorithms for video-based driver-drowsiness applications.
Pelatihan Implementasi Artificial Intelligence Menggunakan Teachable Machine berbasis Project-Based Learning bagi Siswa SMA/SMK Wibowo, Dibyo Adi; Hidajat, Moch. Sjamsul; Pramunendar, Ricardus Anggi; Rohman, Muhammad Syaifur; Ratmana, Danny Oka; Megantara, Rama Aria
ABDIMASKU : JURNAL PENGABDIAN MASYARAKAT Vol 9, No 1 (2026): JANUARI 2026
Publisher : LPPM UNIVERSITAS DIAN NUSWANTORO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/ja.v9i1.3226

Abstract

Artificial Intelligence (AI) merupakan teknologi yang berkembang pesat dan penting untuk dikenalkan sejak jenjang pendidikan menengah. Kegiatan pengabdian kepada masyarakat ini bertujuan untuk meningkatkan pemahaman siswa SMA/SMK di Kota dan Kabupaten Kediri terhadap konsep dasar Artificial Intelligence dan machine learning melalui pelatihan implementasi AI menggunakan Teachable Machine berbasis Project-Based Learning (PjBL). Metode pelaksanaan kegiatan mengombinasikan pendekatan PjBL dan experiential learning, di mana peserta dilibatkan secara aktif dalam pengembangan proyek AI sederhana berbasis gambar, suara, dan pose tubuh. Evaluasi pembelajaran dilakukan menggunakan pre-test dan post-test untuk mengukur peningkatan pemahaman peserta. Hasil kegiatan menunjukkan adanya peningkatan yang signifikan pada seluruh kategori materi, termasuk konsep dasar AI, computational thinking, machine learning, penggunaan Teachable Machine, serta implementasi dan evaluasi model AI. Temuan ini menunjukkan bahwa penggunaan Teachable Machine yang dipadukan dengan pendekatan PjBL efektif dalam meningkatkan literasi Artificial Intelligence siswa SMA/SMK serta membantu peserta memahami konsep AI secara lebih konkret dan aplikatif.
Pemanfaatan Artificial Intelligence untuk Meningkatkan Efisiensi Layanan Birokrasi pada Organisasi Perangkat Daerah Pemerintah Provinsi Jawa Tengah: Utilization of Artificial Intelligence to Improve the Efficiency of Bureaucratic Services in Regional Government Organizations of Central Java Province Farrikh Alzami; Naufal, Muhammad; Santoso, Dewi Agustini; Pergiwati, Dewi; Heni Indrayani; Karis Widyatmoko; Megantara, Rama Aria
JAMU : Jurnal Abdi Masyarakat UMUS Vol. 6 No. 02 (2026): Februari
Publisher : LPPM Universitas Muhadi Setiabudi

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Transformasi digital birokrasi menuntut pemerintah daerah untuk meningkatkan efisiensi dan kualitas layanan publik. Artificial intelligence (AI) merupakan salah satu teknologi yang memiliki potensi besar dalam mendukung otomasi administrasi, pengolahan data, serta peningkatan responsivitas layanan pemerintahan. Namun, tingkat pemahaman dan kesiapan aparatur sipil negara (ASN) dalam memanfaatkan AI masih belum merata, terutama terkait aspek etika dan pelindungan data. Kegiatan pengabdian kepada masyarakat ini bertujuan meningkatkan pemahaman dan kapasitas ASN Organisasi Perangkat Daerah (OPD) Pemerintah Provinsi Jawa Tengah dalam memanfaatkan AI secara tepat, aman, dan bertanggung jawab guna mendukung efisiensi layanan birokrasi. Metode pelaksanaan kegiatan berupa workshop tatap muka yang meliputi penyampaian materi konseptual, studi kasus pemanfaatan AI di sektor publik, diskusi interaktif, serta praktik penggunaan AI dalam konteks administrasi pemerintahan. Hasil kegiatan menunjukkan peningkatan pemahaman peserta terhadap konsep AI, kemampuan mengidentifikasi potensi penerapan AI dalam tugas birokrasi, serta meningkatnya kesadaran terhadap aspek etika dan keamanan data. Kegiatan ini menunjukkan bahwa pendampingan akademik melalui workshop praktis mampu memberikan kontribusi nyata dalam mendukung transformasi digital birokrasi di tingkat pemerintah daerah.
Addressing Class Imbalance in Android Backdoor Malware DetectionUsing Ensemble Models Megantara, Rama Aria; Pergiwati, Dewi; Alzami, Farrikh; Pramunendar, Ricardus Anggi; Prabowo, Dwi Puji; Naufal, Muhammad; Brilianto, Rivaldo Mersis
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 25 No. 2 (2026)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v15i2.6198

Abstract

Backdoor malware represents one of the most critical threats in the Android ecosystem due to its capability to enable covert remote access, escalate privileges, and exfiltrate sensitive data without user awareness. Although the CCCS-CIC-AndMal-2020 dataset is publicly available, prior studies have not specifically formulated Backdoor detection as a binary classification problem under extreme class imbalance, nor systematically evaluated the impact of oversampling and cost-sensitive weighting using imbalance-aware performance metrics. This study proposes a comprehensive detection pipeline that integrates ensemble learning, class imbalance handling strategies, and explainability-based analysis to extract behavioral signatures of Backdoor malware. A two-stage feature selection process is employed to reduce the original 9,502-dimensional feature space to 500 informative features. Subsequently, five classification algorithms are evaluated under three imbalance-handling scenarios using a composite ranking criterion based on F1-score, Area Under the Receiver Operating Characteristic Curve (AUC), Geometric Mean (G-Mean), and Matthews Correlation Coefficient (MCC). The experimental results demonstrate that the Random Forest model combined with Synthetic Minority Oversampling Technique (SMOTE) achieves the best performance, with an F1-score of 0.9043, AUC of 0.9909, G-Mean of 0.9422, and MCC of 0.8948. Furthermore, SHAP analysis identifies 39 Android permissions related to account access, covert communication, and privilege escalation as key behavioral signatures, with the permissions feature group contributing 2.31 times higher discriminative importance than nonpermission features. These findings indicate that interpretable ensemble learning not only improves detection performance but also provides actionable insights for static malware analysis.
Co-Authors Abu Salam Adrian, Aurell Zulfa Angger Ahmad Akrom Ahmad Khotibul Umam, Ahmad Khotibul Akrom, Ahmad Al zami, Farrikh Al-Azies, Harun Alzami, Farrikh Anggi Pramunendar, Ricardus Ashari, Ayu Asih Rohmani, Asih Brilianto, Rivaldo Mersis Budi, Setyo Chaerul Umam Dewi Agustini Santoso Diana Aqmala Dibyo Adi Wibowo Dwi Puji Prabowo Dwi Puji Prabowo, Dwi Puji Erika Devi Udayanti Fahmi Amiq Fauzi Adi Rafrastara Fikri Diva Sambasri Firman Wahyudi, Firman Go, Agnestia Agustine Djoenaidi Guruh Fajar Shidik Hadi, Heru Pramono Harun Al Azies Heni Indrayani Herfiani, Kheisya Talitha Ifan Rizqa Ika Novita Dewi Irwan, Rhedy ISWAHYUDI ISWAHYUDI Khoirunnisa, Emila Kurniawan Aji Saputra Kurniawan, Defri Kusumawati, Yupie L. Budi Handoko Lalang Erawan Lesmarna, Salsabila Putri Mahendra, Syafrie Naufal Maulana, Isa Iant Moch. Sjamsul Hidajat Moh Yusuf, Moh Moh. Yusuf Mohammad Arif Muhammad Naufal Muslih Muslih Nazella, Desvita Dian Nurhindarto, Aris Ocky Saputra, Filmada Pergiwati, Dewi Puji Prabowo, Dwi Pulung Nurtantio Andono Puri Sulistiyawati Puri Sulistiyawati Ramadhan Rakhmat Sani Ratmana, Danny Oka Ricardus Anggi Pramunendar Rifqi Mulya Kiswanto Rini Anggraeni Ritzkal, Ritzkal Rofiani, Rofiani Rohman, Muhammad Syaifur Saputra, Filmada Ocky Saputra, Resha Mahardhika Saputri, Pungky Nabella Saraswati, Galuh Wilujeng Sasono Wibowo Sinaga, Daurat Soeleman, M Arief Sri Winarno Suharnawi Suharnawi Widyatmoko Karis Yuventius Tyas Catur Pramudi Zahro, Azzula Cerliana