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All Journal Sistemasi: Jurnal Sistem Informasi Cyber Security dan Forensik Digital (CSFD) Journal of Applied Data Sciences Jurnal Pengabdian kepada Masyarakat
Chazar, Chalifa
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Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Environmental Science Languange, Linguistic, Communication & Media Public Health Social Sciences Other

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PENGEMBANGAN PEMBELAJARAN TATA SURYA (ASTRA QUEST) BERBASIS KUIS GAME AR UNTUK PANTI ASUHAN PYI YATIM & ZAKAT CAB. CIKUTRA Chazar, Chalifa; Setyaningrum, Anisa Putri; Faa’iz, Prayoga Anwar; Weninggalih, Sanjaya Raga; Wijaya, Freza Taruna Nugraha; Rivano, Kevin Faza
J-ABDI: Jurnal Pengabdian kepada Masyarakat Vol. 4 No. 10: Maret 2025
Publisher : Bajang Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53625/jabdi.v4i10.9453

Abstract

Penelitian ini membahas penerapan teknologi Augmented Reality (AR) sebagai media pembelajaran interaktif untuk meningkatkan motivasi belajar anak-anak di Panti Asuhan PYI Yatim & Zakat Cabang Cikutra, Bandung. Metode yang digunakan mencakup perancangan sistem, pengembangan aplikasi kuis berbasis AR, serta implementasi dan evaluasi hasilnya. Aplikasi yang dikembangkan, bernama Astra Quest, mengintegrasikan fitur visualisasi tiga dimensi dan kuis interaktif untuk memperkaya pengalaman belajar. Hasil implementasi menunjukkan bahwa aplikasi ini berhasil meningkatkan keterlibatan dan motivasi belajar anak-anak, dengan pengurus panti mampu melanjutkan program secara mandiri setelah diberikan pelatihan. Uji coba aplikasi membuktikan fungsionalitas yang stabil dan antarmuka yang intuitif, menjadikannya alat bantu pembelajaran yang efektif. Penelitian ini menyimpulkan bahwa teknologi AR berpotensi besar dalam menciptakan pengalaman belajar yang lebih menarik dan efektif, serta merekomendasikan pengembangan fitur tambahan di masa depan untuk memperluas manfaat aplikasi ini.
Deteksi Serangan Pada Jaringan Internet Of Things Medis Menggunakan Machine Learning Dengan Algoritma XGBoost: Attack Detection On Internet Medical Of Things Using Machine Learning With Xgboost Algorithm Diash Firdaus; Afin, Afin; Sumardi, Idi; Chazar, Chalifa
Cyber Security dan Forensik Digital Vol. 8 No. 1 (2025): Edisi Mei 2025
Publisher : Fakultas Sains dan Teknologi UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/csecurity.2025.8.1.5036

Abstract

Internet of Things (IoT) telah memberikan dampak besar pada sektor kesehatan, memungkinkan pengumpulan data pasien secara real-time dan meningkatkan efisiensi layanan kesehatan. Namun, adopsi perangkat IoT medis juga membawa tantangan baru terkait keamanan, terutama serangan Distributed Denial of Service (DDoS) yang dapat mengganggu layanan kritis. Penelitian ini melakukan deteksi terhadap lima jenis serangan, yaitu ARP Spoofing, Recon Attack, MQTT Attack, TCP/IP DoS, dan DDoS, menggunakan model machine learning dengan algoritma XGBoost. Dataset yang digunakan adalah CICIoMT2024, yang dirancang khusus untuk menilai keamanan perangkat medis terhubung, melibatkan 40 perangkat IoMT. XGBoost menunjukkan performa terbaik dengan akurasi, recall, presisi, dan F1-score yang unggul, mencapai akurasi 99.8%, presisi 92.4%, recall 96%, dan F1-score 93.8%. Sebelumnya, algoritma lain seperti Logistic Regression dan Naive Bayes menunjukkan akurasi masing-masing sebesar 79% dan 92% dalam mendeteksi serangan serupa, hal ini menunjukan keterbatasan dalam menangani pola yang lebih kompleks. Hasil ini menegaskan efektivitas XGBoost dalam mendeteksi ancaman keamanan dalam ekosistem IoT medis, memberikan perlindungan lebih baik terhadap potensi gangguan pada layanan kesehatan kritis. Kata kunci: Machine Learning, Keamanan Siber, xgboost, deteksi, Internet Medical of Things ------------------------- Abstract The Internet of Things (IoT) has significantly impacted the healthcare sector, enabling real-time patient data collection and enhancing service efficiency. However, the adoption of medical IoT devices also introduces new security challenges, particularly Distributed Denial of Service (DDoS) attacks that can disrupt critical services. This study detects five types of attacks: ARP Spoofing, Recon Attack, MQTT Attack, TCP/IP DoS, and DDoS, using machine learning models with the XGBoost algorithm. The dataset used is CICIoMT2024, specifically designed to assess the security of connected medical devices, involving 40 IoMT devices. XGBoost demonstrated the best performance with superior accuracy, recall, precision, and F1-score, achieving 99.8% accuracy, 92.4% precision, 96% recall, and 93.8% F1-score. Previously, other algorithms such as Logistic Regression and Naive Bayes showed accuracies of 79% and 92% respectively in detecting similar attacks, but with limitations in handling more complex patterns. These results underscore the effectiveness of XGBoost in detecting security threats in the medical IoT ecosystem, providing enhanced protection against potential disruptions to critical healthcare services.   Keywords: Machine Learning, Cybersecurity, xgboost, detection, Internet Medical of Things
Image-Based Malware Multiclass Classification Using Vision Transformer Architecture: Multiclass Klasifikasi Malware Berbasis Gambar Menggunakan Vision Transformer Architecture Diash Firdaus; Sumardi, Idi; Chazar, Chalifa; Dafy, Muhamad Zufar
Cyber Security dan Forensik Digital Vol. 8 No. 1 (2025): Edisi Mei 2025
Publisher : Fakultas Sains dan Teknologi UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/csecurity.2025.8.1.5107

Abstract

Perkembangan malware yang semakin canggih telah menjadi ancaman serius bagi keamanan siber global, mengakibatkan kerugian finansial yang signifikan. Metode deteksi tradisional seperti deteksi berbasis tanda tangan dan analisis dinamis memiliki keterbatasan dalam mendeteksi varian malware baru. Sebagai solusi inovatif, analisis malware berbasis gambar mengubah file biner malware menjadi representasi gambar, memanfaatkan pemrosesan citra digital dan pembelajaran mesin untuk identifikasi yang lebih efisien. Penelitian ini menggunakan arsitektur Vision Transformer (ViT) untuk klasifikasi malware multikelas berbasis gambar, menawarkan pendekatan yang lebih efektif dibandingkan CNN tradisional seperti EfficientNet dan VGG16. ViT muncul sebagai pendekatan baru yang menarik karena fleksibilitasnya dalam memahami hubungan objek dalam gambar dan mendeteksi pola penting. Dengan kemampuannya mempelajari hubungan jangka panjang antar data, ViT dapat mendeteksi perbedaan halus antara berbagai jenis malware dan mencapai akurasi lebih tinggi. Dataset yang digunakan adalah Malimg, yang merupakan hasil konversi malware biner menjadi format gambar. Hasil penelitian menunjukkan Vision Transformers mencapai akurasi pelatihan 99.96%, validasi 98.05%, dan pengujian 97.49%, meningkatkan akurasi dibandingkan CNN. Keberhasilan ini menunjukkan kemajuan signifikan dalam akurasi deteksi, mengindikasikan arah menjanjikan untuk penelitian dan aplikasi keamanan siber di masa depan. Studi ini menekankan pentingnya teknik pembelajaran mesin yang canggih untuk meningkatkan deteksi malware. Kata kunci: Vision Transformers, Klasifikasi Malware, Deep learning ------------------------- The increasing sophistication of malware has become a serious threat to global cybersecurity, resulting in significant financial losses for individuals and organizations. Traditional detection methods, such as signature-based detection and dynamic analysis, face limitations in identifying new or modified malware variants. As an innovative solution, image-based malware analysis converts malware binary files into image representations, leveraging digital image processing and machine learning for safer and more efficient identification. This study employs the Vision Transformer (ViT) architecture for multiclass image-based malware classification, offering a more effective approach compared to traditional CNNs. The Vision Transformer (ViT) has emerged as an exciting new approach, gaining attention for its flexibility in understanding object relationships within images and detecting important patterns. ViT, with its ability to learn long-range relationships between data, can detect subtle differences between various types and subtypes of malware, achieving higher classification accuracy. The results of this study show that Vision Transformers achieve the highest training accuracy of 99.96%, the highest validation accuracy of 98.05%, and a testing accuracy of 97.49%. The success of Vision Transformers in malware classification indicates significant advancements in detection accuracy, suggesting a promising direction for future research and applications in cybersecurity. This study underscores the importance of leveraging advanced machine learning techniques to enhance malware detection capabilities Keywords: Vision Transformers, Malware Classification, Deep learning  
Implementation of COBIT 2019 for Designing Hospital Performance Improvement Recommendations Radja, Carrisa Adnyana Putri; Budiraharjo, Raden; Chazar, Chalifa
Sistemasi: Jurnal Sistem Informasi Vol 14, No 3 (2025): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v14i3.5076

Abstract

Service delivery at Kiwari Regional Public Hospital (RSUD Kiwari) in Bandung continues to face various challenges in meeting established standards. These challenges primarily stem from inefficiencies in business processes, particularly in patient registration, medical consultations, inpatient services, and patient transfers. These issues were previously identified through bottleneck analysis using a process mining approach. The identified bottlenecks indicate delays in several stages of the hospital service process. This study aims to provide improvement recommendations to enhance the hospital's business process capabilities based on the bottleneck analysis results obtained from process mining. The research adopts the COBIT 2019 framework to assess and improve business process capabilities, with a focus on the DSS06 (Deliver, Service, and Support) domain, deemed most relevant to the hospital’s current challenges. COBIT 2019 was selected for its systematic approach to measuring and enhancing IT process capabilities. The findings indicate that the current business process capability level at RSUD Kiwari Bandung is at Level 2, while the target level is Level 3. To achieve this goal, the study proposes a set of improvement recommendations that can serve as an evaluation tool and a guide to improving IT governance and service efficiency at RSUD Kiwari Bandung.
Egg Weight Estimation Based on Image Processing using Mask R-CNN and XGBoost Pardede, Jasman; Rawosi, Muhammad Fadlansyah Zikri Akhiruddin; Setyaningrum, Anisa Putri; Milenio, Rizka Milandga; Chazar, Chalifa
Journal of Applied Data Sciences Vol 6, No 4: December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i4.1004

Abstract

Manually measuring egg weight in the context of livestock and the food industry can pose various problems, including time and labor requirements, the risk of egg damage, consistency and accuracy, and limitations on production scale. To address these issues, an automated egg weight estimation system is essential. This study proposes integrating computer vision and machine learning into a unified workflow that combines segmentation, classification, and regression for practical weight estimation. The proposed pipeline employs Mask R-CNN for egg segmentation, Random Forest (RF) classifier for egg type classification based on color features, and XGBoost for regression using morphological, geometric, color features, and egg type as predictors. The dataset used is 720 images, consisting of 20 eggs (10 chicken and 10 duck), each photographed from 36 rotational angles, and was collected with Ground Truth (GT) weights obtained from a digital scale. Experimental findings show that the RF classifier achieved perfect accuracy (precision, recall, and F1-score = 1.00) in distinguishing chicken and duck eggs. The XGBoost regressor obtained a training performance of MAE = 1.07 g and R² = 0.68, and a validation performance of MAE = 0.23 g and R² = 0.80 under 10-fold grouped cross-validation. Although a Support Vector Regressor baseline reached higher training accuracy (MAE = 0.22 g, R² = 0.96), it failed to generalize on validation (R² 0), highlighting XGBoost’s robustness. The feature importance analysis revealed that there are 4 (four) important features for building an estimation model, namely: Hu moments, eccentricity, elongation, and diagonal length, while color statistics played a complementary role. The novelty of this work lies in combining deep segmentation, color-based classification, and feature-driven regression into a unified framework specifically for egg weight estimation, showing its feasibility as a proof of concept and laying the foundation for future large-scale, calibrated, and externally validated deployment.
Co-Authors Afin, Afin Budiraharjo, Raden Dafy, Muhamad Zufar Diash Firdaus Faa’iz, Prayoga Anwar Idi Sumardi, Idi Jasman Pardede Milenio, Rizka Milandga Radja, Carrisa Adnyana Putri Rawosi, Muhammad Fadlansyah Zikri Akhiruddin Rivano, Kevin Faza Setyaningrum, Anisa Putri Weninggalih, Sanjaya Raga Wijaya, Freza Taruna Nugraha