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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Algoritma JUITA : Jurnal Informatika Sistemasi: Jurnal Sistem Informasi Jurnal Teknik Komputer AMIK BSI Journal of Information Technology and Computer Science (JOINTECS) Jiko (Jurnal Informatika dan komputer) MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer RESISTOR (Elektronika Kendali Telekomunikasi Tenaga Listrik Komputer) Abdimas Umtas : Jurnal Pengabdian kepada Masyarakat Jurnal Fundadikdas (Fundamental Pendidikan Dasar) Jurnal Algoritma Techno semanTIK Jurnal Pengabdian Masyarakat: Bisnis dan Iptek (JPMBISTEK)
Herman Herman
Universitas Ahmad Dahlan
Religion Agriculture, Biological Sciences & Forestry Arts Automotive Engineering Chemical Engineering, Chemistry & Bioengineering 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 Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Social Sciences Other

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Mobile Forensic of Vaccine Hoaxes on Signal Messenger using DFRWS Framework Imam Riadi; Herman Herman; Nur Hamida Siregar
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 21 No. 3 (2022)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v21i3.1620

Abstract

The COVID-19 pandemic is one of the factors that has increased the use of social media. One of the negative impacts of using social media is the occurrence of cybercrime. The possibility of cybercrime can also happen on one of the social media platforms, such as the Signal Messenger application. In the investigation process, law enforcement needs mobile forensic methods and appropriate forensic tools so that the digital evidence found on the perpetrator's smartphone can be accepted by the court. This research aims to get digital evidence from cases of spreading the COVID-19 vaccine hoaxes. The method used in this research is a mobile forensics method based on the Digital Forensic Research Workshop (DFRWS) framework. The DFRWS framework consists of identification, preservation, collection, examination, analysis, and preservation. The results showed that the MOBILedit tool could reveal digital evidence in the form of application information and contact information with a performance value of 22.22%. Meanwhile, Magnet AXIOM cannot reveal digital evidence at all. The research results were obtained following the expected research objectives.
Interactive 3D Rendering of the Human Heart on Mobile Web Using WebGL and Three.js Sunardi Sunardi; Herman Herman; Krisna Astianingrum
JUITA: Jurnal Informatika JUITA Vol. 14 Issue 1, March 2026
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v14i1.27793

Abstract

The advancement of web-based 3D visualization technology has created new opportunities for interactive medical learning, particularly in anatomy education. The existing rendering techniques for the mobile web still face challenges due to limitations of cellular and mobile device capacity This study focuses on optimizing real-time rendering of an interactive 3D heart model for mobile web platforms using WebGL and Three.js. Several optimization techniques were applied, including Draco compression, polygon reduction, and the GLB file format, to achieve high rendering performance while maintaining anatomical accuracy. Performance testing was conducted on three device tiers—low-, mid-, and high-end—under different network conditions. Key metrics such as frame rate, loading time, and memory usage were systematically measured. The optimized system achieved stable rendering at 58–60 FPS with a reduced loading time from 6.2 seconds to 1.4 seconds, demonstrating strong scalability and responsiveness. From an educational perspective, this interactive 3D heart model enables medical students, trainees, and patients to dynamically explore cardiac anatomy, improving their spatial understanding of complex structures without requiring high-end VR hardware. The novelty of this work lies in its optimization pipeline tailored for mobile web, making real-time anatomical visualization lightweight and accessible. Future research will involve larger user studies to evaluate educational effectiveness.
HUMAN DIGITAL TWIN MODELING FOR ADVANCING ARRHYTHMIA TREATMENT herman herman; Moch. Nasheh Annafii; Muhammad Kunta Biddinika
JIKO (Jurnal Informatika dan Komputer) Vol 8 No 3 (2025)
Publisher : Program Studi Teknik Informatika Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v8i3.10656

Abstract

Heart disease in all its forms remains a significant health threat. Arrhythmia is a type of heart disease whose diagnosis and treatment still primarily rely on conventional electrocardiogram-based diagnosis. However, this approach is limited, as it is reactive and captures cardiac conditions only at the time of electrocardiogram measurement, making it unable to continuously and individually monitor arrhythmia progression for each patient. This study explores digital twin technology and develops human digital twin models for the treatment of arrhythmia patients. The modeling framework integrates three core components: geometrical modeling, physical modeling, and data-driven modeling to represent the human heart and cardiovascular system in a digital environment. The output of this integrative process has been implemented in the initial prototype of the Human Digital Twin Cockpit, which is designed to treat arrhythmia. This prototype enhances the existing diagnosis and treatment, and also incorporates a proactive simulation system. Evaluation and system testing have successfully demonstrated their ability to integrate geometric data from medical imaging and physical data from electrophysiological sensors to predict arrhythmia in various scenarios
Comparison of Filter and Wrapper Feature Selection Methods for Heart Disease Risk Classification using K-Nearest Neighbors (k-NN) Deni Kuswandani; Herman Herman; Rusydi Umar
Sistemasi: Jurnal Sistem Informasi Vol 15, No 3 (2026): 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.v15i3.5989

Abstract

Feature selection plays a crucial role in improving the effectiveness of medical classification models. This study compares two feature selection approaches—filter and wrapper methods—in developing a k-Nearest Neighbors (k-NN) model for heart disease risk classification. The dataset consists of patients’ demographic data, lifestyle factors, and clinical indicators. In this study, the filter method was applied by considering data types: Pearson Correlation was used for numerical features, while the Chi-Square test was applied to categorical features. The selected features from both techniques were then combined, reducing the initial 20 features to four key variables considered most relevant for heart disease risk classification: BMI, homocysteine level, blood pressure, and stress level. This approach achieved high computational efficiency; however, it resulted in only a modest accuracy improvement (76.8%) and a low recall for the minority class (0.07). In contrast, the wrapper method using Sequential Forward Selection (SFS) produced a more informative subset of 11 features, achieving higher accuracy (80.00%) and a ROC-AUC of 0.657, indicating better discrimination capability for the minority class. These findings suggest that while the filter method excels in simplicity and computational efficiency, the wrapper method is more effective in improving classification performance. This study provides empirical insights into selecting appropriate feature selection strategies based on analytical objectives, particularly for clinical decision support systems.
Artificial Intelligence (AI) Integration Training and Mentoring in Productive Curriculum Development at Ilhami Kemiri Vocational School, Tangerang Regency Muhammad Syukri; Bambang Suhartono; Murinto Murinto; Herman Herman
Jurnal Pengabdian Masyarakat: Bisnis dan Iptek (JPMBISTEK) Vol. 3 No. 1 (2026): Jurnal Pengabdian Masyarakat: Bisnis dan Iptek (JPMBISTEK)
Publisher : LPPM STMIK Mardira Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56447/jpmbistek.v3i1.01

Abstract

The swift advancement of Artificial Intelligence (AI) technology requires vocational education institutions to revise their curricula to meet the demands of the digital industry. This study examines the implementation of training and mentorship for AI integration in the development of productive curricula at SMK Ilhami Kemiri, Tangerang Regency, while assessing teachers' understanding and preparedness to incorporate AI into the educational process. The researcher utilized a mixed-methods approach consisting of a systematic sequence of stages: identifying training needs, developing materials and instruments, conducting training sessions, facilitating AI integration, gathering questionnaire data, and executing both quantitative and qualitative analyses. Data were collected from effective subject teachers, who served as the principal participants. The findings indicate that the training significantly improves teachers' foundational understanding of AI ideas and applications in education. At the same time, mentorship is crucial for helping instructors implement AI in instructional tools and classroom practices. Data analysis reveals a rise in perceived teacher preparedness, especially in the application of AI for material creation, learning assessment, and effective curriculum improvement. This research advocates for the implementation of sustainable training programs, enhanced digital infrastructure support, and the creation of an integrated AI-based competency curriculum to facilitate the transition of vocational education at the Vocational High School level.
Integrating Off-Chain and On-Chain Mechanisms to Enhance Medical Data Management Using Blockchain Technology herman herman; Rinday Zildjiani Salji; Herman Yuliansyah
SemanTIK : Teknik Informasi Vol. 11 No. 2 (2025): SemanTIK : Teknik Informasi
Publisher : Informatics Engineering Department of Halu Oleo University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55679/semantik.v11i2.235

Abstract

Manajemen data medis yang aman dan andal merupakan tantangan utama dalam sistem perawatan kesehatan digital, di mana pertumbuhan eksponensial rekam medis elektronik meningkatkan permintaan akan solusi penyimpanan yang scalable dan efisien. Teknologi blockchain, terutama melalui kontrak pintar, menyediakan metode terdesentralisasi dan tahan gangguan untuk mengelola data medis. Namun, menyimpan data skala besar langsung di blockchain menyebabkan biaya transaksi yang jauh lebih tinggi (biaya gas), membuat prosesnya tidak efisien secara ekonomi dan secara teknis tidak praktis. Untuk mengatasi keterbatasan ini, penelitian ini meningkatkan integrasi kontrak pintar blockchain dengan InterPlanetary File System (IPFS) dengan mengembangkan kerangka kerja manajemen data yang aman, andal, dan hemat biaya. Arsitektur hibrida yang diusulkan menggabungkan kontrak pintar on-chain untuk kontrol akses dan IPFS off-chain untuk penyimpanan data, secara efektif mengurangi biaya gas sekaligus menjaga keamanan dan transparansi. Hasil eksperimen menunjukkan bahwa pendekatan hibrida memastikan penanganan data medis yang tidak dapat diubah, dapat diverifikasi dan efisien. Evaluasi terhadap prototipe sistem yang ditawarkan mengkonfirmasi integritas data dan pengaksesan data dengan cepat. Analisis kinerja menunjukkan sistem berhasil mengurangi tantangan biaya, meningkatkan skalabilitas dan keamanan dalam manajemen data medis secara terdesentralisasi. Secure and reliable medical data management is a major challenge in digital healthcare systems, where the exponential growth of electronic medical records increases the demand for scalable and efficient storage solutions. Blockchain technology, particularly through smart contracts, provides a decentralized and tamper-resistant method for managing medical data. However, storing large-scale data directly on the blockchain leads to significantly higher transaction costs (gas fees), making the process economically inefficient and technically impractical. To address this limitation, this study investigates the integration of blockchain smart contracts with the InterPlanetary File System (IPFS) to develop a secure, reliable, and cost-effective data management framework. The proposed hybrid architecture combines on-chain smart contracts for access control and off-chain IPFS for data storage, effectively reducing gas costs while maintaining security and transparency. Experimental results show that the hybrid approach ensures immutable, verifiable, and efficient medical data handling. Field evaluations confirm data integrity and rapid retrieval, while performance analysis demonstrates that the model successfully mitigates cost, scalability, and security challenges in decentralized healthcare data management.
Co-Authors Anton Yudhana Bambang Suhartono Deni Kuswandani Dikky Praseptian M Faiz Isnan Abdurrachman Fatima Tul Zuhra Herman Yuliansyah Hidayat, Panji Imam Riadi Imam Riadi Ismail Fauzi Isnin Kamalrulnizam Abu Bakar Krisna Astianingrum M. Zulkifli M. Zaki Moch. Nasheh Annafii Mohammad Hafiz Mohamed Muhammad Kunta Biddinika Muhammad Syukri Murinto Murinto Nur Hamida Siregar Rinday Zildjiani Salji Rusydi Umar Sarjimin Sarjimin Satriya Dwi Putra Sunardi, Sunardi Syahrani Lonang Syinta Brata