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All Journal TEKNIK INFORMATIKA Sainteks JUITA : Jurnal Informatika PengabdianMu: Jurnal Ilmiah Pengabdian kepada Masyarakat Infotekmesin Indonesian Journal of Electrical Engineering and Computer Science JPM: JURNAL PENGABDIAN MASYARAKAT Jurnal Pendidikan dan Teknologi Indonesia Journal of Dinda : Data Science, Information Technology, and Data Analytics Emerging Information Science and Technology Melek IT: Information Technology Journal IJCOSIN : Indonesian Journal of Community Service and Innovation eProceedings of Engineering Jurnal Pengabdian Teknik dan Sains (JPTS) Edu Komputika Journal Prosiding Seminar Nasional CORISINDO Smatika Jurnal : STIKI Informatika Jurnal IJCOSIN : Indonesian Journal of Community Service and Innovation Journal of Software Engineering and Multimedia (JASMED)
Dany Candra Febrianto
Telkom University
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Astronomy Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mechanical Engineering Medicine & Pharmacology Public Health Social Sciences Transportation Other

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A smart-contract framework for patient identity management in digital health platforms Cahyo Prihantoro; Dany Candra Febrianto; Maie Istighosah; Ahmad Uffi Lestrasi Ma’ruf; Angger Taufiqur Rohman Winarno; Yudha Islami Sulistya
Indonesian Journal of Electrical Engineering and Computer Science Vol 41, No 3: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v41.i3.pp1025-1039

Abstract

Asmart-contract framework for patient identity management in digital health platforms. A major gap in current digital health ecosystems is the absence of a portable and verifiable patient identity layer across fragmented electronic health record (EHR) systems. The problem addressed is the lack of a portable, verifiable, and patient-centric identity layer across fragmented electronic health record systems, which weakens access accountability and privacy. The proposed solution couples fast healthcare interoperability resources (FHIR) with self-sovereign identity (SSI), storing FHIR payloads off-chain in the InterPlanetary file system (IPFS) and committing only encrypted pointers and policies on Polygon smart contracts. Patient identifiers and content addresses are protected with AES-256 GCMauthenticated encryption and elliptic-curve key wrapping (ECIES) for both the healthcare administrator and the patient. A web implementation in Next.js using thirdweb automates wallet creation, keystore handling, encryption, and on-chain commits. In evaluation with 50 synthetic registrations, success reached 100 percent, median end-to-end latency was 5.86 seconds, mean on-chain latency 3.77 seconds, average transaction fee 0.0401 POL/MATIC, encryption time 13.9 milliseconds, and all decryptions validated. The results indicate practical feasibility for portable identity and auditable access, with on-chain latency as the main bottleneck to be reduced through batching, cheaper layers, and broader field trials. However, this study is limited because the evaluation uses only synthetic data and singleprovider testing, without real-world patients or multi-institutional environments. Zero-knowledge proofs (ZKP) are discussed conceptually as future integration and are not implemented or benchmarked in this work.
Design of the "SRIKANDI" Image Processing Application for Detecting Rice Leaf Diseases using the NASNetMobile Convolutional Neural Network Architecture Dodi Bayu Prasanto; Fauzaandhiyaa Shafiananto; Muhammad Rakan Atha; Septiyadi Susanto; Putri Anggrani Sijabat; Cindy Anti; Dany Candra Febrianto
Journal of Software Engineering and Multimedia (JASMED) Vol. 4 No. 1 (2026): Journal of Software Engineering and Multimedia (JASMED)
Publisher : Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/jasmed.v4i1.10235

Abstract

Rice leaf diseases can cause a significant decrease in productivity if not treated early, while currently still using a manual diagnosis process that is often slow, inconsistent, and dependent on extension workers. In this study, the SRIKANDI application was developed, a mobile application for image processing for rice leaf diseases using the NASNetMobile Convolutional Neural Network (CNN) architecture. This system is designed using five labels, namely bacterial, blast, brownspot, leafsmut, and healthy leaves. The dataset used consists of 2500 images collected from Kaggle, Mendeley Data, and taken directly. All images go through preprocessing stages of resizing, pixel normalization, and augmentation, then divided into 80% train, 10% test, and 10% validation. The model training was carried out in two stages, namely, 40 epochs of fine-tuning with a learning rate of 0.0008 followed by 20 epochs of fine-tuning with a learning rate of 1e-5, the results obtained by the model with a test set accuracy rate of 96.40%. The trained model is then saved in TFLite format to be integrated into the SRIKANDI mobile application so that it can help farmers detect rice leaf diseases in real-time via camera or taken from the gallery.
Sentiment Analysis of User Reviews on the Bus Simulator Indonesia Application Using Machine Learning Approaches Iswatun Maimanah; Rakhmad Maulidi; Dany Candra Febrianto
SMATIKA JURNAL : STIKI Informatika Jurnal Vol 16 No 02 (2026): SMATIKA Jurnal : STIKI Informatika Jurnal
Publisher : LPPM Universitas Bhinneka Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/smatika.v16i02.2285

Abstract

User reviews on the Google Play Store serve as a primary source of information for assessing the quality and satisfaction levels of an application. As expectations for game quality and realism continue to rise, Bus Simulator Indonesia still faces a range of technical issues — including bugs, glitches, visual disturbances, and suboptimal controls — that directly impact user comfort and overall experience. This study aims to classify the sentiments expressed by Bus Simulator Indonesia users into three categories: positive, neutral, and negative. In addition, it compares the performance of three machine learning algorithms: Naive Bayes, Support Vector Machine (SVM), and Logistic Regression. The research pipeline consists of data collection, text preprocessing, word weighting, modeling, and model evaluation. The dataset comprises 19,482 Indonesian-language reviews gathered from the Google Play Store between 2021 and 2025, evaluated across three data-splitting scenarios: 90:10, 80:20, and 70:30. The results show that Support Vector Machine consistently achieved the highest accuracy across all scenarios, recording 84.3%, 82.0%, and 81.0%, with weighted F1-score values of 0.83, 0.82, and 0.81, respectively, indicating a more stable classification performance. Logistic Regression followed with accuracies of 81.1%, 79.9%, and 79.0%, with weighted F1-score values of 0.79, 0.78, and 0.77, respectively, while Naive Bayes produced the lowest scores overall. These findings confirm that Support Vector Machine is the most effective algorithm for sentiment classification of Bus Simulator Indonesia user reviews.
YOLO26-Based ASL Sign Language Gesture Detection on Android Isnaeni, Nenen; Wisesa, Bradika Almandin; Febrianto, Dany Candra
Emerging Information Science and Technology Vol. 7 No. 1 (2026): May
Publisher : Universitas Muhammadiyah Yogyakarta

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

Abstract

This study proposes a real-time Android-based American Sign Language (ASL) gesture detection system using YOLO26. The model recognizes 26 static gesture classes consisting of 24 alphabet gestures (a-i and k-y) and two common expressions, namely “I love you” and “thank you”, from images captured by a smartphone camera. A custom dataset of 7,800 images was prepared and divided into 5,460 training images, 1,560 validation images, and 780 testing images. Preprocessing included resizing, normalization, horizontal flipping, random rotation, brightness adjustment, contrast variation, and zoom augmentation to improve robustness under different acquisition conditions. Three YOLO26 variants, namely YOLO26s, YOLO26n, and YOLO26m, were trained and evaluated using precision, recall, F1-score, mAP@50, mAP@50-95, latency, frames per second, and lighting robustness. Experimental results show that YOLO26n provided the most balanced deployment performance with 96.4% precision, 95.8% recall, 96.1% F1-score, 97.8% mAP@50, and 84.2% mAP@50-95. Real-time testing on an Infinix X6726 device produced an average latency of 118 ms per frame or 8.47 FPS. Robustness testing under high, medium, and low lighting produced detection success rates of 98.3%, 96.7%, and 93.3%, respectively. The findings indicate that YOLO26 is feasible as an exploratory architecture for Android-based ASL gesture detection, although broader cross-device and cross-dataset validation is still required before claiming general real-world superiority.
Implementasi Augmented Reality Berbasis Marker pada Brosur Perumahan untuk Visualisasi Model 3D Rumah dengan Metode MDLC Satiaka Nugraha Sasmita Padmanagara; Muhamad Azrino Gustalika; Dany Candra Febrianto
Jurnal Pendidikan dan Teknologi Indonesia Vol 6 No 7 (2026): JPTI - Juli 2026
Publisher : CV Infinite Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jpti.2110

Abstract

Industri properti yang berkembang pesat mendorong pengembang untuk mencari metode pemasaran yang lebih inovatif. Brosur biasa yang hanya menampilkan informasi berupa teks dan gambar dua dimensi masih kurang mampu menjelaskan secara jelas tentang desain dan penataan ruangan rumah kepada calon pembeli. Penelitian ini bertujuan untuk membuat aplikasi Augmented Reality (AR) yang menggunakan marker dan dapat dijalankan di platform Android. Aplikasi ini terhubung dengan brosur Perumahan Griya Teluk Indah dan berfungsi menampilkan gambar rumah tipe 36 dalam bentuk 3D secara interaktif. Metode yang digunakan dalam pengembangan adalah Multimedia Development Life Cycle (MDLC) yang terdiri dari enam tahap, yakni tahap konsep, desain, pengumpulan materi, pembuatan, pengujian, dan distribusi. Aplikasi ini dikembangkan dengan menggunakan Unity 3D, Vuforia SDK, dan bahasa pemrograman C#. Hasil pengujian Black Box menunjukkan semua fitur aplikasi berjalan sesuai dengan fungsi yang dibutuhkan, dengan kemampuan mendeteksi Marker yang cukup baik dalam jarak 10 hingga 50 sentimeter serta sudut 10 derajat sampai 90 derajat. Hasil UAT yang melibatkan empat orang Sales Freelance mendapatkan skor rata-rata sebesar 85,00%, yang masuk ke dalam kategori sangat baik. Penelitian menunjukkan bahwa aplikasi AR yang dibuat berhasil membantu para penjual dalam memberikan informasi tentang rumah dengan cara yang lebih menarik dan jelas, Penelitian ini berkontribusi sebagai inovasi yang mengintegrasikan teknologi AR berbasis Marker langsung ke dalam brosur fisik perumahan subsidi menggunakan metode MDLC, sehingga menawarkan pendekatan baru dalam strategi pemasaran properti yang lebih interaktif dan terukur.
Co-Authors Affan Hilmy Natsir Agung Purwo Wicaksono Ahmad Uffi Lestrasi Ma’ruf Akmal Burhansyah, Helmy Andi Prademon Yunus Angger Taufiqur Rohman Winarno Arief Rais Bahtiar Arif Amrulloh Arya Amirul Jawad, Bagas Aulia Desy Nur Utomo Bradika Almandin Wisesa Cahyo Prihantoro Choo, Yit Hong Cindy Anti Dodi Bayu Prasanto Fauzaandhiyaa Shafiananto Fitriani, Maulida Ayu Ghifari, Abu Dzar Al Hindayati Mustafidah Iswatun Maimanah Khadija, Mutiara Auliya Kusumastuti, Rajnaparamitha Laily Nurlina Lisda Lisda Lisda, Lisda M Yoka Fathoni Mahazam Afrad Maie Istighosah Maryona Septiara Maulida Ayu Fitriani Maulida Ayu Fitriani Muhamad Azrino Gustalika Muhammad Rakan Atha Nenen Isnaeni Nicolaus Euclides Wahyu Nugroho Novian Adi Prasetyo Nur Alim, Muhammad Putri Anggrani Sijabat Rakhmad Maulidi Satiaka Nugraha Sasmita Padmanagara Septiyadi Susanto Wahyu Andi Saputra Yesy Diah Rosita Yit Hong Choo Yudha Islami Sulistya Zikra Ismunandar, Az