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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 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 Jurnal Pengabdian Teknik dan Sains (JPTS) Edu Komputika Journal Prosiding Seminar Nasional CORISINDO 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 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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Designed a Waste Management Application by Applying Requirements Engineering Methods to Meet User Needs and Expectations Lisda, Lisda; Febrianto, Dany Candra; Kusumastuti, Rajnaparamitha
Infotekmesin Vol 16 No 1 (2025): Infotekmesin: Januari 2025
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v16i1.2509

Abstract

Efforts to manage waste through recycling have been implemented frequently but continue to receive minimal attention from the public, who are daily contributors to waste generation. As a result, the volume of waste keeps increasing, leading to environmental pollution, such as ecosystem damage, unpleasant odors, and blockages in waterways. This research aims to demonstrate that waste management can be enhanced by integrating data to uncover insights that can inform new strategies for addressing excess waste. In this study, a prototype for a waste recycling application was developed, focusing on digital-based waste management using IoT technology. The system incorporates sensors capable of measuring waste volume as a supporting tool developed using the requirements engineering method. Questionnaires were distributed to 30 respondents to gather feedback on platform designs and IoT product designs. Through requirements validation testing, the results showed that 70% of the 30 respondents approved the platform design, while 63.2% approved the IoT product design.
Small Object Detection and Object Counting for Primary Roe Dataset Based on Yolo Saputra, Wahyu Andi; Nugroho, Nicolaus Euclides Wahyu; Febrianto, Dany Candra; Yunus, Andi Prademon; Gustalika, Muhammad Azrino; Choo, Yit Hong
JURNAL TEKNIK INFORMATIKA Vol. 18 No. 1: JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jti.v18i1.46063

Abstract

This research offers an initial exploration into the effectiveness of three variations of the YOLOv8 model original, trimmed, and YOLOv8n.pt in combination with two distinct datasets characterized by tight and loose distributions of roe, aimed at enhancing small object detection and counting accuracy. Utilizing a primary roe dataset across 776 images, the research systematically compares these model-dataset configurations to identify the most effective combination for precise object detection. The experimental results reveal that the YOLOv8n.pt model combined with the loosely distributed dataset achieves the highest detection performance, with a mean Average Precision (mAP) of 53.86%. This outcome underscores the critical impact of both model selection and data distribution on the detection accuracy in machine learning applications. The findings highlight the importance of tailored model and dataset synergies in optimizing detection tasks, particularly in complex scenarios involving small, densely clustered objects. This research contributes valuable insights into the strategic deployment of neural network architectures for refined object detection challenges.
Sentiment Analysis of Visitor Reviews on Baturaden Tourist Attraction Using Machine Learning Methods Afrad, Mahazam; Febrianto, Dany Candra; Wijayanto, Sena; Fathoni, M. Yoka
Edu Komputika Journal Vol. 11 No. 1 (2024): Edu Komputika Journal
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/edukom.v11i1.10561

Abstract

This study evaluates the performance of four machine learning models: Support Vector Machine (SVM), Random Forest, K-Nearest Neighbors (KNN), and Naive Bayes in analyzing visitor reviews of the Lokawisata Baturaden tourist attraction. Using 5-fold cross-validation, the study aims to determine which machine learning model best suits sentiment analysis on the Baturaden review data. This study was conducted through several stages, including data preprocessing, feature extraction, and the data training process. Case folding, text cleaning, tokenization, stopword removal, and stemming were performed during the data preprocessing stage. The feature extraction method used was TF-IDF. SMOTE was applied to increase data variation and address the data imbalance in the dataset. The results show that SVM provides the best performance with an accuracy of 0.937, an F1-score of 0.937, a precision of 0.943, and a recall of 0.937. Random Forest also performs well with an accuracy of 0.918 and an F1-score of 0.918, though slightly below SVM. KNN shows the lowest performance with an accuracy of 0.651 and an F1-score of 0.544, while Naive Bayes performs adequately with an accuracy of 0.845 and an F1-score of 0.841. Based on this evaluation, SVM is recommended as the best model for sentiment analysis of reviews, followed by Random Forest as a good alternative. The KNN model is not recommended due to its lower performance, while Naive Bayes can be considered for its speed and simplicity, although its results are not as good as SVM and Random Forest. These conclusions guide the selection of the optimal model to enhance understanding and visitor experience at the Baturaden tourist attraction.
Sistem Pakar Diagnosa Penyakit Kura-Kura Berbasis Android Menggunakan MetodeForward Chaining dan Certainty Factor Fitriani, Maulida Ayu; Ghifari, Abu Dzar Al; Mustafidah, Hindayati; Febrianto, Dany Candra
CORISINDO 2025 Vol. 1 (2025): Prosiding Seminar Nasional CORISINDO 2025
Publisher : CORISINDO 2025

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/corisindo.v1.5653

Abstract

Kura-kura merupakan hewan yang memiliki umur panjang, namun tetap rentan terhadap penyakit. Banyak pemelihara kura-kura yeng kesulitan merawat kura-kura yang terkena penyakit, karena keterbatasan akses ke klinik hewan atau dokter spesialis hewan. Oleh karena itu, dibutuhkan suatu alat atau sistem yang dapat mendiagnosa penyakit pada kura-kura dengan kemampuan layaknya dokter spesialis hewan. Tujuan dari penelitian ini ialah membangun sistem pakar berbasis android untuk mendiagnosa 9 jenis penyakit pada kura-kura, data penyakit didapatkan melalui studi literatur dan wawancara dengan 3 pakar hewan. Sistem pakar dibangun menggunakan metode Forward Chaining sebagai metode inferensi untuk mempermudah pengambilan keputusan. Namun, metode ini memiliki kekurangan dalam menentukan tingkat keyakinan mendiagnosa, metode Certainty Factor ditambahkan sebagai metode perhitungan untuk menentukan tingkat keyakinan diagnosa. Berdasarkan hasil uji perbandingan anatara output pada sistem dan perhitungan manual, sistem pakar dapat melakukan diagnosa dengan persentase keakuratan mencapai 97%.
Pelatihan “Artificial Intelligence for Kids” bagi Tenaga Pendidik di Sekolah Dasar Muhammadiyah Danaraja: Training on “Artificial Intelligence for Kids” for Educators at Muhammadiyah Danaraja Elementary School Fitriani, Maulida Ayu; Nurlina, Laily; Febrianto, Dany Candra
PengabdianMu: Jurnal Ilmiah Pengabdian kepada Masyarakat Vol. 10 No. 12 (2025): PengabdianMu: Jurnal Ilmiah Pengabdian kepada Masyarakat
Publisher : Institute for Research and Community Services Universitas Muhammadiyah Palangkaraya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33084/pengabdianmu.v10i12.10248

Abstract

The artificial intelligence (AI) training program for teachers focused on developing animation-based teaching media that integrates AI concepts. This training produced dynamic and reusable instructional materials, enabling teachers to reintroduce the content effectively to students. By helping educators incorporate AI into their lessons, the program aims to prepare the younger generation to understand, manage, and actively participate in an increasingly advanced digital world, supporting government efforts to enhance national competitiveness. Through this training, teachers learned to embed AI concepts into various subject areas. Examples of the resulting teaching media include applications for animal recognition, growth stages, Arabic vocabulary, and more. The platforms used for media development included Pose Block from media.mit.edu and Teachable Machine by Google. The satisfaction survey revealed that 74.5% of participants rated the training as very satisfactory.
Pelatihan Pembelajaran Berbasis Pemanfaatan AI untuk Meningkatkan Produktivitas Guru di Era Digital Affan Hilmy Natsir; Dany Candra Febrianto
Indonesian Journal of Community Service and Innovation Vol. 6 No. 1 (2026): April 2026
Publisher : LPPM IT Telkom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/ijcosin.v6i1.10495

Abstract

Perkembangan teknologi digital, khususnya Artificial Intelligence (AI), memberikan peluang strategis dalam meningkatkan produktivitas guru dan kualitas pembelajaran di era digital. Namun, pemanfaatan AI oleh guru masih terbatas akibat rendahnya literasi digital, kurangnya pemahaman etika penggunaan AI, serta minimnya pendampingan praktis. Artikel ini bertujuan untuk mendeskripsikan pelaksanaan dan dampak kegiatan Pengabdian kepada Masyarakat berupa pelatihan pengembangan pembelajaran berbasis pemanfaatan AI yang dilaksanakan oleh Himpunan Mahasiswa Teknik Informatika Telkom University Purwokerto di SDIT Top Kids. Metode kegiatan meliputi pemaparan materi, workshop praktik langsung, dan diskusi interaktif yang difokuskan pada penggunaan ChatGPT untuk penyusunan materi ajar, pembuatan soal, perencanaan pembelajaran, serta pemahaman etika dan risiko penggunaan AI dalam pendidikan. Evaluasi dilakukan melalui pre-test dan post-test serta observasi selama kegiatan. Hasil menunjukkan peningkatan rata-rata pemahaman peserta sebesar 27%, mencakup aspek konseptual, pedagogis, dan etika pemanfaatan AI. Temuan ini menunjukkan bahwa pelatihan berbasis praktik AI efektif dalam meningkatkan produktivitas dan kompetensi digital guru secara bertanggung jawab serta mendukung inovasi pembelajaran berkelanjutan.
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.
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.
Co-Authors Affan Hilmy Natsir Agung Purwo Wicaksono Ahmad Uffi Lestrasi Ma’ruf Andi Prademon Yunus Angger Taufiqur Rohman Winarno 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 Gustalika, Muhammad Azrino Hindayati Mustafidah 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 Putri Anggrani Sijabat Septiyadi Susanto Wahyu Andi Saputra Yesy Diah Rosita Yit Hong Choo Yudha Islami Sulistya