Article Per Year (5 Year)
p-Index From 2021 - 2026
2.724
P-Index
This Author published in this journals
All Journal Jurnal Simetris Jurnal Teknologi Informasi dan Ilmu Komputer RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Jurnal Teknik Informatika (JUTIF) Mitra Mahajana: Jurnal Pengabdian Masyarakat Decode: Jurnal Pendidikan Teknologi Informasi Jurnal Pengabdian Masyarakat Sains dan Teknologi Jurnal Pengabdian Informatika (JUPITA) Journal of Software Engineering and Information System (SEIS) Jurnal Riset Sistem dan Teknologi Informasi Jurnal Multidisiplin Pengabdian Masyarakat (JMPM) Jurnal Pengabdian kepada Masyarakat (PEMAS) Scientific Journal of Engineering Research Scientific: Journal of Computer Science and Informatics Scientific Journal of Computer Science Jurnal Informatika, Komputasi, Aplikasi & Bisnis (INFOKABIN)
Tri Stiyo Famuji
Department Of Informatics, Universitas Al-Irsyad Cilacap
Religion Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Public Health Social Sciences Other

Published : 19 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 19 Documents
Search

 1   2 
Machine Learning 5.0 In-depth Analysis Trends in Classification Dianda Rifaldi; Tri Stiyo Famuji; Setiawan Ardi Wijaya; Ahmed Jaber Abougarair; Phichitphon Chotikunnan; Alfian Ma'arif; Furizal
Scientific Journal of Computer Science Vol. 1 No. 1 (2025): June
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjcs.v1i1.2025.18

Abstract

In the era of Technology 5.0 Machine Learning continues to show significant advancements across various sectors. This study aims to examine the latest trends in Machine Learning classification, focusing on four key approaches Explainable Artificial Intelligence, Federated Learning, Transfer Learning, and Generative Adversarial Networks. The methodology involves a comprehensive literature review of research in Asia and experimentation with related datasets. The findings indicate that Explainable Artificial Intelligence enhances transparency and accuracy in data classification, Federated Learning enables decentralized learning while safeguarding data privacy, Transfer Learning improves accuracy with small datasets, and Generative Adversarial Networks aids in data augmentation for better model training. In conclusion, these techniques not only enhance the efficiency and accuracy of classification but also open up new opportunities for innovation in various fields, including healthcare, transportation, and cybersecurity.
MODEL PERANCANGAN SISTEM TERDESENTRALISASI UNTUK KEAMANAN DATA GENETIKA MANUSIA BERBASIS BLOCKCHAIN DAN IPFS Tri Stiyo Famuji; Alya Masitha; Maulana Muhammad Jogo Samodro; Galih Pramuja Inngam Fanani; Yuniariana Pertiwi
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 10 No 2 (2025): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v10i2.6015

Abstract

The rapid development of genomic technology has heightened the urgency to address vulnerabilities in centralized systems managing sensitive human genetic data. This study proposes a decentralized system integrating blockchain and IPFS to enhance data security, integrity, and accessibility. Blockchain ensures immutable audit trails and dynamic access control via smart contracts, while IPFS provides scalable off-chain storage using cryptographic hashes (CIDs). The hybrid design separates raw data storage (encrypted via homomorphic encryption) on IPFS from access management on the Ethereum blockchain. A user interface built with React.js and Web3.js enables encrypted data uploads, role-based access requests, and real-time audit monitoring, complying with GDPR/HIPAA standards. Testing demonstrated the system’s effectiveness in preventing unauthorized access and ensuring data traceability. Challenges such as energy efficiency and regulatory compliance were addressed through sharding, layer-2 protocols, and selective data deletion mechanisms. This framework supports secure cross-institutional collaboration in genomic research, promoting public participation and accelerating biomedical innovation. Future work will focus on optimizing computational efficiency and expanding datasets.
Trends and Impact of the Viola-Jones Algorithm: A Bibliometric Analysis of Face Detection Research (2001-2024) Setiawan Ardi Wijaya; Tri Stiyo Famuji; Muhammad Amirul Mu'min; Yana Safitri; Novi Tristanti; Abdennasser Dahmani; Zied Driss; Abdel-Nasser Sharkawy; Raheem Al-Sabur
Scientific Journal of Engineering Research Vol. 1 No. 1 (2025): March
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjer.v1i1.2025.8

Abstract

The Viola-Jones algorithm remains a cornerstone in computer vision, particularly for object and face detection. This bibliometric study provides a comprehensive analysis of the algorithm’s academic impact and research trends, encompassing publication patterns, citation metrics, influential authors, and co-occurrence of keywords. The findings indicate a significant rise in research outputs and citations between 2016 and 2020, reflecting the algorithm's sustained relevance and application in various domains. Network visualization maps further reveal the algorithm's integration with diverse fields, including machine learning, image processing, and neural networks, emphasizing its versatility and adaptability to emerging technological challenges. Key research contributions include advancements in hybrid approaches, combining the Viola-Jones framework with techniques such as convolutional neural networks and HOG-SVM for improved detection accuracy. However, limitations such as computational inefficiency and sensitivity to environmental factors persist, presenting opportunities for innovation. This study concludes by highlighting future research directions, such as integrating deep learning and edge computing to enhance algorithmic performance in real-time and complex scenarios. This study provides a valuable reference for researchers and practitioners aiming to extend the Viola-Jones algorithm’s capabilities and applications by consolidating existing knowledge and identifying research gaps.
A Thirdweb-Based Smart Contract Framework for Secure Sharing of Human Genetic Data on the Ethereum Blockchain Tri Stiyo Famuji; Bernadine Grancho; Galih Pramuja Inggam Fanani; Hidear Talirongan; Raden Bagus Bambang Sumantri
Scientific Journal of Engineering Research Vol. 1 No. 3 (2025): September
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjer.v1i3.2025.30

Abstract

Human genetic data, crucial for advancing personalized medicine, requires secure and privacy-preserving management solutions. Traditional approaches face challenges in scalability, security, and decentralized access control. This study proposes a blockchain-based framework leveraging Thirdweb and Ethereum smart contracts to address these issues. The framework integrates decentralized storage via IPFS for cost-efficient off-chain genetic data storage, while on-chain smart contracts manage access control, encryption, and audit trails. Utilizing Solidity for smart contract development, the system ensures role-based permissions, wallet-based authentication, and immutable transaction logging. Genetic data in FASTA format, sourced from NCBI, is encrypted and linked to IPFS hashes stored on the blockchain. The architecture supports dual interfaces—command-line for developers and a Thirdweb dashboard for end-users—enabling secure data upload, access, and monitoring. Testing demonstrated functional efficacy in data integrity, access verification, and audit capabilities. Results highlight the system’s ability to enhance privacy, eliminate intermediaries, and provide transparent data governance. The integration of Thirdweb further decentralizes operations, aligning with Web 3.0 principles. Key contributions include a scalable model for genetic data sharing, a customizable smart contract template, and a user-centric design. Future work should explore advanced encryption, real-world healthcare integration, and performance optimization under high-throughput conditions. This research bridges biotechnology and blockchain, offering a robust foundation for secure genomic data ecosystems.
Penggunaan Algoritma Naive Bayes Untuk Analisis Sentimen pada Ulasan Aplikasi Threads Di Google Play Store Raden Bagus Bambang Sumantri; Dede yusuf; Tri Stiyo Famuji; Walidy Rahman Hakim; Retno Agus Setiawan
INFOKABIN (Informatika Komputasi Aplikasi dan Bisnis) Vol 1 No 2 (2026): Juli 2026
Publisher : Universitas Al-Irsyad Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36760/ifkb.v1i2.758

Abstract

Kemajuan teknologi informasi mendorong pengguna untuk memberikan ulasan terhadap aplikasi digital, termasuk Threads, platform berbasis teks yang dirilis oleh Meta Platforms Dengan menggunakan Algoritma Naive Bayes, penelitian ini bertujuan menganalisis ulasan pengguna aplikasi Threads di Google Play Store. Tahapan penelitian meliputi pengumpulan data melalui teknik scraping, pemrosesan awal data (preprocessing), pembobotan menggunakan metode TF-IDF, serta klasifikasi sentimen dengan membagi data ke dalam set pelatihan (training) dan pengujian (testing). Hasil penelitian menunjukkan bahwa sebagian besar ulasan pengguna memiliki sentimen yang positif, dengan akurasi klasifikasi sebesar 84%. Untuk sentimen negatif, precision, recall, dan f1-score masing-masing mencapai 74%, 65%, dan 69%. Sementara itu, sentimen positif memiliki precision sebesar 84%, recall 89%, dan f1-score 86%. Meskipun model berhasil mengklasifikasikan sentimen dengan baik, masih terdapat kesalahan identifikasi pada ulasan negatif, disebabkan oleh hal-hal seperti bahasa yang tidak jelas dan sentimen yang beragam. Penelitian ini membantu pengembang meningkatkan pengalaman pengguna dan kualitas aplikasi. Untuk penelitian selanjutnya, disarankan membandingkan algoritma lain seperti k-Nearest Neighbour atau kernel RBF serta menerapkan metode k-fold cross validation untuk meningkatkan keakuratan model.
EKSPLORASI BAHAN ALAM CILACAP UNTUK PRODUK KESEHATAN PADA KEGIATAN KARYA ILMIAH REMAJA Muhammad Yogie Prastowo; Tri Stiyo Famuji; Yuhansyah Nur Fauzi; Tri Fitri Yana Utami; Yuniariana Pertiwi; Tri Kusuma Wardani
Mitra Mahajana: Jurnal Pengabdian Masyarakat Vol. 6 No. 3 (2025): Volume 6 Nomor 3 November 2025
Publisher : LPPM Universitas Flores

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37478/mahajana.v6i3.6957

Abstract

This community service activity focused on conducting an exploration of local natural materials from Cilacap for health products within the Student Scientific Group (KIR) at SMP Negeri 5 Cilacap. The activity aimed to enhance students' understanding and scientific skills regarding the potential of their local biodiversity through a contextual learning approach. The implementation method consisted of three main stages: (1) Delivery of core material on the identification and utilization of natural materials, (2) Independent exploration and discussion by students to observe material samples, and (3) Understanding evaluation through interactive quizzes and reflection. The results demonstrated high enthusiasm and active engagement from the participants. Students were not only able to identify various natural materials and their potential benefits but also showed improved observation and collaboration skills. This activity successfully raised awareness of the value of local wisdom and opened insights into opportunities for developing health products based on local resources. For follow-up, it is recommended to develop product prototypes, compile a digital catalogue, and establish collaborations with relevant parties for program sustainability.
Peningkatan Literasi Digital Masyarakat untuk Identifikasi Informasi Kesehatan Valid di Puskesmas Kempas Jaya Dianda Rifaldi; Muhammad Rahmad; Galih Fanani; Alya Masitha; Mesy Yulandari; Tri Stiyo Famuji
Jurnal Pengabdian kepada Masyarakat (PEMAS) Vol. 3 No. 2 (2026): Mei 2026
Publisher : Yayasan Ran Edu Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63866/pemas.v3i2.133

Abstract

Akses internet yang semakin mudah mendorong masyarakat memanfaatkan media sosial dan aplikasi percakapan sebagai sumber informasi kesehatan, sekaligus meningkatkan risiko paparan informasi yang keliru, tidak lengkap, atau menyesatkan. Kegiatan pengabdian ini bertujuan meningkatkan literasi digital masyarakat di wilayah kerja Puskesmas Kempas Jaya dalam mencari, mengevaluasi, memverifikasi, dan memanfaatkan informasi kesehatan digital secara bertanggung jawab. Kegiatan dilaksanakan pada 21 April 2026 dengan melibatkan 30 peserta melalui pendekatan Community-Based Research dan desain one-group pretest-posttest. Tahapan meliputi identifikasi kebutuhan, pretest, penyuluhan, demonstrasi pemeriksaan fakta, praktik pencarian informasi menggunakan mesin pencari, Google Fact Check Explorer, Google Lens, kecerdasan buatan secara kritis, serta situs resmi kesehatan, dilanjutkan dengan diskusi kasus, posttest, dan evaluasi. Instrumen divalidasi melalui expert judgment oleh dua ahli bidang Informatika dan Kesehatan Masyarakat. Data dianalisis secara deskriptif dan menggunakan Wilcoxon signed-rank test dengan tingkat signifikansi 5%. Hasil menunjukkan peningkatan rata-rata skor pengetahuan dari 52,30 menjadi 83,70, dengan selisih 31,40 poin atau peningkatan relatif 60,04%. Peserta berkategori baik meningkat dari 4 orang (13,3%) menjadi 23 orang (76,7%), dengan hasil uji Wilcoxon menunjukkan perbedaan signifikan (p < 0,001). Tingkat kepuasan peserta mencapai 96,7%. Hasil ini menunjukkan bahwa integrasi Informatika dan Kesehatan Masyarakat dapat meningkatkan pengetahuan masyarakat dalam mengevaluasi informasi kesehatan digital. Keberlanjutan program perlu didukung melalui pendampingan berkala dan penyediaan modul digital.
A Performance Trade-Off Analysis Between Minutiae-Based Algorithm and Convolutional Neural Networks in Fingerprint Image Identification Raden Bagus Bambang Sumantri; Fajar Mahardika; Dede Yusuf; Tri Stiyo Famuji
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 4 (2026): JUTIF Volume 7, Number 4, August 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.4.5609

Abstract

Fingerprint image identification plays a crucial role in biometric authentication systems; however, selecting an appropriate algorithm remains challenging due to trade-offs between identification accuracy and computational efficiency. This study aims to comparatively evaluate the performance of a traditional Minutiae-Based algorithm and a Convolutional Neural Network (CNN) for fingerprint image identification to determine their respective strengths and limitations. The Minutiae-Based method extracts distinctive ridge features, such as ridge endings and bifurcations, followed by a similarity-based matching process. In contrast, the CNN model automatically learns discriminative features from raw fingerprint images through deep learning. Experiments were conducted on a multi-subject fingerprint dataset, and performance was assessed using identification accuracy, False Acceptance Rate (FAR), False Rejection Rate (FRR), and computation time per image. The results show that CNN achieved a higher identification accuracy of 98.6%, with a FAR of 1.2% and FRR of 1.4%, outperforming the Minutiae-Based algorithm, which obtained 92.3% accuracy, 4.8% FAR, and 3.9% FRR. However, the Minutiae-Based approach demonstrated superior computational efficiency, requiring an average processing time of 0.42 seconds per image compared to 1.35 seconds for CNN. These findings highlight a clear performance trade-off between accuracy and processing speed. The novelty of this study lies in providing a structured quantitative comparison that integrates accuracy, security metrics, and computational cost within a unified evaluation framework. The results contribute to the development of biometric systems by offering practical guidance for selecting fingerprint identification algorithms based on application-specific requirements, whether prioritizing high recognition accuracy or real-time computational efficiency.
Sistem Analisis Kemiskinan Berbasis Website dengan Model Knn untuk Kelayakan Penerimaan PKH di Kabupaten Cilacap Dede Yusuf; Tri Stiyo Famuji; Imam Agus Faizal; Setiawan Ardi Wijaya
Decode: Jurnal Pendidikan Teknologi Informasi Vol. 5 No. 3: NOVEMBER 2025
Publisher : Program Studi Pendidikan Teknologi Infromasi UMK

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51454/decode.v5i3.1477

Abstract

Kemiskinan menjadi permasalahan strategis di Kabupaten Cilacap dengan angka mencapai 12,43% pada tahun 2023. Penelitian ini bertujuan mengembangkan Sistem SIPANDU Cilacap (Sistem Prediksi dan Analisis Data untuk Subsidi) berbasis website untuk mendukung penentuan kelayakan penerima Program Keluarga Harapan (PKH). Metode penelitian menggunakan pendekatan Research and Development (R&amp;D) dengan desain mixed-methods. Pengumpulan data dilakukan melalui studi dokumentasi, kuesioner terstruktur, dan wawancara terhadap 350 kepala keluarga di lima kecamatan prioritas. Sistem dikembangkan menggunakan arsitektur three-tier dengan teknologi PHP, MySQL, dan Bootstrap 5. Hasil penelitian menghasilkan sistem berbasis website yang terintegrasi dengan empat fitur utama: Pemetaan Wilayah untuk visualisasi sebaran kemiskinan, Cek Status PKH untuk input data calon penerima, Berita untuk informasi program, dan Tentang SIPANDU untuk profil sistem. Sistem ini dirancang untuk menghasilkan data terstruktur sesuai format yang diperlukan implementasi algoritma K-Nearest Neighbor dalam analisis kelayakan. Hasil validasi model KNN menunjukkan akurasi sebesar 83,33% dengan nilai K optimal = 5 berdasarkan evaluasi menggunakan 5-fold cross validation. Implementasi sistem menunjukkan kemampuan dalam konsolidasi data sosio-demografi yang komprehensif meliputi 21 variabel prediktor. Pengujian sistem mencakup User Acceptance Test (UAT) dengan stakeholder Dinas Sosial Kabupaten Cilacap yang menunjukkan tingkat kepuasan 92% pada aspek usability dan functionality. Hasil pengujian blackbox terhadap seluruh modul sistem menunjukkan tingkat keberhasilan 100%, membuktikan keandalan sistem untuk diimplementasikan. SIPANDU Cilacap diharapkan dapat menjadi solusi teknologi untuk meningkatkan akurasi, transparansi, dan akuntabilitas dalam penyaluran bantuan sosial PKH, sekaligus mendukung evidence-based policy dalam perumusan kebijakan penanggulangan kemiskinan di tingkat daerah.
Co-Authors Abdel-Nasser Sharkawy Abdennasser Dahmani Abdul Fadlil Adiyah Mahiruna Ahmed Jaber Abougarair Alfian Ma'arif ALYA MASITHA Alya Masitha Alya Masitha Bella Okta Sari Miranda Bernadine Grancho Dede Yusuf Dede Yusuf, Dede Dianda Rifaldi Dianda Rifaldi Dianda Rifaldi Dinar Danan Sukmawati Erin Eka Citra Fajar Mahardika Fanani, Galih Pramuja Inngam Fathir Fauzan Purma Ramadhan Furizal Galih Fanani Galih Pramuja Inggam Fanani Galih Pramuja Inngam Fanani Hakim, Walidy Rahman Hidear Talirongan Imam Agus Faizal Iriene Putri Mulyadi Maulana Muhammad Jogo Samodro Maulana Muhammad Jogo Samudro Mayji Danardono Mesy Yulandari Muhammad Amirul Mu'min Muhammad Amirul Mu'min Muhammad Kunta Biddinika Muhammad Rahmad Muhammad Yogie Prastowo Mulyadi, Iriene Putri Novi Tristanti Novi Tristanti Phichitphon Chotikunnan Raden Bagus Bambang Sumantri Raheem Al-Sabur Rahmat Riansyah Ramadhan, Fauzan purma Retno Agus Setiawan Rezki Rio Anggara Saputra, Vanji Setiawan Ardi Wijaya Setiawan Ardi Wijaya Siti Mutmainah Sunardi Sunardi Tri Fitri Yana Utami Tri Kusuma Wardani Vanji Saputra6 Veithzal Rivai Zainal Walidy Rahman Hakim Walidy Rahman Hakim Wijaya, Setiawan Ardi Yana Safitri Yana Safitri Yuhansyah Nur Fauzi Yuniariana Pertiwi Yuniariana Pertiwi Zied Driss