Article Per Year (5 Year)
p-Index From 2021 - 2026
4.174
P-Index
This Author published in this journals
All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Sainteks Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Teknologi Informasi dan Ilmu Komputer InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) InComTech: Jurnal Telekomunikasi dan Komputer JITK (Jurnal Ilmu Pengetahuan dan Komputer) JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Teknologi Sistem Informasi dan Aplikasi Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) JURIKOM (Jurnal Riset Komputer) EDUMATIC: Jurnal Pendidikan Informatika Journal of Information Systems and Informatics Zonasi: Jurnal Sistem Informasi JIKA (Jurnal Informatika) Jurnal Sistem Komputer dan Informatika (JSON) TIN: TERAPAN INFORMATIKA NUSANTARA Idealis : Indonesia Journal Information System Journal of Applied Data Sciences Jurnal Pendidikan dan Teknologi Indonesia Journal Automation Computer Information System (JACIS) INTERNATIONAL JOURNAL OF EDUCATION, INFORMATION TECHNOLOGY, AND OTHERS Amare J-Icon : Jurnal Komputer dan Informatika Journal of Computing Theories and Applications Teknomatika: Jurnal Informatika dan Komputer
Erik Iman Heri Ujianto
Universitas Teknologi Yogyakarta
Religion Aerospace Engineering Arts Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Social Sciences Veterinary Other

Published : 29 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 29 Documents
Search

 1   2   3 
Sistem Pengelolaan Inventori Real-Time untuk UMKM Berbasis Flutter dan QR Code Menggunakan Metode R&D Saputra, Faiz; Ujianto, Erik Iman Heri
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 6 (2025): Desember 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i6.9309

Abstract

The efficiency and accuracy of stock recording in MSMEs are enhanced through the development of a mobile-based inventory management application, addressing the challenges of manual systems, which are prone to human error and data delays. This study implements a real-time system by integrating three key technologies: Flutter as a cross-platform framework for efficient development, QR Codes for rapid item identification, and Firebase as a database for instant data synchronization. By applying the R&D method through the Waterfall model, the application was developed and tested in a case study at Warung Laras. The application is equipped with essential features for daily operations, including security verification using OTP via WhatsApp, unique QR code generation for each item, and the ability to automatically generate inventory reports in PDF format. The results from Black Box testing and user trials show a significant quantitative impact: data recording accuracy increased to 95%, while the average time per transaction was drastically reduced by 70%, from approximately 30 seconds to just 9 seconds. This finding proves that the technological integration has successfully created a reliable and effective solution to drive digital transformation in MSME-scale stock management
MENUJU TATA KELOLA KEPEGAWAIAN YANG EFEKTIF: ANALISIS DAN PERANCANGAN SISTEM DI KECAMATAN GIRINTONTRO, WONOGIRI Sujoko, Sujoko; Darmawan, Surya; Dewanto, Aditya Dimas; Sujarwadi, Agus; Ujianto, Erik Iman Heri; Mahardhika, Chaidar Panji
Amare Vol. 4 No. 2 (2025): Juli - Desember 2025
Publisher : Sekolah Tinggi Agama Katolik Negeri Pontianak

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52075/sh0bkw35

Abstract

Pengelolaan kepegawaian di Kecamatan Giritontro masih dilakukan secara manual, yang menimbulkan berbagai kendala seperti keterlambatan proses administrasi, kesulitan pencarian data, serta tingginya risiko inkonsistensi dan ketidakakuratan data. Untuk mengatasi permasalahan tersebut, kegiatan pengabdian kepada masyarakat ini bertujuan menganalisis dan merancang sistem manajemen kepegawaian kecamatan guna meningkatkan efisiensi dalam pengelolaan data aparatur. Metode yang digunakan meliputi analisis kebutuhan melalui observasi dan wawancara untuk mengidentifikasi permasalahan utama. Selanjutnya dilakukan perancangan alur informasi menggunakan Data Flow Diagram (DFD), serta perancangan basis data dengan Entity-Relationship Diagram (ERD) guna memastikan sistem mampu menyimpan dan mengelola data pegawai secara terstruktur. Rancangan mencakup fitur utama seperti pencatatan data pegawai, pengelolaan jabatan, penilaian kinerja, serta riwayat mutasi dan pengangkatan. Selain itu, dibuat desain antarmuka untuk menggambarkan interaksi pengguna dengan sistem. Hasil dari perancangan ini diharapkan dapat menjadi acuan pengembangan sistem digital kepegawaian di Kecamatan Giritontro, sehingga pengelolaan kepegawaian dapat berjalan lebih optimal, efektif, dan efisien, serta mendukung peningkatan kualitas layanan administrasi di lingkungan pemerintahan kecamatan.
Mobile Based E-Commerce Application with Whatsapp Notification for Transactions Tuasikal, Afrizal Halim Naindhy; Ujianto, Erik Iman Heri
Jurnal Teknologi Sistem Informasi dan Aplikasi Vol. 9 No. 1 (2026): Jurnal Teknologi Sistem Informasi dan Aplikasi
Publisher : Program Studi Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/jtsi.v9i1.55662

Abstract

Online shopping has become an essential part of modern society. Various e-commerce platforms have emerged offering a wide range of products, yet most still focus primarily on selling new items. In fact, there is a growing need for a platform that can accommodate both new and used goods transactions within a single, user-friendly application. Moreover, many existing platforms lack direct integration with instant messaging services such as WhatsApp, resulting in delayed transaction information and potentially leading to miscommunication. To address these challenges, a mobile-based e-commerce application was developed using Flutter as the frontend framework, Flask as the backend API, and MySQL as the database. The main advantage of this application lies in its automatic WhatsApp integration, which enables real-time transaction notifications. Testing results show that the application functions properly and remains stable. The main features include registration, login, product addition, and product listing, along with additional features such as favorites marking, live streaming, and automatic WhatsApp notifications. This application is expected to serve as an effective solution to overcome current platform limitations and to support efficient and convenient buying and selling of both new and used goods.
Face Detection Based on Anti-Spoofing with FaceNet Method for Filtering Contract Cheating in Online Exam Ujianto, Erik Iman Heri; Diyasa, I Gede Susrama Mas; Junaidi, Achmad; Fatullah, Ryan Reynickha; Permanasari, Wahyu Melinda; Sari, Allan Ruhui Fatmah
Journal of Applied Data Sciences Vol 7, No 1: January 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i1.1167

Abstract

This study develops a reliable face-based verification system for online examinations by integrating a face recognition model with a blink detection mechanism to minimize the risk of identity fraud, also known as "contract cheating," and static image manipulation. "Contract cheating" refers to the practice where students hire others to complete their exams or assignments, compromising academic integrity. The growing reliance on online exams has raised concerns about the credibility of facial verification, as conventional methods are often vulnerable to spoofing attempts. To address this issue, the proposed system combines FaceNet, a deep learning model for identity recognition, with Dlib’s eye blink detection to provide a stronger layer of protection. The system was evaluated using 5-fold and 10-fold K-fold cross-validation, and additional testing assessed the impact of different video frame rates on performance. The results show that the system performs effectively in identifying legitimate users and detecting spoofing. FaceNet achieved an accuracy of 96.67 percent, outperforming DeepFace, which showed poorer results in precision, recall, and F1 score for some participants. Both models were evaluated on the same dataset, consisting of 150 images. The preprocessing pipeline, including face detection using MTCNN, cropping, and resizing, was applied consistently to both models to ensure a fair comparison of their performance. The system also demonstrated adaptability, achieving correct classifications at both 15 and 30 frames per second. Anti-spoofing tests based on the eye blink detection system detected all real faces, while static images were classified as spoofing. These results confirm that combining face recognition with liveness detection enhances the security of online examination platforms. The findings demonstrate the system's potential to reduce contract cheating and impersonation fraud, making online examinations more credible. Future work may focus on implementing adaptive thresholding for blink detection and integrating multimodal verification techniques to improve robustness across diverse real-world environments.
BATINARA: Hybrid LLM-BERT-ML Chatbot for Safe Mental Health Support Walangitan, Jeanette; Ujianto, Erik Iman Heri
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 20, No 2 (2026): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.112432

Abstract

This study addresses the pressing need for safe and personalized digital mental health support by mitigating the inherent risk of Large Language Models (LLMs) generating unsafe or unethical responses during high-risk psychological crises. We developed BATINARA, a chatbot system based on a Neuro-Symbolic Hybrid framework. This architecture integrates a Predictive Module (IndoBERT for crisis detection, Random Forest for multi-label emotion classification) with a Generative LLM Module (OpenAI API). Ethical control is enforced by the Dynamic Context Integration Logic (D-CIL), which utilizes clinical rules to uphold the Principle of Nonmaleficence. Key results demonstrate the system’s ability to: (1) Enforce safety protocols through the automatic override of LLM responses when suicidal ideation is detected (Recall IndoBERT 0.9977). (2) Achieve high contextual accuracy in multi-label emotion detection (F1 = 0.94), which supports dynamic personalization via Dynamic Prompt Modulation based on specific therapeutic styles and user PHQ-9/GAD-7 clinical scores. (3) Enhance interaction transparency through the real-time visualization of detected emotions. This Neuro-Symbolic hybrid approach proves effective in mitigating clinical risks associated with generative AI, resulting in adaptive and ethically sound therapeutic interactions.
Deteksi Dini Disleksia Berbasis Analisis Tulisan Tangan Menggunakan Transfer Learning MobileNetV2 Difa Nur Sahara; Erik Iman Heri Ujianto
InComTech : Jurnal Telekomunikasi dan Komputer Vol. 16 No. 1 (2026)
Publisher : Department of Electrical Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/incomtech.v16i1.36696

Abstract

Keterlambatan dalam mengidentifikasi disleksia dapat secara signifikan memengaruhi perkembangan akademik serta emosional anak, sebuah tantangan yang di Indonesia masih sering dihadapi akibat ketergantungan pada metode observasi manual. Untuk mengatasi masalah tersebut, sebuah prototipe aplikasi berbasis web dirancang sebagai alat bantu untuk mengidentifikasi potensi disleksia secara dini melalui analisis pola tulisan tangan. Sistem ini diimplementasikan menggunakan algoritma Convolutional Neural Network (CNN) dengan pendekatan transfer learning pada arsitektur MobileNetV2. Model dilatih menggunakan dataset primer yang terdiri dari 75 citra tulisan tangan siswa dari SD Negeri Klegenwonosari, Kabupaten Kebumen yang diperkaya variasinya melalui teknik augmentasi data. Evaluasi model melalui K-Fold Cross-Validation menunjukkan capaian akurasi rata-rata yang menjanjikan sebesar 0,7200±0,0980. Penambahan volume data terbukti secara positif memengaruhi stabilitas kinerja model serta kemampuannya untuk mulai mengenali kelas minoritas. Prototipe aplikasi ini diharapkan dapat berfungsi sebagai alat skrining awal yang praktis bagi para pendidik dan orang tua.
Mapping Biometric Security Paradox: A Behavioral Study of Perception and Awareness Among Indonesian Digital Natives Erik Iman Heri Ujianto; Rianto Rianto
Journal of Computing Theories and Applications Vol. 3 No. 4 (2026): JCTA 3(4) 2026
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.15935

Abstract

 The rapid adoption of smartphones among Indonesian digital natives has increased reliance on biometric authentication systems. However, empirical evidence regarding the relationship between user satisfaction and security risk awareness remains limited, particularly in developing-country contexts. This study investigates the behavioral dynamics of biometric security perception among 266 respondents, consisting of 221 high school students and 45 university students in Indonesia. A Python-based computational pipeline incorporating Akaike Information Criterion (AIC) validation and 1,000-iteration stochastic bootstrapping was employed to evaluate nonlinear behavioral patterns using Polynomial Regression and Ordinary Least Squares (OLS) multivariate analysis. The results confirm the existence of a nonlinear Security Paradox. While the overall population demonstrates a positive quadratic trajectory, the university student group exhibits a concave-down parabolic relationship (a=−0.0460), indicating a decline in perceived utility beyond a specific security threshold. The identified behavioral breaking point occurs at X≈5.45 (95% CI: 2.99–20.77), suggesting that excessive security hardening may reduce perceived usability and increase cognitive friction. Furthermore, the ablation analysis reveals that security risk awareness (p<0.001) is the strongest predictor of user satisfaction, exceeding the influence of daily usage intensity. Segment-level analysis further demonstrates behavioral divergence between respondent groups. High school students exhibit relatively uniform satisfaction toward biometric systems, whereas university students display greater variability and more critical perceptions regarding authentication friction. These findings indicate that highly rigid security configurations may become less effective for users with higher digital literacy and risk awareness. This study contributes a computationally validated behavioral framework for understanding security–utility trade-offs and provides a conceptual foundation for developing adaptive, user-centric, and friction-aware biometric authentication systems.
Klasifikasi Performa Video Free Fire di YouTube Menggunakan Pelebelan Otomatis Berbasis K-Means, Random Forest, dan SHAP Wayan Praka; Erik Iman Heri Ujianto
Journal Automation Computer Information System Vol. 6 No. 2 (2026): November (In Progress Issue)
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/jacis.v6i2.203

Abstract

Perkembangan platform YouTube mendorong kreator konten Free Fire untuk memahami faktor-faktor yang memengaruhi performa video untuk meningkatkan strategi publikasi yang lebih efektif. Namun, penelitian yang mengintegrasikan pelebelan otomatis, klasifikasi, dan interpretabilitas model untuk mengungkap faktor penentu masih terbatas. Penelitian ini bertujuan mengembangkan model klasifikasi performa video menggunakan pelabelan otomatis berbasis K-Means, algoritma Random Forest, dan Explainable Artificial Intelligence berbasis SHAP. Dataset yang digunakan terdiri atas 4.000 video Free Fire dari kanal YouTube Indonesia. Proses pelabelan menggunakan algoritma K-Means berdasarkan evaluasi Elbow Method, Silhouette Score, Calinski-Harabasz Index, dan Davies-Bouldin Index. Hasil evaluasi menunjukkan bahwa K=2 memberikan kualitas klaster terbaik dengan nilai Silhouette Score tertinggi, dipilih untuk membentuk dua label, yaitu High Performance sebesar 74,23% dan Low Performance sebesar 25,77%. Selanjutnya, model Random Forest yang dioptimalkan dengan GridSearchCV serta pembagian 80% data pelatihan dan 20% data pengujian. Hasil dari pengujian Random Forest mencapai akurasi sebesar 92,37% dan akurasi 10-Fold Cross Validation rata-rata sebesar 91,59%. Analisis SHAP menunjukkan bahwa Subscriber_Channel merupakan faktor yang paling berpengaruh, diikuti oleh Duration_Sec, Title_Length, Video_Age_Days, Upload_Hour_WIB dan Upload_day_WIB. Kebaruan penelitian ini terletak pada integrasi pelabelan otomatis berbasis K-Means, optimasi Random Forest menggunakan GridSearchCV, serta interpretasi model menggunakan SHAP dalam analisis performa video YouTube. Penelitian ini masih terbatas pada video Free Fire dari kanal YouTube Indonesia sehingga generalisasi terhadap kategori konten maupun wilayah lain memerlukan penelitian lanjutan
Uncovering Security Vulnerabilities in Electronic Medical Record Systems: A Comprehensive Review of Threats and Recommendations for Enhancement Dian Wijayanti; Erik Iman Heri Ujianto; Rianto Rianto
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 1 (2024): March
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i1.28192

Abstract

Cybersecurity is a critical concern for healthcare organizations in the digital era, as patient data privacy faces significant risks from numerous vulnerabilities. Given the escalating cyberattacks in healthcare, understanding EMR system vulnerabilities has become imperative. This study aimed to find the main weaknesses in Electronic Health Record (EHR) systems and suggest proven methods to improve security and keep patient information private. Utilizing a cross-sectional analysis, we assessed the effectiveness of current security protocols against identified threats. We systematically reviewed 25 recent, high-quality articles (from 2020 to 2023) on EMR vulnerabilities, selected based on their relevance and the efficacy of their proposed solutions. Our analysis revealed that system architecture flaws and credential misuse represented the most significant threats, with hacking incidents most frequently targeting these weaknesses. The analysis identified six key threat categories to EMR security: compromised access, system architecture flaws, data sharing challenges, hacking, credential misuse, and non-compliance with regulations. This framework introduced a multi-layered defense strategy, unique in incorporating both technical and behavioral security measures. The study provided a novel framework combining technological and management safeguards, offering a fresh perspective on modern EMR vulnerabilities. The detailed threat categorization gave healthcare organizations a strategic basis for improved security planning and resource allocation. The actionable insights from this study could greatly enhance EMR security protocols in healthcare settings, potentially reducing data breaches and improving patient trust. Further research was warranted to test the effectiveness of the proposed framework across various healthcare environments.
Co-Authors Achmad Junaidi Adhi Prahara ADITYA HERMAWAN Agus Rakhmadi Mido Agus Sujarwadi Amelia Citra Dewi Apriansyah, Ferryma Arba Azmi, Falah Ilhan Darmawan, Surya Dewanto, Aditya Dimas Dian Wijayanti Difa Nur Sahara Diyasa, I Gede Susrama Mas Enggar Novianto Fatullah, Ryan Reynickha Fauzi, Rizky Gunawan Wibisono Iman, Asri Khayatul Kastella, Thufail Bintang Mahardhika, Chaidar Panji Mido, Agus Rakhmadi Muhammad Husni Wahid Muhammad Rizki Murinto Murinto Permanasari, Wahyu Melinda Purwanto, Candra Ihsan Rianto Rianto Rianto Rianto Santoso, Firdaus Restu Rafi Saputra, Candra Heru Saputra, Faiz Sari, Allan Ruhui Fatmah Sujoko Sujoko Syahab, Alfin Syarifuddin Tri Waluyo Triyas Hevianto Saputro Tuasikal, Afrizal Halim Naindhy Walangitan, Jeanette Wayan Praka Wibowo, Wahyu Andre Zharifa, Anjana Haya Atha