Prosiding Seminar
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3 Juli 2026
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Fakultas Bisnis & Manajemen / Bisnis Digital
Penulis: Satria Audria Sakti, Aldi Akbar Al Hafid, Arni Retno Mariana, Afrizal, Alfiah Khoirunisa
Today, there are still various obstacles faced by the community in protecting themselves from COVID-19
infection and providing valid health services. The purpose of this paper is to explain how healthcare systems
interact with IoT technology to provide warnings to users when they are on COVID-19 zoning and provide
information on the implementation of health protocols according to zones. The research method used is the
SLR (Systematic Literature Review) method, where the data collection process is carried out by documenting
all research articles that match the criteria, search string from 2000 to 2020. The results of the discussion of
this paper are how to design a framework for improving health information services as an early prevention
effort against the dangers of COVID-19. The conclusion from the healthcare system framework can be used as
a reference for building IoT-based applications in self-monitoring of the dangers of COVID-19
Jurnal Ilmiah
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26 Mei 2026
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Fakultas Teknologi Informasi & Komunikasi / Sistem Informasi
Penulis: Sidik
Inaccurate targeting in subsidized LPG distribution remains a persistent policy challenge in Indonesia, where manual verification processes are vulnerable to misuse and administrative error. Addressing this gap, the present study develops and evaluates a biometric identity verification system based on Convolutional Neural Networks (CNNs) to improve the accuracy and accountability of subsidy allocation at the point of distribution. Following the CRISP-DM framework, two CNN architectures with fundamentally different design philosophies were compared: ResNet-IR, optimized for representational depth and recognition accuracy, and MobileFaceNet, designed for computational efficiency on resource-constrained hardware. Both models were sourced from the InsightFace framework as pre-trained models and evaluated on a locally acquired dataset of 111 registered subsidy recipients from Pajang Village, Tangerang City. Evaluation across face identification (1:N) and face verification (1:1) tasks reveals that ResNet-IR consistently outperforms MobileFaceNet, achieving an accuracy of 94.7% with a precision, recall, and F1-score of 0.9043, compared to MobileFaceNet’s accuracy of 93.7% and F1-score of 0.8862. The primary contribution of this work is to demonstrate, for the first time in the Indonesian subsidy distribution context, that deep learning-based facial recognition can serve as a viable, eployable mechanism for biometric identity verification in public service programs offering a technically grounded pathway toward more transparent and equitable subsidy targeting.
Jurnal Ilmiah
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4 Mei 2026
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Fakultas Teknologi Informasi & Komunikasi / Sistem Informasi
Penulis: Achmad Sidik, M. Bucci Ryando, M. Ramaddan Julianti, Agus Rifaldi
Inaccurate targeting in subsidized LPG distribution remains a persistent policy challenge in Indonesia, where manual verification processes are vulnerable to misuse and administrative error. Addressing this gap, the present study develops and evaluates a biometric identity verification system based on Convolutional Neural Networks (CNNs) to improve the accuracy and accountability of subsidy allocation at the point of distribution. Following the CRISP-DM framework, two CNN architectures with fundamentally different design philosophies were compared: ResNet-IR, optimized for representational depth and recognition accuracy, and MobileFaceNet, designed for computational efficiency on resource-constrained hardware. Both models were sourced from the InsightFace framework as pre-trained models and evaluated on a locally acquired dataset of 111 registered subsidy recipients from Pajang Village, Tangerang City. Evaluation across face identification (1:N) and face verification (1:1) tasks reveals that ResNet-IR consistently outperforms MobileFaceNet, achieving an accuracy of 94.7% with a precision, recall, and F1-score of 0.9043, compared to MobileFaceNet’s accuracy of 93.7% and F1-score of 0.8862. The primary contribution of this work is to demonstrate, for the first time in the Indonesian subsidy distribution context, that deep learning-based facial recognition can serve as a viable, deployable mechanism for biometric identity verification in public service programs offering a technically grounded pathway toward more transparent and equitable subsidy targeting.
Jurnal Ilmiah
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2 Oktober 2025
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Fakultas Teknologi Informasi & Komunikasi / Teknik Informatika
Penulis: Rusmawan Mawardi, Erwana Amarulloh Sunarya, Muhammad Luthfi Prabowo, Triono
Perkembangan ilmu pengetahuan dan teknologi yang pesat telah memberikan pengaruh signifikan dalam bidang teknologi informasi, khususnya pada sistem berbasis internet. Di tingkat Sekolah Menengah Pertama (SMP), masih banyak sekolah yang belum mengenal dan
memanfaatkan teknologi ini secara optimal. SMP Negeri 2 Pakuhaji merupakan salah satu sekolah yang telah memiliki fasilitas yang memadai untuk mendukung proses pembelajaran siswa. Meskipun beberapa sistem telah terkomputerisasi, sekolah ini masih menghadapi permasalahan yang cukup kompleks, terutama dalam hal monitoring nilai, pengumpulan data
nilai, serta proses entri nilai ke dalam komputer. Saat ini, pengolahan nilai siswa masih dilakukan secara manual oleh bagian tata usaha, yangmenyebabkan proses menjadi lambat, sulit, dan kurang akurat. Untuk mengatasi permasalahan tersebut, diperlukan pengembangan sistem yang dapat mengoptimalkan waktu pemrosesan dan menjaga keakuratan data. Penelitian ini menggunakan metode System Development Life Cycle (SDLC) dengan tahapan analisis, perancangan, pemrograman, pengujian, operasi, dan pemeliharaan. Bahasa pemrograman yang digunakan adalah UML dan MySQL. Hasil yang diharapkan dari penelitian ini adalah terciptanya sistem informasi nilai berbasis daring yang dapat memberikan kemudahan bagi
siswa serta membantu guru dalam proses pengolahan nilai siswa di SMP Negeri 2 Pakuhaji agar lebih efisien, mudah, tepat, dan akurat.
Jurnal Ilmiah
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21 Agustus 2025
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Fakultas Teknologi Informasi & Komunikasi / Sistem Informasi
Penulis: Qothrun Nada Alfath, Ruslan Rizki Hidayat, Zainul Hakim, Sri Rahayu
Digital transformation is essential in modern education, particularly at SMK Bina Am Ma’mur, where traditional learning methods, such as printed materials and the WhatsApp application, are still primarily used. These conventional methods limit flexibility, interactivity, and student engagement, which are crucial for effective learning. To address these challenges, this study develops an interactive web-based e-learning platform designed to enhance the learning experience at SMK Bina Am Ma’mur. The research employs a Research & Development (R&D) methodology, guided by the ADDIE model, which includes five phases: Analyze, Design, Development, Implementation, and Evaluation. Data were collected through literature reviews, observations, interviews with teachers, expert validation, and student feedback, followed by descriptive analysis to interpret the results. The validation process revealed that the developed platform is highly feasible for educational use. Media experts rated it 95% suitable, while subject matter experts provided a 91% rating. Additionally, the platform received an 89% approval rating from students, indicating its effectiveness in improving material comprehension and engagement. These findings suggest that the interactive e-learning platform is a highly effective tool for enhancing the learning process at SMK Bina Am Ma’mur, making learning more flexible, accessible, and engaging. This innovative platform has the potential to overcome the limitations of traditional media, fostering greater student motivation, interactivity, and overall learning outcomes, particularly in vocational education.
