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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Fisika : Fisika Sains dan Aplikasinya Jurnal Teknik Informatika (JUTIF) COREAI: Jurnal Kecerdasan Buatan, Komputasi dan Teknologi Informasi Journal of Innovation in Applied Natural Science
Prihanto, Surya
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Aerospace Engineering Agriculture, Biological Sciences & Forestry Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Electrical & Electronics Engineering Energy Engineering Environmental Science Immunology & microbiology Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Transportation

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Hand gesture-based automatic door security system using squeeze and excitation residual networks Prihanto, Surya; Effendy, Nazrul; Nopriadi, Nopriadi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp1619-1624

Abstract

Viruses can be transmitted in various ways; one spreads through airborne droplets or the touch of multiple objects. This can occur in any area, including the entrance to the house or access to a room or deposit box. The spread of viruses that cause diseases like COVID-19 has caused many human casualties, and there is still the possibility of similar conditions appearing in the future. Several things need to be done to reduce the chances of spreading disease due to viruses, including developing contactless security support methods. This paper proposes a security system using hand gesture recognition using squeeze and excitation residual networks (SE-ResNet). This research offers a hand gesture recognition system for an automatic door system using SE-ResNet and the residual network (ResNet).
HARDWARE DESIGN OF THE TOUCHLESS HAND CODE AND CONVOLUTIONAL NEURAL NETWORKS - BASED AUTOMATIC DOOR SECURITY SYSTEM Prihanto, Surya; Effendy, Nazrul; Nopriadi, Nopriadi
Jurnal Teknik Informatika (Jutif) Vol. 4 No. 6 (2023): JUTIF Volume 4, Number 6, Desember 2023
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2023.4.6.1117

Abstract

The spread of viruses and bacteria through touching door surfaces is essential in maintaining public hygiene and health. In this context, a hand-coded touchless automatic door hardware design has been developed to reduce the spread of diseases through touch. This research aims to create a plan that includes interface development and hardware design to open and close doors automatically without contact. In this research, the automatic door hardware response is tested based on the numeric input from the hand code represented by the numeric database. The input and output control is connected to Python's graphical user interface (GUI). The GUI system design involves tools to connect the Python programming language and the Arduino microcontroller. Based on the experimental results, the hardware design of the automatic door security system based on hand code and Convolutional Neural Networks functions appropriately.
Augmented Learning untuk Pembelajaran Anatomi Wajah Berbasis Computer Vision Interaktif Prihanto, Surya; Fath, Yusril Al; Satrio, Satrio; Naufal, Muhammad Rifqi
COREAI: Jurnal Kecerdasan Buatan, Komputasi dan Teknologi Informasi Vol 6, No 2 (2025): Pemanfaatan Artificial Intelligence dalam Transformasi Digital
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/coreai.v6i2.13207

Abstract

Perkembangan teknologi Computer Vision memungkinkan terciptanya media pembelajaran interaktif yang membantu siswa memahami anatomi wajah secara visual dan praktis melalui interaksi dengan model digital. Penelitian ini bertujuan untuk mengembangkan media pembelajaran anatomi wajah interaktif berbasis Computer Vision dengan integrasi MediaPipe Face Mesh dan Hand Tracking. Sistem dirancang untuk meningkatkan pemahaman konsep anatomi melalui interaksi visual, auditori, dan kinestetik. Metode yang digunakan adalah Research and Development (R&D) dengan tahapan analisis kebutuhan, perancangan sistem, pengembangan, dan uji coba. Aplikasi memiliki dua fitur utama, yaitu fitur praktikum (Edu-Touch, 3D Board, dan Game Anatomi) serta fitur materi (kuis, buku digital, dan video learning). Hasil pengujian menunjukkan bahwa sistem mampu mendeteksi 468 titik landmark wajah dan 21 titik landmark tangan secara real-time dengan akurasi tinggi. Fitur Edu-Touch berhasil menampilkan animasi 3D dan suara penjelasan saat titik wajah disentuh. Fitur 3D Board mengenali pertemuan landmark jari telunjuk (titik 8) dan ibu jari (titik 4) untuk mengontrol rotasi model 3D, sedangkan Game Anatomi berhasil mendeteksi ekspresi wajah sebagai respons terhadap soal. Hasil ini menunjukkan bahwa integrasi MediaPipe efektif dalam menciptakan pembelajaran yang interaktif, menarik, dan adaptif terhadap berbagai gaya belajar
Beyond the Canopy: Resolving Topographic and Acoustic Complexities with Machine Learning for Karst Avifauna Monitoring Fitryan, Anggyta; Abdurrahman, Ahmad Faruq; Nuryani; Prihanto, Surya; Al Fath, Yusril; Aprilia, Ayu; Junaidi; Surtono, Arif
Journal of Innovation in Applied Natural Science Vol. 1 No. 1 (2025): Journal of Innovation in Applied Natural Science
Publisher : CV Media Inti Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/jinas.v1i1.52

Abstract

Background of study: Tropical karst landscapes harbor exceptional avian biodiversity but pose unique monitoring challenges due to complex topography, cave reverberation, and humidity-driven sound distortion. Conventional ecoacoustic methods fail in these environments, with indices showing weak correlations (r=0.20-0.43) for avian diversity due to insect masking and abiotic interference. Over 83% of karst-endemic birds lack standardized monitoring protocols despite escalating extinction risks.Aims and scope of paper: This review aims to: (1) quantify limitations of current ecoacoustic methods in karst ecosystems, (2) develop a machine learning-enhanced framework addressing topographic and reverberation effects, and (3) establish conservation-ready protocols for endangered karst avifauna. The study synthesizes evidence from 29 studies across hardware innovation, signal processing, and policy applications.Methods: We systematically analyzed 29 studies on acoustic monitoring in karst ecosystems, focusing on machine learning innovations, topographic adaptations, and conservation applications.Result: Topography drives 47% of soundscape variation, surpassing vegetation effects. Machine learning (CNNs/MFCCs) boosts detection accuracy by 22-80% in reverberant caves. Hybrid protocols enable 25-m resolution habitat mapping and precise disturbance monitoring, overcoming tropical "latitude paradox" limitations.Conclusion: This review establishes the first karst-adapted ecoacoustic framework, integrating machine learning with topographic variables to transform monitoring from biodiversity proxy to precision tool. Critical next steps include developing species-specific call libraries, wind-reverberation filters, and policy integration of acoustic baselines for IUCN assessments. The proposed protocols address urgent conservation needs for Earth's most threatened avian sanctuaries.
STUDI KOMPARASI PELARUT ORGANIK PADA ELEKTROLIT TEABF4 TERHADAP KINERJA ELEKTROKIMIA SUPERKAPASITOR KARBON Fath, Yusril Al; Prihanto, Surya; Abdurrahman, Ahmad Faruq; Aprilia, Ayu; Suprihatin, Suprihatin; Firdaus, Iqbal
Jurnal Fisika : Fisika Sains dan Aplikasinya Vol 11 No 1 (2026): Jurnal Fisika : Fisika Sains dan Aplikasinya
Publisher : Universitas Nusa Cendana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35508/fisa.v11i1.27833

Abstract

Superkapasitor berbasis karbon merupakan perangkat penyimpanan energi yang memiliki rapat daya tinggi, siklus hidup panjang, serta kemampuan pengisian–pengosongan cepat. Kinerja superkapasitor sangat dipengaruhi oleh interaksi antara elektroda, separator, dan elektrolit. Penelitian ini bertujuan untuk mengkaji pengaruh variasi pelarut organik Acetonitrile (ACN) dan Propylene Carbonate (PC) pada elektrolit Tetraethylammonium tetrafluoroborate (TEABF4) terhadap kinerja elektrokimia superkapasitor berbasis karbon aktif dengan separator polietilen. Pembuatan elektroda dilakukan dengan metode blending menggunakan komposisi 9 (karbon aktif) : 1 (carbon black) : 1 (PVDF). Karakterisasi struktur dilakukan menggunakan XRD, sedangkan morfologi dianalisis melalui SEM. Hasil XRD menunjukkan karbon aktif bersifat amorf dengan puncak (002), sementara polietilen memiliki struktur kristalin dengan puncak utama pada 2θ ≈ 21° (110) dan 23° (200). Citra SEM memperlihatkan karbon aktif memiliki struktur berpori yang mendukung adsorpsi ion, sedangkan separator polietilen menunjukkan jaringan fibril yang memungkinkan transport ion. Pengujian GCD pada tegangan 1 V menunjukkan bahwa penggunaan pelarut ACN menghasilkan kapasitansi lebih tinggi (47,01 F/g) dibandingkan PC, serta nilai iR drop yang lebih rendah (0,06 V vs 0,57 V). Pada rapat arus tinggi, kepadatan daya meningkat dari 166,6 W/kg hingga 676,04 W/kg, menunjukkan respons energi yang cepat. Hasil ini menegaskan bahwa pemilihan pelarut berperan penting dalam meningkatkan performa superkapasitor.
Co-Authors Abdurrahman, Ahmad Faruq Al Fath, Yusril Aprilia, Ayu Fath, Yusril Al Firdaus, Iqbal Fitryan, Anggyta Junaidi Junaidi Naufal, Muhammad Rifqi Nazrul Effendy Nopriadi Nopriadi Nuryani Satrio Satrio Suprihatin Suprihatin Surtono, Arif