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All Journal JITK (Jurnal Ilmu Pengetahuan dan Komputer) Tematik : Jurnal Teknologi Informasi Komunikasi AMMA : Jurnal Pengabdian Masyarakat
Ahmad Rifai
STMIK IKMI Cirebon
Humanities Chemistry Computer Science & IT Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Nursing

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SMART ATTENDANCE TRACKING SYSTEM EMPLOYING DEEP LEARNING FOR FACE ANTI-SPOOFING PROTECTION Bani Nurhakim; Ahmad Rifai; Dian Ade Kurnia; Dadang Sudrajat; Ujang Supriatna
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 3 (2025): JITK Issue February 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i3.5992

Abstract

Conventional attendance systems face challenges in accuracy and efficiency, often vulnerable to spoofing and data manipulation. This study addresses these issues by developing a smart attendance system integrating Deep Learning-based facial recognition with anti-spoofing technology. The system ensures secure and reliable attendance authentication while automating and enhancing management processes. Utilizing a convolutional neural network (CNN) architecture, the system processes raw facial images directly without additional feature extraction, improving accuracy and efficiency. A novel training strategy, termed 50 Random Samples-30 Sub-epochs Count-1 Epoch, is introduced to optimize the training process. This strategy involves random sampling during each forward pass and grouping 30 passes as one epoch, enabling the use of complex CNN architectures and automatic dataset expansion. The system achieves 98.90% accuracy in identifying genuine attendance, maintaining a confidence level above 80%, significantly reducing spoofing risks and errors. This innovative solution has significant implications, particularly for educational institutions. It automates attendance tracking, minimizes manual effort, reduces errors, and supports disciplinary enforcement through accurate data. Moreover, its scalability allows for application across various environments, offering benefits to a wide range of institutions. By enhancing data accuracy and operational efficiency, this system sets a foundation for smarter, more reliable attendance management, strengthening administrative practices in education and beyond.
Peningkatan Kreativitas Karang Taruna Melalui Pelatihan Desain Grafis dan Konten Digital Ahmad Faqih; Ahmad Rifai; Mohamad Riad Solihin; Muhammad Daffa Ayyasy
AMMA : Jurnal Pengabdian Masyarakat Vol. 1 No. 03 (2022): AMMA : Jurnal Pengabdian Masyarakat
Publisher : CV. Multi Kreasi Media

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Abstract

The role of youth in village development is becoming increasingly important in the digital era, particularly in promoting local potential, social activities, and creative economic initiatives. One of the main challenges faced by youth organizations such as Karang Taruna is the limited ability to produce engaging digital content and graphic designs, despite the great potential of social media as a means of promotion and communication. This Community Service Program (PKM) aims to enhance the capacity of Karang Taruna members in creating creative content and graphic design using simple and accessible digital applications. The program was carried out in several stages: identifying participants’ needs, developing training modules, conducting in-person training sessions, and providing post-training assistance. The training materials included the basics of graphic design, understanding visual elements (color, typography, layout), simple photography and videography techniques using smartphones, and the use of design applications such as Canva, CapCut, and Pixellab. Participants also practiced creating social media content to promote village activities, local MSMEs, and social campaigns managed by Karang Taruna. The results show a significant improvement in participants' skills in designing posters, Instagram feeds, and short videos for publication purposes. Some of the participants' works have been uploaded to Karang Taruna’s official social media accounts and received positive responses from the community. This program not only enhanced technical skills but also fostered confidence, creativity, and a spirit of collaboration among members. It makes a tangible contribution to empowering village youth through digital literacy and creative media. Moving forward, this training can be developed into a sustainable program to strengthen the village’s digital identity and promote local potential through community-based efforts.
Penerapan Model LSTM Univariat dengan Walk-Forward Validation untuk Estimasi Harga Saham Nokia Ahmad Rifai; Roni Saputra; Dian Ade Kurnia; Fatihanursari Dikanandafatiha.dikananda@gmail.com
TEMATIK Vol. 13 No. 1 (2026): Tematik : Jurnal Teknologi Informasi Komunikasi (e-Journal) - Juni 2026
Publisher : LPPM POLITEKNIK LP3I BANDUNG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38204/tematik.v13i1.3000

Abstract

Prediksi harga saham merupakan permasalahan yang kompleks karena karakteristik data deret waktu finansial yang bersifat non-linear, volatil, dan dinamis. Meskipun algoritma Long Short-Term Memory (LSTM) terbukti efektif dalam menangkap pola temporal, banyak penelitian sebelumnya menggunakan pendekatan multivariat yang melibatkan variabel dengan korelasi sangat tinggi sehingga berpotensi menimbulkan redundansi informasi dan meningkatkan kompleksitas model. Penelitian ini mengusulkan model LSTM univariat untuk memprediksi harga saham Nokia Corporation (NOK) dengan menggunakan harga penutupan sebagai variabel masukan tunggal. Data historis harian periode 1 Oktober 2015 hingga 24 Oktober 2025 sebanyak 2.532 observasi diperoleh dari Yahoo Finance. Sebelum proses pemodelan, dilakukan analisis korelasi terhadap variabel Open, High, Low, Close, dan Volume. Hasil analisis menunjukkan bahwa variabel harga memiliki korelasi yang sangat tinggi (r > 0,99), sedangkan variabel Volume memiliki korelasi yang sangat rendah terhadap variabel harga (−0,052 ≤ r ≤ −0,043). Berdasarkan hasil tersebut, harga penutupan dipilih sebagai fitur utama dalam pemodelan. Untuk mengevaluasi performa model pada kondisi prediksi yang realistis, diterapkan metode Walk-Forward Validation (WFV) sebanyak 30 iterasi. Hasil pengujian menunjukkan bahwa model memperoleh nilai MSE sebesar 0,0260, RMSE sebesar 0,1613, MAE sebesar 0,1086, MAPE sebesar 2,75%, dan koefisien determinasi (R²) sebesar 0,9446. Hasil tersebut menunjukkan bahwa model mampu menjelaskan 94,46% variasi harga saham dengan tingkat kesalahan prediksi yang rendah. Penelitian ini menyimpulkan bahwa model LSTM univariat yang didukung oleh proses seleksi fitur yang sistematis dan validasi temporal yang robust mampu menghasilkan prediksi harga saham yang andal dengan kompleksitas yang lebih rendah dibandingkan pendekatan multivariat konvensional.
Co-Authors Ahmad Faqih Bani Nurhakim Dadang Sudrajat Dian Ade Kurnia Fatihanursari Dikanandafatiha.dikananda@gmail.com Mohamad Riad Solihin Muhammad Daffa Ayyasy Roni Saputra Ujang Supriatna