Article Per Year (5 Year)
p-Index From 2021 - 2026
8.851
P-Index
This Author published in this journals
All Journal JURNAL PASTI (PENELITIAN DAN APLIKASI SISTEM DAN TEKNIK INDUSTRI) Indonesian Journal of Applied Informatics Antivirus : Jurnal Ilmiah Teknik Informatika Jurnal Ilmiah ILKOMINFO - Ilmu Komputer & Informatika Jurnal Mnemonic JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Teknika JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) JIKA (Jurnal Informatika) Jurnal Bumigora Information Technology (BITe) Jurnal Informatika Teknologi dan Sains (Jinteks) JASTEN (Jurnal Aplikasi Sains Teknologi Nasional
Nurlaily Vendyansyah
Institut Teknologi Nasional Malang
Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Physics Transportation Other

Published : 81 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search
Journal : Indonesian Journal of Applied Informatics

 1 
Implementasi Haar Cascade Untuk Deteksi Kendaraan Bermotor Pada Pemantauan Lalu Lintas Kota Malang Rian Setya Budi; Deddy Rudhistiar; Nurlaily Vendyansyah
IJAI (Indonesian Journal of Applied Informatics) Vol 9, No 2 (2025)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/ijai.v9i2.95558

Abstract

Abstrak : Pemantauan lalu lintas di kota besar seperti Kota Malang semakin penting untuk mendukung ketertiban dan analisis jalan raya. Meskipun infrastruktur CCTV telah tersedia, teknologi deteksi kendaraan secara otomatis masih terbatas. Penelitian ini mengimplementasikan metode Haar Cascade untuk deteksi kendaraan bermotor pada rekaman CCTV guna meningkatkan akurasi dan efektivitas pemantauan lalu lintas. Evaluasi kinerja sistem dilakukan menggunakan metrik akurasi, presisi, recall, F1 score, serta pengujian blackbox untuk mengukur keandalan fungsi sistem. Hasil menunjukkan performa deteksi terbaik pada pagi hari, dengan akurasi 94.84%, presisi 84.21%, recall 100%, dan F1 score 91.43% untuk mobil, serta akurasi 93.81%, presisi 92.45%, recall 94.23%, dan F1 score 93.33% untuk motor. Namun, performa menurun pada malam hari, dengan akurasi 88.89% dan F1 score 71.43% untuk mobil, serta akurasi 46.15% dan F1 score 63.64% untuk motor. Pengujian blackbox menunjukkan bahwa setiap fungsi sistem berjalan sesuai dengan yang diharapkan tanpa ditemukan error dalam pengoperasian fitur utama. Penelitian ini menunjukkan bahwa metode Haar Cascade efektif untuk deteksi kendaraan di siang hari, namun membutuhkan peningkatan untuk kondisi pencahayaan rendah.=================================================Abstract :Traffic monitoring in major cities like Malang has become increasingly important to support order and road analysis. Although CCTV infrastructure is available, the technology for automatic vehicle detection remains limited. This study implements the Haar Cascade method for motor vehicle detection on CCTV recordings to improve the accuracy and effectiveness of traffic monitoring. The system's performance evaluation was conducted using metrics such as accuracy, precision, recall, F1 score, and blackbox testing to assess system reliability. The results show the best detection performance in the morning, with an accuracy of 94.84%, precision of 84.21%, recall of 100%, and an F1 score of 91.43% for cars, and an accuracy of 93.81%, precision of 92.45%, recall of 94.23%, and an F1 score of 93.33% for motorcycles. However, performance decreased at night, with an accuracy of 88.89% and an F1 score of 71.43% for cars, and an accuracy of 46.15% and an F1 score of 63.64% for motorcycles. Blackbox testing shows that all system functions operate as expected without any errors in the main features. This study demonstrates that the Haar Cascade method is effective for vehicle detection during the day but requires improvement for low-light conditions.
Implementasi Convolutional Neural Network (CNN) untuk Face Recognition pada Sistem Presensi Kehadiran Moch Arif Rochmanullah; Nurlaily Vendyansyah; Febriana Santi Wahyuni
IJAI (Indonesian Journal of Applied Informatics) Vol 9, No 2 (2025)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/ijai.v9i2.95563

Abstract

Abstrak : Sistem presensi merupakan elemen penting dalam memastikan kehadiran, terutama di lingkungan pendidikan dan pekerjaan. Penelitian ini bertujuan mengembangkan sistem presensi berbasis face recognition menggunakan metode Convolutional Neural Network (CNN) untuk mengatasi kelemahan presensi manual yang rentan terhadap kecurangan, seperti di Prodi Teknik Informatika ITN Malang. Model CNN dilatih dengan deep learning menggunakan dataset wajah mahasiswa untuk mengenali pola unik fitur wajah. Hasilnya, model mencapai training accuracy sebesar 97%, validation accuracy sebesar 90%, dan pengujian mencapai accuracy 93%. Sistem ini meningkatkan efisiensi absensi dan akurasi identifikasi hingga 93%, sekaligus mengurangi potensi kecurangan.CNN terbukti andal dalam mendukung presensi berbasis teknologi dengan pengelolaan lebih praktis. Kendati demikian, performa model masih dapat ditingkatkan melalui pengayaan dataset dan optimasi model. Sistem ini berpotensi besar meningkatkan keandalan dan keamanan proses presensi, menjadi solusi inovatif dalam pengelolaan kehadiran di era digital.=====================================================Abstract :The attendance system is a crucial element in ensuring presence, especially in educational and workplace settings. This study aims to develop a face recognition-based attendance system using the Convolutional Neural Network (CNN) method to address the weaknesses of manual attendance prone to fraud, as observed in the Informatics Engineering Study Program at ITN Malang. The CNN model was trained using deep learning techniques with a student face dataset to recognize unique facial features. The results show the model achieved a training accuracy of 97%, validation accuracy of 90%, and testing accuracy of 93%. This system improves attendance efficiency and identification accuracy by 93%, while reducing the potential for fraud. CNN has proven reliable in supporting technology-based attendance with more practical management. However, the model’s performance can still be improved through dataset enrichment and optimization. This system holds significant potential to enhance the reliability and security of attendance processes, providing an innovative solution for managing attendance in the digital era.
Aplikasi Presensi Siswa Berbasis Location Based Services (LBS) Dengan Haversine Formula Di SMK Islam Al-Futuhiyyah Mohammad Harifin; Nurlaily Vendyansyah; Febriana Santi Wahyuni
IJAI (Indonesian Journal of Applied Informatics) Vol 9, No 2 (2025)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/ijai.v9i2.95566

Abstract

Abstrak : Perkembangan teknologi informasi membawa dampak signifikan di bidang pendidikan, termasuk dalam sistem presensi siswa. Penelitian ini bertujuan untuk mengembangkan aplikasi presensi siswa berbasis Android dengan menggunakan metode Location Based Services (LBS) di SMK Islam Al-Futuhiyyah. Metode yang digunakan adalah Research and Development (R&D) dengan model pengembangan perangkat lunak Waterfall, meliputi analisis kebutuhan, desain, implementasi, pengujian, dan evaluasi. Aplikasi ini menggunakan metode Haversine untuk mengukur jarak antara lokasi siswa dan lokasi sekolah, sehingga memastikan presensi dilakukan di area yang telah ditentukan. Hasil pengujian menunjukkan bahwa aplikasi ini berhasil memvalidasi presensi dengan akurasi tinggi, di mana Haversine Formula menghasilkan akurasi presensi hingga 100%. Pengujian Black Box memastikan semua fungsi aplikasi berjalan sesuai spesifikasi, sedangkan pengujian LBS membuktikan keakuratan dalam mendeteksi lokasi siswa. Selain itu, berdasarkan User Acceptance Testing (UAT) yang melibatkan siswa dan guru, aplikasi ini memperoleh skor kepuasan 81,7%. Aplikasi ini juga mempermudah siswa dalam melakukan presensi, sekaligus membantu guru dan operator sekolah dalam memantau dan mengelola data presensi. Implementasi aplikasi ini memberikan solusi efektif dan efisien untuk menggantikan metode presensi manual yang kurang praktis. Penelitian ini diharapkan dapat meningkatkan kualitas layanan pendidikan di SMK Islam Al-Futuhiyyah dan menjadi referensi bagi pengembangan sistem presensi berbasis teknologi di lembaga pendidikan lainnya===================================================Abstract : The development of information technology has a significant impact on the field of education, including in the student attendance system. This study aims to develop an Android-based student attendance application using the Location Based Services (LBS) method at SMK Islam Al-Futuhiyyah. The method used is Research and Development (R&D) with the Waterfall software development model, including needs analysis, design, implementation, testing, and evaluation. This application uses the Haversine method to measure the distance between the student's location and the school location, thus ensuring that attendance is carried out in a predetermined area. The test results show that this application has successfully validated attendance with high accuracy, where the Haversine Formula produces attendance accuracy of up to 100%. Black Box testing ensures that all application functions run according to specifications, while LBS testing proves accuracy in detecting student locations. In addition, based on User Acceptance Testing (UAT) involving students and teachers, this application received a satisfaction score of 81,7% This application also makes it easier for students to take attendance, while helping teachers and school operators to monitor and manage attendance data. The implementation of this application provides an effective and efficient solution to replace the less practical manual attendance method. This research is expected to improve the quality of educational services at Al-Futuhiyyah Islamic Vocational School and become a reference for the development of technology-based attendance systems in other educational institutions.
Co-Authors A. Asrul Achmad Fauzi Adi Prasetyo, Guntur Agung Panji Sasmito Agung Prasetyo, Yudha Ahmad Fahrudi Setiawan Ahmad Faisol AHMAD FAISOL Akbar, Muhamad Faisol Al Biruni, Muhammad Ali Mahmudi Ali Ridho Alif Fajar Fadhillah, Dimas Amin, Muhammad Yusuf Angesti Fitri, Anggie Anggara, Arul Anggriati, Shafira Aulia, Lalu Muhammad Fatwa Bayu Samudro, Agusti Benny Korain, Lukas Bugis, Syahroni Chintia Devi, Annisa Davi Firmansyah, Muhammad Deddy Rudhistiar Deswandi Yahya, Raflizar Dyah Agustin, Praditasari Edgar Septandita Hariyanto, Nicollas Eka Pradina, Ayuni Fahrudi Setiawan, Ahmad Fajrul Mujab, Akhmad Febriana Santi Wahyuni Febrianti, Fitri Fikri, Rizal Firmansah, Ridho Yuli Gilang Ramadhan Guntur Adi Prasetyo Hadi Al Haddad, Abdullah Haifan, Azrul Hamdani, Andrian Irfie Hani Zulfia Zahro Hernoko, Marvelina Gracia Kartiko Ardi Widodo Khoiru Nurdina, Arista Khoirul Muwahidin, Habib Kurnia Sella, Fernanda Lusi Damayanti Ma'rif, Ikhsan Masini, Joi Mira Orisa Moch Arif Rochmanullah Mohammad Harifin Mohammad Hasan Basri Mukhamad Alfian, Deska Mutiara Shandhini Maylita, Nefa Muzaky, Mohammad Rizal Nelly Budiharti Nugroho, Dwi Yulianto Nur Cahyo, Dwi Peniel Immanuel Gultom Peniel Immanuel Gultom Pratama, Wahyu Tedy Prayoga, Moch. Irfan Primaswara Prasetiya , Renaldi Priyo Kartiko, Sandi Puka, Madelbertha Fridolin Putra, Dzulfidho Wijianto Putri, Kiky Rachmad Agung Laksono Rafi, Mochammad Renaldi Prasetya Renaldi Primaswara Prasetya Rian Setya Budi Riziq Gyfari, Aghisna Rizky Genta Ananda, Muhammad Satwikayana, Sujud Sentot Achmadi Setyohadi Pratama, Dhimas Suryani, Tutut Suryo Adi Wibowo Taralandu, Deriatno Tuasikal, Nyongpati Rahmat Vernanda, Arninda Agnes WAHYUDI, RIZAL Wangsa Nata Asmara, Krisna Xaverius Ariwibisono, Fransiskus Yacub, Dhiky Wardany Yosep Agus Pranoto Yunianto, Muhammad Fauzan Yuris Wijayanto, Fajar Zahro’, Hani Zulfia Zulfia Zahro’, Hani