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All Journal snimed Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Techno.Com: Jurnal Teknologi Informasi Jurnal Teknologi dan Manajemen Informatika Jurnal Informatika dan Teknik Elektro Terapan Sistemasi: Jurnal Sistem Informasi JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Sisfokom (Sistem Informasi dan Komputer) Technomedia Journal ScientiCO : Computer Science and Informatics Journal IT (INFORMATIC TECHNIQUE) JOURNAL Advance Sustainable Science, Engineering and Technology (ASSET) DedikasiMU: Journal of Community Service PROSISKO : Jurnal Pengembangan Riset dan observasi Rekayasa Sistem Komputer Jurnal MENTARI: Manajemen, Pendidikan dan Teknologi Informasi Jurnal Ecotipe (Electronic, Control, Telecommunication, Information, and Power Engineering) The Indonesian Journal of Computer Science Tadulako Science and Technology Journal
Hajra Rasmita Ngemba
Information System Department, Faculty Of Engineering, Tadulako University, Jl. Soekarno Hatta No KM 9 Palu 94148, Central Sulawesi, Indonesia
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Journal : JOURNAL OF APPLIED INFORMATICS AND COMPUTING

 1 
Analysis of the Use of MTCNN and Landmark Technology to Improve the Accuracy of Facial Recognition on Official Documents Chandra, Ferri Rama; Ngemba, Hajra Rasmita; Hamid, Odai Amer; Lapatta, Nouval Trezandy; Hendra, Syaiful; Nugraha, Deny Wiria; Syahrullah, Syahrullah
Journal of Applied Informatics and Computing Vol. 9 No. 1 (2025): February 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i1.8814

Abstract

A face recognition system consists of two stages: face detection and face recognition. Detection of features such as eyes and mouth is important in facial image processing, especially for official documents such as identity cards. To ensure identification accuracy, this research applies facial landmark extraction technology and MTCNN (Multi-Task Cascaded Convolutional Neural Network). The purpose of this research is to evaluate the accuracy of MTCNN in detecting facial features at the Department of Population and Civil Registration (dukcapil) Palu City, using facial landmarks and waterfall methods as an application development methodology. The evaluation results show that MTCNN has high face recognition accuracy and good positioning ability regardless of what GPU in use as long have right CPU and System Operation. In comparison, the Viola-Jones algorithm is effective for high-speed applications, while SSD offers balanced performance with GPU device requirements for optimal performance. While MTCNN proved to be effective, challenges still exist, such as false positives and false negatives, especially in poor lighting conditions and extreme poses. Image and camera quality, including resolution and facial expression, also affects detection accuracy. These findings suggest that the application of MTCNN can improve face recognition accuracy for official documents, although it requires addressing existing challenges. With this technology, it is expected that errors in facial recognition can be minimized, resulting in more reliable data that meets the standards for issuing identity documents. This research contributes to the development of a more accurate and efficient face recognition system for personal identification applications.
Utilization of EfficientNet-B0 to Identify Oncomelania Hupensis Lindoensis as a Schistosomiasis Host Lamadjido, Moh. Raihan Dirga Putra; Laila, Rahmah; Pusadan, Mohammad Yazdi; Yudhaswana, Yuri; Lapatta, Nouval Trezandy; Ngemba, Hajra Rasmita
Journal of Applied Informatics and Computing Vol. 9 No. 3 (2025): June 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i3.9058

Abstract

Schistosomiasis caused by the Schistosoma japonicum worm is a significant health problem in Indonesia, especially in endemic areas such as the Napu Plateau and Bada Plateau. The main problem in controlling this disease is the difficulty in rapid and accurate identification of Oncomelania hupensis lindoensis snails as intermediate hosts of the parasite. This research aims to develop an artificial intelligence-based system that can efficiently identify the snail species. The stages of this research include collecting snail image data from the Central Sulawesi Provincial Health Office, consisting of 2100 images covering seven snail species, then processed through preprocessing and augmentation stages. The model applied was EfficientNet-B0. The results showed that the EfficientNet-B0 model achieved 98.80% training accuracy and 98.33% validation accuracy. Confusion matrix testing showed good performance, with an accuracy of 98% and for the species Oncomelania hupensis lindoensis had a recall of 93%, precision of 100%, F1-score of 97%, and the resulting AUC value of 99.7%. This research successfully developed an efficient identification system, which is expected to help health surveillance personnel in accelerating the identification process of schistosomiasis intermediate hosts.
Co-Authors - Ifandi A Djufri, Isdar Abd. Rahman Sholeh Abdillah Sani, Ilham Abdul Mahatir Najar Aimar Anand Alamsyah - Alamsyah Alamsyah Alda Nur Pradinda Angraeni, Dwi Shinta Anita Ahmad Kasim Anugrah Aidin Yotolembah Anwar, Asriani Azizah Azizah Chandra, Ferri Rama Deny Wiria Nugraha Dharmakirti, Dharmakirti Djohari, Riyandi Dwitama Dwi Shinta Angreni Dwi Wijaya, Kadek Agus Dwiwijaya, Kadek Agus Elyana Aulia Chandra Fahlevi, Mohammad Fazrin Fathul Wahid Habibu, Rahmawati Hamid, Odai Amer Hardianti Hardianti, Hardianti I Gusti Ngurah Agung Kade Dwi Arsana Iman Setiawan Indrajaya, Muhammad Aristo Junaidi Junaidi Junus Widjaja Kade Dwi Arsana, I Gusti Ngurah Agung Laila, Rahma Lakatjinda, Adiatma Lamadjido, Moh. Raihan Dirga Putra Lapatta, Nouval Trezand Liza Amalia Mohamad Irfan, Mohamad Mohammad Fajri Mohammad Yazdi Pusadan Muh Alif Alghifari Muh. Aristo Indrajaya Muhammad Jindan Muhammad Nauval Daffa Ulhaq Muhammad Yazdi Nouval Trezandy Lapatta Nurpati Nurpati Nursalim Pratama, Moh. Asry Eka Putri Febrina, Annisa Rahmah Laila Raivandy, I Made Randhy Rina Marlina Risaldi Pata’Dungan, Adi Rizana Fauzi, Rizana Rizka Ardiansyah Rosmala Nur Ryfial Azhar, Ryfial Setijadi, Eko Smith, Jennifer Suryani Putri, Fadiah Syahrullah Syahrullah Syahrullah Syaiful Hendra Ulhaq, Muhammad Naufal Daffa Winarta, Ardhiansyah Wirdayanti Yudhaswana, Yuri Yuri Yudhaswana Joefrie Yusuf Anshori Zaharuddin