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All Journal ILKOM Jurnal Ilmiah Indonesia Berdaya
Muhammad Na'im Al Jum'ah
Universitas Sembilanbelas November Kolaka
Arts Humanities Computer Science & IT Economics, Econometrics & Finance Education Health Professions Medicine & Pharmacology Nursing Public Health Social Sciences Veterinary Other

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Sosialisasi Penggunaan Webiste Sistem Informasi Pengelolaan Dokumen Arsip Kepegawaian Berbasis Digital di Fakultas Teknologi Informasi Universitas Sembilanbelas November Kolaka Hamid Wijaya; Rima Ruktiari; Muhammad Na'im Al Jum'ah
Indonesia Berdaya Vol 4, No 2 (2023)
Publisher : UKInstitute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47679/ib.2023456

Abstract

The technologies are growing rapidly nowadays including the of information of technology. In the era of using digital information technology, websites make the employees as a user itself easy to provide a service, improve the work performance, convenience, time, effort and resources, so that can make employees do their jobs more quickly and efficiently. Department of Information Technology of Sembilanbelas November Kolaka University has collected many documents and saved them as physical archives. However, the number of documents will continue to increase every day if there is no rule to manage it well. The purpose of this socialization is to help lecturers and staff to archive the physical documents of employees into digital documents. Those physical documents are changed into Portable Documents Format (PDF) form or digital images and uploaded into the website of Digital Employee’s Archive Document Management Information System. So that there is no longer a collection of physical documents. The socialization activities have been doing well and showed by the enthusiasm of the participants by doing the activities. Abstrak: Perkembangan teknologi saat ini sangat berkembang pesat termasuk di dalamnya yaitu perkembangan teknologi informasi. Di era penggunaan teknologi informasi berbasis digital, penggunaan website telah memudahkan para pegawai untuk memberikan pelayanan, meningkatkan efisiensi kinerja, kenyamanan, waktu, tenaga dan sumber daya, sehingga membuat pegawai melakukan tugasnya lebih cepat dan efisien. Universitas Sembilanbelas November Kolaka terkhususnya pada Fakultas Teknologi informasi setiap harinya telah banyak mengumpulkan dokumen dan menyimpan dokumen tersebut sebagai arsip fisik Peningkatan jumlah arsip setiap harinya jika tidak dikelola dengan benar maka akan terjadi penumpukan. Tujuan dari kegiatan sosialisasi ini adalah untuk membantu para pegawai yaitu dosen maupun tenaga kependidikan untuk melakukan pengarsipan dokumen kepegawaian berbasis digital, dalam hal ini tidak perlu lagi menyimpan dokumen secara fisik, akan tetapi dalam bentuk file PDF atau gambar untuk diunggah ke dalam website Sistem Informasi Pengelolaan Dokumen Arsip Kepegawaian Berbasis Digital, sehingga tidak terjadi lagi penumpukan dokumen fisik. Dari hasil kegiatan sosialisasi yang telah dilakukan telah berjalan dengan baik serta para peserta kegiatan sangat antusias mengikuti kegiatan karena merasa terbantu dengan adanya kegiatan sosialiasi ini dalam hal pengarsipan dokumen kepegawaian berbasis digital. 
Forensic Analysis for Detecting Deep-Fake Images Using A Convolutional Neural Network (CNN) and The National Institute of Standards and Technology (NIST) Methods Muhammad Na'im Al Jum'ah; Hamid Wijaya; Muh. Hajar Akbar; Suwito Pomalingo
ILKOM Jurnal Ilmiah Vol 18, No 2 (2026)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v18i2.3178.281-291

Abstract

The development of Artificial Intelligence (AI) has significantly influenced audio, video, and image manipulation techniques, commonly known as deepfakes. Image forensics faces an urgent challenge in identifying and mitigating the impact of deepfake content to maintain the integrity and credibility of digital information. This research aims to perform forensic analysis in accordance with NIST standards and to implement Convolutional Neural Network (CNN) methods to detect deepfake images. Based on the test results, the Convolutional Neural Network (CNN) method can be effectively applied to deepfake image detection. The CNN architecture used can identify the distinct visual characteristics of deepfake images with high performance. The model demonstrates the ability to learn and minimize prediction errors on training data. Accuracy graphs indicate that the model has successfully learned data patterns, as evidenced by consistent improvements in both training and validation data as the number of epochs increases. Furthermore, the loss graph shows a downward trend, signifying a continuous reduction in model error. The precision graph demonstrates the model's effectiveness in reducing false positives, thereby minimizing errors in detecting the original data. The recall graph also indicates improved detection performance on the training data. The ROC curve suggests that the model possesses superior classification capabilities compared to random guessing. Additionally, the Area Under the Curve (AUC) of 0.6544 serves as a quantitative indicator of performance, indicating that the model has moderate capability for class differentiation. Detection results from the CNN model on a dataset of real and deepfake images show that the Confidence and Raw Score values can distinguish between the two; however, the confidence levels still fluctuate around the classification threshold. Low confidence values in certain images suggest that the extracted features are not yet optimal at distinguishing between real faces and manipulated images. Moreover, the application of the National Institute of Standards and Technology (NIST) standards (Collection, Examination, Analysis, and Reporting) for forensic analysis ensures that the evidence gathered is legally accountable in court. Thus, these standards can serve as a scientific reference to ensure a more structured and standardized investigation process for deepfake images.
Co-Authors Hamid Wijaya Muh. Hajar Akbar Rima Ruktiari Suwito Pomalingo