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All Journal Jurnas Nasional Teknologi dan Sistem Informasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Insect (Informatics and Security) : Jurnal Teknik Informatika ILKOM Jurnal Ilmiah Simtek : Jurnal Sistem Informasi dan Teknik Komputer JISKa (Jurnal Informatika Sunan Kalijaga) Journal of Information Systems and Informatics ANOA: Jurnal Pengabdian Masyarakat Fakultas Teknik SAINTEK Journal of Information Systems and Technology (JISTech) JIKTEKS : Jurnal Ilmu Komputer dan Teknologi Informasi TENANG : Teknologi, Edukasi, dan Pengabdian Multidisiplin Nusantara Gemilang JROCK (Journal Research on Computing Knowledge)
Muh. Hajar Akbar
Universitas Sembilanbelas November Kolaka
Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Library & Information Science Mechanical Engineering Social Sciences

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Rancang Bangun Sistem Informasi Lowongan Kerja Berbasis Mobile di Kota Kendari Fadillah Maharani; Muh. Hajar Akbar; Ilcham
Prosiding Sains dan Teknologi Vol. 4 No. 1 (2025): Seminar Nasional Sains dan Teknologi (SAINTEK) ke 4 - Februari 2025
Publisher : DPPM Universitas Pelita Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Kota Kendari mengalami peningkatan jumlah pencari kerja yang membutuhkan informasi lowongan pekerjaan secara cepat dan akurat. Penelitian ini bertujuan untuk merancang dan membangun sistem informasi lowongan kerja berbasis mobile di Kota Kendari, guna memberikan solusi efektif bagi pencari kerja dan perusahaan dalam menyebarluaskan dan mendapatkan informasi lowongan kerja. Sistem ini dirancang untuk memfasilitasi proses pencarian kerja, pendaftaran lowongan, dan manajemen data pencari kerja serta perusahaan secara efisien. Metode penelitian yang digunakan adalah metode prototipe, yang meliputi proses iteratif pengumpulan kebutuhan, perancangan sistem menggunakan Unified Modeling Language (UML), pengembangan prototipe, umpan balik pengguna, dan penyempurnaan. Aplikasi ini dilengkapi dengan fitur notifikasi, pencarian lowongan berdasarkan kategori, dan profil pengguna. Berdasarkan hasil pengujian black box menunjukkan bahwa aplikasi ini berjalan dengan baik dan memenuhi kebutuhan pengguna.
SISTEM MONITORING PENGAJUAN DAN VALIDASI KEMIRIPAN JUDUL SKRIPSI BERBASIS ALGORITMA RABIN-KARP DAN SWR MENGGUNAKAN NEXT.JS Ilcham Ilcham; Sitti Najmia Rifai; Muh. Hajar Akbar; Mardianto Mardianto
Simtek : jurnal sistem informasi dan teknik komputer Vol. 11 No. 1 (2026): April 2026
Publisher : STMIK Catur Sakti Kendari

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51876/simtek.v11i1.1750

Abstract

Program Studi Sistem Informasi Universitas Sembilanbelas November Kolaka masih memproses pengajuan judul skripsi secara konvensional, sehingga memicu inefisiensi waktu dan rentan meloloskan duplikasi riset mahasiswa. Penelitian ini merancang aplikasi web interaktif berbasis framework Next.js untuk memantau alur pengajuan dan memvalidasi orisinalitas judul skripsi secara otomatis. Sistem menerapkan algoritma Rabin-Karp melalui tahapan stopword removal dan pembentukan K-Gram, kemudian menghitung tingkat kemiripan terhadap database alumni menggunakan Jaccard Coefficient dengan batas toleransi 40 persen. Peneliti mengintegrasikan strategi Stale-While-Revalidate (SWR) untuk memperbarui status pengajuan secara real-time. Implementasi sistem sukses memangkas birokrasi akademik, mewujudkan tata kelola paperless, dan menyaring keaslian dokumen secara akurat. Penerapan teknologi ini secara efektif mempercepat koordinasi multi-peran dan menjamin integritas karya ilmiah sejak tahap pendaftaran awal.
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.
Hybrid CNN-RNN Architecture with MFCC-LFCC Features for Audio Deepfake Detection Muh. Hajar Akbar; Nurfitria Ningsi; Aldi; Muhammad Na’im Al Jum’ah; Ilcham
Journal of Information System and Informatics Vol 8 No 4 (2026): August
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i4.1723

Abstract

The proliferation of sophisticated audio deepfake technology poses a significant threat to digital voice authentication and forensic verification systems. This research addresses this challenge by developing and evaluating a lightweight hybrid Convolutional Neural Network and Recurrent Neural Network (CNN-RNN) architecture for audio deepfake detection. The proposed model integrates a CNN for spatial feature extraction with a bidirectional RNN for temporal dependency modeling, utilizing an early vertically fused feature set of Mel-Frequency Cepstral Coefficients (MFCC) and Linear Frequency Cepstral Coefficients (LFCC) stabilized via utterance-level Cepstral Mean and Variance Normalization (CMVN). We assessed the proposed framework on the official ASVspoof 2019 Logical Access (LA) evaluation benchmark dataset (71,237 trials). Comprehensive evaluation on the official evaluation set demonstrated promising performance within the evaluated benchmark, achieving a Global Equal Error Rate (EER) of 8.24% and an Area Under the Receiver Operating Characteristic (ROC-AUC) of 0.9680, while maintaining an internal validation EER of 0.33% on known attacks. While showing high sensitivity to bona fide speech and robust resilience against advanced Neural Text-to-Speech synthesis (EER < 0.25% for A07–A10), the framework exhibits notable vulnerability to phase-preserving voice conversion attacks. Consequently, without real-world forensic operational testing, the model serves as an initial diagnostic screening approach rather than a fully operational forensic solution, and still requires further cross-repository and noisy-condition validation.
Enhancing Tuberculosis Classification Performance in Chest X-Rays through Efficient Net B0 and CLAHE Muh. Hajar Akbar; Aldi Aldi; Waode Hartina; Imam Mustafaenal Akhyar; Elsa Nur Khotima
JIKTEKS : Jurnal Ilmu Komputer dan Teknologi Informasi Vol. 4 No. 03 (2026): Agustus
Publisher : Faatuatua Media Karya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70404/jikteks.v4i03.729

Abstract

Tuberculosis (TB) remains a significant global health challenge, requiring rapid and accurate diagnostic tools to prevent transmission. Chest X-ray (CXR) imaging is a primary screening method, yet manual interpretation is often subjective and prone to inconsistency. This study proposes an efficient automated detection framework using the EfficientNet-B0 architecture integrated with Contrast Limited Adaptive Histogram Equalization (CLAHE). The research utilizes the Shenzhen Dataset, employing CLAHE to enhance the visibility of pulmonary features by mitigating non-uniform illumination in radiographs. The model was modified with a Global Average Pooling (GAP) layer and a 0.5 dropout rate to optimize performance for binary classification. Experimental results demonstrate that the proposed framework achieved an Accuracy of 87.21%, a Sensitivity of 89.70%, and an Area Under the Curve (AUC) of 0.9350. Furthermore, the model exhibits high computational efficiency with a compact size of 20.5 MB and only 5.3 million parameters, significantly outperforming heavier architectures like ResNet-50. This study concludes that the combination of CLAHE-based enhancement and EfficientNet-B0 provides a robust and lightweight solution for TB screening, particularly suitable for deployment in resource-constrained clinical environments.
Co-Authors Al Jum'ah, Muhammad Na'im alders paliling Aldi Aldi Aldi Anjar Pradipta Budi Wijaya Rauf Elsa Nur Khotima Fadillah Maharani Hamid Wijaya Ilcham Ilcham Ilcham Ilcham, Ilcham Imam Mustafaenal Akhyar Imam Riadi Imam Riadi Imam Riadi Irwan Jaya Jimsan Jimsan La Ode Hasnuddin S. Sagala Mardianto Mardianto Mardianto Mardianto Muh Nurtanzis Sutoyo Muh. Dhion. R Muh. Nurtanzis Sutoyo Nasrullah Nasrullah Nurfitria Ningsi Sitti Najmia Rifai Sitti Najmia Rifai Sunardi - Sunardi, Sunardi Suwito Pomalingo Syahraeni Rahmadi Waode Hartina