Article Per Year (5 Year)
p-Index From 2021 - 2026
0.947
P-Index
This Author published in this journals
All Journal Sistemasi: Jurnal Sistem Informasi JOURNAL OF APPLIED INFORMATICS AND COMPUTING MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer SmartComp
Brilianto, Rivaldo Mersis
Unknown Affiliation
Computer Science & IT Electrical & Electronics Engineering

Published : 5 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 5 Documents
Search

 1 
Enhanced Brain Tumor Classification through Gamma Correction in Deep Learning Naufal, Muhammad; Al Azies, Harun; Brilianto, Rivaldo Mersis
Sistemasi: Jurnal Sistem Informasi Vol 13, No 6 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i6.4474

Abstract

Classification of brain tumors is a problem in computer-aided diagnosis (CAD). This study classifies three classes of brain tumors: gliomas, meningiomas, and pituitary tumors. Image enhancement is useful for improving the quality of images to be recognized by Computer-Aided Diagnosis (CAD) systems. Gamma correction is one spatial method aimed at manipulating contrast. This method operates with a spatial approach and has relatively low computational time but yields satisfactory results in certain cases. This research compares Gamma Correction with Convolutional Neural Network (CNN) in the classification of brain tumor types. The CNN method without Gamma Correction achieves an accuracy of 86.52%, precision of 83.63%, sensitivity of 86.11%, and specificity of 93.27%. The application of Gamma Correction at 1.5 results in improved performance with an accuracy of 88.80%, precision of 86.49%, sensitivity of 88.06%, and specificity of 94.50%. Meanwhile, Gamma Correction at 0.5 shows an accuracy of 88.59%, precision of 87.59%, sensitivity of 86.68%, and specificity of 94.17%. Overall, the implementation of Gamma Correction in the classification of brain tumor types successfully enhances the CNN classification performance in terms of precision, sensitivity, and specificity compared to without its use.
Sistem Informasi Kemahasiswaan Politeknik Harapan Bersama (SIKEMAS) Rakhman, Arif; Basit, Abdul; Brilianto, Rivaldo Mersis
Smart Comp :Jurnalnya Orang Pintar Komputer Vol 10, No 3 (2021): Smart Comp: Jurnalnya Orang Pintar Komputer
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/smartcomp.v10i3.2566

Abstract

Mahasiswa menjadi elemen yang sangat penting pada suatu perguruan tinggi khususnya di Prodi D-3 Teknik Komputer Politeknik Harapan Bersama. Politeknik Harapan Bersama merupakan pendidikan vokasi yang terletak di Kota Tegal, jumlah mahasiswa di perguruan tinggi ini mencapai +4000 mahasiswa  dan tenaga pendidik serta tenaga kependidikan yang tidak sedikit terdiri dari berbagai program studi. Program studi DIII Teknik komputer adalah Prodi yang mempunyai peserta didik  yang cukup banyak yaitu semester dua 302, semester empat 368 dan semester enam 699 dan total seluruh angkatan 1067 . Sistem informasi mahasiswa ini di beri nama SIKEMAS (sistem Informasi Kemahasiswaan agar mudah di ucapkan dan familiar bagi pengguna. SIKEMAS menggambarkan interaksi antara pengguna dan sistem, yang setiap pengguna bebas mengakses informasi yang berbeda sesuai ketentuan yang bertujuan agar mahasiswa  mendapatkan informasi secara cepat,  seperti informasi mengenai aktif studi, cuti studi, prestasi mahasiswa, mahasiswa bermasalah karena melakukan beberapa kali pelanggaran hingga himpunan mahasiswa Prodi. Oleh sebab itu, penelitian ini bertujuan membangun sistem informasi kemahasiswaan untuk Prodi D-3 Teknik Komputer dengan menggunakan metode waterfall. Kumpulan Data yang digunakan adalah metode observasi dan wawancara. Software yang digunakan untuk membangun sistem ini adalah UML dengan Enterprise arsitec untuk perancangan sistem, Bahasa pemrograman yang digunakan adalah PHP dengan framework Code igniter, dan database MySQL. Sistem ini di harapkan bisa mempercepat dan mempermudah proses dalam mendata mahasiswa dari mulai mahasiswa berprestasi hingga yang bermasalah agar Riwayat mahasiswa selama perkuliahan bisa tersimpan dengan baik.        Kata kunci : Student interest,  Sistem Informasi, UML, PHP, CI, MySQL
Evaluation of Histogram-Based Image Enhancement Methods for Facial Images in Drowsy Driver Using No-Reference Metrics Naufal, Muhammad; Al Azies, Harun; Alzami, Farrikh; Brilianto, Rivaldo Mersis
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.12055

Abstract

Low-light facial images suffer significant quality degradation, leading to performance degradation in surveillance and face recognition systems, where conventional enhancement methods often produce over-enhancement or unnatural noise artifacts. This study compares three histogram equalization methods, namely HE, AHE, and CLAHE, for low-light facial image enhancement, with evaluation using no-reference quality assessment metrics, including NIQE, LOE, and Entropy, as well as visual analysis and histogram distribution. The results showed that AHE produced the lowest NIQE (4.96 ± 1.38) and the highest entropy (7.86 ± 0.11) but had significant noise artifacts, HE produced an overly even distribution with NIQE of 6.34 ± 1.41, while CLAHE showed the most balanced performance with the lowest LOE (0.07 ± 0.02) and the best visual quality when using the optimal clip limit in the range of 1.2-2.0, providing an optimal trade-off between contrast enhancement, naturalness preservation, and artifact minimization with computational efficiency below 1 ms.
Optimizing Driver Drowsiness Detection: Evaluating CLAHE and AHE Enhancement Techniques Naufal, Muhammad; Al Azies, Harun; Al Zami, Farrikh; Brilianto, Rivaldo Mersis
SISTEMASI Vol 15, No 2 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i2.5206

Abstract

Driver drowsiness is a critical factor in road safety, and early detection can be key to preventing accidents. This research focuses on improving the accuracy of drowsiness detection by enhancing the contrast of driver facial images using image processing techniques. Specifically, the study explores the effectiveness of Adaptive Histogram Equalization (AHE) and Contrast Limited Adaptive Histogram Equalization (CLAHE) in this context. The research utilizes the Drowsy Driver Detection (DDD) dataset, which includes facial images categorized into Drowsy and Non-Drowsy classes. AHE and CLAHE techniques are applied to preprocess these images, aiming to improve contrast and subsequently enhance drowsiness detection accuracy. Evaluation metrics such as Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), and Signal-to-Noise Ratio (SNR) are employed to assess the quality of the processed images. The findings indicate that CLAHE performs better than AHE in terms of image enhancement. CLAHE achieves significantly lower MSE (93.90) compared to AHE (103.92), along with higher PSNR (28.41 for CLAHE vs. 27.97 for AHE) and SNR (0.49 for CLAHE vs. 0.04 for AHE) values. These results suggest that CLAHE effectively enhances contrast and improves image clarity. The success of CLAHE as a contrast enhancement technique highlights its potential application in real-time driver monitoring systems. In conclusion, this research underscores the importance of image preprocessing techniques like CLAHE in advancing driver safety technologies, emphasizing their potential to enhance the performance of drowsiness detection systems in practical driving scenarios.
Addressing Class Imbalance in Android Backdoor Malware DetectionUsing Ensemble Models Megantara, Rama Aria; Pergiwati, Dewi; Alzami, Farrikh; Pramunendar, Ricardus Anggi; Prabowo, Dwi Puji; Naufal, Muhammad; Brilianto, Rivaldo Mersis
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 25 No. 2 (2026)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v15i2.6198

Abstract

Backdoor malware represents one of the most critical threats in the Android ecosystem due to its capability to enable covert remote access, escalate privileges, and exfiltrate sensitive data without user awareness. Although the CCCS-CIC-AndMal-2020 dataset is publicly available, prior studies have not specifically formulated Backdoor detection as a binary classification problem under extreme class imbalance, nor systematically evaluated the impact of oversampling and cost-sensitive weighting using imbalance-aware performance metrics. This study proposes a comprehensive detection pipeline that integrates ensemble learning, class imbalance handling strategies, and explainability-based analysis to extract behavioral signatures of Backdoor malware. A two-stage feature selection process is employed to reduce the original 9,502-dimensional feature space to 500 informative features. Subsequently, five classification algorithms are evaluated under three imbalance-handling scenarios using a composite ranking criterion based on F1-score, Area Under the Receiver Operating Characteristic Curve (AUC), Geometric Mean (G-Mean), and Matthews Correlation Coefficient (MCC). The experimental results demonstrate that the Random Forest model combined with Synthetic Minority Oversampling Technique (SMOTE) achieves the best performance, with an F1-score of 0.9043, AUC of 0.9909, G-Mean of 0.9422, and MCC of 0.8948. Furthermore, SHAP analysis identifies 39 Android permissions related to account access, covert communication, and privilege escalation as key behavioral signatures, with the permissions feature group contributing 2.31 times higher discriminative importance than nonpermission features. These findings indicate that interpretable ensemble learning not only improves detection performance but also provides actionable insights for static malware analysis.
Co-Authors Abdul Basit Al zami, Farrikh Alzami, Farrikh Arif Rakhman Dwi Puji Prabowo, Dwi Puji Harun Al Azies Megantara, Rama Aria Muhammad Naufal Pergiwati, Dewi Ricardus Anggi Pramunendar