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All Journal Jurnal Informatika dan Teknik Elektro Terapan Infotech Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JOURNAL OF APPLIED INFORMATICS AND COMPUTING EPIC : Journal of Electrical Power, Instrumentation and Control Techno Xplore : Jurnal Ilmu Komputer dan Teknologi Informasi Jurnal Ilmiah Wahana Pendidikan Jurnal Info Sains : Informatika dan Sains
Aries Suharso
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Religion Computer Science & IT Education Electrical & Electronics Engineering Engineering Social Sciences Other

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PENERAPAN PENGENALAN WAJAH MENGGUNAKAN CNN DAN DETEKSI LOKASI HAVERSINE UNTUK PRESENSI SEKOLAH BERBASIS WEB Muhammad Fitra Fajar Rusamsi; Aries Suharso; Chaerur Rozikin
Jurnal Informatika dan Teknik Elektro Terapan Vol. 13 No. 3S1 (2025)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v13i3S1.7485

Abstract

Kehadiran guru merupakan faktor utama dalam mendukung kelancaran proses pembelajaran dan administrasi akademik di sekolah. Namun, sistem presensi manual yang masih digunakan, memiliki berbagai kendala seperti kurangnya transparansi data, tidak efisien, risiko manipulasi data, hingga kesulitan memverifikasi lokasi dan wajah guru secara akurat. Menyikapi tantangan tersebut, dibutuhkan inovasi yang dapat meningkatkan efisiensi dan transparansi data, khususnya dalam hal kehadiran guru. Penelitian ini bertujuan untuk mengembangkan sistem presensi guru berbasis web dengan teknologi pengenalan wajah (face recognition) menggunakan OpenCV dan model CNN pre-trained, serta validasi lokasi berbasis metode Haversine. Integrasi teknologi ini memungkinkan presensi tidak hanya dapat memverifikasi identitas guru, tetapi juga memastikan kehadiran dilakukan di tempat dan waktu yang sesuai. Total citra wajah yang didapatkan kurang lebih 50 guru. Metode yang digunakan dalam proses ini adalah Waterfall. Pada pengembangan sistem ini menggunakan laravel serta python sebagai face recognition yang nantinya dikirim sebagai API lalu diterima oleh Laravel. Proses pengujian dilakukan dengan tiga kondisi untuk masing-masing metode. Pada pengujian pengenalan wajah, dari tiga sampel wajah yang diuji, hanya satu yang tidak berhasil dikenali, yaitu wajah yang tertutup masker. Sementara itu, pada pengujian validasi lokasi, sistem berhasil mendeteksi lokasi guru dengan akurat.
Sentiment Analysis of ChatGPT Application Reviews Using the BERT Algorithm Sukmana, Farhan Naufal; Aji Primajaya; Aries Suharso
Jurnal Info Sains : Informatika dan Sains Vol. 15 No. 02 (2025): Info sains, Desember 2025
Publisher : SEAN Institute

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

Abstract

The rapid growth of generative artificial intelligence applications, particularly ChatGPT, has resulted in a significant increase in user reviews on the Google Play Store. These reviews serve as valuable sources for understanding user perceptions, experiences, and concerns. This study aims to analyze sentiment in Indonesian-language reviews of the ChatGPT application using the Bidirectional Encoder Representations from Transformers (BERT) algorithm combined with the Knowledge Discovery in Database (KDD) methodology. The dataset was collected using web scraping via the google-play-scraper library, producing 1,806 reviews after data cleaning and preprocessing. The dataset was divided into training and testing sets with an 80:20 ratio. IndoBERT was employed as the pre-trained model. Evaluation results show that the model successfully classified positive, negative, and neutral sentiments with an accuracy of 93%, precision of 87%, recall of 83%, and an F1-score of 85%. Although performance on the neutral class was lower due to dataset imbalance, the model demonstrated strong overall results. This study confirms that BERT is effective for sentiment analysis of application reviews and can serve as a reference for improving application service quality by understanding user opinions.
Enhancing IT/OT Security Posture Against Erlang/OTP SSH Exploits Through Threat Campaign Assessment Nabila Latifa Tullaili; Ridwan Satrio Hadikusuma; Aries Suharso
EPIC Journal of Electrical Power Instrumentation and Control Vol 8 No 1 (2025): EPIC
Publisher : Universitas Pamulang, Prodi teknik Elektro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/epic.v8i1.58567

Abstract

The convergence of Information Technology (IT) and Operational Technology (OT) infrastructures exposes organizations to new risks, particularly when facing critical vulnerabilities. This research evaluates the security posture of IT/OT environments against CVE-2025-32433, a severe vulnerability in Erlang/OTP’s SSH daemon that allows unauthenticated remote code execution. The assessment was conducted in a real environment using the Keysight Threat Simulator, where simulated threats were injected from the darkcloud, passed through a Palo Alto Networks firewall, and targeted a host system (Windows Server 2016) with Keysight Agent version 25.7.3-1751647889 and ATI version 25.5.4181.502994. This campaign involving seven malware scenarios using remote hosts and DNS callbacks. The results showed 43 prevention outcomes, 0 detection events, and 9 security recommendations. While the firewall prevented part of the attacks, the detection capability at the host level failed entirely, indicating potential blind spots in monitoring and response.The study concludes that proactive threat simulation is essential for identifying prevention gaps and detection weaknesses in converged IT/OT networks. Recommendations include strengthening host-based detection, improving IT/OT segmentation, and enhancing monitoring of DNS traffic to mitigate exploitation risks.
Co-Authors Agung Susilo Yuda Irawan Amelia Pratiwi Chaerur Rozikin Dinar Sugiri Diva Ahmad Maulana Fricylia Rusdiana Putri Hannie, Hannie Hanny Hikmayanti Handayani Muammar Fahraz Pratama Muhammad Fitra Fajar Rusamsi Muhammad Rafi Santosa Nabila Latifa Tullaili Purwantoro Purwantoro Ragil Aria Dewanto Ridwan Satrio Hadikusuma Sukmana, Farhan Naufal Suria Darma Tarigan Virna Miftahuljannah Yandra Arkeman