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All Journal Building of Informatics, Technology and Science Journal of Artificial Intelligence and Digital Business Majority Science Journal Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen)
Hutagalung, Carli Apriansyah
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Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Law, Crime, Criminology & Criminal Justice

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Pengembangan Aplikasi Ojek Online (BLOON) Berbasis Android Studi Kasus Provinsi Bengkulu Hutagalung, Carli Apriansyah; Rizki, Sestri Novia
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 5, No 3 (2024): Edisi Juli
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v5i3.425

Abstract

Online motorcycle taxi services have become a trending topic as they are considered an innovative solution to enhance the efficiency of traditional motorcycle taxis. Many province in Bengkulu Regency face difficulties in finding reliable transportation services for delivery purposes due to the unavailability of online motorcycle taxi services like Gojek, Grab, or Maxim, which are common in larger cities. This research aims to design the Bengkulu Online Motorcycle Taxi (BLOON) application for Android using the Object-Oriented Analysis and Design (OOAD) methodology. OOAD is an analytical approach that evaluates requirements from the perspective of classes and objects within the problem's domain, surpassing traditional software architecture through the manipulation of system or subsystem objects. The Rapid Application Development (RAD) model is employed in this research. Through thorough analysis, design, testing, and implementation, the Bengkulu Online Motorcycle Taxi application demonstrates successful operation in line with the research objectives and plans. The application of the OOAD methodology proves effective in developing structured applications. 
Development of AI-Based Presentation Application using Deep Learning for Individuals With Disabilities Hutagalung, Carli Apriansyah; Fitrianto, Adi; Akbar, Gebran
Building of Informatics, Technology and Science (BITS) Vol 6 No 3 (2024): December 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i3.6162

Abstract

This study addresses the challenges individuals with disabilities face in controlling presentation devices, particularly in noisy environments, by developing an AI-based application using a hybrid LSTM-GRU model. The primary objective is to improve voice command recognition accuracy for commonly used presentation commands, such as “next” and “back,” even under varying noise conditions. The research employs a hybrid deep learning architecture combining Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) with an attention mechanism to focus on the most relevant temporal features. The model was trained using the Speech Commands Dataset and further fine-tuned with noise-augmented data to simulate real-world environments. Results show that the LSTM-GRU model achieved high accuracy in clean environments and maintained reasonable performance in noisy conditions, outperforming traditional models like Hidden Markov Model (HMM) and Gaussian Mixture Model (GMM). The fine-tuned model, at its optimal epoch, demonstrated robust performance with a balanced precision and recall, making it suitable for deployment in real-world scenarios. The study concludes that while deep learning models offer significant improvements, further refinement is necessary to enhance noise resilience in practical applications
Technological Innovation in Clean Water Management to Improve the Quality of Life of the Community Ridha, Muhammad Abdi; Hutagalung, Carli Apriansyah
MSJ : Majority Science Journal Vol. 3 No. 2 (2025): MSJ-MAY
Publisher : PT. Hafasy Dwi Nawasena

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61942/msj.v3i2.365

Abstract

Clean water is a basic need that is very important for human health, well-being, and quality of life. However, many regions in the world, including Indonesia, still face serious challenges in providing clean water due to population growth, urbanization, environmental pollution, climate change, and inadequate infrastructure. This article aims to examine various technological innovations in clean water management and analyze their impact on people's quality of life. This study uses the Systematic Literature Review (SLR) method by examining various scientific articles, policy reports, and relevant technical documents from various reliable sources. The results of the study show that the application of technologies such as membrane filtration, Internet of Things (IoT)-based water quality monitoring, integrated wastewater treatment, desalination technology, and the use of renewable energy significantly increase access to clean water, reduce the rate of disease, and support economic welfare and environmental resilience of the community. This article recommends the integration of inclusive and collaborative clean water technology policies to realize efficient, sustainable, and equitable water management at all levels of society
Deteksi Deepfake Real-Time pada Perangkat Mobile Menggunakan Arsitektur MobileViT-CBAM Teroptimasi Hutagalung, Carli Apriansyah; Akhmad, Dinar Munggaran; Resita, Ersa; Rahmi, Talita
RIGGS: Journal of Artificial Intelligence and Digital Business Vol. 4 No. 4 (2026): November - January
Publisher : Prodi Bisnis Digital Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/riggs.v4i4.3634

Abstract

Perkembangan teknologi deepfake menghadirkan ancaman serius terhadap autentisitas informasi digital, terutama di media sosial dan konteks politik. Namun, sebagian besar model deteksi deepfake masih berukuran besar dan memiliki latensi tinggi, sehingga tidak efisien untuk dijalankan pada perangkat mobile. Penelitian ini mengusulkan arsitektur deteksi deepfake ringan berbasis MobileViT yang dipadukan dengan modified Convolutional Block Attention Module (CBAM) serta rangkaian optimasi model untuk memungkinkan inferensi real-time pada smartphone. MobileViT digunakan karena kemampuannya mengintegrasikan representasi lokal dan global secara efisien, sementara modified CBAM ditambahkan untuk meningkatkan fokus model pada area wajah yang sering dimanipulasi. Proses optimasi mencakup pruning 40%, quantization 8-bit, dan konversi TensorFlow Lite. Model dilatih menggunakan dataset FaceForensics++ dan Celeb-DF dengan total 84.690 frame yang diproses melalui MTCNN dan normalisasi 224×224 piksel. Hasil evaluasi menunjukkan bahwa model mencapai AUC 0.993 dan akurasi 96.4%, dengan ukuran akhir hanya 0.80 MB dan kecepatan 15.8 FPS pada perangkat simulasi MacBook M1 Pro. Ablation study mengonfirmasi kontribusi signifikan modified CBAM terhadap peningkatan performa, serta efektivitas quantization dalam menurunkan ukuran model tanpa mengorbankan akurasi. Temuan ini menunjukkan bahwa model MobileViT-CBAM teroptimasi mampu memberikan solusi deteksi deepfake yang akurat, ringan, dan dapat dijalankan secara real-time pada perangkat mobile tanpa ketergantungan cloud, sehingga berpotensi mendukung verifikasi konten multimedia dan mitigasi disinformasi di masyarakat.
Co-Authors Akbar, Gebran Dinar Munggaran Akhmad, Dinar Munggaran Fitrianto, Adi Rahmi, Talita Resita, Ersa Ridha, Muhammad Abdi Rizki, Sestri Novia