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All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics Jurnal Algoritma Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Dinamisia: Jurnal Pengabdian Kepada Masyarakat JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI CARADDE: Jurnal Pengabdian Kepada Masyarakat Jurnal Sistem Cerdas Jurnal Abdi Insani Indonesian Journal of Electrical Engineering and Computer Science Journal of Energy and Electrical Engineering (JEEE) Jurnal Teknik Informatika (JUTIF) Teknika Mosharafa: Jurnal Pendidikan Matematika Transformasi Algoritme Jurnal Mahasiswa Teknik Informatika Konstelasi: Konvergensi Teknologi dan Sistem Informasi Jurnal Algoritma Fuse-teknik Elektro MDP Student Conference
Helfy Susilawati
Fakultas Teknik Universitas Garut
Agriculture, Biological Sciences & Forestry Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Social Sciences Other

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Enhancing Image Quality in Facial Recognition Systems with GAN-Based Reconstruction Techniques Wijaya, Beni; Satyawan, Arief Suryadi; Haqiqi, Mokh. Mirza Etnisa; Susilawati, Helfy; Artemysia, Khaulyca Arva; Sopian, Sani Moch.; Shamie, M. Ikbal; Firman
Teknika Vol. 14 No. 1 (2025): March 2025
Publisher : Center for Research and Community Service, Institut Informatika Indonesia (IKADO) Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34148/teknika.v14i1.1180

Abstract

Facial recognition systems are pivotal in modern applications such as security, healthcare, and public services, where accurate identification is crucial. However, environmental factors, transmission errors, or deliberate obfuscations often degrade facial image quality, leading to misidentification and service disruptions. This study employs Generative Adversarial Networks (GANs) to address these challenges by reconstructing corrupted or occluded facial images with high fidelity. The proposed methodology integrates advanced GAN architectures, multi-scale feature extraction, and contextual loss functions to enhance reconstruction quality. Six experimental modifications to the GAN model were implemented, incorporating additional residual blocks, enhanced loss functions combining adversarial, perceptual, and reconstruction losses, and skip connections for improved spatial consistency. Extensive testing was conducted using Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) to quantify reconstruction quality, alongside face detection validation using SFace. The final model achieved an average PSNR of 26.93 and an average SSIM of 0.90, with confidence levels exceeding 0.55 in face detection tests, demonstrating its ability to preserve identity and structural integrity under challenging conditions, including occlusion and noise.  The results highlight that advanced GAN-based methods effectively restore degraded facial images, ensuring accurate face detection and robust identity preservation. This research provides a significant contribution to facial image processing, offering practical solutions for applications requiring high-quality image reconstruction and reliable facial recognition.
Custom LiDAR Dataset for 3D Object Recognition in Restricted Spaces Using Voxel-RCNN Firman, Firman; Susilawati, Helfy; Setyawan, Arief Suryadi; Haqiqi, Mokh. Mirza Etnisa
MDP Student Conference Vol 4 No 1 (2025): The 4th MDP Student Conference 2025
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/mdp-sc.v4i1.11209

Abstract

Autonomous vehicles play a crucial role in logistics, agriculture, and warehousing, requiring precise object detection and recognition for safe navigation in confined spaces. Traditional 2D sensor-based methods and simple LiDAR applications often struggle with depth perception and classification accuracy, limiting real-time decision-making. This study addresses these challenges by developing a custom LiDAR-based dataset for object recognition within the Voxel-RCNN framework, focusing on six object categories to enhance recognition accuracy. The Voxel-RCNN model was trained on this custom dataset without architectural modifications, assessing its generalization to non-standard data and performance in constrained environments. The training process demonstrated stable convergence, with loss decreasing from 6.09 to 2.37 after 600 epochs. Quantitative evaluations using BEV and 3D Average Precision (AP) revealed strong performance in detecting structured objects like cars (68.14% BEV AP, 55.83% 3D AP in Easy cases) but significant challenges with occluded and irregularly shaped objects such as trees and cyclists. Despite these challenges, the study highlights the potential of Voxel-RCNN for 3D object recognition in autonomous navigation. Future improvements include dataset augmentation, multi-scale feature fusion, and advanced voxelization techniques to enhance recognition performance. These findings contribute to the advancement of LiDAR-based perception systems, supporting safer and more intelligent autonomous vehicle operations.
Restorasi Citra Wajah Terdegradasi Menggunakan Model GAN dan Fungsi Loss Wijaya, Beni; Haqiqi, Mokh. Mirza Etnisa; Satyawan, Arief Suryadi; Susilawati, Helfy
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 5 No 2 (2025): April 2025 || Algoritme Jurnal Mahasiswa Teknik Informatika
Publisher : Program Studi Teknik Informatika Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/algoritme.v5i2.11487

Abstract

This study develops a Generative Adversarial Network (GAN)-based model to restore partially degraded facial images by reconstructing missing regions while preserving the structural integrity of the face. The model adopts an encoder-decoder architecture enhanced with skip connections and residual blocks to improve restoration accuracy. The training process utilizes 1,000 paired images, comprising 500 original and 500 occluded facial images, with 200 images allocated for testing. The model was trained over 50 epochs, resulting in a consistent reduction of generator loss from 0.80 to 0.67 and stabilization of discriminator loss at 0.70. Qualitative evaluation indicates the model’s capability to reconstruct facial features such as eyes, nose, and mouth with high visual fidelity, although minor artifacts remain in areas with complex textures. These findings demonstrate the effectiveness of GAN-based approaches in facial image restoration and suggest potential improvements through the exploration of alternative network architectures and more diverse training datasets. The proposed model shows promise for applications in digital forensics and historical image recovery.
Thermal Image-Based Multi-Class Semantic Segmentation for Autonomous Vehicle Navigation in Restricted Environments Fazri, Nurul; Susilawati, Helfy; Haqiqi, Mokh. Mirza Etnisa; Satyawan, Arief Suryadi
Jurnal Sistem Cerdas Vol. 8 No. 1 (2025)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v8i1.489

Abstract

Technological advancements have propelled the development of environmentally friendly transportation, with autonomous vehicles (AVs) and thermal imaging playing pivotal roles in achieving sustainable urban mobility. This study explores the application of the SegNet deep learning architecture for multi-class semantic segmentation of thermal images in constrained environments. The methodology encompasses data acquisition using a thermal camera in urban settings, annotation of 3,001 thermal images across 10 object classes, and rigorous model training with a high-performance system. SegNet demonstrated robust learning capabilities, achieving a training accuracy of 96.7% and a final loss of 0.096 after 120 epochs. Testing results revealed strong performance for distinct objects like motorcycles (F1 score: 0.63) and poles (F1 score: 0.84), but challenges in segmenting complex patterns such as buildings (F1 score: 0.34) and trees (F1 score: 0.42). Visual analysis corroborated these findings, highlighting strengths in segmenting well-defined objects while addressing difficulties in handling variability and elongated structures. Despite these limitations, the study establishes SegNet's potential for thermal image segmentation in AV systems. This research contributes to the advancement of computer vision in autonomous navigation, fostering sustainable and green transportation solutions while emphasizing areas for further refinement to enhance performance in complex environments.
Deteksi Baut Kereta Api Menggunakan Artificial Intelligence Firman; Helfy Susilawati; Ginaldi Ari Nugroho
KONSTELASI: Konvergensi Teknologi dan Sistem Informasi Vol. 5 No. 1 (2025): Juni 2025
Publisher : Program Studi Sistem Informasi Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/konstelasi.v5i1.11379

Abstract

Baut merupakan salah satu dari bagian rel kereta api. Baut memiliki peran yang sangat penting dalam rel kereta api karena berfungsi sebagai pengencang antar rel. Apabila baut rel kereta api mengalami masalah maka hal ini juga akan berpengaruh pada performa rel kereta pi bahkan lebih jauhnya dapat mengakibatkan kecelakaan. Mengingat pentingnya baut pada rel kereta api maka setiap hari saat pagi hari akan ada petugas yang ditugaskan untuk menyusuri rel kereta api untuk melakukan pengecekan baut. Pengecekan tersebut dilakukan secara manual yaitu dengan berjalan kaki atau dengan menaiki kendaraan untuk menyusuri rel kereta api. Pengecekan yang dilakukan secara manual memiliki kemungkinan kesalahan dalam mengenali kondisi baut, selain itu jika dilakukan secara manual belum adanya catatan terkait kondisi baut. Oleh karena itu diperlukan sistem cerdas yang dapat mengenali kondisi baut yang ada pada rel kereta api, yang mana data ini akan diberikan kepada pusat informasi. Data tersebut akan menjadi pertimbangan apakah kondisi baut masih layak digunakan atau harus diganti. Langkah pertama yang harus dilakukan dalam penelitian adalah mengenali objek baut pada rel kereta api, agar selanjutnya dapat dilakukan penelitian lebih lanjut. Penelitian ini telah berhasil mendeteksi baut pada rel kereta api. Penelitian ini menggunakan dataset sebanyak 200 dataset, dengan 183 dataset training dan 17 dataset testing. Model yang digunakan pada penelitian adalah model SSD Resnet 50 V1, yang mana dengan menggunakan 2.000 step didapat mAP senilai 92,64%.
Design and Development of A Semi-Automatic Catfish Feeder Device Based on Arduino Sugandi, Gandi; Gusman, Dilla Oktaviani; Susilawati, Helfy; Wiharso, Tri Arif
Journal of Energy and Electrical Engineering Vol 6, No 2: April 2025
Publisher : Teknik Elektro Universitas Siliwangi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/jeee.v6i2.15028

Abstract

Fish feed is one of the most important factors in fish farming and plays a crucial role in determining the success of the cultivation. One of the most commonly farmed fish species in the community is catfish. The feeding of catfish must be carefully managed to optimize their growth by providing feed accurately and regularly so that they can be harvested with maximum results. In this thesis research, a semi-automatic catfish feeder was designed to optimize the feeding process. The device is designed with a semi-automatic system so that when monitoring the catfish pond, farmers can simultaneously feed the fish. This device also uses a solar panel as an energy source to make it more efficient, especially in areas far from electrical sources. The feeder operates using Bluetooth transmission and can be controlled via a smartphone. It includes a feed dispensing system and a feed weight monitoring system, which displays the feed weight in grams on an LCD screen. On the smartphone, a parameter called "Feed Capacity" is displayed to indicate the amount of feed based on its height inside the feed container. This device has a load cell sensor with an average error of 0.5% and a servo motor with an average error of 0.6%.
Perancangan Sistem Shutdown Komputer Berbasis Pengolahan Citra Pada Laboratorium Komputer Lubis, Anggi Muhammad; Susilawati, Helfy; Malikmatin, Iik Muhammad
Fuse-teknik Elektro Vol 5 No 1 (2025): Fuse-teknik Elektro
Publisher : Fakultas Teknik Universitas Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52434/jft.v5i1.2763

Abstract

Perkembangan teknologi yang pesat mendorong terciptanya sistem kendali jarak jauh dan monitoring real-time untuk meningkatkan efisiensi kerja. Penelitian ini merancang sistem otomatisasi shutdown komputer berbasis pengolahan citra pada laboratorium komputer. Sistem menggunakan Raspberry Pi sebagai mikrokontroler dan sensor arus untuk mendeteksi status komputer (menyala atau mati). Data dikirim ke website sebagai sistem monitoring, dan kontrol dilakukan melalui integrasi dengan TeamViewer. Metode Single Shot Detector diterapkan untuk mendeteksi keberadaan manusia di ruangan. Hasil pengujian menunjukkan bahwa sistem mampu mendeteksi keberadaan manusia dengan rata-rata delay 3,43 detik dan mengenali status komputer secara akurat. Waktu koneksi ke TeamViewer rata-rata 3,10 detik. Sistem ini efektif sebagai solusi otomatisasi shutdown komputer berbasis keberadaan pengguna.
Optimizing Autonomous Navigation: Advances in LiDAR-based Object Recognition with Modified Voxel-RCNN Firman; Satyawan, Arief Suryadi; Susilawati, Helfy; Haqiqi, Mokh. Mirza Etnisa; Artemysia, Khaulyca Arva; Sopian, Sani Moch; Wijaya, Beni; Samie, Muhammad Ikbal
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 10, No. 2, May 2025
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v10i2.2199

Abstract

This study aimed to enhance the object recognition capabilities of autonomous vehicles in constrained and dynamic environments. By integrating Light Detection and Ranging (LiDAR) technology with a modified Voxel-RCNN framework, the system detected and classified six object classes: human, wall, car, cyclist, tree, and cart. This integration improved the safety and reliability of autonomous navigation. The methodology included the preparation of a point cloud dataset, conversion into the KITTI format for compatibility with the Voxel-RCNN pipeline, and comprehensive model training. The framework was evaluated using metrics such as precision, recall, F1-score, and mean average precision (mAP). Modifications to the Voxel-RCNN framework were introduced to improve classification accuracy, addressing challenges encountered in complex navigation scenarios. Experimental results demonstrated the robustness of the proposed modifications. Modification 2 consistently outperformed the baseline, with 3D detection scores for the car class in hard scenarios increasing from 4.39 to 10.31. Modification 3 achieved the lowest training loss of 1.68 after 600 epochs, indicating significant improvements in model optimization. However, variability in the real-world performance of Modification 3 highlighted the need for balancing optimized training with practical applicability. Overall, the study found that the training loss decreased up to 29.1% and achieved substantial improvements in detection accuracy under challenging conditions. These findings underscored the potential of the proposed system to advance the safety and intelligence of autonomous vehicles, providing a solid foundation for future research in autonomous navigation and object recognition.
KEPUTUSAN MANUSIA VS KEPUTUSAN MESIN: STUDI KOMPARATIF TERHADAP AKURASI DAN KONSISTENSI DALAM PENGAMBILAN KEPUTUSAN Nurfalah, Rifki; Susilawati, Helfy; Khoerunnisa, Ica
TRANSFORMASI Vol 20, No 2 (2024): TRANSFORMASI
Publisher : STMIK BINA PATRIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56357/jt.v20i2.414

Abstract

This study aims to analyze and compare the accuracy, consistency, and decision-making efficiency between humans and machine learning (ML) algorithms in tabular data classification tasks. The dataset comprises 50 classification cases containing both numerical and categorical features with binary decision labels. Two groups were compared: 10 human participants, and six ML algorithms—Logistic Regression, Support Vector Machine, Random Forest, Decision Tree, k-Nearest Neighbors, and Naive Bayes. ML models were trained on 80% of the data and tested on the remaining 20%, while human participants manually classified all 50 test cases. The results showed that the average human accuracy was 76.2%, while ML algorithms achieved between 78.9% and 91.8%, with Random Forest yielding the highest performance. Human decision-making took an average of 18 seconds per case, significantly slower than the algorithmic predictions completed within milliseconds. Additionally, high variability in human responses indicated lower consistency compared to deterministic outputs from ML models. These findings support the integration of ML algorithms as a decision support or replacement tool in data-driven domains, with the potential to reduce human error in high-stakes environments. Nevertheless, human involvement remains essential in contexts requiring ethical consideration and interpretability.
7 Curug Cimanganten: Eduwisata Berkelanjutan yang Mengedukasi Melalui Energi Terbarukan Rukmana, Ade; Susilawati, Helfy; Adiatma, Dani; Fadillah, Ardi; Ihsan, Muhammad
CARADDE: Jurnal Pengabdian Kepada Masyarakat Vol. 8 No. 1 (2025): Agustus
Publisher : Ilin Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31960/caradde.v8i1.3146

Abstract

Pengabdian eduwisata berkelanjutan di kawasan 7 Curug Cimanganten dilakukan melalui sosialisasi energi terbarukan dengan fokus pada Pembangkit Listrik Tenaga Mikrohidro. Kegiatan ini dilatarbelakangi oleh potensi sumber daya air yang melimpah, yang tidak hanya berfungsi sebagai daya tarik wisata, tetapi juga dapat dimanfaatkan sebagai sumber energi ramah lingkungan. Tujuan utama kegiatan adalah meningkatkan pemahaman masyarakat mengenai energi terbarukan, menjelaskan cara kerja pembangkit mikrohidro, serta memperkenalkan konsep eduwisata berkelanjutan yang menggabungkan keindahan alam dan nilai edukasi. Metode yang digunakan berupa sosialisasi langsung dan survei kepuasan masyarakat. Hasil menunjukkan bahwa 80–95 persen responden setuju kegiatan ini bermanfaat dalam meningkatkan pengetahuan, pemahaman manfaat mikrohidro, serta motivasi menjaga kelestarian lingkungan. Responden juga menyarankan kegiatan rutin, praktik lapangan, dan pelibatan komunitas lokal. Kesimpulannya, sosialisasi memberikan dampak positif pada pengetahuan, sikap, dan motivasi, serta memperkuat posisi 7 Curug Cimanganten sebagai model eduwisata berbasis energi terbarukan.
Co-Authors Ade Rukmana Ade Rukmana Adhitya Yusuf Wibysono Adiatma, Dani Aeni, Dinda Noorfaidah Afina Carmelya, Anindya Ahmad Noor Jaman ahmad rizal Akhmad Fauzi Ikhsan Akhmad Fauzi Ikhsan Alfaz Arva Baihaqi Aloysius Adya Pramudita Artemysia, Khaulyca Arva Baihaqi, Alfaz Arva Dani Prasetyo Adi, Puput Dhiky Juansyah Dinda Noorfaidah Aeni Dini Fajriani Etnisa, Moch Mirza Evi Novitasari Fadillah, Ardi Fajriani, Dini Fauzi, Moch Zulfi Fazri, Nurul Firman Firman Firman Fitri Nuraeni Galura Muhammad Suranegara Ghofur, Shaefan Afuan Ginaldi Ari Nugroho Gusman, Dilla Oktaviani Hamdani, Nizar Alam Haqiqi, Mokh. Mirza Etnisa Iik Muhammad Malik Matin Iik Muhammad Malik Matin Ikhsan, Akhmad Fauzi Jaman, Ahmad Noor Juansyah, Dhiky Juniawan, Ega Rizki Khoerunnisa, Ica Latukolan, Merlyn Inova Christie Lubis, Anggi Muhammad M.Angdarun, M.Angdarun Malikmatin, Iik Muhammad Matin, Iik Muhammad Malik Mirza Etnisa Haqiqi, Mokhamamad Muhamad, Reza Muhammad Ihsan Mutmainah, Rina Nasrullah Armi Novitasari, Evi Nurdin, Agung Ihwan Nurfalah, Rifki Nurfitriani, Nabila Nurichsan, Irman Reza Muhamad Rifki Nurfalah Rina Mutmainah RR. Ella Evrita Hestiandari Rukmana, Ade Samie, Muhammad Ikbal Satyawan, Arief Suryadi Sediono, Wahju Setyawan, Arief Suryadi Shaefan Afuan Ghofur Shamie, M. Ikbal Sifa Nurpadillah Sobari, Acep Hasan Sopian, Sani Moch Sopian, Sani Moch. Sri Nuraeni, Sri Sugandi, Gandi Sunardi, Dede Suryadi Satyawan, Arief Syarif Saeful Yusup TRI ARIF WIHARSO Tri Arif Wiharso Wibysono, Adhitya Yusuf Wiharso, Tri Arif Wiwik Handayani Yusup, Syarif Saeful