Article Per Year (5 Year)
p-Index From 2021 - 2026
0.61
P-Index
This Author published in this journals
All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Simantec JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika)
Jauharis Saputra, Wahyu Syaifullah
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Education

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Optimization of facial recognition authentication system using InceptionResNetV1 with Pretrained VGGFACE2 Gunawan, Ellexia Leonie; Mas Diyasa, I Gede Susrama; Jauharis Saputra, Wahyu Syaifullah
Jurnal Simantec Vol 13, No 2 (2025): Jurnal Simantec Juni 2025
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/simantec.v13i2.29776

Abstract

Face recognition as a biometric authentication method continues to evolve due to its high security and ease of use. However, training models from scratch faces challenges such as the need for large datasets and high computational resources. This study aims to optimize the face authentication system using the InceptionResNetV1 architecture with a transfer learning approach from the pretrained VGGFace2 model and to compare its performance with CASIA-WebFace. Face detection is conducted using YOLOv8, face embeddings are generated by InceptionResNetV1, and authentication is performed by calculating the Euclidean distance between embeddings. Face data were collected from university students and divided into training and testing datasets. Performance evaluation includes accuracy, precision, recall, F1-score, and the confusion matrix. The results show that the VGGFace2 model achieved an accuracy of 98.75%, a recall of 100%, and an F1-score of 99.26%, with no False Negatives, while CASIA-WebFace achieved an accuracy of 86.25% with a recall of 85.07%. The main contribution of this study is to demonstrate that the use of transfer learning with the pretrained VGGFace2 model can significantly improve the accuracy of face authentication systems and to show its effectiveness for developing systems with limited data and computational resources. This study contributes by highlighting the superiority of the pretrained VGGFace2 model in face authentication systems and emphasizing the effectiveness of transfer learning for implementing accurate systems under resource constraints.Keywords: Authentication System, InceptionResNetV1, Face Recognition, Transfer Learning, VGGFace2
Optimization of Palm Fruit Ripeness Detection With Yolov11 on CPU Anniswa, Iqbal Ramadhan; JAUHARIS SAPUTRA, Wahyu Syaifullah; Idhom, Mohammad; Rizaldy Pratama, Alfan; Susrama Mas Diyasa, I Gede
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 19, No 4 (2025): October
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.111253

Abstract

The palm oil industry is one of the strategic sectors that contributes significantly to the Indonesian economy. However, this industry still faces various challenges, particularly in terms of operational efficiency and the implementation of digitalization, especially at the level of independent farmers who often still use manual methods to determine the ripeness of the fruit. This manual process is prone to subjectivity, which can impact harvest quality and supply chain efficiency. To address this issue, this study proposes a palm oil fruit ripeness detection system based on the YOLOv11 algorithm, chosen for its advantages in inference speed and detection accuracy, especially when run on devices with limited resources. The developed model was then implemented using the ONNX Runtime Framework. This enables accelerated inference processes and supports portability on hardware with limited resources. Test results show that the model achieves an mAP@50 accuracy of 90.2% with an average latency of around 255 ms to 300 ms. With these achievements, this system is not only reliable in detecting fruit ripeness, but also efficient in processing time and relevant to support digital transformation in the palm oil plantation sector.
OPTIMASI PUSAT CLUSTER K-PROTOTYPES PADA PENGELOMPOKAN PENERIMAAN BANTUAN REHABILITASI RUTILAHU DI KOTA SURABAYA Ningrum, Lisya Septyo; Hindrayani, Kartika Maulida; Jauharis Saputra, Wahyu Syaifullah
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 11, No 1 (2026)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v11i1.7656

Abstract

Clustering merupakan teknik penting dalam data mining yang digunakan untuk mengelompokkan data berdasarkan kesamaan karak-teristik. Algoritma K-Prototypes sering digunakan pada data bertipe campuran karena mengombinasikan K-Means untuk atribut numerik dan K-Modes untuk atribut kategorikal. Namun, kinerjanya sangat ber-gantung pada inisialisasi pusat klaster awal. Penelitian ini men-gusulkan penerapan tiga algoritma optimasi yaitu Particle Swarm Op-timization (PSO), Genetic Algorithm (GA), dan Flower Pollination Algorithm (FPA) untuk meningkatkan performa K-Prototypes dalam pengelompokan calon penerima program rehabilitasi Rumah Tidak Layak Huni (Rutilahu) di Kota Surabaya. Evaluasi dilakukan menggunakan Davies-Bouldin Index (DBI), Silhouette Score, dan wak-tu komputasi. Berdasarkan hasil penelitian menunjukkan bahwa PSO memberikan hasil terbaik dengan DBI terendah sebesar 0,6467, Silhou-ette Score tertinggi sebesar 0,5498, dan waktu komputasi tercepat yaitu 23,5168 detik. GA menghasilkan DBI tertinggi sebesar 0,7134, Silhou-ette Score sebesar 0,5143, serta waktu komputasi terlama yaitu 7220,6384 detik. FPA memiliki DBI 0,6467 dan Silhouette Score yang sama dengan PSO, tetapi dengan waktu komputasi sebesar 3415,9175 detik. Dengan demikian, PSO terbukti paling efektif dalam meningkat-kan akurasi dan efisiensi clustering K-Prototypes, serta mendukung distribusi bantuan yang lebih adil dan tepat sasaran.
Co-Authors Alfan Rizaldy Pratama Anniswa, Iqbal Ramadhan Gunawan, Ellexia Leonie I Gede Susrama Mas Diyasa idhom, Mohammad Kartika Maulida Hindrayani Ningrum, Lisya Septyo Susrama Mas Diyasa, I Gede