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Automatic Door Access Model Based on Face Recognition using Convolutional Neural Network Tjut Awaliyah Zuraiyah; Sufiatul Maryana; Asep Kohar
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 22 No. 1 (2022)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v22i1.2350

Abstract

Automatic door access technology by utilizing biometrics such as fingerprints, retinas and facial structures is constantly evolving. The use of masks during the Covid-19 Pandemic and post-pandemic has become an obligation wherever humans are active. The study aimed to create an automated door access model using Convolutional Neural Network (CNN) algorithms and Amazon Rekognition as cloud-based software. The CNN algorithm is applied to classify faces wearing masks or not wearing masks. The CNN architecture model uses sequential, convolution2D, max polling 2D, flatten dan dense. The hardware includes the Raspberry Pi, USB Webcam, Relay, and Magnetic Doorlock. The test results were obtained from the results of the accuracy plot on the Convolutional Neural Network model with an accuracy rate of 99% at an epoch value of 8 with a learning time of 67 seconds.
Decision Support System for Evaluating Textile Supplier Performance Based on Weights by Envelope and Slope and Mixed Aggregation by Comprehensive Normalization Technique for Multi-Criteria Setiawansyah Setiawansyah; Junhai Wang; Pritasari Palupiningsih; Sufiatul Maryana
Journal of Computer Science, Information Technology and Telecommunication Engineering Vol 7, No 1 (2026)
Publisher : Universitas Muhammadiyah Sumatera Utara, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30596/jcositte.v7i1.29131

Abstract

The textile industry is highly dependent on supplier performance in ensuring the quality of raw materials, timely delivery, price stability, and supply continuity. The complexity of supplier evaluation involving many criteria often leads to subjectivity and inconsistencies in decision-making when using conventional approaches. This study proposes a decision support system to evaluate textile supplier performance based on a combination of Weights by Envelope and Slope (WENSLO) and Mixed Aggregation by Comprehensive Normalization Technique for Multi-Criteria (MACONT). The WENSLO method is used to determine the weight of criteria objectively based on data distribution characteristics, while MACONT is applied to assess and rank supplier alternatives through a comprehensive normalization and aggregation process. The case study was conducted involving nine suppliers and five evaluation criteria, namely material quality, timeliness, price, supply capacity, and responsiveness. The results of the study indicate that the proposed model is capable of producing clear and stable supplier rankings, with Supplier A9, Supplier A7, and Supplier A2 occupying the top three positions. These findings demonstrate that the integration of WENSLO and MACONT can enhance the objectivity and consistency of decision-making, as well as provide a more reliable and relevant framework for evaluating textile suppliers to support data-driven supply chain management.
Implementasi Certainty Factor Untuk Diagnosa Penyakit Sapi Sufiatul Maryana; Dini Suhartini
CHAIN: Journal of Computer Technology, Computer Engineering, and Informatics Vol. 1 No. 1 (2023): Volume 1 Number 1 January 2023
Publisher : PT. Tech Cart Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58602/chain.v1i1.5

Abstract

Sistem pakar untuk diagnosa penyakit sapi merupakan sistem pakar yang dirancang sebagai alat bantu untuk mendiagnosa jenis penyakit sapi secara khusus. Pengetahuan ini didapat dari berbagai sumber diantaranya penelitian dan seminar yang dilakukan pakar dalam bidangnya serta buku yang berhubungan dengan penyakit sapi. Sistem Pakar ini dilakukan dengan cara nantinya pengguna sistem memasukan nilai-nilai yang telah disediakan kedalam sistem yang kemudian diproses berdasarkan aturan-aturan atau rule yang di peroleh dari pakar sehingga nantinya didapatkan hasil kesimpulan diagnosa serta memberikan solusi terhadap masalah yang dihadapi oleh pengguna. Hasil penilaian sistem pakar menggunakan metode certainty factor untuk pengguna berdasarkan parameter yang ada, maka mendapatkan hasil tingkat keyakinan menggunakan metode certainty factor adalah penyakit kudis (scabies) dengan tingkat keyakinan sebesar 60%, penyakit sapi ingusan dengan tingkat keyakinan sebesar 12%, penyakit sapi ngorok dengan tingkat keyakinan sebesar 0%, penyakit sapi demam dengan tingkat keyakinan sebesar 16%, penyakit sapi surra dengan tingkat keyakinan sebesar 80%. maka hasil sistem pakar menggunakan metode certainty factor untuk pengguna berdasarkan parameter sapi sulit bernafas dan gemetaran mendiagnosa penyakit sapi surra dengan tingkat keyakinan 80%.