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All Journal Proceedings of KNASTIK Jurnal Ilmu Komputer dan Agri-Informatika JPSriwijaya JOIV : International Journal on Informatics Visualization Jurnal Teknoinfo MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Krea-TIF: Jurnal Teknik Informatika Journal of Science Innovare Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika IJEEIT : International Journal of Electrical Engineering and Information Technology Building of Informatics, Technology and Science Jurnal Leverage, Engagement, Empowerment of Community (LeECOM) JTIK (Jurnal Teknik Informatika Kaputama) Journal of Computer Science, Information Technology and Telecommunication Engineering (JCoSITTE) Journal Social Science And Technology For Community Service Prosiding Seminar Nasional Sains Teknologi dan Inovasi Indonesia (Senastindo) BEES: Bulletin of Electrical and Electronics Engineering Jurnal Aplikasi Bisnis dan Komputer J-Icon : Jurnal Komputer dan Informatika Bulletin of Informatics and Data Science CHAIN: Journal of Computer Technology, Computer Engineering and Informatics Journal of Artificial Intelligence and Technology Information JIPETIK:Jurnal Ilmiah Penelitian Teknologi Informasi & Komputer Prosiding SeNTIK STI&K Jurnal Ilmiah Sistem Informasi Akuntansi (JIMASIA)
Sufiatul Maryana
Universitas Pakuan
Agriculture, Biological Sciences & Forestry Humanities Chemical Engineering, Chemistry & Bioengineering 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 Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mechanical Engineering Medicine & Pharmacology Neuroscience Public Health Social Sciences Other

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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%.
IMPLEMENTASI ALGORITMA AES (ADVANCE ENCRYPTION STANDARD) RIJNDAEL PADA APLIKASI KEAMANAN DATA Prajuhana Putra, Agung; Herfina, Herfina; Maryana, Sufiatul; Setiawan, Andrian
JIPETIK:Jurnal Ilmiah Penelitian Teknologi Informasi & Komputer Vol 1, No 2 (2020): JIPETIK : Jurnal Ilmiah Pendidikan Teknologi Informasi & Komputer
Publisher : Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/jipetik.v1i2.4599

Abstract

AES Rijndael algorithm is a modern cryptographic algorithm published by NIST (National Institute of Standards and Technology) in 2001 using block cipher mode and using symmetric keys. AES Rijndael as a substitute for DES (Data Encryption Standard) algorithm whose use began in 1977 and has ended. The length of the block cipher used is 128 bits and variations in key length are 128 bits, 192 bits and 256 bits. AES Rijndael's algorithm has a reliability that is the simplicity of the bytes transformation process so that it can streamline encryption and decryption, and has high security.Implementation of the AES Rijndael algorithm will be done on Android devices to secure digital files, which can be used for all types of file types. The parameters used in the analysis are testing of the encryption and decryption processing time, changes in size and bits of the encrypted and decrypted files, then proving the strength of the key length against the robustness of the AES Rijndael algorithm as a symmetric algorithm.
Penerapan Model Convolutional Neural Network (Cnn) Untuk Klasifikasi Penyakit Daun Pada Tanaman Singkong (Manihot Esculenta Crantz) Gilang Surya Surya; Sufiatul Maryana; Siska Andriani
Jurnal Ilmiah Sistem Informasi Akuntansi Vol. 6 No. 1 (2026): Volume 6, Nomor 1, June 2026
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jimasia.v6i1.1505

Abstract

Penyakit daun singkong merupakan salah satu faktor utama yang menyebabkan penurunan produktivitas tanaman singkong apabila tidak dideteksi sejak dini. Proses identifikasi penyakit secara manual masih memiliki keterbatasan karena bergantung pada pengamatan manusia yang memerlukan waktu dan ketelitian tinggi. Penelitian ini bertujuan untuk mengembangkan sistem klasifikasi penyakit daun singkong secara otomatis menggunakan metode Convolutional Neural Network (CNN) dengan arsitektur ResNet152V2. Dataset yang digunakan diperoleh dari Kaggle dengan total 21.397 citra daun singkong yang terbagi ke dalam lima kelas, yaitu Cassava Bacterial Blight, Brown Streak Disease, Green Mottle, Mosaic Disease, dan Healthy. Tahapan penelitian meliputi persiapan data, exploratory data analysis, prapemrosesan citra dengan ukuran 32×32 piksel, penggunaan batch size 64, serta penerapan random oversampling untuk mengatasi ketidakseimbangan data. Data kemudian dibagi menjadi data latih dan data uji dengan rasio 80:20. Hasil pengujian menunjukkan bahwa model CNN ResNet152V2 mampu mencapai akurasi sebesar 97,2%. Meskipun masih terdapat beberapa kesalahan klasifikasi akibat kemiripan visual antar kelas penyakit yang terlihat pada confusion matrix, model secara keseluruhan menunjukkan performa yang sangat baik. Model yang telah dilatih selanjutnya diimplementasikan ke dalam aplikasi web berbasis Streamlit sehingga dapat digunakan sebagai alat bantu deteksi dini penyakit daun singkong. Penelitian ini diharapkan dapat membantu petani dan masyarakat dalam mencegah penyebaran penyakit serta meningkatkan produktivitas hasil panen singkong.
Co-Authors Abdat, Misel Umar Agung Prajuhana Putra Agung Prajuhana Putra Agung Prajuhana Putra Alfiansyah Alfiansyah Andi Mulyono Anggraeni, Ade Irma Arie Arie Qur’ania Arshad, Muhammad Waqas Asep Kohar Benny Nofika Benny Rachman Dian Kartika Utami Dieke Aprianto Edi Santosa Eneng Tita Tosida Fani Susanti Gilang Surya Surya Hamzah, Rizki Maulana Heni Sulistiani Herfina Herfina Herlinda Herlinda Hermawan Hermawan Hifshan Riesvicky Iryanto Chandra Iyan Mulyana Junhai Wang Kamel Mahdi Lita Karlitasari Lita Karlitasari Mehta, Abhishek Mubarok, Yasir Pahmi mulyono, andi Mutiara Prihatini Nofika, Benny Oktaviani, Nabila Oktaviani Palupiningsih, Pritasari Prajuhana Putra, Agung Pritasari Palupiningsih Putri Dwi Lestari Rahma, Yunita Reno Cahyo Hutomo Setiawan, Andrian Setiawansyah Setiawansyah Siska Andriani Sitna Hajar Hadad Soewarto Hardhienata Sri Wahjuni Sudrajat, Cecep Suriansyah, Muhammad Iqbal Tjut Awaliyah Zuraiyah Yudhia Mulya Yuli Wahyuni Yuri Rahmanto Zebua, Mitra Darma Zolla Zolla