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All Journal Sainteks Jurnal Mantik Jurnal Teknologi Sistem Informasi dan Sistem Komputer TGD
Diana Yusuf
Institut Teknologi Dan Bisnis Ahmad Dahlan
Computer Science & IT Economics, Econometrics & Finance Languange, Linguistic, Communication & Media Mechanical Engineering Medicine & Pharmacology

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Infographic of Internet Usage Data for Learning Process in the Province of Indonesia Nofirman; Pandu Adi Cakranegara; Diana Yusuf; Nanny Mayasari; Arifin
Jurnal Mantik Vol. 6 No. 3 (2022): November: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

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Abstract

The development of telecommunications in Indonesia has significantly affected the emergence of the digital age, as the internet has become a daily necessity for community activities. Information and communication technology (ICT)-based learning is influencing teaching methods and learning media to use the internet for the learning process in the field of education. This study aims to analyze and graphically present data on the internet usage of rural and urban residents in all Indonesian provinces concerning their education levels. The presented data is in the form of types of internet usage that support the learning process so that it can become predominant in the use of the internet by rural and urban areas and serve as a positive input for internet service providers in the improvement of education-related services. The results indicated that using the Internet to support the learning process by communities in rural and urban areas of Indonesian provinces is one of the goals of using the Internet to obtain information for the learning process is 10%. D.I.Yogyakarta has the highest percentage 58,1%, indicating the most significant number of internet users for educational purposes. When students and student users access the internet to support the learning process, they engage in a variety of activities, including doing assignments, accessing e-learning tools, accessing media information to support learning, using web browsers to display social media, and accessing e-mail to submit assignments using smartphone media, which is the most prevalent form of internet access.
Implementasi Teknik Clustering Untuk Pengelompokan Mobil Bekas Berdasarkan Grade Pada Mobi Auto Diana Yusuf; Ellya Sestri; Fahrul Razi
Jurnal Teknologi Sistem Informasi dan Sistem Komputer TGD Vol. 6 No. 2 (2023): J-SISKO TECH EDISI JULI
Publisher : STMIK Triguna Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53513/jsk.v6i2.8352

Abstract

Penelitian ini bertujuan untuk mengimplementasikan teknik clustering dalam pengelompokan mobil bekas berdasarkan grade pada perusahaan Mobi Auto. Metode clustering yang digunakan ialah K-Means Clustering untuk mengelompokkan mobil bekas ke dalam  3 (tiga) grade yakni grade A (Mobil Kualitas Sangat Baik), grade B (Mobil Kualitas Baik), dan grade C (Mobil Kualitas Rata-Rata). Data fitur kendararaan, kondisi fisik, riwayat perawatan, harga dan atribut tambahan dikumpulkan dan digunakan sebagai variabel dalam analisis klasterisasi. Hasil penelitian ini dapat memberikan pengelompokkan mobil bekas sesuai dengan grade yang ditetapkan oleh perusahaan. Hal ini akan membantu Mobi Auto dalam mengelola stok mobil bekas dengan lebih efisien, memberikan informasi yang akurat kepada pelanggan dan meningkatkan pengalaman pembelian mobil bekas. Implementasi K-Means Clustering ini dapat menjadi alat yang bermanfaat dalam pengelompokkan mobil bekas berdasarkan grade di perusahaan Mobi Auto. Penelitian ini memberikan dasar bagi pengembangan sistem pengelompokkan yang lebih canggih dan efektif di masa depan, serta memberikan manfaat dalam pengolahan dan analisis data secara keseluruhan untuk perusahaan penjualan mobil bekas.    
Comparison of EfficientNet B5-B6 for Detection of 29 Diseases of Fruit Plants Vany Terisia; Widi Hastomo; Adhitio Satyo Bayangkari Karno; Ellya Sestri; Diana Yusuf; Shevty Arbekti Arman; Nada Kamilia
Sainteks Vol 20, No 2 (2023): Oktober
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat (LPPM)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/sainteks.v20i2.18691

Abstract

In initiatives to meet food needs and enhance the wellbeing of farmers and society at large, crop production performance is essential. For early attempts to be made for quick handling to prevent crop failure, farmers must be able to readily and quickly receive information in order to detect plant illnesses. In this study, two Convolutional Neural Network (CNN) architectures namely, EfficientNet versions B5 and B6 are used to develop a classification model for plant disease using Deep Learning (DL). The 66,556 visuals in the dataset, which is from Kaggle.com, are used. To create a model, the training method uses 57,067 images data and 3,170 image data for validation. The EfficientNet architecture versions B5 and B6 received very good accuracy scores for the total test results, namely 0.9905 and 0.9927. The model testing phase was carried out through testing phases utilising 3.171 images data. Future analysis can compare CNN architectures and try it with different datasets.
Co-Authors Adhitio Satyo Bayangkari Karno Arifin, Arifin Cakranegara, Pandu Adi Elliya Sestri Fahrul Razi Nada Kamilia Nanny Mayasari Nofirman, Nofirman Shevti Arbekti Arman Vany Terisia Widi Hastomo