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All Journal Jurnal Media Computer Science PEDAMAS (Pengabdian Kepada Masyarakat)
Hajar Rojandra
Universitas Muhammadiyah Bengkulu
Humanities Computer Science & IT Economics, Econometrics & Finance Education Energy Engineering Health Professions Law, Crime, Criminology & Criminal Justice Social Sciences

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PEMBUATAN ABSENSI GURU BERBASIS WEBSITE MENGUNAKAN INIFINITY FREE DISEKOLAH SMP MUHAMMADIYAH TERPADU Hajar Rojandra; Muhammad Ale Gufron; Jerry Ario Zhonata; Imelia Okta Ramayanti; Nabela Hermy Nathania; RG Guntur Alam
PEDAMAS (PENGABDIAN KEPADA MASYARAKAT) Vol. 4 No. 02 (2026): MARET 2026
Publisher : MEDIA INOVASI PENDIDIKAN DAN PUBLIKASI

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Abstract

kegiatan ini memiliki tujuan untuk merancang serta mengimplementasikan suatu sistem pencatatan kehadiran guru yang berbasis website dengan memanfaatkan layanan hosting gratis InfinityFree pada SMP Muhammadiyah Terpadu Kota Bengkulu. Proses pengembangan sistem dilaksanakan dengan menerapkan model pengembangan perangkat lunak waterfall, yang mencakup serangkaian tahapan sistematis meliputi analisis kebutuhan sistem, perancangan arsitektur sistem, implementasi kode program, pengujian fungsionalitas, serta tahap pemeliharaan sistem. Adapun teknologi yang diimplementasikan dalam proses pengembangan sistem ini mencakup bahasa pemrograman PHP sebagai logika backend, sistem manajemen basis data MySQL untuk pengelolaan data, serta framework Bootstrap guna menghasilkan tampilan antarmuka pengguna yang responsif dan adaptif terhadap berbagai perangkat. Pemilihan InfinityFree sebagai platform hosting didasarkan pada pertimbangan bahwa layanan tersebut menyediakan fasilitas web hosting secara cuma-cuma dengan spesifikasi teknis yang memadai, sehingga sistem dapat dioperasikan secara daring tanpa menimbulkan beban biaya operasional bagi pihak institusi.
The Application Of The Transfer Learning Method In The Classification Of Eggplant Diseases Using The Densenet-201 Architecture Hajar Rojandra; Sastya Hendri Wibowo; Erwin Dwika Putra; Harry Witriyono
Jurnal Media Computer Science Vol 5 No 4 (2026): Oktober
Publisher : LPPJPHKI Universitas Dehasen Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jmcs.v5i4.12587

Abstract

Disease affecting eggplant fruit (Solanum melongena L.) is one of the main obstacles that reduce crop productivity, while conventional detection methods are considered inefficient as they require expert knowledge and a considerable amount of time. This study aims to apply the transfer learning method with the DenseNet201 architecture to classify eggplant fruit diseases into three categories, namely healthy fruit, rotten fruit, and fruit borer, based on digital images. The research dataset consisted of 2,417 images, divided into training, validation, and testing data with a 70:15:15 ratio, which were then processed through resizing, normalization, denoising, and data augmentation on the training set so that each class contained 1,000 images. The model was built using pre-trained ImageNet weights and trained for 20 epochs with the Adam optimizer, achieving a training accuracy of 90.0% and a validation accuracy of 87.5% with no significant indication of overfitting. During testing, the model achieved a test accuracy of 81.69% with a weighted F1-score of 81.68%. The healthy fruit class showed the best performance with an F1-score of 90.77%, while the rotten fruit and fruit borer classes achieved F1-scores of 78.48% and 74.11%, respectively, due to the visual similarity between the two classes. System testing on sample images from each class resulted in entirely accurate classifications, with confidence values ranging from 78.35% to 99.69%. These results indicate that the DenseNet201 model based on transfer learning performs well and has the potential to be used as an early detection support system for eggplant fruit disease.
Co-Authors Guntur Alam Harry Witriyono Imelia Okta Ramayanti Jerry Ario Zhonata Muhammad Ale Gufron Nabela Hermy Nathania Putra, Erwin Dwika Sastya Hendri Wibowo