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All Journal JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Mitra Jurnal Pengabdian Masyarakat Multidisiplin (MJPMM)
Stephanus Priyowidodo
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Computer Science & IT Education Engineering Languange, Linguistic, Communication & Media Social Sciences Other

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Pelatihan Penggunaan Aplikasi GIS untuk Pendataan Peralatan dan Pelanggan PDAM Makassar Berbasis Web Stephanus Priyowidodo; Susilawati; Nukhe Andri Silviana
Mitra Jurnal Pengabdian Masyarakat Multidisiplin (MJPMM) Vol. 1 No. 2 (2025): September
Publisher : Marasofi International Media and Publishing (MIMP)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64123/mjpmm.v1.i2.3

Abstract

Pengelolaan data infrastruktur dan pelanggan merupakan tantangan bagi PDAM Makassar dalam meningkatkan efektivitas dan efisiensi layanan. Kegiatan pengabdian ini bertujuan mengimplementasikan aplikasi sistem informasi geografis (GIS) berbasis web untuk pendataan jaringan pipa, peralatan jaringan, titik kebocoran, serta data pelanggan beserta posisi water meter. Metode pelaksanaan meliputi analisis kebutuhan melalui diskusi dengan PDAM, perancangan sistem menggunakan teknologi web GIS, pengumpulan data melalui survei lapangan dengan GPS berakurasi tinggi, serta implementasi dan pelatihan penggunaan aplikasi. Hasil menunjukkan aplikasi mampu menampilkan visualisasi peta interaktif yang memuat posisi peralatan dan pelanggan, mempermudah pemantauan kebocoran, mendukung analisis spasial wilayah layanan, serta menyediakan fitur pencarian dan filter data. Penerapan sistem ini meningkatkan akurasi pendataan, mempercepat pemeliharaan jaringan, dan memperbaiki respons terhadap keluhan. Kesimpulan dari kegiatan ini adalah bahwa GIS berbasis web efektif dalam mendukung manajemen infrastruktur dan data pelanggan PDAM. Rekomendasi pengembangan mencakup integrasi sistem mobile untuk pengambilan data lapangan dan integrasi dengan sistem penagihan pelanggan.
A Comparative Analysis of ResNet50, ConvNeXtTiny, and Vision Transformer for Rice Leaf Disease Classification susilawati susilawati; Stephanus Priyowidodo; Andre Hasudungan Lubis
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 10 No. 1 (2026): Issues July 2026
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v10i1.18397

Abstract

Rice leaf diseases can significantly reduce crop productivity and grain quality, making rapid, objective, and accurate identification essential. This study compares the performance of three deep learning architectures, namely ResNet50, ConvNeXtTiny, and Vision Transformer (ViT-B16), for rice leaf disease classification. The dataset consists of 5,932 images representing four disease classes: Bacterial Blight, Blast, Brown Spot, and Tungro. The data were divided into 3,559 training images, 1,186 validation images, and 1,186 testing images using the same random seed. All models were trained using transfer learning with ImageNet pretrained weights, data augmentation applied only to the training set, dropout regularization, and a two-stage training strategy involving feature extraction and fine-tuning. Performance was evaluated using validation loss, accuracy, precision, recall, F1-score, training curves, and confusion matrices. The results show that ResNet50 achieved the best performance, with a validation accuracy of 99.92% and a validation loss of 0.00326, followed by ViT-B16 (99.16%) and ConvNeXtTiny (98.99%). All models classified the Tungro class with high accuracy, while minor misclassifications occurred among the Bacterial Blight, Blast, and Brown Spot classes due to their similar visual characteristics. Overall, ResNet50 proved to be the most effective model for rice leaf disease classification, although validation using real-world field images is still required before practical deployment
Co-Authors Andre Hasudungan Lubis Susilawati Susilawati