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All Journal Journal of Information Systems and Informatics Jurnal Abdimas Mandiri Journal of Information Technology Ampera
Hadi Syaputra
Universitas Sumatera Selatan
Computer Science & IT Education Social Sciences

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Pelatihan Google Gemini dan Google Colab untuk Pengembangan Aplikasi OBE bagi Dosen UNSELA Darius Antoni; Selvia; Hadi Syaputra; Muhammad Bitrayoga
Jurnal Abdimas Mandiri Vol. 10 No. 2 (2026)
Publisher : UNIVERSITAS INDO GLOBAL MANDIRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36982/jam.v10i2.7614

Abstract

Implementasi Outcome-Based Education (OBE) di perguruan tinggi memerlukan dukungan teknologi digital agar proses perencanaan, pelaksanaan, dan evaluasi pembelajaran dapat dilakukan secara efektif. Namun, sebagian besar dosen Universitas Serelo Lahat belum memanfaatkan teknologi Artificial Intelligence (AI) seperti Google Gemini dan Google Colab untuk mendukung pengembangan aplikasi OBE. Oleh karena itu, kegiatan Pengabdian kepada Masyarakat ini bertujuan untuk meningkatkan kompetensi dosen dalam memanfaatkan Google Gemini dan Google Colab untuk merancang dan mengembangkan aplikasi OBE. Kegiatan dilaksanakan melalui tahapan analisis kebutuhan, persiapan, pelatihan, praktik langsung, pendampingan, dan evaluasi. Peserta kegiatan berjumlah 35 dosen yang berasal dari lima program studi di Universitas Serelo Lahat. Evaluasi dilakukan menggunakan metode pre-test, post-test, dan kuesioner kepuasan peserta. Hasil kegiatan menunjukkan adanya peningkatan kompetensi peserta yang ditunjukkan oleh kenaikan nilai rata-rata pre-test dari 55,69 menjadi 88,00 pada post-test atau meningkat sebesar 32,31 poin. Selain itu, hasil evaluasi kepuasan menunjukkan rata-rata tingkat kepuasan peserta mencapai 97,36%, yang mengindikasikan bahwa materi, metode pelatihan, narasumber, dan pendampingan telah memenuhi kebutuhan peserta. Melalui pelatihan ini, peserta mampu memanfaatkan Google Gemini untuk menghasilkan rancangan sistem, prompt engineering, desain basis data, antarmuka aplikasi, dan kode program, serta menggunakan Google Colab sebagai lingkungan pengembangan dan pengujian aplikasi berbasis cloud. Dengan demikian, kegiatan ini berhasil meningkatkan kompetensi digital dosen dan mendukung implementasi OBE berbasis AI di Universitas Serelo Lahat
Pengembangan Sistem Informasi Pelayanan Administrasi Berbasis Web Pada Desa Paya Angus Tommy Saputra; Ruswa Dwipa; Yudha Pratomo; Hadi Syaputra
Journal of Information Technology Ampera Vol. 7 No. 2 (2026): Vol. 7 No. 2 (2026): Journal of Information Technology Ampera
Publisher : APTIKOM SUMSEL

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Pelayanan administrasi pada Pemerintah Desa Paya Angus, Kecamatan Sungai Rotan, Kabupaten Muara Enim ini masih dilakukan secara manual, khususnya dalam proses pengajuan dan pengelolaan surat administrasi masyarakat. Kondisi tersebut menyebabkan proses pelayanan menjadi kurang efektif karena masyarakat harus datang ke kantor desa untuk mengajukan surat dan kembali datang untuk mengambil surat yang telah selesai, sehingga proses pelayanan administrasi tersebut kurang efektif. Penelitian ini bertujuan untuk merancang dan membangun sistem informasi pelayanan masyarakat berbasis web pada Desa Paya Angus yang dapat mempermudah masyarakat dalam memperoleh informasi desa serta mengajukan surat administrasi secara online. Dalam pengembangan sistem menggunakan metode Waterfall, yang meliputi tahap analisis kebutuhan, percangan, implementasi, pengujian dan pemeliharaan. Pengujian sistem dilakukan menggunakan metode Black Box Testing dan hasil penelitian ini berupa sistem informasi pelayanan masyarakat berbasis web yang dapat membantu masyarakat mengajukan surat tanpa harus datang ke kantor desa pada tahap awal serta membantu perangkat desa dalam mengelola data dan proses administrasi surat secara lebih terstruktur. Sistem ini diharapkan dapat meningkatkan efektivitas, efisiensi, dan kualitas pelayanan administrasi masyarakat di Desa Paya Angus.
Comparing CNN Models for Rice Disease Detection: ResNet50, VGG16, and MobileNetV3-Small Muhammad Taufik Roseno; Serly Oktarina; Yuwinti Nearti; Hadi Syaputra; Nirmala Jayanti
Journal of Information System and Informatics Vol 6 No 3 (2024): September
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i3.865

Abstract

The Oryza sativa (rice) plant is an important staple food source, especially in the Asian region. Rice production is often disrupted by diseases such as Brown Spot, Leaf Scald, Rice Blast, Rice Tungro, and Sheath Blight, which can reduce yield and crop quality. This research aims to classify rice plant diseases using a deep learning approach with Convolutional Neural Networks (CNN) architecture, namely ResNet50, VGG16, and MobileNetV3-Small. The dataset used is Rice Leaf Disease Classification which consists of 1305 images with five disease labels. The data is divided into training, validation, and testing sets with proportions of 70%, 15%, and 15%. The results showed that the MobileNetV3-Small model provided the best accuracy on the test data of 79%, while VGG16 achieved the validation accuracy of 78.84%. Based on these results, MobileNetV3-Small is considered the most superior model for rice disease classification. This research shows the great potential of applying deep learning in automatic rice disease detection.
Development of a Digital Village Concept based on Information Technology Infrastructure and Strategy Management to Facilitate SPBE Ogan Ilir Regency Serly Oktarina; Muhammad Taufik Roseno; Ubaidillah Ubaidillah; Darius Antoni; Lailatuz Zahro; Hadi Syaputra
Journal of Information System and Informatics Vol 6 No 4 (2024): December
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i4.875

Abstract

Technology in the digital era is currently progressing very rapidly. This is marked by the increasingly massive number of social media users in everyday life. Survey results from the Indonesian Internet Service Providers Association (APJII) in 2022 recorded that the number of internet users in Indonesia reached 196.7 million people. This number increased by 23.5 million or 8.9% compared to 2018. With information technology and the internet, information is now becoming more easily spread and can be accessed by all levels of society thanks to the internet, not just people in urban areas, but people living there. in rural areas too. The Ogan Ilir Regency Government initiated information technology infrastructure including village internet or Digital Village to solve the problem of inequality in digitalization of society in rural and urban areas. Development and implementation of a digital village is a program that implements electronic-based government system (SPBE) services to the community and empowers the community based on the use of technology. This research aims to conduct a survey of information technology infrastructure to identify village potential, marketing and accelerating access and public services. Apart from that, this research also identifies digital-based life patterns of people in rural and urban areas, as well as to advance economic development in rural areas to improve SPBE services in Ogan Ilir Regency. The method used is a quantitative method for surveying and mapping the use of information technology in villages and ultimately producing the concept of an independent digital village. Research data was obtained from surveys and FGDs with the Ogan Ilir district government, village heads, village communities and micro, small and medium enterprises. Meanwhile, secondary data will be obtained through the results of MSME and village profiles from the Central Statistics Agency.
Performance Analysis of Convolutional Neural Network in Pempek Food Image Classification with MobileNetV2 and GoogLeNet Architecture Yudha Pratomo; Muhammad Taufik Roseno; Hadi Syaputra; Darius Antoni
Journal of Information System and Informatics Vol 7 No 1 (2025): March
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i1.1026

Abstract

This research develops a pempek food image classification system using two Deep Learning architectures, namely MobileNetV2 and GoogLeNet. The dataset consists of five types of pempek with a total of 446 images, which are divided for training (70%), validation (15%), and testing (15%). The model was evaluated based on accuracy, precision, recall, and F1-score. The results showed that GoogLeNet achieved a validation accuracy of 96.21%, higher than MobileNetV2 which was only 70.58%. GoogLeNet is also more stable in convergence and more accurate in recognizing different types of pempek. This research shows that GoogLeNet is more optimal for pempek classification. In the future, this research can be extended by adding more datasets, exploring more sophisticated models, and developing mobile or web-based applications.
Co-Authors Darius Antoni Darius Antoni Lailatuz Zahro Muhammad Bitrayoga Muhammad Taufik Roseno Nirmala Jayanti Ruswa Dwipa Selvia Serly Oktarina Tommy Saputra Ubaidillah Ubaidillah Yudha Pratomo Yuwinti Nearti