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All Journal JOIV : International Journal on Informatics Visualization Jurnal Teknologi Informasi dan Pendidikan Jurnal Ipteks Terapan : research of applied science and education Journal of Systems Engineering and Information Technology
Ideva Gaputra
Politeknik Negeri Padang
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Study on Blockchain Visualization Tri Sundara; Ideva Gaputra; Siska Aulia
JOIV : International Journal on Informatics Visualization Vol 1, No 3 (2017)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1476.287 KB) | DOI: 10.30630/joiv.1.3.23

Abstract

Blockchain as a distributed ledger system which provide underlying technology behind Bitcoin. Blockchain paradigm can be extended to provide a generalized framework for implementing decentralized compute resources. Some attempts has been made to visualize Blockchain transaction flow. This research aims to assess those attempts through systematic review.
PEMESANAN TRAVEL BERBASIS SMS GATEWAY DAN JAVA NETBEANS PADA CV.RATU PASAMAN TRAVEL Rifa Turaina; Ideva Gaputra; Sri Ayu Vivi Eliza
Jurnal Teknologi Informasi dan Pendidikan Vol 9 No 1 (2016): Jurnal Teknologi Informasi dan Pendidikan
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/tip.v9i1.47

Abstract

Pemesanan travel berbasis sms gateway dapat memberikan solusi dan juga permasalahan kepada pelanggan, seperti kesalahan data pelanggan yang telah memesan travel, jam keberangkatan dan pembuatan laporan keuangan. Proses pembuatan sms gateway juga membutuhkan beberapa peralatan, seperti modem, komputer/laptop dan sms gateway software, gammu. Gammu merupakan sebuah aplikasi yang dapat digunakan untuk mengelola berbagai macam fungsi pada telepon dan modem. Fungsi yang dikelola oleh gammu adalah sejumlah kontak dan sms. Aplikasi bahasa pemrogramannya menggunakan aplikasi java yang merupakan bahasa pemograman yang berorientasi objek.
SMART IRRIGATION SYSTEM PROTOTYPE FOR RICE PLANTS USING THE AWD METHOD Ideva Gaputra; Harfebi Fryonanda; Ulya Ilhami Arsyah; Yori Adi Atma
Jurnal Ipteks Terapan (Research Of Applied Science And Education ) Vol. 17 No. 1 (2023): Jurnal Ipteks Terapan : research of applied science and education
Publisher : Lembaga Layanan Pendidikan Tinggi Wilayah X

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1200.483 KB) | DOI: 10.22216/jit.v17i1.1958

Abstract

Irrigation is a system for irrigation in the form of waterways used to distribute water. In fulfilling the need for water, especially in rice farming, it is necessary to increase agricultural yields and reduce crop failure. All of that can be done with a well-controlled irrigation system to increase agricultural profits. Irrigation systems, especially in rice farming, have irrigation systems that have been widely implemented in Indonesia and have been proven to increase agricultural yields. The farming system is called the wet-dry irrigation system. To control the irrigation system and to be able to monitor it regularly, an IoT-based irrigation system was created. The device used for the main control is Arduino Uno. The results of the smart irrigation system produce a prototype that can control irrigation based on soil moisture and close irrigation using a water level sensor. After creating the prototype of the IoT device, the process of irrigating the land can be carried out automatically according to the wet and dry irrigation system, and monitoring the condition of irrigating rice fields via the internet can be carried out to increase the effectiveness of farming
An Empirical Evaluation of ChatGPT as an Automated Machine Learning Code Generator for Image Classification Dedi Mardianto; Milla Apriliana; Eva Oktavia; Ideva Gaputra; Widya Wahyuni
Journal of Systems Engineering and Information Technology (JOSEIT) Vol 3 No 2 (2025)
Publisher : Ikatan Ahli Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/joseit.v4i2.7174

Abstract

The emergence of large language models such as ChatGPT has created unprecedented opportunities for automating software development processes, particularly within the machine learning domain. This study aims to empirically evaluate the effectiveness of ChatGPT in generating machine learning code for image classification tasks using the Keras framework. The research employs an experimental methodology utilizing the MNIST dataset, comprising 70,000 handwritten digit images. A systematic series of experiments was conducted through progressive prompting strategies, ranging from basic model construction to comprehensive evaluation protocols. The findings demonstrate that ChatGPT successfully generated 100% executable code without errors, with the resulting models achieving 99% accuracy on the test dataset. A notable discovery emerged in the form of "intelligent deviation" phenomena, wherein ChatGPT autonomously provided Convolutional Neural Network (CNN) architectures despite explicit requests for fully connected layers, demonstrating sophisticated contextual understanding. The generated code quality met professional standards with robust multi-library integration capabilities. This research provides the first systematic empirical contribution regarding large language model capabilities in machine learning code generation, offering significant implications for democratizing artificial intelligence technology access within educational and research contexts.
Co-Authors Arsyah, Ulya Ilhami Dedi Mardianto Eva Oktavia Fryonanda, Harfebi Milla Apriliana Rifa Turaina Siska Aulia Sri Ayu Vivi Eliza Tri Sundara Widya Wahyuni Yori Adi Atma