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Journal : Jurnal Mantik

Development of Management Information System Rental Service Photography Web-Based Using PHP Native Case Study at PT Dwipa Photowork Surabaya Ahmad Habib; Rifki Satya; Balok Hariadi
Jurnal Mantik Vol. 6 No. 1 (2022): May: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

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Abstract

Scheduling is a planning activity to determine when and where each surgery as part of the work in total must be carried out on limited energy sources, and the allocation of energy resources at a certain time by taking into account the available resource capacity. In this research, utilizing the development of information technology, the most important is the application system for data processing or data collection that functions to produce the required information. Every company that wants to develop its business and achieve success, of course, the company must follow the information age by using computer applications that make it easy to process company data.  In this study, the author wants to provide a solution by designing an application for processing work service scheduling flow data based on the manual scheduling system that already exists at the Dwipa Photowork Company which is still less effective and efficient, and creates a database system used in a well-computerized scheduling application. between the database system, the user interface, and the user itself
Implementation Of Convolutional Neural Network For Diagnosing Rice Plant Diseases Using Colab Python Integrated With Streamlit Habib, Ahmad; Yahya, Haydar
Jurnal Mantik Vol. 8 No. 4 (2025): February: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mantik.v8i4.6003

Abstract

Agriculture, particularly rice cultivation, is crucial for Indonesia's food security; however, production is often hindered by pests and diseases. With over 30 million hectares of rice fields and millions of farmers relying on this staple crop, the impact of these challenges is significant, threatening both livelihoods and national food supply. This study aims to develop a rice plant disease diagnosis system using Convolutional Neural Network (CNN) methods implemented in a Streamlit-based application. Data were obtained from an open dataset on Kaggle, which includes images of healthy and infected rice leaves. The Streamlit application facilitates users in uploading images and receiving real-time diagnoses. Results show that the CNN model achieved an accuracy of 96.03% in identifying diseases, demonstrating a strong ability to recognize patterns in leaf images. This system offers an efficient solution to help farmers quickly and accurately detect rice diseases, contributing to increased agricultural productivity and food security in Indonesia