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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Sains dan Teknologi
Nababan, Marlince Novita Karoseri
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Computer Science & IT Economics, Econometrics & Finance Mathematics Physics Social Sciences

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SISTEM INFORMASI PENJUALAN SPAREPART MOBIL BERBASIS WEB PADA CV.CIPTA MANDIRI JAYA Wijaya, Vivi; Nababan, Marlince Novita Karoseri
JURNAL SAINS DAN TEKNOLOGI Vol. 1 No. 2 (2020): Sains dan Teknologi
Publisher : Sisfokomtek

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (523.956 KB)

Abstract

The spare parts that are currently running are less efficient because during the process of selling and recording the spareparts that require a long time, the management of the unbalanced company that is taking place in Cipta Mandiri Jaya is one of thecompanies that sell car parts. The existence of a debate, then we need an information system that can help smooth operational processes.The purpose of designing this system is to provide the required report information quickly for CV. Cipta Mandiri Jaya. Informationsystems for sales of goods are designed using the PHP programming language and MySQL as the database. The application that canreduce and minimize the problem of counting, makes it easier to make purchases, sales and purchase transactions more effectively andefficiently and is supported by a high level of data security
Smart Diagnosis of Coffee Diseases via Web-Based Expert System Ginting, Deo Ekel Pindonta; Sitorus Pane, Siti Anzani; Nababan, Marlince Novita Karoseri; Christnatalis
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 3 (2025): Article Research July 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i3.14974

Abstract

Indonesia’s coffee industry faces persistent threats from plant diseases and pests, which significantly impact crop yield and farmer livelihoods. Many smallholder farmers lack access to timely expert guidance, leading to delays in diagnosis and ineffective treatments. This study proposes a web-based expert system designed to assist farmers in diagnosing coffee plant diseases and pests based on observed symptoms. The system integrates a Bayesian Network (BN) to model the probabilistic relationships between symptoms and diseases. It employs a Breadth-First Search (BFS) algorithm to optimize the exploration of symptom-disease associations. Developed using Node.js, Next.js, and MySQL, the system enables users to input their symptoms and receive probabilistic diagnoses along with treatment suggestions. Validation results show over 85% accuracy compared to expert assessments, highlighting the system's reliability and scalability. This research demonstrates that combining probabilistic reasoning and structured graph traversal provides an effective diagnostic tool, especially for underserved rural communities. Furthermore, the system serves as a foundation for future development of intelligent agricultural support tools, with potential integration of real-time environmental data, mobile platforms, and adaptive learning models to enhance decision-making in precision farming.
Co-Authors Christnatalis Ginting, Deo Ekel Pindonta Sitorus Pane, Siti Anzani Wijaya, Vivi