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Design and Implementation of a Production Forest Monitoring Information System in Central Sulawesi Province Syahrullah, Syahrullah; Najar, Abdul Mahatir; Ngemba, Hajra Rasmita; Hendra, Syaiful
Sistemasi: Jurnal Sistem Informasi Vol 14, No 2 (2025): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v14i2.5073

Abstract

Kesatuan Pengelola Hutan (KPH) Dolago Tanggunung, as one of the production forest managers in Central Sulawesi, faces serious challenges such as illegal logging, forest encroachment, and a high risk of forest fires. The complexity of managing production forests in this region is further hindered by manual data collection and reporting processes, which pose significant limitations. This study develops a Production Forest Management and Monitoring Information System aimed at improving efficiency in recording and monitoring production forests using the Agile-Scrum methodology, allowing for incremental development based on user needs. The system is designed as a web-based platform with key features including data collection for fire-prone areas, illegal logging incidents, and forest encroachment, as well as integration with spatial data visualization technology. Testing results indicate that the system enhances data recording efficiency, transparency in reporting, and accelerates response to on-site incidents. The implementation of this system is expected to support data-driven decision-making and strengthen sustainable forest management.
Revealing the Relationship of Batik Motifs Using Convolutional Neural Network Najar, Abdul Mahatir; Abu, Maulidyani; Ratianingsih, Rina; Jaya, Agus Indra
Sistemasi: Jurnal Sistem Informasi Vol 13, No 5 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i5.4480

Abstract

This study explores the use of Convolutional Neural Network to identify and classify regional batik motifs, a significant aspect of Indonesian cultural heritage. The CNN model was optimized with Adam optimizer and used to extract distinctive features from the batik patterns. Subsequently, a hierarchical clustering method was employed to construct a relationship tree depicting the link between batik motifs based on their region. The research findings demonstrate that the CNN model effectively classifies batik motifs with an accuracy of up to 88%. The study provides insights into the intricate connections between regional batik designs and contributes to the preservation and understanding of Indonesia's cultural heritage.