cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Rizal Furqan Ramadhan
Contact Email
admin@rizaniamedia.com
Phone
+6285257563813
Journal Mail Official
admin@rizaniamedia.com
Editorial Address
RT 10 RW 04 Desa Pucanganak Kec.Tugu Kab. Trenggalek
Location
Kab. trenggalek,
Jawa timur
INDONESIA
Jurnal Ilmiah Informatika dan Komputer
Published by CV.RIZANIA MEDIA PRATAMA
ISSN : -     EISSN : 30474752     DOI : https://doi.crossref.org/10.69533
Core Subject : Science,
Computer Science & IT
INFORMATECH : Jurnal Ilmiah Informatika dan Komputer (E-ISSN : 3047-4752) merupakan Jurnal nasional dengan akses terbuka yang menerbitkan artikel hasil penelitian di bidang Teknik Informatika dan Ilmu Komputer. Ruang Lingkup Jurnal meliputi Kecerdasan Buatan (Artificial Intellegence), Sistem Informasi, Robotika, Jaringan Komputer, Pengolahan Citra (Image Processing), Aplikasi Mobile, Data Mining dan bidang ilmu informatika lainnya. Jurnal INFORMATECH dikelola dan dipublikasikan oleh Rumah Jurnal RIZANIA MEDIA PRATAMA. Jurnal ini diterbitkan sebagai sarana dan wadah para dosen, ilmuan, peneliti maupun pakar bidang Teknik Informatika dan Ilmu Komputer mempublikasikan hasil-hasil penelitiannya untuk menunjang Tugas dan Program Tri Dharma Perguruan Tinggi secara Umum. Jurnal INFORMATECH terbit dua kali dalam setahun pada bulan Juni dan Desember.
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 72 Documents
 4   5   6   7   8 
Development Of An Inventory Management Information System Using The Agile Method Adelia Puspita Sari; Yuntari Purbasari; Andi Chiristian
Jurnal Ilmiah Informatika dan Komputer Vol. 3 No. 1 (2026): June 2026
Publisher : CV.RIZANIA MEDIA PRATAMA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69533/informatech.volume3number1.581

Abstract

PT. Saribumi Sriguna Putra still uses manual inventory recording and reporting processes involving ledgers, Microsoft Excel, and direct counts of remaining items in the warehouse. This situation poses risks of recording errors, data duplication, and delays in obtaining inventory information. This study aims to design and develop a web-based inventory management information system to facilitate a more structured process for managing inventory data. The system development method used is Agile Development, which allows the development process to be carried out in stages and adapted to user needs. The development stages include planning, implementation, testing, documentation, deployment, and maintenance. The developed system provides features for inventory data, incoming goods, outgoing goods, as well as reports on inventory, incoming goods, and outgoing goods. The research result is a web-based inventory management information system that can be used to manage inventory data and generate computerized reports. Test results using the Black-Box Testing method indicate that the developed features function in accordance with the system’s intended functions and requirements. This system is expected to assist PT. Saribumi Sriguna Putra in managing inventory data in a more structured manner and supporting the inventory monitoring process.
Three-Category News Text Classification Using LSTM on a Local Government Portal Febrisari Amalia; Andhy Permadi; Ilham Ilham
Jurnal Ilmiah Informatika dan Komputer Vol. 3 No. 1 (2026): June 2026
Publisher : CV.RIZANIA MEDIA PRATAMA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69533/informatech.volume3number1.582

Abstract

This research develops an automated three-category news classification system for the official news portal of the Regional Revenue Agency (Bapenda) of Surabaya City, using a Long Short-Term Memory (LSTM) deep learning model to reduce reliance on manual, inconsistent categorization. The training corpus combined original institutional data, web-scraped articles from other regional revenue agencies, and GPT-generated synthetic text, all validated by domain experts, then processed through a structured preprocessing pipeline. On an unseen test set of 513 authentic institutional articles, the model achieved an overall accuracy of 77.78% and a weighted F1-score of 0.78, driven mainly by strong performance on the majority "Important Information" class (F1-score 0.87). However, the macro-averaged F1-score was only 0.4434, reflecting substantially weaker performance on minority classes, particularly "Information Technology" (F1-score 0.069). The model was deployed as a human-in-the-loop recommendation feature within the agency's existing dashboard.

Page 8 of 8 | Total Record : 72

 4   5   6   7   8 

Filter by Year

2024 2026


Reset
Filter By Issues
All Issue Vol. 3 No. 1 (2026): June 2026 Vol. 2 No. 2 (2025): Desember 2025 Vol. 2 No. 1 (2025): June 2025 Vol. 1 No. 2 (2024): Desember 2024 Vol. 1 No. 1 (2024): Juni 2024