Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Jurnal Tika
Mahara Bengi
Unknown Affiliation
Aerospace Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Diagnosa Penyakit Bawang Merah Dengan Metode Forward Chaining Dan Backward Chaining Mukti Qamal; Fadlisyah; Mahara Bengi; Mukarramah
Jurnal Tika Vol 7 No 1 (2022): Jurnal Teknik Informatika Aceh
Publisher : Fakultas Ilmu Komputer Universitas Almuslim Bireuen - Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (407.308 KB) | DOI: 10.51179/tika.v7i1.1002

Abstract

Plant diseases are the main enemy of farmers. Many farmers fail to harvest or reduce their agricultural yields because they are not able to properly deal with the diseases that attack their crops. One of the plants that are susceptible to disease is the onion plant. To properly handle the disease that attacks the shallot plant, an agricultural expert is needed. While the number of agricultural experts is limited and unable to deal with the problems of a large number of farmers at the same time, so we need a system that has the capabilities of an agricultural expert, which in this system contains the expertise of an agricultural expert regarding diseases, symptoms and diseases treatment of onion plants. In this study, a Web-based expert system was designed and built using rule-based reasoning with forward chaining and backward chaining inference methods which were intended to assist farmers in diagnosing diseases in shallots, and how to handle them. In this study, forward chaining and backward chaining methods will be compared so that the results will be obtained which method is more suitable for diagnosing a disease. From the results of the comparison analysis of the two methods, it was found that the Forward Chaining method was better and more efficient for diagnosing diseases in shallot plants.
Application of Natural Language Processing and LSTM in A Travel Chatbot for Medan City Atika, Syarifah; Bengi, Mahara; Sardeng, Shekainah Kim A.
Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Vol. 9 No. 1 (2025)
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/eltikom.v9i1.1481

Abstract

The tourism sector plays a vital role in economic growth and regional development. Medan, a major city in North Sumatra, offers rich religious, historical, and cultural attractions. However, fragmented and inconsistent information presents challenges for both tourists and destination managers, often complicating travel planning. To address this issue, this study proposes the development of an AI-based chatbot aimed at enhancing the tourism experience in Medan. By integrating Natural Language Processing (NLP) and Long Short-Term Memory (LSTM), the chatbot is designed to deliver accurate, contextual, and conversational responses tailored to users' tourism-related queries. It was trained on a comprehensive dataset compiled from various sources concerning Medan’s tourism. The training ran over 100 epochs, achieving an accuracy of 84.31% and a loss of 0.7594. Validation testing yielded an accuracy of 77.14% and a loss of 2.4233, indicating good generalization to unseen data. End-to-end testing with 312 queries covering all defined intents resulted in a testing accuracy of 75.64%, confirming the model’s practical effectiveness. The findings demonstrate that the chatbot can accurately interpret user input, classify information, and enhance user interaction. supports the digital transformation of Medan’s tourism sector by introducing a reliable, AI-driven tool for seamless travel planning and engagement.
Co-Authors Atika, Syarifah Fadlisyah Mukarramah Mukti Qamal Sardeng, Shekainah Kim A.