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All Journal Journal of Information Technology and Computer Science Indonesian Journal of Artificial Intelligence and Data Mining Buletin Ilmiah Sarjana Teknik Elektro Equiva
Gusti Ahmad Fanshuri Alfarisy, Gusti Ahmad Fanshuri
Universitas Brawijaya
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Mathematics

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Automatic Plant Disease Classification with Unknown Class Rejection using Siamese Networks Putra, Rizal Kusuma; Alfarisy, Gusti Ahmad Fanshuri; Nugraha, Faizal Widya; Nuryono, Aninditya Anggari
Buletin Ilmiah Sarjana Teknik Elektro Vol. 6 No. 3 (2024): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v6i3.11619

Abstract

Potatoes are one of the horticultural commodities with significant trade value both domestically and internationally. To produce high-quality potatoes, healthy and disease-free potato plants are essential. The most common diseases affecting potato plants are late blight and early blight. These diseases appear randomly in different positions and sizes on potato leaves, resulting in numerous combinations of infected leaves. This study proposes an architecture focused on a similarity-based approach, namely the Siamese Neural Network (SNN). SNN can recognize images by comparing two or more images and categorizing the test image accordingly. Thus, SNN has an advantage over classification-based approaches as it can identify various combinations of disease spots on potato plants using a similarity-based approach. This study is divided into two main scenarios: testing with data categories which were previously seen during the training process (traditional testing) and testing with the addition of new data categories that were not seen during training. In the first scenario, SNN showed better accuracy with an accuracy rate of 98.4%, while in the second scenario, SNN achieved an accuracy of 97.1%. That result suggests that SNN can categorize data very well, even recognizing data which never seen during training. These results offer hope that SNN can recognize more disease spots/patterns on potato plants or even identify new diseases by adding these new diseases to the SNN support set without retraining.
Rainfall Forecasting in Banyuwangi Using Adaptive Neuro Fuzzy Inference System Alfarisy, Gusti Ahmad Fanshuri; Mahmudy, Wayan Firdaus
Journal of Information Technology and Computer Science Vol. 1 No. 2: November 2016
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (590.164 KB) | DOI: 10.25126/jitecs.20161212

Abstract

Rainfall forcasting is a non-linear forecasting process that varies according to area and strongly influenced by climate change. It is a difficult process due to complexity of rainfall trend in the previous event and the popularity of Adaptive Neuro Fuzzy Inference System (ANFIS) with hybrid learning method give high prediction for rainfall as a forecasting model. Thus, in this study we investigate the efficient membership function of ANFIS for predicting rainfall in Banyuwangi, Indonesia. The number of different membership functions that use hybrid learning method is compared. The validation process shows that 3 or 4 membership function gives minimum RMSE results that use temperature, wind speed and relative humidity as parameters.
Hybrid Genetic Algorithm and Simulated Annealing for Function Optimization Fatyanosa, Tirana Noor; Sihananto, Andreas Nugroho; Alfarisy, Gusti Ahmad Fanshuri; Burhan, M Shochibul; Mahmudy, Wayan Firdaus
Journal of Information Technology and Computer Science Vol. 1 No. 2: November 2016
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (879.719 KB) | DOI: 10.25126/jitecs.20161215

Abstract

The optimization problems on real-world usually have non-linear characteristics. Solving non-linear problems is time-consuming, thus heuristic approaches usually are being used to speed up the solution’s searching. Among of the heuristic-based algorithms, Genetic Algorithm (GA) and Simulated Annealing (SA) are two among most popular. The GA is powerful to get a nearly optimal solution on the broad searching area while SA is useful to looking for a solution in the narrow searching area. This study is comparing performance between GA, SA, and three types of Hybrid GA-SA to solve some non-linear optimization cases. The study shows that Hybrid GA-SA can enhance GA and SA to provide a better result
Ecommerce-price-scraper: Pustaka Ekstraksi Harga E-Commerce Indonesia Melalui Web Scraping Alfarisy, Gusti Ahmad Fanshuri; Richard Owen Hoan; Helmi; Nur Ali Rajab; Bhagavatgita Mahardika Kesuma Putra
Equiva Journal Vol 3 No 1 (2025)
Publisher : Jurusan Matematika dan Teknologi Informasi

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

This research focuses on the development of a Python library based on BeautifulSoup for automating web scraping on e-commerce platforms Lazada and Bukalapak. The primary goal of this study is to provide a practical solution for efficiently extracting product prices without the need to switch between platforms. The methodology involves integrating BeautifulSoup and Selenium to ensure accurate data retrieval, even when the HTML structure of the sites frequently changes. The developed system is capable of extracting information such as product names, prices, and product links based on user search keywords. The results demonstrate that the library can automate the data retrieval process with high accuracy while offering opportunities for the development of more complex price comparison applications. Additionally, the library provides developers with functions that can be extended to other e-commerce environments. However, adapting to changes in the HTML structure of e-commerce platforms remains a challenge for future development. This solution is expected to improve the efficiency of online shopping for users.
A RAG-Based Academic Information Chatbot Using Lightweight LLaMA and Indo-Sencence-BERT Saman, Muhamad; Alfarisy, Gusti Ahmad Fanshuri; Amelia, Rizky; Fadhliana, Nisa Rizqiza
Indonesian Journal of Artificial Intelligence and Data Mining Vol 8, No 3 (2025): November 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v8i3.38150

Abstract

In the current digital era, Institut Teknologi Kalimantan (ITK) encounters challenges in delivering academic information that is fast, accurate, and easily accessible to students, lecturers, and academic staff. Access to important information such as administrative procedures, report writing guidelines, and academic policies remains largely reliant on manual systems and static handbooks. To address this issue, this study investigates a chatbot system utilizing the Retrieval-Augmented Generation (RAG) framework through LLaMA model. The chatbot combines semantic retrieval and natural language generation to provide relevant and accurate answers based on existing academic documents. Evaluation was conducted on two lightweight LlaMA models: 1.5 and 3B parameters. Furthermore, different embedding vector also evaluated along with Indo-Sentence-BERT as well as the chunking size. The most optimal configuration was achieved using LLaMA 3B as the generative model and Indo-Sentence-BERT as the retriever, with a chunk size of 200 tokens and an overlap of 10 tokens. This setup achieved a RAGAS score of approximately 0.9, a competitive MRR of 0.5, and response latency under 1 second. Although LLaMA 1B recorded a higher MRR (0.6), its low RAGAS score made it less favorable. Overall, the LLaMA 3B and Indo-Sentence-BERT configuration is recommended to enhance the efficiency of academic information retrieval at ITK.
Co-Authors Andreas Nugroho Sihananto Bhagavatgita Mahardika Kesuma Putra Fadhliana, Nisa Rizqiza Helmi M Shochibul Burhan, M Shochibul Nugraha, Faizal Widya Nur Ali Rajab Nuryono, Aninditya Anggari Putra, Rizal Kusuma Richard Owen Hoan Rizky Amelia Saman, Muhamad Tirana Noor Fatyanosa, Tirana Noor Wayan Firdaus Mahmudy