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All Journal ILKOM Jurnal Ilmiah Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Media Elektrik
Isra, Ali
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering

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Apriori Algorithm Application for Consumer Purchase Patterns Analysis Situmorang, Boldson Herdianto; Isra, Ali; Paragya, Dhatu; Akbar Adhieputra, David Aulia
Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Vol 21, No 1 (2024): Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika
Publisher : Ilmu Komputer, FMIPA, Universitas Pakuan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33751/komputasi.v21i1.9260

Abstract

The Apriori algorithm is a data mining association rule algorithm for finding relationship patterns between one or more items in a dataset. Apriori algorithm is often used in transaction data analysis or market basket analysis. Apriori algorithm is used to find out consumer purchase patterns in e-commerce systems and provide product recommendations to consumer by extaracting associations or events from transactional data. This study is purposed for deeply analyze the steps, performance of Apriori algorithm, and give relevant an example of case study to better explain the steps of Apriori algorithm application, as well as the results achieved. 
REAL-TIME SOLAR PANEL FAULT DETECTION USING YOLOv8-BASED DEEP LEARNING APPROACH Nur Faisal, Andi; Isra, Ali
Jurnal Media Elektrik Vol. 22 No. 3 (2025): MEDIA ELEKTRIK
Publisher : Jurusan Pendidikan Teknik Elektro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/metrik.v22i3.9182

Abstract

This study presents the implementation of the YOLOv8s-cls model for automatic classification of solar panel surface conditions into six categories: Clean, Dusty, Bird-drop, Snow-Covered, Electrical-Damage, and Physical-Damage. A dataset comprising 619 images was used to train the modified YOLOv8s-cls architecture, spanning 50 epochs with a batch size of 16, input dimensions set to 128×128, and the AdamW optimizer applied throughout. The training was conducted on a CPU-only system, yet the inference benchmark was performed in a separate testing phase, yielding an average inference time of 0.032 seconds per image, indicating strong feasibility for real-time deployment. The achieved accuracies were 85.88% for Top-1 and 99.44% for Top-5 predictions, demonstrating robust performance in multi-class classification tasks. Nonetheless, some visual ambiguities remained between similar classes such as Dusty vs. Snow-Covered and Electrical-Damage vs. Physical-Damage. These results affirm the effectiveness of YOLOv8s-cls as a lightweight and adaptable deep learning solution for solar panel condition monitoring. Future enhancements are proposed, including targeted data augmentation, texture-based preprocessing, and deployment on GPU-accelerated or edge-optimized platforms to improve generalization and deployment flexibility in real-world settings.
A Deep Learning Approach for Tourism Destination Recommendation Using IndoBERT and TF-IDF Silfianti, Widya; Syah, Rama Dian; Suhendra, Adang; Isra, Ali; Darmayantie, Astie; Ohorella, Noviawan Rasyid
ILKOM Jurnal Ilmiah Vol 17, No 3 (2025)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v17i3.3069.241-251

Abstract

The rapid development of information technology has transformed various sectors, including tourism, where recommendation systems play a vital role in providing personalized services. Tourists are often faced with a wide range of destination choices, making decision-making increasingly complex. To address this, Artificial Intelligence (AI) and Natural Language Processing (NLP) can be leveraged to enhance recommendation accuracy through deeper analysis of destination descriptions. This study proposes a tourism destination recommendation system combining IndoBERT, SimCSE, and TF-IDF methods. IndoBERT was applied to capture semantic and contextual meaning in the Indonesian language, SimCSE improved sentence-level embeddings, and TF-IDF extracted essential keywords from descriptions. The system was implemented on a website to generate personalized recommendations based on user input. Evaluation results demonstrated that the composition of IndoBERT and TF-IDF achieved strong performance, with precision, recall, and F1-score values of 1.0 at a similarity threshold of 0.20. However, higher thresholds reduced recall and F1-score, indicating that a lower threshold provided a better balance between accuracy and coverage. The recommendation outputs matched user preferences, and functional testing showed that all website features performed successfully. These findings highlight the effectiveness of combining semantic and keyword-based methods for tourism recommendation. Future work could expand the dataset, integrate user feedback, and benchmark against other state-of-the-art models to further enhance system performance.
Co-Authors Adang Suhendra Akbar Adhieputra, David Aulia Darmayantie, Astie Nur Faisal, Andi Ohorella, Noviawan Rasyid PARAGYA, DHATU Silfianti, Widya Situmorang, Boldson Herdianto Syah, Rama Dian