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All Journal Jurnal Mantik Penusa INTECOMS: Journal of Information Technology and Computer Science The IJICS (International Journal of Informatics and Computer Science) Jurnal Riset Informatika ComTech: Computer, Mathematics and Engineering Applications Jurnal Mantik bit-Tech Journal of Intelligent Decision Support System (IDSS) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Bulletin of Information Technology (BIT) Jurnal Hukum dan Sosial Politik Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Jurnal Testing dan Implementasi Sistem Informasi The Journal of Algorithmic Digital Engineering and Networks (JADEN)
Muhammad Iqbal
Universitas Pembangunan Panca Budi
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics

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Analysis of E-Commerce Consumer Purchasing Patterns Using the Naïve Bayes Algorithm Khairunnisa Khairunnisa; Muhammad Iqbal
The IJICS (International Journal of Informatics and Computer Science) Vol. 10 No. 1 (2026): Maret
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/ijics.v10i1.9531

Abstract

The rapid growth of e-commerce has generated a large volume of transactional data; however, its utilization is often limited to sales reports. In fact, transaction data holds significant strategic potential to be analyzed in order to understand customer purchasing patterns. This study aims to analyze consumer purchasing behavior in e-commerce by applying the Naïve Bayes algorithm as a probabilistic classification method. The dataset used in this research consists of 30 transaction records, including attributes such as purchase frequency, transaction value, product type, payment method, and promotion, with the purchasing decision (buy or not buy) as the class attribute. The research stages include calculating prior probabilities and conditional probabilities for each attribute with respect to the purchasing decision classes. The results indicate that the majority of transactions belong to the buy class with a probability of 0.63, while the not-buy class has a probability of 0.37. Purchase frequency and transaction value are identified as the most influential factors, where medium to high purchase frequency and medium to large transaction values show a strong tendency toward buying decisions. Furthermore, electronic products and non-cash payment methods, particularly e-wallets and credit cards, exhibit high probabilities associated with purchase decisions. Promotions also contribute positively to encouraging purchases, although they are not the sole determining factor. Overall, this study demonstrates that the Naïve Bayes algorithm is effective in identifying customer purchasing patterns and can support strategic decision-making in e-commerce businesses.
Fungal Disease Detection Using CNN Deep Learning Method Dika Dika; Muhammad Iqbal
JADEN : Journal of Algorithmic Digital Engineering and Networks Vol. 1 No. 2 (2026): The Journal of Algorithmic Digital Engineering and Networks
Publisher : Cv. Data Sinergi Digital

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65853/jaden.v1i2.120

Abstract

This study aims to detect mushroom diseases based on digital images using the Deep Learning Convolutional Neural Network (CNN) method. Fungal diseases are often the main cause of decreased quality and yield, so a fast and accurate detection method is needed. The dataset used consists of images of healthy mushrooms and diseased mushrooms obtained through direct image capture at the cultivation location. The research stages include image preprocessing, CNN model training, and performance evaluation using accuracy, precision, recall, and F1-score metrics. The results show that the CNN model is able to detect mushroom diseases with a high level of accuracy, so this method has the potential to be used as a decision support system in mushroom cultivation.
Co-Authors , Arpan Abdul Muin Nasution Ahkyar Khadafi Ardiansyah Ardiansyah Astri Aprilia Pratiwi Atja Ramadhan Boy Rizky Akbar Chairul Rizal Dika Dika DWI SURYANTO Efendi, Syahril Eko Hariyanto Firdaus Firdaus Hafizh Sallam Kairul Fahmi Khairunnisa Khairunnisa Kurnia Wira Syahputra Muhammad Adli Afdika Muhammad Eka Muhammad Israr Fathoni Muhammad Noor Hasan Siregar Niska, Debi Yandra Putra, Randi Rian Rindi Antika Sapna Indah Br Ginting Sartika Siburian Siti Hapsoh Lubis Sumarno Supiyandi Supiyandi Syaiful Asmi Hasibuan