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All Journal International Journal of Research and Applied Technology (INJURATECH) Jurnal Pendidikan Sains dan Komputer
Alfiah, Ananda
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Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Immunology & microbiology Materials Science & Nanotechnology Mathematics Physics Other

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Marketing Strategic for Hydroponic Vegetable Product to Improve Marketing Performance Base on Digital Technology Miftah, Himmatul; Alfiah, Ananda; Kusumawati, Riny; Mubarokah, Syaima Lailatul; Pramartaa, Ikhsan Qodri; Susanti, Aishah Rini; Aidah, Yuyu
International Journal of Research and Applied Technology (INJURATECH) Vol. 4 No. 1 (2024): International Journal of Research and Applied Technology (INJURATECH)
Publisher : Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/injuratech.v4i1.14479

Abstract

Agriculture is one of the sectors that has been the backbone of human civilization since the beginning, undergoing major transformation along with technological. The hydroponic vegetable market continues to grow along with increasing demand for healthy and sustainable agricultural products. In this digital era, marketing strategies based on digital technology are the key to increasing visibility and sales of hydroponic vegetable products. The purpose of this study is to investigate different digital marketing tactics that may be used to enhance the marketing effectiveness of hydroponically grown vegetables. By examining digital marketing techniques like social media marketing and the use of e-commerce platforms, this research identifies effective ways to strengthen the market position of hydroponic vegetable products in the digital era. The study also highlights the important role digital technology plays in changing the way manufacturers interact with consumers, measure marketing performance, and develop long-term relationships with customers. It is believed that the research's conclusions would give hydroponic vegetable growers insightful information and marketing practitioners in optimizing their marketing strategies in this increasingly digital market
Analisis Kinerja Decision Tree dan Random Forest Menggunakan Dataset Breast Cancer: Performance Analysis of Decision Tree and Random Forest Using Breast Cancer Dataset Firdaus, Uus; Alfiah, Ananda; Mohdo, Lorina
Jurnal Pendidikan Sains dan Komputer Vol. 6 No. 01 (2026): Artikel Riset, February 2026
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/jpsk.v6i01.7892

Abstract

Breast cancer is a disease with a high mortality rate in women worldwide, making early detection a crucial factor in increasing the chances of successful treatment and patient survival. Advances in computing technology, particularly machine learning, have created opportunities to use medical data to inform decision-making in the diagnostic process. This study aims to analyze and compare the performance of the Decision Tree and Random Forest algorithms in classifying breast cancer using the Wisconsin Breast Cancer Dataset. The dataset comprises 569 data points with 30 numeric attributes representing cancer cell characteristics and a class label indicating benign or malignant cancer. The research stages include data preprocessing, splitting the data into training and test sets (80:20), applying the classification algorithm, and evaluating model performance using accuracy, precision, recall, and F1-score metrics. The test results show that the Random Forest algorithm outperforms the Decision Tree. Random Forest achieved an accuracy of 98.68% on the training data and 95.61% on the test data, while Decision Tree achieved an accuracy of 96.92% on the training data and 91.23% on the test data. This difference indicates that Random Forest has better generalization capabilities and is more resistant to overfitting. The findings of this study indicate that Random Forest is more effective for data-based breast cancer classification than Decision Tree. Therefore, the Random Forest algorithm is recommended as a more reliable method to support decision support systems in early breast cancer detection.
Co-Authors Aidah, Yuyu Himmatul Miftah Ikhsan Qodri Pramartaa Mohdo, Lorina Riny Kusumawati Susanti, Aishah Rini Syaima Lailatul Mubarokah Uus Firdaus