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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Teknik Informatika UNIKA Santo Thomas Building of Informatics, Technology and Science Jurnal Tekinkom (Teknik Informasi dan Komputer) MEANS (Media Informasi Analisa dan Sistem) Jurnal Ilmu Komputer dan Sistem Informasi (JIKOMSI) International Journal Of Science, Technology & Management (IJSTM) Jurnal Media Informatika Jurnal Sistem Informasi, Teknik Informatika dan Teknologi Pendidikan (JUSTIKPEN) Jurnal Teknik Informatika Unika Santo Thomas (JTIUST) Journal of Computer Science and Information Systems (JCoInS) IKA BINA EN PABOLO : PENGABDIAN KEPADA MASYARAKAT Jurnal Pengabdian Masyarakat Gemilang (JPMG) Internet of Things and Artificial Intelligence Journal Jurnal Pengabdian Harapan Bangsa
Irmayani, Deci
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Aerospace Engineering Agriculture, Biological Sciences & Forestry Automotive Engineering 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 Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Public Health Social Sciences Transportation Other

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Journal : International Journal Of Science, Technology

 1 
Comparative Analysis of K-Nearest Neighbors and Decision Tree Methods in Determining Students’ Purchase Interest in MacBook Laptops Fadilla Hasibuan, Intan; Irmayani, Deci; Sihombing, Volvo
International Journal of Science, Technology & Management Vol. 5 No. 5 (2024): September 2024
Publisher : Publisher Cv. Inara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46729/ijstm.v5i5.1169

Abstract

In the context of increasingly competitive technology markets, companies need to know consumer preferences accurately to optimize product offerings and increase sales. Two classification methods that are often used in data mining, namely K-Nearest Neighbors and Decision Tree, have their own advantages and disadvantages. This study proposes a solution that involves processing student data using both classification methods to identify the most accurate and effective method for identifying purchase intentions. This study aims to compare the performance of the two methods in determining student purchase intentions for MacBook laptops. The research methodology includes collecting data from 100 students covering various factors such as user experience, design and portability, technical specifications, price, and security. This data is then classified using the K-Nearest Neighbors and Decision Tree methods. Furthermore, a confusion matrix is used to provide a more detailed picture of the performance of the two methods. The results of the study show that the Decision Tree method has a higher accuracy (91%) compared to K-Nearest Neighbors (88%). In addition, Decision Tree excels in other metrics such as precision (87.18% vs. 85.71%), recall (89.47% vs. 85.71%), specificity (91.94% vs. 89.66%), and F1-Score (88.31% vs. 85.71%). The decision tree also has a higher NPV value and lower FPR and FNR rates than K-Nearest Neighbors, indicating that it is superior in avoiding misclassification. The study's conclusion is that the Decision Tree method is more effective and accurate than K-Nearest Neighbors in determining students' purchase intentions for MacBook laptops. The decision tree shows better performance in almost all evaluation metrics, making it a more reliable method to use in consumer data analysis. The results of this study are expected to help companies choose a more appropriate and effective analysis method for their marketing strategies, as well as provide a basis for further research in the field of consumer purchase intention classification.
Analysis Of Public Interest In Smartfren SIM Cards Using The K-Nearest Neighbors Method Tawanta Natalia Sembiring, Sri; Sihombing, Volvo; Irmayani, Deci
International Journal of Science, Technology & Management Vol. 5 No. 5 (2024): September 2024
Publisher : Publisher Cv. Inara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46729/ijstm.v5i5.1177

Abstract

The use of Smartfren SIM cards is increasing along with the public's need for fast and stable internet services. However, a deep understanding of public interest in the SIM card is necessary to optimize marketing strategies and increase sales. Proper analysis can help companies identify potential target markets and develop effective marketing strategies. We chose the K-Nearest Neighbors method to analyze public interest in using Smartfren SIM cards. This study aims to develop and evaluate the K-Nearest Neighbors model in predicting public interest in using Smartfren SIM cards. This study uses a dataset containing information about Smartfren SIM card users. We divide the data into two sets: a training set for model building and a test set for evaluating model performance. We apply the K-Nearest Neighbors method to classify the data into two categories: interested and not interested. We evaluate the model performance using accuracy, precision, recall, and F1-score metrics. We present the evaluation results as a confusion matrix. The developed K-Nearest Neighbors model showed excellent performance with an accuracy of 94.29%, a precision of 94.20%, a recall of 100%, and an F1-score of 97.01%. These results indicate that the K-Nearest Neighbors model is effective in predicting people's interest in Smartfren SIM cards. The high recall value indicates that the model is able to identify all interested individuals without missing any, while the high precision value indicates that the model rarely makes false positive prediction errors. This study concludes that the K-Nearest Neighbors method is very effective for use in analyzing people's interest in using Smartfren SIM cards. We can rely on the developed model's strong performance for real-world applications in marketing strategies.
Co-Authors Adi, Puput Dani Prasetyo Afnita Afnita Agusti, Nelly Aini, Putri Andriani, Reza Silvia Anjar, Agus Aris Munandar Bangun, Budianto Bilatasya, Yolanda Dalimunthe, Ersandi Ahmad Delima Harahap, Risma Dewi, Rahayu Kusnita Dwiyanti, Dida Dwiyanti, Didan Efendi, Davina Rizky Fadilla Hasibuan, Intan Fitriani, Sinta Gultom, Gregorius Apri K Harahap, Raja Pahlevi Harahap, Vivi Nadenia Hasibuan, Mila Nirmala Sari Hsb, Elysa Rohayani imam maulana, imam Iwan Purnama JP, Gafar Ilyaz Juledi, Angga Putra Lesmana, Yandri Lukman Hakim Munthe, Ibnu Rasyid Muti’ah, Rahma Muwanti, Apri Nasution , Fitri Aini Nasution, Fitri Aini Nasution, Marnis Pakto, Dedi Pane, Rahmadani Purba, Mila Hanim Rambe, Aida Zahrah Hasanati Br Rani Rahayu, Rani Rasyid Munthe, Ibnu Ritonga, Aldi Rapandi Ritonga, Ali Akbar Rohanita Hasibuan, Lily Sari Hasibuan, Mila Nirmala Sari, Ely Novita Sari, Mila Nirmala Sentosa Zega, Agus Mentro Sianipar, Vitasari Sihombing, Volvo Simanjuntak, Ebrika Nadia Sinaga, Fransisco Alexander Sinaga, Novica Handayani Siregar, Adinda Pratiwi Siregar, Siti Wahdina Siregar, Zakia El Husna Sitompul, Muhammad Sofyan Sudarman, Dita Auliya Suryadi, Sudi Suryadi, Sudi Syahputra, Rahmat Syaiful Zuhri Harahap Tawanta Natalia Sembiring, Sri Utami, Ulfa Yanris, Gomal Juni Yullyana, Yullyana