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Sihombing, Volvo
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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.
Implementation of K-Nearest Neighbors Algorithm in Analyzing Public Interest in Shoping at Supermarkets Siti Kholijah, Siti Kholijah; 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.v5i5.1183

Abstract

People's shopping patterns and behaviors continue to develop along with technological advances and lifestyle changes, thus requiring retail business actors, especially supermarkets, to better understand their customers' interests and preferences. In this context, accurate analysis of customer shopping interests is very important to improve customer satisfaction and optimize marketing strategies. One solution that can be implemented to analyze people's shopping interests is the application of the K-Nearest Neighbors algorithm, a simple yet effective nearest neighbor-based classification method for recognizing patterns from existing data. This study aims to apply the K-Nearest Neighbors algorithm to classify people's interest in shopping at supermarkets. This study also evaluates the effectiveness and performance of the algorithm in the context of business decision-making in the retail sector. The research methodology includes collecting data on people's shopping interests, data pre-processing, implementing the K-Nearest Neighbors algorithm, and evaluating model performance using evaluation metrics such as accuracy, precision, recall, and F1-score. The results of this study indicate that the K-Nearest Neighbors algorithm is able to achieve an accuracy of 88%, with precision, recall, and F1-score all reaching 92.86%. These results indicate that the K-Nearest Neighbors model is very effective in classifying people's shopping interests, with a low error rate. The resulting confusion matrix also shows the model's ability to identify customers who are interested in shopping with little prediction error. This study concludes that we can rely on the K-Nearest Neighbors algorithm to analyze people's shopping interests in supermarkets. This model not only shows good performance in classification but also has great potential to be implemented in recommendation systems and customer segmentation in the real world. This study contributes to the development of consumer behavior analysis methods in the retail sector, as well as providing a basis for further research to explore other algorithms or combinations of techniques to improve the accuracy and effectiveness of classification models.
Web-based Palm Oil Seedling Sales Information System (Case Study: CV. XYZ) Ramadhani, Siska; Sihombing, Volvo; Juni Yanris, Gomal
International Journal of Science, Technology & Management Vol. 6 No. 1 (2025): January 2025
Publisher : Publisher Cv. Inara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46729/ijstm.v6i1.1213

Abstract

Oil palm seedling sales are one of the important aspects of the agricultural industry in Indonesia. However, conventional sales processes often encounter various obstacles, such as limited access to information for consumers, difficulties in inventory management, and low operational efficiency. To overcome these problems, this study aims to develop a Web-based Oil Palm Seedling Sales Information System at CV. XYZ. We designed this system to offer a comprehensive solution for sales management, encompassing stock recording and real-time transaction processing. This study uses a system development method with the waterfall method, in which the development stages include requirements, system design, implementation, verification, and maintenance. We collected data through direct observation at CV XYZ, interviews with management and customers, and related literature studies. Users then tested the prototype to identify potential problems and gather feedback for system improvement. According to the study's findings, the implementation of this web-based information system has resulted in increased efficiency in sales and inventory management. Users can easily access product information, place orders, and track the status of their orders. In addition, this system also allows integration with payment gateways, so that the transaction process becomes faster and safer. This system simplifies customer data management, sales recording, and financial report analysis from a management standpoint. At CV. XYZ, the development of the Web-based Palm Oil Seedling Sales Information System positively impacted operational efficiency and effectiveness. This system not only increases customer satisfaction by providing faster and more transparent services, but it also helps management make more accurate decisions based on available data. We need to add more advanced data analysis features to forecast sales trends and market demands, and integrate the system with e-commerce platforms to broaden our market reach.
Implementation Of The Support Vector Machine Method In Predicting Student Graduation Abdillah, Syakira; Juni Yanris, Gomal; Sihombing, Volvo
International Journal of Science, Technology & Management Vol. 6 No. 1 (2025): January 2025
Publisher : Publisher Cv. Inara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46729/ijstm.v6i1.1265

Abstract

Student graduation is an important indicator of the quality of education at a higher education institution. A high graduation rate not only indicates success in the learning process but also has a positive impact on the reputation of the institution. Conversely, a low graduation rate can be a signal of problems that require special attention. Various factors, both academic and non-academic, influence higher education institutions in ensuring timely student graduation. Therefore, we need a method that can accurately predict student graduation to carry out early intervention. This study aims to apply the support vector machine method in predicting student graduation. We chose this method due to its capacity to classify complex data. We use historical student data, such as Semester Achievement Index scores, as input variables to build a prediction model. We evaluate the model using precision, recall, and f1-score metrics. According to the study's findings, the support vector machine model's accuracy level is 71.20%. This method is good at predicting students who graduate with a precision of 95%, recall of 72%, and f1-score of 82%. However, the model's performance in predicting students who failed was less than satisfactory, with a precision of only 17%, a recall of 62%, and an f1-score of 26%. The imbalance in data between passed and failed students contributed to this result. The Support Vector Machine method effectively predicts student graduation for the majority class (passed), but requires special handling of the data imbalance to enhance the accuracy of predictions for the minority class (failed). Universities expect to use the results of this study to carry out early intervention and increase student graduation rates.
Co-Authors Abdillah, Ihsan Abdillah, Syakira Adi, Puput Dani Prasetyo Adi, Puput Dani Prasetyo Adi Aditya, Nanda Ajeng Lestari, Dinda Alam, Dewi Pathimah Alpiansyah, Fredy Andriyani, Wahyu Fitri Angreani Hulu, Linca Elma Popi Ariska, Fevi Aurianda, Rieke Bangun, Budianto Barasa, Kristina Dewi Utami, Yarma Agustya Dwiyanti, Dida Dwiyanti, Didan Efendi, Davina Rizky Esterlin, Esterlin Fachry Abda El Rahman Fadilla Hasibuan, Intan Gultom, Gregorius Apri K Gustriyadi, Eko Halawa, Prianus Harefa, Sinema Hasibuan, Dilla Puspita Hasibuan, Mila Nirmala Sari Hasibuan, Sabdi Albi Hendriyanti, Yurika Cici Hia, Faomaha Hutagalung, Charles Efendy Irmayani , Deci Irmayani Irmayani Irmayani, Deci Jannah , Ely Jati, Dewi Sekar Juledi, Angga Putra Juni Yanris, Gomal Latifah Hanum Lumban Gaol, Tid Verawati Maizura, Safrina Manurung, Romtika mawarni, Putri Sigit Mega, Mega Muhammad Halmi Dar Muliani, Sonia Sri Munthe , Ibnu Rasyid Munthe, Ibnu Rasyid Nasution, Fitri Aini Nasution, Marnis Novilda, Mustamu Elizabeth Nurdila, Nurdila Pakto, Dedi Pertiwi, Nur Fajar Kurnia Petromak Sinaga, Gandhi Naek Purba, Mila Hanim Purwati Purwati Putra Juledi, Angga Ramadhani, Siska Rangkuti, M. Andri Gautama Rani Rahayu, Rani Rasyid Munthe, Ibnu Rizky Mangunsong, Annisa Rusmiana, Rusmiana Rusmina, Eva Safitri, Nina Siddik, Rasid Simamora, Rikardo Lasroha Sinaga, Veranica siregar, Victor M.M. Siti Kholijah, Siti Kholijah Sitio, Arjon Samuel Sitompul, Joni Awendri Sitorus, Anggiat Selamat Suci Ramadhani, Suci Suhaylah, Suhaylah Syafitri, Risma Syahputra, Rapian Tasyabila, Tasyabila Tawanta Natalia Sembiring, Sri Tiara, Dewi Tria Wulandari Wiranti, Dea Yanris, Gomal Juni