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Errisya Rasywir
Universitas Dinamika Bangsa
Computer Science & IT

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Penerapan Data Mining Algoritma Naive Bayes Clasifier Untuk Mengetahui Minat Beli Pelanggan Terhadap INDIHOME Adi Wahyudi; Silvia Ovelia Tampubolon; Nurul afrilia Putri; Aji Ghassa; Errisya Rasywir; Desi Kisbianty
Jurnal Informatika Dan Rekayasa Komputer(JAKAKOM) Vol 2 No 2 (2022): JAKAKOM Vol 2 No 2 September 2022
Publisher : LPPM Universitas Dinamika Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1240.111 KB) | DOI: 10.33998/jakakom.2022.2.2.111

Abstract

Abstrak− Persaingan yang terjadi dalam dunia bisnis memaksa para pelakunya untuk selalu memikirkan strategi-strategi dan terobosan yang dapat menjamin kelangsungan dari bisnis yang dijalankannya. Hal ini akan memunculkan persaingan antar sesama provider kartu terhadap kartu internet. Para provider kartu internet berlomba-lomba menarik minat pelanggan dengan berbagai macam strategi pemasaran agar tidak kalah saing dan tetap eksis. Dan perusahaan ingin selalu meluncurkan kartu internet terbaru tanpa memikirkan kartu internet tersebut akan laku atau tidak dipasaran. Maka, metode klasifikasi mampu menemukan model yang membedakan konsep atau kelas data, dengan tujuan untuk dapat memperkirakan kelas dari suatu objek yang labelnya tidak diketahui. Oleh sebab itu, algoritma naive bayes dapat memprediksi peluang di masa depan berdasarkan pengalaman dimasa sebelumnya. Hasil dari penelitian ini agar dapat memprediksi atau memperkirakan laku atau tidak kartu internet yang baru, sehingga pemilik usaha dapat mengambil keputusan dan meningkatkan strategi pemasaran. Kata Kunci: Data Mining; Kartu Internet; Klasifikasi; Algoritma Naïve Bayes Abstract− Competition is happening in the business world to force the perpetrators to always think of the strategy - and a breakthrough strategy that can guarantee the continuity of the business being operated. This leads to competition among card providers on the internet card. The provider of internet card race-the race to attract customers with a variety of marketing strategies in order not to lose competitiveness and still exist. And the company wants to always launch the latest internet card without thinking of internet cards will be sold in the market or not. Thus, the classification method is able to find a model that distinguishes the concept or class of data, in order to be able to predict the class of an unknown object label. Therefore, Naive Bayes algorithm can predict future opportunities based on the experience of earlier. Results from this study in order to predict or estimate the behavior or the new Internet card, so that the bussines owner can make decisions and improve marketing strategy. Keywords: Data Mining; Internet Cards; Classification; Naïve Bayes Algorithm
Comparative Performance Analysis of Random Forest dand Support Vector Machine Algorithms for Coronary Heart Disease Risk Classification Maria Borroek; Ujang Suhendra; Errisya Rasywir
Jurnal Informatika Dan Rekayasa Komputer(JAKAKOM) Vol 6 No 2 (2026): JAKAKOM Vol 6 No 2 SEPTEMBER 2026
Publisher : LPPM Universitas Dinamika Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33998/jakakom.2026.6.2.2884

Abstract

Abstract− Coronary heart disease (CHD) is one of the leading causes of death in Indonesia, with an increasing incidence rate, thereby requiring early, rapid, and accurate risk detection to improve patients’ quality of life. CHD was selected in this study because it involves various measurable risk factors derived from clinical data, such as blood pressure, cholesterol levels, blood glucose levels, alcohol consumption, and patients’ medical history, making it relevant for analysis using a machine learning–based approach. This study aims to analyze and compare the performance of the Random Forest and Support Vector Machine (SVM) algorithms in classifying the risk of coronary heart disease based on a dataset consisting of 1,000 patient records with 16 clinical attributes and two target classes, namely high-risk and low-risk. The research stages include data exploration, preprocessing, class balancing using the Synthetic Minority Over-sampling Technique (SMOTE), train-test data splitting, model training, and hyperparameter optimization using GridSearchCV. Model performance was evaluated using Accuracy, Precision, Recall, F1-Score, and ROC-AUC metrics. The results indicate that the Random Forest model achieved the best performance with 99% Accuracy, 100% Precision, 99% Recall, 99% F1-Score, and 100% ROC-AUC after the implementation of SMOTE and hyperparameter tuning, while the SVM model achieved 95% Accuracy, 95% Precision, 91% Recall, 93% F1-Score, and 99% ROC-AUC. Therefore, it can be concluded that the Random Forest algorithm demonstrates more optimal and stable performance in classifying coronary heart disease risk within the dataset used
Co-Authors Adi Wahyudi Aji Ghassa Desi Kisbianty Maria Borroek Nurul afrilia Putri Silvia Ovelia Tampubolon Ujang Suhendra