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Journal : bulletin of network engineer and informatics bufnets

PERFORMANCE ANALYSIS OF NAÏVE BAYES CLASSIFIERS BASED ON THE INFORMATION GAIN-BASED FEATURE SELECTION WITH MULTICOLLINEARITY ANALYSIS Luh Putu Risma Noviana Risma; I Gede Aris Gunadi; I Made Gede Sunarya
Bulletin of Network Engineer and Informatics Vol. 4 No. 1 (2026): BUFNETS (Bulletin of Network Engineer and Informatics) April 2026
Publisher : PT. GWEX NET PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59688/732114

Abstract

This study aims to analyze the performance of the Naïve Bayes Classifier algorithm by comparing several feature selection methods, namely Information Gain-based feature selection, Multicollinearity-based feature selection, and a combination of Information Gain and Multicollinearity. The dataset used in this study consists of 337 toddler stunting cases obtained from Kintamani I and VI Public Health Centers. The experiment was conducted using four testing scenarios: (1) Naïve Bayes Classifier without feature selection, (2) Naïve Bayes Classifier with Information Gain feature selection, (3) Naïve Bayes Classifier with Multicollinearity feature selection, and (4) Naïve Bayes Classifier with a combination of Information Gain and Multicollinearity feature selection. All experiments used a data split of 70% training data and 30% testing data, while model performance was evaluated using a confusion matrix. In the Information Gain feature selection stage, several features achieved the highest gain values, namely BPJS with a gain value of 1.0, immunization with a gain value of 1.0, age with a gain value of 0.842, maternal pregnancy history with a gain value of 0.791, and smoking habits with a gain value of 0.756. These features were retained in the final combined model because they contributed the most to the stunting classification process. In addition to improving predictive performance, the combination of Information Gain and Multicollinearity was also able to reduce feature redundancy, resulting in a more stable classification model. The results showed that the accuracy of the Naïve Bayes Classifier without feature selection was 90.10%, the Naïve Bayes Classifier with Information Gain feature selection achieved 95.05%, the Naïve Bayes Classifier with Multicollinearity feature selection achieved 93.07%, and the Naïve Bayes Classifier with a combination of Information Gain and Multicollinearity achieved the highest accuracy of 96.04%. These findings indicate that the combination of Information Gain and Multicollinearity produced the best performance among all tested methods. In addition, a coefficient of determination (R Square) test was conducted using SPSS, resulting in a value of 0.577, indicating that 57.7% of stunting classification was influenced by independent variables such as age, BPJS, immunization, smoking habits, and maternal pregnancy history, while the remaining 42.3% was influenced by other factors outside the scope of this study. The results also indicate that the Naïve Bayes algorithm combined with Information Gain feature selection and multicollinearity testing can be used as a stable and effective approach for early stunting classification to support decision-making in public health services.
Co-Authors ., Ketut Suma ., Putu Sonia Virgawati Pratiwi Adi Sista, Dewa Nyoman Agus Ariwanta, I Putu Yesha Agus Gunawan Agus Harjoko Agus Harjoko Agus Harjoko Ahmad Asroni Ahmad Asroni, Ahmad Anandita, Ida Bagus Gede Andiny T T Arditaloka, I Wayan Angga Ariasa, Komang Ariyani, Putu Wendy Artama, Made Bella Eka Wahyuningtias Cipta, I Putu Agus Eka Yatna Cokorda Oka Birawidya David Juli Ariyadi Dewa Gede Hendra Divayana, Dewa Gede Hendra Dewi Oktofa Rachmawati Dharmana, I Wayan Diatmika, I Ketut Agus Indra Dinata, I Made Anom Mahartha Erlangga, Anak Agung Gde Wahyu Sukma Fauzi, Muhammad Rizki Galih Cahyaningsih, Agung Ukki Gede Indrawan Gede Rasben Dantes Hajrin, M. Heryanto, I Wayan Agus I Gusti Agung Putu Mahendra I Ketut Paramarta I Made Arya Adinata Dwija Putra I Made Candiasa I Made Gede Sunarya I Made Pradipta I Nyoman Sukajaya I Nyoman Wahyu Semeru Putra I Putu Agus Eka Yatna Cipta I Putu Aris Sanjaya I Putu Aris Sanjaya, I Putu Aris I Putu Dody Suarnatha I Putu Putra Damana I Wayan Agus Heryanto I Wayan Gede Suweca Antara I Wayan Pio Pratama I Wayan Rosiana I Wayan Sadia I Wayan Santyasa I Wayan Sukra Ida Ayu Mirah Cahya Dewi Jana Satvika, Gd. Aditya Kadek Yota Ernanda Aryanto Ketut Suma Ketut Suma . Ketut Suma . Komang Ariasa Komang Setemen Lika Hanifah Luh Joni Erawati Dewi Luh Putu Budi Yasmini Luh Putu Risma Noviana Risma Luh Rumni Oktaria M. Hajrin M.Cs S.Kom I Made Agus Wirawan . Made Artama Made Junindra Maha Arta Sang Made Wahyu Aditya Arta Made Windu Antara Kesiman Made Windu Segara Matius Ivan Bimasena Mimin Yeli Sholekah Moh. Heri Setiawan MS Prof. Dr. Ketut Suma . N Dinda Maharani Ni Kadek Erna Supriathi Ni ketut Lisa Maheni Ni Komang Rai Mirayanti NI LUH PUTU MANIK WIDIYANTI Ni Made Yeni Dwi Rahayu Ni Putu Eka Apriyanthi Nugraha, I Gede Pradipta Adi Nugraha, I Gusti Agung Satria Oktaria, Luh Rumni Pathni, Ida Ayu Wisma Anggaritha pramana, i gede pramana ade saputra Prof. Dr. Ketut Suma, MS . Putra, I Kadek Nurcahyo Putra, I Nyoman Wahyu Semeru Putra, I Putu Arya Putu Eka Parianthana Putu Sonia Virgawati Pratiwi . Rai Sujanem Risha, Nurfa Sandhiyasa, I Made Subrata Saputri, Ni Kadek Tesya Ari Sariyasa Sariyasa Sariyasa Sariyasa Sawitri D U Segara, Made Windu Sidik, Purnama Sisilia Fhelly Djun Sonia Dewi Parna.T Sri Hartati Suputra, I Putu Arsana Suryawan, I Made Yuda Sutarno, Erwan Sutarno, Erwan Suweca Antara, I Wayan Gede T, Andiny T U, Sawitri D Wardana, I Komang Tri Edi Wayan Eka Ariawan Yogi Duwi Antara