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All Journal Jurnal Gamma EKSAKTA: Journal of Sciences and Data Analysis JURNAL SAINS PERTANIAN EQUATOR Jurnal Ekonomi : Journal of Economic Paradigma Geoplanning : Journal of Geomatics and Planning Pro Bisnis Jurnal Dinamika Pengabdian (JDP) JOIV : International Journal on Informatics Visualization Knowledge Engineering and Data Science BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) Jurnal Teknologi Sistem Informasi dan Aplikasi JSiI (Jurnal Sistem Informasi) JSAI (Journal Scientific and Applied Informatics) Jurnal Teknologi Informatika dan Komputer Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Enthusiastic : International Journal of Applied Statistics and Data Science Proceedings Series on Physical & Formal Sciences Healthy Tadulako Journal (Jurnal Kesehatan Tadulako) Blend Sains Jurnal Teknik Jurnal Informatika dan Teknologi Pendidikan Jurnal Ilmiah MEA (Manajemen, Ekonomi, dan Akuntansi) KOLONI Innovative: Journal Of Social Science Research Ahsana: Jurnal Penelitian dan Pengabdian kepada Masyarakat STATISTIKA Buletin Ilmiah Ilmu Komputer dan Multimedia (BIIKMA) Medic Nutricia : Jurnal Ilmu Kesehatan Jurnal Ilmu Komunikasi dan Sosial Politik Jurnal Kajian Islam dan Sosial Keagamaan Journal of Intelligent Systems and Information Technology Indonesian Journal of Islamic Law (IJIL) IERA, Islamic Education and Research Academy Jurnal Sains dan Teknologi Kesehatan Emerging Statistics and Data Science Journal Jurnal Hukum dan Ekonomi Syariah
Achmad Fauzan, Achmad
brawijaya university
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Journal : STATISTIKA

 1 
Pendekatan Naïve Bayes untuk Mengidentifikasi Perokok Tembakau Berdasarkan Faktor Sosio-Demografi dan Kesehatan Faza Izzatul Muttaqin; Fauzan, Achmad
Statistika Vol. 24 No. 2 (2024): Statistika
Publisher : Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Islam Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/statistika.v24i2.4224

Abstract

ABSTRAK Merokok adalah kebiasaan yang berdampak negatif pada kesehatan masyarakat Indonesia. Untuk mengatasi masalah ini, diperlukan pemahaman tentang faktor-faktor demografi, pendidikan, dan kesehatan yang mempengaruhi perilaku merokok. Penggunaan metode klasifikasi Naïve Bayes relevan karena dapat memberikan wawasan menyeluruh mengenai faktor-faktor yang mempengaruhi perilaku perokok tembakau di Kota Tasikmalaya. Penelitian ini menggunakan data dari Survei Sosial Ekonomi Nasional (SUSENAS) Maret 2023 di Kota Tasikmalaya. Data dibagi menjadi data pelatihan dan data pengujian, dan kemudian dibagi lagi untuk validasi model. Model tersebut dibuat dan dievaluasi dengan mengacak sampel, membuat model, dan mengevaluasi sebanyak 100 kali. Hasil evaluasi kinerja model dalam mengklasifikasi data validasi menunjukkan akurasi rata-rata 87% dengan standar deviasi 0,0689 dan standar error 0,00689. Confidence interval antara 85.64% dan 88.35% menunjukkan model yang dapat diandalkan. Pengujian akhir model pada data testing mencapai akurasi 83.33%, menunjukkan model berkemampuan baik dalam mengklasifikasikan perokok tembakau di Kota Tasikmalaya pada Februari 2023. ABSTRACT Smoking is a habit that negatively impacts the health of the Indonesian population. To address this issue, it is essential to understand the demographic, educational, and health factors influencing smoking behavior. The use of the Naïve Bayes classification method is relevant as it provides comprehensive insights into the factors affecting tobacco smoking behavior in the city of Tasikmalaya. This study uses data from the National Socio-Economic Survey (SUSENAS) conducted in March 2023 in Tasikmalaya. The data is divided into training and testing datasets, with the training data further split for model validation. The model was built and evaluated by randomizing samples, creating models, and evaluating them 100 times. The model's performance evaluation in classifying the validation data showed an average accuracy of 87% with a standard deviation of 0.0689 and a standard error of 0.00689. The confidence interval between 85.64% and 88.35% indicates a reliable model. The final testing of the model on the testing data achieved an accuracy of 83.33%, model demonstrating a good capability in classifying tobacco smokers in Tasikmalaya in February 2023.
Peningkatan Distribusi Bantuan Sosial di Pangkalpinang dengan Pengelompokan Berbantuan Algoritma K-Means Dwitra Gusti Alriscki; Fauzan, Achmad
Statistika Vol. 24 No. 2 (2024): Statistika
Publisher : Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Islam Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/statistika.v24i2.4305

Abstract

ABSTRAK Program bantuan sosial (Bansos) merupakan kebijakan penting yang diimplementasikan untuk mengatasi kemiskinan dan meningkatkan kesejahteraan masyarakat. Penelitian ini bertujuan untuk meningkatkan efektivitas program bantuan sosial di Kota Pangkalpinang melalui penerapan metode K-Means Clustering dalam pengelompokan kelurahan dengan tingkat kesejahteraan rendah. Data yang digunakan adalah Data Terpadu Kesejahteraan Sosial (DTKS) yang mencakup penerima bantuan dari berbagai program seperti PBI, BST, dan lainnya. Metode K-Means Clustering diterapkan untuk mengelompokkan kelurahan berdasarkan indikator kemiskinan, yang kemudian dianalisis untuk profilisasi cluster. Analisis Principal Component Analysis (PCA) dilakukan untuk mengatasi multikolinearitas antar variabel. Silhouette coefficient digunakan untuk menentukan jumlah cluster yang ideal untuk memastikan validitas pengelompokan. Hasil penelitian menunjukkan tiga cluster utama dengan nilai silhouette coefficient sebesar 0.458. Cluster pertama memiliki penerima bantuan terbanyak, sedangkan cluster ketiga memiliki penerima bantuan terendah. Penggunaan metode ini diharapkan dapat meningkatkan efektivitas dan efisiensi distribusi bantuan sosial dengan memastikan bantuan tepat sasaran sesuai dengan tingkat kemiskinan masing-masing kelurahan. Oleh karena itu, penelitian ini diharapkan dapat membantu kebijakan bantuan sosial Kota Pangkalpinang. ABSTRACT The social assistance program (Bansos) is an important policy implemented to address poverty and improve community welfare. This research aims to improve the effectiveness of social assistance programs in Pangkalpinang City through the application of the K-Means clustering method in grouping sub-districts with low welfare levels. The data used is the Integrated Social Welfare Data (SWD), which includes recipients of assistance from various programs such as PBI, BST, and others. The K-Means clustering method is applied to group villages based on poverty indicators, which are then analyzed for cluster profiling. Principal Component Analysis (PCA) is conducted to address multicollinearity among variables. The silhouette coefficient is used to determine the ideal number of clusters to ensure the validity of the clustering. The research results show three main clusters with a silhouette coefficient value of 0.458. The first cluster has the most aid recipients, while the third cluster has the fewest aid recipients.The use of this method is expected to improve the effectiveness and efficiency of social assistance distribution by ensuring that aid is targeted according to the poverty levels of each sub-district. Therefore, this research is expected to assist the social assistance policies of Pangkalpinang City.
Co-Authors Abid Yanuar Badharudin Adhar Arifuddin Adhi Pribadi Agung Dwi Ramadhan, Agung Ahmad, A. Sri Sartika Sufiina Amalina, Nur Dina Anang Kurnia Andreas Wahyu Gunawan Annisa Dwi Risqi Ariyadi, Fandy Akhmad Ashari Ashari Ayu Wulandari Bariklana, Muhammad Buana, Arya Galih Cahyani, Laras Niken Dwi Christopher Yudha Erlangga Dafrian, Rafli Danang Bagus Wibowo Dany Hilmanto Daryono Daryono, Daryono Denta, Anggeria Dewi, Sawitri Diana Apriyanti, Diana Dwitra Gusti Alriscki Efi Miftah Faridli, Efi Miftah Elsa Pudji Setiawati Evicenna Naftuchah Riani, Evicenna Naftuchah Fachry Abda El Rahman Faza Izzatul Muttaqin Fazira, Nabila Dwi Febrianti, Nur Qadri Feri Wibowo Fitri Amalia Fitri Yanti Ghefira Nurhaliza, Celine Hafidz, Syauqi Jauzza Hakim, Raden Bagus Fajriya Humairah, Nanda Lailatul Lahan Adi Purwanto Marisi Aritonang Marisma, Murni Maulana, Ashabul Akbar Maziyah Mazza Basya Mohamad Irkham Mamungkas Munandar Munandar Nabilah, Muna Faizatun Najlah Nugraha, Agasta Pratama Nur Faizin Nur Kusmiyati Nursidah, Dea Ratu Oktaviani, Nabila Pradana, Wahyu Aji Pratama, Arizqi Ihsan Pratiwi, Erlina Lutfiayu Primandari, Arum Handini Purwanto, Kalis Purwanto, Muhammad Idris Putri, Sabrina Marta Rahmawati, Octavia Ramadhan, Muhammad Rizal Ramadhan, Rizki Fauzian Ridho Muktiadi Rinaldi Sjahril, Rinaldi Roza Azizah Primatika, Roza Azizah Sadat, Fauzan Safira, Nuzulia Nur Saini, Mukhamat Santoso, Alfian Setya Adhiwibawa, Marcelinus Alfafisurya Shenny Oktoriana Siregar, Ayu Lestari Siregar, Rania Febriyola Sirojudin, Didin  Supriyono Supriyono Suri, Mulhamatus Latifatus Suria, Muh. Yunus Sutisna, Irwan Syahdony, Farrel Nolan Tito Pinandita Wahyudin, M. Abyan Widyastuti, Galuh Widyawati, Dwi Kartika Wilis Dwi Pangesti Wiranti, Ridha Yanasari, Herlinda Yuliana Watiningrum, Rahayu Yuliana, Alfa Zahidah, RA Ghina Zakariyah Zakariyah