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All Journal Variance : Journal of Statistics and Its Applications Community Development Journal: Jurnal Pengabdian Masyarakat Journal of Data Insights
Yunanita, Novia
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Humanities Computer Science & IT Economics, Econometrics & Finance Education Health Professions Mathematics Public Health

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Journal : Journal of Data Insights

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A Pengelompokan Kabupaten/Kota di Jawa Tengah Berdasarkan Kepadatan Penduduk Menggunakan Metode Hierarchical Clustering Asyfani, Yusrisma; Manfaati Nur, Indah; Fathoni Amri, Ihsan; Yunanita, Novia; Anggun Lestari , Febi; Aura Hisani, Zahra; Hikmah Nur Rohim, Febrian
Journal of Data Insights Vol 2 No 1 (2024): Journal of Data Insights
Publisher : Department of Sains Data UNIMUS Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jodi.v2i1.158

Abstract

Jawa Tengah merupakan provinsi dengan urutan kelima di Indonesia berdasarkan kepadatan penduduk pada tahun 2020 sebanyak 1.113 jiwa/km2. Pengaruh kepadatan penduduk yang tinggi dapat menyebabkan berbagai masalah diantaranya kemacetan,pengangguran,kesehatan,kriminalitas serta permasalahan serius lainnya. Kepadatan penduduk dipengaruhi oleh angka kelahiran,angka kematian serta laju pertumbuhan, Untuk mengevaluasi kepadatan penduduk di provinsi Jawa Tengah, kita perlu mengklasifikasikan/mengelompokkan kabupaten/kota yang berada didalamnya. Pengelompokan ini bertujuan agar kebijakan yang dibuat oleh pemerintah dapat tepat sasaran. Metode yang dapat digunakan untuk pengelompokkan kabupaten.kota di provinsi Jawa Tengah berdasarkan kepadatan penduduknya yaitu Clustering Hierarchical Ward. Dari hasil analisis pengelompokan tersebut kabupaten/kota di provinsi Jawa Tengah dibagi menjadi empat kelompok berdasarkan kepadatan penduduknya.
Application of Random Forest Method to Analyze the Effect of Smoking History on The Type and Outcomes of TB Examinations: Penerapan Metode Random Forest Untuk Menganalisis Pengaruh Riawayat Merokok Terhadap Tipe dan Hasil Pemeriksaan Pasien TBC Purwanto, Dannu; Yunanita, Novia
Journal of Data Insights Vol 2 No 2 (2024): Journal of Data Insights
Publisher : Department of Sains Data UNIMUS Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jodi.v2i2.651

Abstract

Tuberculosis (TB) continues to pose a major global health challenge, especially in developing countries. One of the key risk factors that exacerbates the condition of TB patients is smoking, which increases susceptibility to infections and worsens disease prognosis. This study aims to evaluate the influence of smoking history on the type and outcomes of TB diagnoses using a Random Forest machine learning model. The dataset comprises information from TB-diagnosed patients, including demographic details such as age, gender, smoking status, patient type, and diagnostic results. The Random Forest model achieved an accuracy of 87.36%, performing best in classifying non-TB-infected patients. However, the model struggled to accurately identify healthy individuals without TB, likely due to data imbalance. This research offers fresh insights into the potential of machine learning to enhance TB diagnosis and prevention, while deepening the understanding of smoking as a risk factor in TB management.
Co-Authors Alwan Fadlurohman Anggun Lestari , Febi Ariska Fitriyana Ningrum Asyfani, Yusrisma Aura Hisani, Zahra Dannu Purwanto Fathoni Amri, Ihsan Hikmah Nur Rohim, Febrian Manfaati Nur, Indah Rohim, Febrian Hikmah Nur Wardani, Amelia Kusuma Yusrin, Yusrin