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All Journal Techno.Com: Jurnal Teknologi Informasi Jurnal Penelitian Kesehatan Suara Forikes Journal of Information Technology and Computer Science (JOINTECS) J-Dinamika: Jurnal Pengabdian Kepada Masyarakat Jurnal Kesehatan Community Engagement and Emergence Journal (CEEJ) ARTERI : Jurnal Ilmu Kesehatan J-REMI : Jurnal Rekam Medik dan Informasi Kesehatan BIOS : Jurnal Teknologi Informasi dan Rekayasa Komputer Jurnal Rekam Medik & Manajemen Informasi Kesehatan (RAMMIK) JOURNAL OF MEDICAL RECORDS AND HELATH INFORMATION Health & Medical Sciences International Journal of Healthcare and Information Technology Manajemen Pelayanan Kesehatan KOPEMAS SEJAGAT : Jurnal Pengabdian Masyarakat
Mochammad Choirur Roziqin
Politeknik Negeri Jember
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Library & Information Science Mathematics Medicine & Pharmacology Nursing Public Health Social Sciences Veterinary Other

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Analisis Spasial Intensitas Kasus Hipertensi untuk Identifikasi Wilayah Prioritas Intervensi Kesehatan di Kabupaten Probolinggo Berbasis QGIS Dian Kartika Sari; Iwan Abdi Suandana; Malinda Capri Nurul Satya; Mochammad Choirur Roziqin
Community Engagement and Emergence Journal (CEEJ) Vol. 7 No. 2 (2026): Community Engagement & Emergence Journal (CEEJ)
Publisher : Yayasan Riset dan Pengembangan Intelektual

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/ceej.v7i2.11735

Abstract

Hipertensi merupakan salah satu penyakit tidak menular yang masih menjadi masalah kesehatan masyarakat karena prevalensinya terus meningkat. Hipertensi didefinisikan sebagai kondisi tekanan darah ≥140/90 mmHg yang dapat meningkatkan risiko penyakit jantung, stroke, dan gangguan ginjal. Distribusi kasus hipertensi yang tidak merata antarwilayah menuntut adanya pendekatan analisis spasial untuk mengidentifikasi wilayah dengan intensitas kasus tinggi sebagai dasar penentuan prioritas intervensi kesehatan. Penelitian ini bertujuan untuk menganalisis intensitas spasial kasus hipertensi dan mengidentifikasi wilayah prioritas intervensi kesehatan di Kabupaten Probolinggo berbasis aplikasi QGIS. Penelitian ini menggunakan desain kuantitatif deskriptif dengan pendekatan Sistem Informasi Geografis (SIG). Data penelitian berupa data sekunder jumlah kasus hipertensi per kecamatan tahun 2023 yang diperoleh dari Dinas Kesehatan Kabupaten Probolinggo serta data batas administrasi wilayah dalam format shapefile. Analisis dilakukan melalui tahapan data cleaning, join attribute, perhitungan Indeks Intensitas Kasus Hipertensi (IIKH) menggunakan normalisasi Min–Max, klasifikasi intensitas kasus dengan metode Natural Breaks (Jenks), dan visualisasi peta tematik. Hasil penelitian menunjukkan nilai Indeks Intensitas Kasus Hipertensi tertinggi terletak di Kecamatan Tongas (IIKH=1) dan Kecamatan Sukapura terendah (IIKH=0). Sebanyak 2 kecamatan termasuk kategori sangat tinggi, yaitu Tongas dan Leces, serta 3 kecamatan berada pada kategori tinggi, yaitu Gending, Sumberasih, dan Banyuanyar. Analisis spasial berbasis QGIS efektif untuk memetakan intensitas kasus hipertensi dan mendukung penentuan prioritas intervensi kesehatan berbasis wilayah secara objektif.
Analisis Klasifikasi Stadium Kanker Payudara Menggunakan Algoritma Naïve Bayes Berdasarkan Data Rekam Medis Mudafiq Riyan Pratama; Sefia Ayu Maharani; Mochammad Choirur Roziqin; Dony Setiawan Hendyca Putra
BIOS : Jurnal Teknologi Informasi dan Rekayasa Komputer Vol 7 No 2 (2026): September (In Progress)
Publisher : Puslitbang Sinergis Asa Professional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37148/bios.v7i2.243

Abstract

Breast cancer is one of the leading causes of cancer-related mortality among women, highlighting the need for faster and more accurate approaches to support disease staging. The utilisation of electronic medical records through data mining techniques provides an alternative approach for breast cancer stage classification. This study aimed to analyse breast cancer stage classification using the Naïve Bayes algorithm based on electronic medical record data from patients at Baladhika Husada Level III Hospital, Jember. A quantitative approach was employed using secondary data consisting of 476 breast cancer medical records selected from a total of 1,082 records. The research stages included data selection, data cleaning, categorical encoding, model development using the Naïve Bayes algorithm, and model evaluation using a Confusion Matrix based on accuracy, precision, and recall. Model performance was evaluated using nine training-testing split scenarios ranging from 10:90 to 90:10. The experimental results showed that the 90:10 split scenario achieved the best performance, with an accuracy of 87.50%, precision of 86.36%, and recall of 86.36%. These findings indicate that the Naïve Bayes algorithm is capable of classifying breast cancer stages effectively based on patients' clinical characteristics recorded in electronic medical records. The proposed approach demonstrates the potential of integrating electronic medical records and the Naïve Bayes algorithm to support the development of clinical decision support systems for breast cancer stage classification.
Co-Authors Adinda Dwi Ajeng Prameswari Ain Azzam Izulhaq Aisyah Fajri Filani Andi Muhammad Zulkifli Andri Permana Wicaksono Andri Permana Wicaksono Andy Malarangai Angga Rahagiyanto Anggi Maulidya Anggia Lutfi Rohmawati Arga Kanya Dhanastra Assyifa Itsnainia Mustika Atma Deharja Bakhtiar Hadi Prakoso Denisistya Krisworo Dian Kartika Sari Dirga Putra Darmawan Dony Setiawan dony setiawan hendyca putra Dony Setiawan Hendyca Putra Dony Setiawan HP Efri Tri Ardianto Eltigeka Devi Apriliani Erna Selviyanti Farah Dilla Wulandari Feby Erawantini Ihwan Huda Al Mujib Irda Nazia Safitri Khomsatun Ni’mah Lutfiatun Nadibah Herman M Mahbub Jauhar Qolbi M. Mubarot Isa Maya Weka Santi Mochammad Arief Darmawan Moh Lutfi Rizalul Hakim Mubarot Isa Mudafiq Riyan Pratama Muhammad Ifantara Putra Muhammad Yunus Nabilah Khoirun Nissa' Nada Aprilika Putri Wahyudi Nadhifa Dwi Rahmalia Novita Nuraini Nur Aggun S. Nurrahma Harris Rachmawati, Ervina Rindiani Rindiani Riza Umami Agustin Rossalina Adi Wijayanti Sabran Sabran Satya, Malinda Capri Nurul Sefia Ayu Maharani Sitti Fa'Isil Karomah Suandana, Iwan Abdi Suhartanty Afifah Rahmatillah Sustin Farlinda Tegar Wahyu Yudha Pratama Veronika Vestine Wita Windari Ziani Said