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All Journal Jurnal Riset Informatika Infokes : Jurnal Ilmiah Rekam Medis dan Informasi Kesehatan J-REMI : Jurnal Rekam Medik dan Informasi Kesehatan Indonesian Journal of Health Information Management Services (IJHIMS) Jurnal Ilmiah Kesehatan Mandira Cendikia
Reka Dwi Syaputra
Sekolah Tinggi Ilmu Kesehatan Sapta Bakti
Computer Science & IT Decision Sciences, Operations Research & Management Dentistry Health Professions Medicine & Pharmacology Neuroscience Nursing Public Health Social Sciences Veterinary

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Pregnancy Risk Level Classification Using The CRISP-DM Method Reka Dwi Syaputra; Achmad Solichin
Jurnal Riset Informatika Vol 5 No 1 (2022): Priode of December 2022
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v5i1.487

Abstract

Independent midwife practices have the task of reminding and maintaining the quality of standardized reproductive health services for pregnant women. Independent midwife practices have had patient visits since the covid-19 pandemic from 2020 to 2021, especially at the yetti puranama midwife, which consists of 320 pregnancy examinations, 130 delivery care, and 50 referrals. The covid-19 pandemic has impacted maternal mortality rates because there are still many restrictions on all services. Maternal health services include pregnant women who are routinely unable to go to the puskesmas or other healthcare facilities due to fear of contracting covid-19, which delays the examination of pregnancy gravida, abortion, temperature, pregnancy distance, haemoglobin, blood pressure, ideal weight, and decisions. So that the problem that occurs is an increase in the risk of pregnancy, resulting in death and increased maternal mortality. In solving this problem, the research takes a machine-learning approach. The research aims to build a classification of pregnancy risk levels that can predict early treatment in this study using the random forest method with cross-validation 2. This study obtained the results of an accuracy value of 98%, precision of 94%, and recalled 100% in the random forest method.
Pregnancy Risk Level Classification using the Crisp-DM Method Reka Dwi Syaputra; Achmad Solichin
Jurnal Riset Informatika Vol. 5 No. 1 (2022): December 2022
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1095.021 KB) | DOI: 10.34288/jri.v5i1.195

Abstract

Independent midwife practices are tasked with reminding and maintaining the quality of standardized reproductive health services for pregnant women. Independent midwife practices have had patient visits since the COVID-19 pandemic from 2020 to 2021, especially at the Yetti puranama midwife, which consists of 320 pregnancy examinations, 130 delivery care, and 50 referrals. The COVID-19 pandemic has impacted maternal mortality rates because there are still many restrictions on all services. Maternal health services include pregnant women who are routinely unable to go to the puskesmas or other healthcare facilities due to fear of contracting COVID-19, which delays the examination of pregnancy gravida, abortion, temperature, pregnancy distance, hemoglobin, blood pressure, ideal weight, and decisions. So that the problem that occurs is an increase in the risk of pregnancy, resulting in death and increased maternal mortality. In solving this problem, the research takes a machine-learning approach. The research aims to build a classification of pregnancy risk levels that can predict early treatment in this study using the random forest method with cross-validation 2. This study obtained the results of an accuracy value of 98%, precision of 94%, and recalled 100% in the random forest method.
Perancangan Aplikasi Pelayanan Rekam Medis Elektronik Berbasis Smartphone di Rumah Sakit Rafflesia Saputra, Ripo Andi; Syaputra, Reka Dwi; Harmanto, Deno
J-REMI : Jurnal Rekam Medik dan Informasi Kesehatan Vol 6 No 1 (2024): December
Publisher : Politeknik Negeri Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25047/j-remi.v6i1.5428

Abstract

Rafflesia Hospital faces challenges in managing medical records as it still relies on a manual paper-based system. This method can lead to data loss, limited access, and human error, which hinder efficiency and service quality. According to Indonesia’s Ministry of Health Regulation No. 24 of 2022 on Medical Records, all healthcare facilities are required to implement Electronic Medical Records by December 31, 2023. This study aims to design a smartphone-based electronic medical record application for Rafflesia Hospital using the Waterfall development method. The proposed application can improve patient queue management through online registration, provide real-time waiting time information, and enhance communication and coordination among medical teams via a fast information exchange platform. Additionally, the application improves responsiveness and efficiency by granting instant access to critical patient information in emergencies. Clear operational guidelines are established to ensure the application is user-friendly and easy to understand. The hospital needs to prepare for the implementation of this application to integrate it with the Hospital Information System (SIMRS), supporting fast, efficient, and high-quality services.
SOSIALISASI INFORMASI TERHADAP PROSEDUR PELAYANAN DAN PENANGAN KLINIS MEDIS PRATAMA TERHADAP JAMINAN KESEHATAN Syaputra, Reka Dwi; Ameliasasi, Dinda; Sari, Suci Junita; Sakoci, Siti; Oktarina, Ayu; Methania, Delara; Aprilia, Sinta; Nengsih, Elia Septia; Saputra, Romadan
Indonesian Journal of Health Information Management Services Vol. 4 No. 1 (2024): Indonesian Journal of Health Information Management Services (IJHIMS)
Publisher : APTIRMIKI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33560/ijhims.v4i1.100

Abstract

BPJS Kesehatan di Indonesia memberikan jaminan kesehatan dan akses layanan kesehatankepada Warga Negara Indonesia, terutama bagi yang kesulitan finansial, untuk mewujudkan hak atas layanankesehatan bagi semua, termasuk kelompok miskin. Mereka juga memfasilitasi pelayanan klinis, mempercepatpengobatan, dan meningkatkan mutu layanan rumah sakit. kurangnya pemahaman masyarakat tentang BPJS 9Kesehatan dan prosedur yang terkait dengannya. Hal ini dapat mempengaruhi akses terhadap layanan kesehatan yang disediakan oleh BPJS Kesehatan. Diperlukan upaya sosialisasi dan edukasi yang lebih luas untukmeningkatkan pemahaman masyarakat terhadap manfaat, prosedur, dan layanan yang dapat diberikan oleh BPJSKesehatan.
SISTEM APLIKASI DETEKSI TINGKAT RISIKO KEHAMILAN PADA AKI DI PUSKESMAS TELAGA DEWA Syaputra, Reka Dwi; Ningsih, Sari Widya; Harmanto, Deno
Jurnal Infokes Vol 15 No 2 (2025): Jurnal Ilmiah Rekam Medis dan Informatika Kesehatan
Publisher : Universitas Duta Bangsa Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47701/11bks828

Abstract

Angka Kematian Ibu (AKI) yang persisten tinggi di Indonesia, khususnya yang disebabkan oleh komplikasi selama masa kehamilan, menuntut adanya inovasi dalam metode skrining dini di fasilitas pelayanan kesehatan primer. Penelitian ini bertujuan untuk mengembangkan, memvalidasi, dan membangun aplikasi untuk prediksi tingkat risiko kehamilan di Puskesmas Telaga Dewa, Bengkulu. Dengan menggunakan kerangka kerja Cross-Industry Standard Process for Data Mining (CRISP-DM), sebuah dataset yang terdiri dari 488 rekam medis pasien ibu hamil dengan 13 variabel klinis objektif dianalisis. Dua algoritma klasifikasi, yaitu Decision Tree dan Random Forest, dievaluasi menggunakan strategi validasi silang 10-fold (10-fold cross-validation) untuk memastikan estimasi kinerja yang robust. Hasil evaluasi menunjukkan keunggulan signifikan dari model Random Forest, yang mencapai nilai rata-rata akurasi 0.97, presisi 0.94, recall 0.99, dan F1-score 0.96. Kinerja ini secara konsisten melampaui model Decision Tree (akurasi 0.85, F1-score 0.85). Analisis feature importance mengidentifikasi tekanan darah, usia, dan riwayat abortus sebagai prediktor paling berpengaruh. Temuan ini menggarisbawahi potensi besar model Random Forest sebagai alat bantu keputusan klinis (Clinical Decision Support) yang akurat dan andal bagi bidan di tingkat Puskesmas. Implementasi model ini dalam bentuk aplikasi berbasis web dapat memfasilitasi stratifikasi risiko pasien secara efisien, memungkinkan alokasi sumber daya yang lebih terfokus, dan mendukung intervensi dini untuk menekan AKI, sejalan dengan agenda transformasi digital kesehatan nasional.
Gambaran Kelengkapan Formulir Catatan Perkembangan Pasien Terintegrasi (CPPT) Pada Pasien Rawat Inap Di Ruang Mawar Rumah Sakit Umum Daerah Dr. M.Yunus Carona Cahayu Putri; Dinda Sri Rahayu; Anggia Budiarti; Reka Dwi Syaputra
Jurnal Ilmiah Kesehatan Mandira Cendikia Vol. 4 No. 1 (2025)
Publisher : YAYASAN PENDIDIKAN MANDIRA CENDIKIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70570/jikmc.v4i1.1588

Abstract

Penelitian Gambaran Kelengkapan Formulir Catatan Perkembangan Pasien Terintegrasi (CPPT) Pada Pasien Rawat Inap Ruang Mawar Rumah Sakit Umum Daerah Dr. M. Yunus Kota Bengkulu bertujuan untuk mengetahui gambaran kelengkapan formulir CPPT pasien rawat inap di ruang mawar Rumah Sakit Umum Daerah Dr. M. Yunus. Penelitian ini menggunakan desain deskriptif dengan metode observasi. Subjek penelitian ini adalah 135 berkas rekam medis pasien rawat inap yaitu pada Formulir CPPT pada ruangan mawar, Teknik pengumpulan data menggunakan data sekunder yang diperoleh peneliti dengan cara melihat berkas rekam medis rawat inap. Dari 135 berkas rekam medis pada formulir CPPT ditemukan kelengkapan identifikasi pasien sebanyak (87%) di isi dengan lengkap dan (14%) yang tidak lengkap. Dari135 berkas rekam medis pada formulir CPPT terdapat sebanyak (73%) terisi lengkap sedangkan (28%) tidak lengkap. Dari 135 berkas rekam medis pada formulir CPPT terdapat (66%) lengkap, (21%) tidak lengkap dan (15%) yang hanya mencantumkan tanda tangan tanpa membubuhi nama terang. Dari 135 berkas rekam medis pada formulir CPPT terdapat sebanyak (47%) sesuai, dan (54%) tidak sesuai.
Co-Authors Achmad Solichin Ameliasasi, Dinda Anggia Budiarti Aprilia, Sinta Carona Cahayu Putri Dinda Sri Rahayu Harmanto, Deno Methania, Delara Nengsih, Elia Septia Ningsih, Sari Widya Oktarina, Ayu Sakoci, Siti Saputra, Ripo Andi Saputra, Romadan Sari, Suci Junita