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All Journal Jurnal Kebijakan Kesehatan Indonesia Ekonomikawan : Jurnal Ilmu Ekonomi dan Studi Pembangunan Public Health of Indonesia Paediatrica Indonesiana PEDAGOGIA Indonesian Journal of Artificial Intelligence and Data Mining Jurnal Manajemen Kesehatan Yayasan RS.Dr. Soetomo Jurnal Pengabdian Masyarakat AbdiMas JOURNAL OF SCIENCE AND SOCIAL RESEARCH Jurnal Manajemen Informatika Jurnal Manajemen Informasi Kesehatan Indonesia (JMIKI) Indonesian of Health Information Management Journal (INOHIM) Poltekita : Jurnal Ilmu Kesehatan PREPOTIF : Jurnal Kesehatan Masyarakat Jurnal Ners Jurnal Ilmiah Perekam dan Informasi Kesehatan Imelda (JIPIKI) J-REMI : Jurnal Rekam Medik dan Informasi Kesehatan KESANS : International Journal of Health and Science NUSRA: Jurnal Penelitian dan Ilmu Pendidikan PELS (Procedia of Engineering and Life Science) Jurnal Ilmu Kesehatan Masyarakat Jurnal Rekam Medik & Manajemen Informasi Kesehatan (RAMMIK) Malcom: Indonesian Journal of Machine Learning and Computer Science Prosiding Seminar Nasional Official Statistics Cerdika: Jurnal Ilmiah Indonesia Indonesian Journal of Health Information Management Services (IJHIMS) International Journal of Health and Information System (IJHIS) Jurnal Karya untuk Masyarakat (JKuM) Jurnal Bioteknologi & Biosains Indonesia (JBBI) Jurnal Bioteknologi & Biosains Indonesia
Nurmalasari, Mieke
Universitas Esa Unggul Jakarta
Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemistry 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 Physics Public Health Social Sciences Veterinary Other

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BIMBINGAN TEKNIS PERAMALAN JUMLAH KUNJUNGAN PASIEN DENGAN TABLEAU Mieke Nurmalasari; Witri Zuama Qomarania; Nauri Anggita Temesvari; Tria Saras Pertiwi
Indonesian Journal of Health Information Management Services Vol. 1 No. 1 (2021): Indonesian Journal of Health Information Management Services (IJHIMS)
Publisher : APTIRMIKI

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (618.851 KB) | DOI: 10.33560/ijhims.v1i1.4

Abstract

ABSTRAK. Peramalan jumlah kunjungan pasien berguna untuk membantu manajemen dalam membuat kebijakan dan perencanaan yang efektif dan efisien. Pesatnya perkembangan teknologi menjadikan data kesehatan digital sebagai salah satu sumber big data. Perlu dilakukan peningkatan pengetahuan pada mahasiswa dan tenaga Perekan Medis dan Manajemen Informasi Kesehatan dalam menganalisis data kunjungan pasien. Metode yang digunakan dalam kegiatan ini adalah pelatihan atau bimbingan teknis yang bersifat teoritis dan praktis. Hasil dari pelatihan ini adalah peningkatan pengetahuan peserta dalam menganalisis data peramalan kunjungan pasien menggunakan software statistik A Tableau. Kata kunci: kunjungan pasien; peramalan; analisis data; public tableau ABSTRACT. Forecasting number of visits is useful to help management to make effective and efficient policies and plans. The rapid development of technology makes digital health data as a one of big data sources. It is necessary to increase the knowledge of student and Professional Health Information Management in analyzing the patient visit data. The method used in this activity is a training or technical guidance which is namely theoretical and practical. The result of this training is an increase in participants' knowledge in analyzing the forecasting of patient visit data using a statistical software Tableau. Keywords: patient visit; forecasting; data analytics; public tableau
Spatial patterns of maternal mortality causes in West Kalimantan, Indonesia Pertiwi, Tria Saras; Temesvari, Nauri Anggita; Nurmalasari, Mieke
Public Health of Indonesia Vol. 7 No. 3 (2021): July - September
Publisher : YCAB Publisher & IAKMI SULTRA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36685/phi.v7i3.441

Abstract

Background: Maternal Mortality Rate (MMR) is one of the health indicators to see maternal survival in general and is a component in the health development index. Maternal Mortality Rate is also an important indicator of the quality of health services and the performance of the Health system.Objective: This study aimed to analyze the spatial patterns of maternal mortality based on the mortality causes in Sambas District, West Kalimantan, Indonesia.Methods: This study used a descriptive and exploratory approach to be able to see the distribution of maternal mortality and the coverage of the distribution of health care facilities. A spatial pattern was carried out to analyze the distribution pattern of maternal mortality cases using the Average Nearest Neighbor.Results: The results showed that most maternal mortality causes include bleeding, pregnancy hypertension, circulation system disorders (heart, stroke), metabolic disorders (diabetes mellitus), and other causes, such as pulmonary embolism. The analysis using a buffer of 3 kilometers and 5 kilometers show that not all the areas are covered by health service facilities in the Sambas district. Analysis of the mean of the nearest neighbors showed that the Nearest Neighbor ratio was 1.039398 with a z-score of 1.022396, which means that the pattern of distribution of maternal death according to the cause of death has a random pattern.Conclusion: The spatial pattern of cases of maternal death according to the cause of death in the Sambas district, West Kalimantan, Indonesia, has a random pattern. This finding can be used as a basis for decreasing the maternal mortality rate.
Fraud in healthcare facilities: A Narrative Review Mauren Michaela, Sarah; Nurmalasari, Mieke; Hosizah, Hosizah
Public Health of Indonesia Vol. 7 No. 4 (2021): October - December
Publisher : YCAB Publisher & IAKMI SULTRA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36685/phi.v7i4.465

Abstract

Every country needs to develop Universal Health Coverage (UHC) to promote optimal levels of public health. But in realizing UHC, there must be some problems, one of which is fraud. Based on the Corruption Eradication Commission (KPK) data, potential fraud is detected from 175,774 claims of Advanced Referral Health Facilities (FKRTL) or worth Rp. 440 billion until June 2015. This review article describes the incidence of fraud in health care facilities. Out of a total of 12,736 cases of fraud, readmission occupies the most cases of fraud, which is 4,827 cases or 37.9%.
Evaluation of the Accuracy of Medical Terminology and Its Relationship to the Accuracy of Clinical Coding in Health Facilities: Systematic Literature Review Firdayana Firdayana; Mieke Nurmalasari; Hosizah Hosizah; Sandra Hakiem Afrizal
Jurnal Ners Vol. 7 No. 2 (2023): OKTOBER 2023
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jn.v7i2.16381

Abstract

Clinical Coding will be accurate if the proper medical language is used while writing. Clinical coding that is appropriate may help healthcare organizations with things like accurate care, billing for services, strategic planning, and statistical and financial analysis. The purpose of the study, which employed a methodical literature analysis, was to ascertain the link between the clinical coding accuracy in healthcare institutions and the correctness of medical terminology. This study is an organized review of the literature. Google Scholar, Garuda, Neliti, the Indonesian Scientific Journal Database (ISJD), Semantic Scholar, PubMed, and ScienceDirect are the databases that were used. PRISMA (Preferred Reporting Items for Systematic Review and Meta-analysis) is the strategy used for the literature selection. The Reinforcement of Observational investigation Reporting in Epidemiology (STROBE) tool was used to evaluate the caliber of the literature in this investigation. The study's findings show that the average value of clinical coding accuracy in healthcare facilities is 48%, with 14 articles having accuracy levels above or below 50%. The average value of medical terminology accuracy in healthcare facilities is 55%, with 11 articles having accuracy levels below 50. Up to 79% of the study's findings indicated a connection between the precision of clinical coding and the precision of medical language. Comparatively, 21% of respondents claimed there was no connection between clinical coding accuracy and medical terminology correctness.
Penyuluhan Sistem Informasi Posyandu sebagai Upaya Mewujudkan Bebas Stunting Arief Ichwani; Mieke Nurmalasari; Nizirwan Anwar; Widia Sari; Badie Uddin
Jurnal Karya untuk Masyarakat (JKuM) Vol 5, No 1 (2024): Jurnal Karya untuk Masyarakat
Publisher : STARKI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36914/jkum.v5i1.1090

Abstract

Desa Pasirwaru merupakan desa dengan jumlah penduduk 5.511 jiwa dan memiliki program unggulan di bidang kesehatan yaitu posyandu. Posyandu Anggrek I adalah posyandu di Desa Pasirwaru yang memiliki anggota 60 balita, 10 ibu hamil. Adapun peranan dari posyandu ini yaitu untuk memberdayakan, memberikan kemudahaan layanan kesehatan, penyuluhan untuk mengatasi stunting dengan pemenuhan kebutuhan gizi bagi ibu hamil, memberikan ASI dan MPASI, akses air bersih, dan memantau pertumbuhan balita di posyandu. Kegiatan pemantauan pertumbuhan balita, dan ibu hamil di posyandu harus didukung oleh data yang lengkap dan akurat dari hasil setiap kegiatan posyandu. Adapun laporan data balita, ibu hamil saat ini di Posyandu Anggrek I masih menggunakan buku tulis. Hal tersebut menyebabkan beberapa permasalahan seperti buku mudah rusak, hilang, ketidak sesuaian dan ketidak akuratan pelaporan, duplikasi data , tidak konsisten, hak akses data yang tidak terkondisikan, media penyimpanan bersifat sementara, sulit dilakukan pengolahan data untuk menghasilkan informasi penting tentang gambaran kondisi balita dan ibu hamil di lingkungan posyandu. Oleh karena itu posyandu harus memiliki Sistem Informasi Posyandu untuk pelaporan data, pencarian data, pemantauan pertumbuhan balita dan ibu hamil yang diakses dengan mudah dan cepat sehingga memiliki keakuratan, keamanan, ketersediaan, kelengkapan data berkelanjutan dan mendukung pengambilan keputusan dengan efektif dan efesien. Metode yang digunakan pada pengabdian masyarakat ini yaitu penyuluhan kesehatan dan workshop penggunaan Sistem Informasi Posyandu. Adapun hasil dari kegiatan ini adalah peningkatan kesadarana akan pentingnya kesehatan, tersedianya sistem informasi posyandu yang dapat digunakan masyarakat dan kader posyandu dengan baik dan benar untuk pemantauan tumbuh kembang anak dan ibu hamil. ABSTRACT Pasirwaru Village is a village with a population of 5,511 people and has a superior program in the health sector, namely posyandu. Posyandu Anggrek I is a posyandu in Pasirwaru Village which has 60 toddlers and 10 pregnant women. The role of this posyandu is to empower, provide easy health services, provide counseling to overcome stunting by fulfilling the nutritional needs of pregnant women, provide breast milk and MPASI, access clean water, and monitor the growth of toddlers at the posyandu. Activities to monitor the growth of toddlers and pregnant women at posyandu must be supported by complete and accurate data from the results of each posyandu activity. As for the toddler data report, currently pregnant women at Posyandu Anggrek I still use notebooks. This causes several problems such as books being easily damaged, lost, inconsistencies and inaccuracies in reporting, duplication of data, inconsistencies, data access rights that are not conditioned, storage media is temporary, data processing is difficult to produce important information about the description of the condition of toddlers and pregnant women in the posyandu environment. Therefore, posyandu must have a Posyandu Information System for reporting data, searching for data, monitoring the growth of toddlers and pregnant women which is accessed easily and quickly so that it has accuracy, security, availability, completeness of continuous data and supports effective and efficient decision making. The methods used in this community service are health education and workshops on using the Posyandu Information System. The results of this activity are increased awareness of the importance of health, the availability of a posyandu information system that can be used by the community and posyandu cadres properly and correctly to monitor the growth and development of children and pregnant women.
Autocorrelation Spatial Based on Specific Nutritional Interventions Achievement with Stunting Cases in Toddlers at Kendari City Using Local Indicator of Spatial Autocorrelation (LISA) Method Pertiwi, Tria Saras; Nurmalasari, Mieke; Qomarania, Witri Zuama; Supryatno, Adi; Saputra, Alief Imran; Salim, Agus
Public Health of Indonesia Vol. 10 No. 3 (2024): July - September
Publisher : YCAB Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36685/phi.v10i3.834

Abstract

Background:Stunting is a priority target both globally and in Indonesia. There are 10 provinces in Indonesia that are the main focus of the stunting reduction program, one of which is Southeast Sulawesi Province. Kendari City, located in Southeast Sulawesi, has experienced an increase in stunting incidence over the past three years. However, progress in reducing stunting in Kendari City has not been evenly distributed across its regions and sub-regions, with significant disparities in stunting rates between different sub-districts. Objective:To determine the spatial autocorrelation based on the achievement of specific nutritional interventions for toddlers and the incidence of stunting in Kendari City using the Local Indicator of Spatial Autocorrelation (LISA). Method:This quantitative study used the Local Indicator of Spatial Autocorrelation (LISA) method. The data on stunting incidence consisted of the number of stunting cases among toddlers in 2023 across 11 sub-districts in Kendari City. The sub-districts analyzed were Abeli, Baruga, Kadia, Kambu, Kendari, West Kendari, Mandonga, Nambo, Poasia, Puuwatu, and Wua-Wua. The study was conducted from November 2023 to May 2024 in Kendari City. A local autocorrelation test with LISA was performed to determine the spatial relationships among the sub-districts based on the research variables, with results displayed in the form of Moran's scatterplot, cluster map, and significance map. Results:The results of Moran's local bivariate test (LISA) indicated that the majority of sub-districts, particularly Kambu, exhibited significant positive autocorrelation with neighboring sub-districts and fell into the cold-spot category. This indicates that the number of specific nutritional intervention programs for toddlers and the cases of stunting in toddlers in 2023 were low in Kambu and its surrounding sub-districts, which also had similarly low values. Conclusion:There is spatial autocorrelation among the sub-districts in Kendari City. Although the cases of stunting in the Kambu sub-district are low, the achievement of intervention programs should remain optimal, as cases still exist in the area. Additionally, since Kambu has a spatial correlation with its neighboring areas, the government should target these areas for appropriate interventions to accelerate stunting reduction, particularly in Kendari City. Keywords:Spatial Autocorrelation; LISA; Specific Nutrition Interventions; Stunting Toddlers
Implementasi Decision Tree untuk Prediksi Kelahiran Bayi Prematur Rosida, Putri Lailatul; Nurmalasari, Mieke; Hosizah, Hosizah; Krismawati, Dewi
Jurnal Manajemen Informatika JAMIKA Vol 14 No 2 (2024): Jurnal Manajemen Informatika (JAMIKA)
Publisher : Program Studi Manajemen Informatika, Fakultas Teknik dan Ilmu Komputer, Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/jamika.v14i2.12797

Abstract

The early birth of baby in Indonesia is a case that has a very high incidence rate. According to data from the Ministry of Health in 2021, the presentation of premature babies in Indonesia is 84%. The number of infant deaths in Indonesia is still relatively high compared to other ASEAN countries. The purpose of this study was to predict the birth of premature babies with the implementation of decision tree, with this type of predictive analysis research. The population in this study is pregnant women patients with a sample of 350 pregnant women patient data covering the variables studied Age, BMI, Vaginal Discharge, History of Miscarriage, History of Prematurity and Pregnancy Spacing. The prediction was made by halving the training data by 245 and the testing data by 105. The results obtained are the variable Body Mass Index (BMI) is the riskiest factor for premature birth The decision tree model yields an AUC of 91.7%, it can be concluded that the decision tree has a good classification accuracy value.
Prediksi Waktu Tunggu Pelayanan Pasien Rawat Jalan dengan Algoritma Random Forest: Predicting Outpatient Service Waiting Times with Random Forest Algorithm Munggaran, Rahayu Putri; Nurmalasari, Mieke; Hosizah, Hosizah; Krismawati, Dewi
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 5 No. 1 (2025): MALCOM January 2025
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v5i1.1529

Abstract

Waktu tunggu pelayanan merupakan salah satu langkah yang harus dilalui pasien untuk mendapatkan pelayanan kesehatan, dimulai dari pendaftaran hingga pemeriksaan oleh dokter. Penelitian ini bertujuan memprediksi waktu tunggu pelayanan pasien rawat jalan menggunakan algoritma Random Forest di Rumah Sakit Jiwa Dr. Soeharto Heerdjan. Prediksi ini diharapkan mempermudah pekerjaan petugas dan dapat diintegrasikan ke dalam aplikasi online untuk mengurangi penumpukan pasien. Metode data mining diterapkan menggunakan aplikasi Orange Data Mining dengan algoritma Random Forest. Penelitian dilakukan menggunakan 2.109 data dari tiga bulan di tahun 2023, yang setelah preprosesing menghasilkan 1.508 data dengan 8 atribut: usia, jenis kelamin, poliklinik, layanan yang dipilih, waktu datang, waktu sebelum bertemu dokter, durasi waktu tunggu, jaminan kesehatan, dan kategori pasien. Data dibagi menjadi dua bagian, yaitu data training sebanyak 1.055 dan data testing sebanyak 452. Hasil prediksi menunjukkan akurasi tinggi dengan nilai AUC 98,2%, CA 97,6%, F1 97,6%, precision 97,6%, dan recall 97,3%. Model ROC-curve dapat memisahkan tiga kategori waktu tunggu yaitu cepat, lambat, dan normal, dengan nilai akurasi mendekati 1. Visualisasi menggunakan Pythagorean Forest membantu mengidentifikasi kategori atau pola waktu tunggu pasien dengan akurasi yang tinggi.
Evaluation of User Satisfaction in the Satusehat Application Sulistianingsih, Sulistianingsih; Nurmalasari, Mieke; Hosizah, Hosizah; Zuama Qomarania, Witri
Jurnal Ilmu Kesehatan Masyarakat Vol. 15 No. 3 (2024): Jurnal Ilmu Kesehatan Masyarakat (JIKM)
Publisher : Association of Public Health Scholars based in Faculty of Public Health, Sriwijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26553/jikm.2024.15.3.346-359

Abstract

Digital transformation in the health sector aims to provide quality, fast, easy, affordable, and measurable services to the community. User satisfaction is paramount when providing services. Key factors influencing user satisfaction include Information, System, and Service Quality. The objective of this study is to evaluate the impact of information quality, system quality, and service quality on user satisfaction with the SATUSEHAT. This research used a quantitative with a cross-sectional design technique. This research using a quota sampling method with 106 respondents. This research using multiple linear regression analysis with univariate and multivariate classical assumption tests. This research focuses on SATUSEHAT application users who actively use social media platforms like Instagram and Twitter. The evaluation aims to provide insights into the impact of information, system, and service quality of the SATUSEHAT application's user satisfaction with the SATUSEHAT application. The results explain that 56.6% of respondents were female, 43.3% were male. 54.7% of this study's respondents were undergraduates aged 19 - 34 years, 67.0% of respondents. Most of the respondents' jobs were employees, 34.9%. Service quality significantly influences SATUSEHAT application The results of the regression coefficient value for user satisfaction is 0.651, and quality of the information, with a regression coefficient of 0.113. The The study found that information, system, and service quality significantly influence user satisfaction with the SATUSEHAT application.
Penerapan Decision Tree dan Neural Network untuk Prediksi Severity Level Pada Kasus Hipertensi di RSUD Khidmat Sehat Afiat (KISA) Depok Rafidah, Arlien Rona; Nurmalasari, Mieke; Hosizah, Hosizah; Larasati , Dhiar Niken
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.v6i1.4963

Abstract

Severity Level is a component of the INA-CBGs code that indicates the severity of a case during treatment, influencing the INA-CBGs tariff rate. The aim of this study is to predict the severity level by implementing decision tree and neural network algorithms using Orange Data Mining. This research was conducted at Khidmat Sehat Afiat Regional Public Hospital in Depok City, utilizing 162 inpatient claim records with primary diagnoses of Hypertension and Hypertensive Heart Disease and secondary diagnoses of CHF, CKD, or both. Prediction was carried out on 114 testing data and 48 training data. Claim data were analyzed using Decision Tree and Neural Network, with testing results showing the highest score in neural network performance with an AUC of 62.5%, CA of 57%, F1 of 56.2%, and precision of 57.7%. Based on calculations from the confusion matrix, the neural network demonstrated better performance, with accuracy at 57.89%, precision at 65.6%, and recall at 80.76%. These results suggest that the neural network is recommended for predicting the severity level of hypertension cases at Khidmat Sehat Afiat Hospital, as it achieves higher accuracy than the Decision Tree.
Co-Authors AA Sudharmawan, AA Azizatul Azza Azza, Azizatul Badie Uddin Dewi, Deasy Rosmala Dewi, Sukmala Firdayana Firdayana Harna, Harna Hosizah Hosizah Husni Abdul Muchlis Ichwani, arief Indawati, Laela Kendrastuti, Nungky Nurkasih Krismawati, Dewi Kurniawati, Anastasia Cyntia Dewi Larasati , Dhiar Niken Lestari, Betri Widya Linta Ifada Linta Ifada Mahadewi, Erlina Puspitaloka Mauren Michaela, Sarah Michaela, Sarah Mauren Munggaran, Rahayu Putri Muniroh, Muniroh Mustikawati, Intan Silviana Nafs, Tazkyatun Nizirwan Anwar Nori wilantika Panutun, Satria Bagus Pramana, Setia Putri, Suci Sri Endah Lestari Mulyanto Putri, Tacyah Kholifah Rafidah, Arlien Rona Rahmawati, Danisa Ocha Rahmayanti R, Andi Rosida, Putri Lailatul Rubina, Tirzhana Jean Ruhmayanti, Nur Ayu Sa'pang, Mertien Sangadji, Namira Wadjir Saputra, Alief Imran Saraswati, Henny Seprianto, Seprianto Septian Bagus Wibisono Silvia Ni'matul Maula Sulistianingsih Sulistianingsih Suprobowati Suprobowati Supryatno, Adi Temesvari, Nauri Anggita Tita Ardianti Tria Saras Pertiwi Widia Sari Widjaja, Lily Witri Zuama Qomarania Zuama, Witri