Article Per Year (5 Year)
p-Index From 2020 - 2025
0.92
P-Index
This Author published in this journals
All Journal Media Statistika Indonesian Journal of Artificial Intelligence and Data Mining Jurnal Manajemen Informatika Malcom: Indonesian Journal of Machine Learning and Computer Science Prosiding Seminar Nasional Official Statistics
Krismawati, Dewi
Unknown Affiliation
Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Social Sciences

Published : 5 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 5 Documents
Search

 1 
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.
Pemetaan Daerah Aktivitas Perikanan Berbasis Data AIS Busaina, Ladisa; Utami, Nandya Rezky; Pramana, Setia; Krismawati, Dewi
Seminar Nasional Official Statistics Vol 2024 No 1 (2024): Seminar Nasional Official Statistics 2024
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/semnasoffstat.v2024i1.2299

Abstract

Digital development brings significant changes in data collection and processing, with big data becoming the main source of official statistics. BPS has been using big data since 2015 for more accurate analysis and statistics. Automatic Identification System (AIS) data, an automated ship navigation system, effectively monitors ship movements and is used for official statistics, improving accuracy and reducing human error. However, monitoring of Indonesia's marine activities is still not optimal, as seen from the low contribution of the fisheries sector to GDP and indications of overfishing due to illegal fishing activities (IUU). The use of AIS is important for monitoring illegal activities, but data quality is often low. Data quality assurance through preprocessing is needed. This research will map fisheries activity areas in the waters around Papua Island using AIS data and the DBSCAN algorithm to cluster fishing vessels, in order to improve monitoring of fisheries activities in Indonesia.
MULTICLASS CLASSIFICATION OF MARKETPLACE PRODUCTS WITH MACHINE LEARNING Aditama, Farhan Satria; Krismawati, Dewi; Pramana, Setia
MEDIA STATISTIKA Vol 17, No 1 (2024): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.17.1.25-35

Abstract

The use of marketplace data and machine learning in the collection of commodity data can provide an opportunity for Statistics Indonesia to complete the commodity directories for various surveys. This research adopts machine learning to train a product classification model based on existing datasets to predict whether a new dataset falls into which KBKI category. The dataset contains more than 32,000 products from 26 classes consisting of product data from two biggest marketplaces in Indonesia. Algorithms used for classification include Random Forests (RF), Support Vector Machines (SVM), and Multinomial Naive Bayes (MNB). Results indicate that MNB is the most effective algorithm when considering the trade-off between accuracy and processing time. MNB achieved the highest micro-average F1 scores, with 91.8% for Tokopedia and 95.4% for Shopee, and has the fastest execution time approximately 5 seconds.
Natural Language Processing for Enhancing Anamnesis Documentation in Typhoid Fever Cases Putri, Tacyah Kholifah; Nurmalasari, Mieke; Hosizah, Hosizah; Krismawati, Dewi; Panutun, Satria Bagus
Indonesian Journal of Artificial Intelligence and Data Mining Vol 8, No 1 (2025): March 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v8i1.33325

Abstract

The implementation of Natural Language Processing (NLP) is crucial for enhancing the quality of medical records. This study aimed to develop an NLP model to improve the accuracy of documenting disease anamnesis for typhoid fever. The problem addressed by this research is the difficulty in analyzing and classifying patient complaints recorded in electronic medical records, which can affect the accuracy of diagnosis and treatment. The urgency of this study lies in ensuring that documented medical information is used accurately to support diagnosis and patient management. A quantitative approach was used, focusing on electronic medical records of patients who underwent anti-salmonella IgM tests in 2023, involving 424 individuals. The study assessed the performance of three models: Support Vector Machines (SVM), Naive Bayes Bernoulli, and Logistic Regression. The SVM model achieved the highest accuracy at 81.4%, compared to 76.7% for Naive Bayes Bernoulli and 79.1% for Logistic Regression. Additionally, four topic models were identified, highlighting common complaint words and their impacts. The most frequently occurring symptoms in the anamnesis of typhoid fever were "defecation," "nausea," "vomiting," "fever," "diarrhea," "heartburn," "weakness," "loss of appetite," "abdominal pain," "cough," and "cold." This study demonstrates that the SVM model provides superior accuracy in analyzing medical records compared to other models.
Co-Authors Aditama, Farhan Satria Busaina, Ladisa Hosizah Hosizah Munggaran, Rahayu Putri Nurmalasari, Mieke Panutun, Satria Bagus Pramana, Setia Putri, Tacyah Kholifah Rosida, Putri Lailatul Utami, Nandya Rezky