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All Journal Journal of Information System, Applied, Management, Accounting and Research Journal of Data Science Methods and Applications
Kultsum, Rahil Urwa
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Biochemistry, Genetics & Molecular Biology Computer Science & IT Decision Sciences, Operations Research & Management Engineering Library & Information Science

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OPTIMASI PENCARIAN RUTE TERPENDEK MENGGUNAKAN ALGORITMA DIJKSTRA Andini, Merry; Kultsum, Rahil Urwa; Raihan, M. Hafizh Rafi; Lestari, Sri
Journal of Information System, Applied, Management, Accounting and Research Vol 9 No 1 (2025): JISAMAR (December-February 2025)
Publisher : Sekolah Tinggi Manajemen Informatika dan Komputer Jayakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52362/jisamar.v9i1.1746

Abstract

Menemukan jalur terpendek merupakan tantangan yang signifikan, khususnya di lingkungan perkotaaan yang dinamis. Kompleksitas pada jaringan dan jumlah rute yang tersedia sering kali menjadi kendala dalam mencapai lokasi tujuan dengan efisien. Dalam mengatasi masalah ini, digunakan Algoritma Dijkstra untuk menentukan rute dengan jarak terpendek yang merupakan salah satu bentuk persoalan optimasi, dimana nilai yang terdapat pada sisi graf mempresentasikan jarak antar simpul. Berdasarkan uji coba yang dilakukan dalam penelitian ini, Algortima Dijkstra terbukti menjadi solusi tepat untuk menentukan rute terpendek menuju Rumah Sakit Abdul Moeloek, sehingga diharapkan dapat meningkatkan efisiensi aksesibilitas enuju fasilitas kesehatan,terutama dalam situasi darurat.
DIAGNOSIS PCOS BERDASARKAN FAKTOR GAYA HIDUP DAN FAKTOR REPRODUKSI MENGGUNAKAN REGRESI LOGISTIK DAN RANDOM FOREST Kurniawan, Hendra; Kultsum, Rahil Urwa; Safitri, Egi; Antonio, Yandi Jaya; Andini, Rekha Aprilia; Syahputra, Lingga; Adytama, Muhammad Rezky
Journal of Data Science Methods and Applications Vol. 2 No. 1 (2026)
Publisher : Program Studi Sains Data - Institut Informatika dan Bisnis Darmajaya

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Abstract

Polycystic Ovary Syndrome (PCOS) is a common endocrine disorder occurring in women of reproductive age, with a global prevalence ranging from 6% to 21%. Current management of PCOS remains limited to symptomatic treatment without addressing the root cause. This study aims to build an accurate predictive model for PCOS diagnosis in Indonesia by analyzing lifestyle and reproductive factors using machine learning algorithms, such as Logistic Regression and Random Forest.The research dataset consists of 541 patient records, which were divided into 80% for training and 20% for testing. The data was normalized using the Min-Max Scaler method, and class imbalance was handled using the SMOTE (Synthetic Minority Oversampling Technique) method. The models were validated using the K-Fold Cross-Validation method and evaluated based on accuracy, precision, recall, and F1-score.The results showed that Logistic Regression with SMOTE in predicting reproductive factors achieved the highest accuracy (82%), while Random Forest with SMOTE demonstrated more stable performance based on average accuracy, particularly for reproductive factors. ROC curve analysis also revealed that Logistic Regression with SMOTE in predicting reproductive factors achieved the highest AUC ($0.84$), making the Logistic Regression model superior in predicting the diagnosis compared to Random Forest. This study confirms that reproductive factors play a more dominant role in predicting PCOS compared to lifestyle factors. Utilizing machine learning algorithms can effectively predict PCOS to support management and prevention, as well as accelerate the early detection process of PCOS.
Co-Authors Adytama, Muhammad Rezky Andini, Merry Andini, Rekha Aprilia Antonio, Yandi Jaya Hendra Kurniawan Raihan, M. Hafizh Rafi Safitri, Egi Syahputra, Lingga