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All Journal Teknika Jupiter Jurnal Teknologi dan Sistem Komputer Jurnal Sistem Komputer & Kecerdasan Buatan Innovation in Research of Informatics (INNOVATICS) Yumary: Jurnal Pengabdian kepada Masyarakat JUSTIN (Jurnal Sistem dan Teknologi Informasi) Jurnal abimana (Jurnal Pengabdian Kepada Masyarakat Nasional) Journal of Data Science Methods and Applications
Safitri, Egi
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Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Library & Information Science Social Sciences Other

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Implementasi Sistem Manajemen Parkir Menggunakan Teknologi QR-CODE Berbasis Web Nurjoko, Nurjoko; Julius, Felix; K, Hendra; Karnila, Sri; Safitri, Egi; Purwati, Neni; Rizal, Ruki
TEKNIKA Vol. 18 No. 1 (2024): Teknika Januari - Juni 2024
Publisher : Politeknik Negeri Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.10570439

Abstract

Kemajuan teknologi telah mendorong penegembangan sistem parkir cerdas di lingkungan perguruan tinggi khususnya Institut Informatika dan Bisnis (IIB) Darmajaya. Seiring dengan pertumbuhan populasi mahasiswa dan staf, menjadi tantangan dalam manajemen perparkiran. Permasalahan parkir, seperti kesulitan dalam mencari tempat parkir yang tersedia dan memanajemen waktu untuk parkir dapat menghambat efisiensi dan kenyamanan pengguna di lingkungan kampus. Penelitian ini bertujuan merancang sistem E-parkir berbasis web di perguruan tinggi yang menyajikan solusi efisien untuk masalah parkir. Sistem e-parkir ini menggunakan teknologi QR-Code berbasis web untuk mempermudah pengguna dalam menemukan, mengelola, dan mengurangi resiko kehilangan kendaraan di tempat parkir. Metode pengembangan sistem pada penelitian ini menggunakan pendekatan metode prototype yang berfokus pada pengguna, dengan integrasi fitur monitoring lokasi ketersediaan tempat parkir guna meningkatkan efisiensi dan kenyamanan pengguna dan petugas parkir. Pengujian sistem dilakukan dengan pendekatan metode black box testing. Berdasarkan kriteria presentasi hasil uji secara keseluruhan dengan nilai kualifikasi sebesar 96% dapat disimpulkan bahwa responden menilai sistem e-parkir yang dibangun telah sesuai dengan fungsinya dan dapat dimplementasikan.
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

Show Abstract | Download Original | Original Source | Check in Google Scholar

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 - Nurjoko Adytama, Muhammad Rezky Amaliah, Khusnatul Andini, Rekha Aprilia Antonio, Yandi Jaya Asmaul Dwi Akbar Bustamam, Alhadi Dani Rofianto Eko Win Kenali Fitra, Jaka Harlianto, Didi Hendra Kurniawan Hendra, Hendra Kurniawan Hijriani, Astria Julius, Felix K Ikshan, Fathurrahman K, Hendra Kultsum, Rahil Urwa Neni Purwati Nicholas Svensons Nurlistiani, Rini Pratama, Raynaldo Syah Rachardi, Andris Rizal, Ruki Rusdah, Deandra Aulia Saputra, M Hardi Sri Karnila SUDARSONO, ELY Syahputra, Lingga Syakur Syakur Syidada, Amran Rahman Triyasri, Novita Windya Harieska Pramujati Yandi Jaya Antonio Yuni Arkhiansyah