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All Journal Jurnal Pengabdian UntukMu NegeRI Jurnal Ilmiah FIFO Bina Insani ICT Journal JURNAL MEDIA INFORMATIKA BUDIDARMA Information System for Educators and Professionals : Journal of Information System Informatics for Educators and Professional : Journal of Informatics Applied Industrial Engineering Journal Explore IT : Jurnal Keilmuan dan Aplikasi Teknik Informatika INTI Nusa Mandiri JTIK (Jurnal Teknik Informatika Kaputama) Jurnal Teknika Jurnal Bumigora Information Technology (BITe) Jurnal Kajian Ilmiah Jurnal AbdiMas Nusa Mandiri Jurnal ABDIMAS (Pengabdian kepada Masyarakat) UBJ Jurnal Mahasiswa Sistem Informasi (JMSI) Journal of Students‘ Research in Computer Science (JSRCS) Reputasi: Jurnal Rekayasa Perangkat Lunak Informatics and Computer Engineering Journal Jurnal Inkofar INFORMATION SYSTEM FOR EDUCATORS AND PROFESSIONALS : Journal of Information System Jurnal Aruna Mengabdi (ARMI) Jurnal Sains dan Teknologi Informasi Prawara Jurnal Pengabdian Masyarakat
Syifa Nur Rakhmah, Syifa Nur
Universitas Nusa Mandiri
Religion Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Medicine & Pharmacology Physics Public Health Social Sciences Transportation Other

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Prediksi dan Pencegahan Risiko Burnout pada Pekerja Fleksibel Menggunakan Algoritma Random Forest Fauziah Mk, Noha Noor; Hakim, Dimas Lukman; Cahyani, Ainun; Sariasih, Findi Ayu; Rakhmah, Syifa Nur; Sutoyo, Imam
Jurnal Sains dan Teknologi Informasi Vol 5 No 1 (2025): Desember 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/jussi.v5i1.8937

Abstract

Flexible workers operating under remote, hybrid, and freelance schemes face burnout risks that are difficult to detect early due to irregular work patterns and blurred work-time boundaries. Conventional burnout monitoring relying on manual surveys is static and lacks sensitivity to the dynamics of workers' psychological changes. This study aims to develop a machine learning-based burnout prediction system for flexible workers capable of providing real-time risk predictions accompanied by personalized prevention recommendations. The method employed is Random Forest Classifier using a dataset from Kaggle titled "Mental Health & Burnout in the Workplace" encompassing 5.000 observations. System development follows the Agile approach and is implemented through a Streamlit-based web application. Preprocessing stages include binary label transformation, data leakage elimination, one-hot encoding, class imbalance handling using SMOTE, and stratified split with a 90:10 ratio. The Random Forest model is configured with 800 trees, max_depth of 20, and other optimal hyperparameters. Evaluation results demonstrate that the model achieves 87% accuracy with precision of 0.89, recall of 0.91, and F1-score of 0.90 for the burnout class. Feature importance analysis identifies CareerGrowthScore, StressLevel, and ProductivityScore as dominant factors. The system provides real-time predictions with latency <2 seconds and prevention recommendations tailored to individual risk profiles. This research contributes a practical solution for self-monitoring mental health among flexible workers and provides organizations with an instrument for monitoring remote workforce well-being. Black-box testing validates that all functionalities operate according to specifications.
Prediksi Risiko Kesehatan Bayi Berbasis Parameter Tumbuh Kembang dengan Menggunakan Gradient Boosting Hulu, Astatia; Aimar, Juan Sebastian; Nabilah, Firyal Aufa; Rakhmah, Syifa Nur; Sariasih, Findi Ayu; Sutoyo, Imam
Informatics and Computer Engineering Journal Vol 6 No 1 (2026): Periode Februari 2026
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat (LPPM) Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/icej.v6i1.11066

Abstract

Kesehatan bayi merupakan indikator penting kualitas generasi masa depan, namun deteksi dini risiko kesehatan sering terkendala keterbatasan tenaga medis dan sistem pemantauan efektif. Penelitian ini mengembangkan sistem prediksi risiko kesehatan bayi berusia 0-30 hari menggunakan algoritma Gradient Boosting berdasarkan parameter tumbuh kembang. Metode pengembangan sistem menggunakan Agile Scrum dengan dataset "Infant Wellness and Risk Evaluation" yang melalui tahap pra-pemrosesan data dan feature engineering. Hasil evaluasi menunjukkan model mencapai akurasi 94%, recall 84% untuk kelas berisiko, dan precision 71%. Analisis feature importance mengidentifikasi age_days, oxygen_saturation, dan heart_rate_zscore sebagai fitur paling berpengaruh. Sistem prediksi berbasis web yang dihasilkan ini nantinya diharapkan dapat menjadi alat bantu yang efektif bagi tenaga medis. Infant health is an important indicator of future generation quality, but early detection of health risks is often constrained by limitations of medical personnel and effective monitoring systems. This research develops a health risk prediction system for infants aged 0-30 days using Gradient Boosting algorithm based on growth and development parameters. The system development method uses Agile Scrum with "Infant Wellness and Risk Evaluation" dataset through data preprocessing and feature engineering stages. Evaluation results show the model achieves 94% accuracy, 84% recall for at-risk class, and 71% precision. Feature importance analysis identifies age_days, oxygen_saturation, and heart_rate_zscore as the most influential features. The resulting web-based system has potential as an effective assistance tool for medical personnel.  
SISTEM REKOMENDASI MAKANAN MULTI – KRITERIA UNTUK KONSUMEN DENGAN ANGGARAN TERBATAS MENGGUNAKAN ALGORITMA CONTENT BASED FILTERING Azhar, Raniah; Shidqin, Dhuha Shobiyan; Prakoso, Azzam Ade; Rakhmah, Syifa Nur; Sariasih, Findi Ayu; Sutoyo, Imam
JTIK (Jurnal Teknik Informatika Kaputama) Vol. 10 No. 1 (2026): Volume 10, Nomor 1, Januari 2026
Publisher : STMIK KAPUTAMA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59697/jtik.v10i1.1186

Abstract

ABSTRACTThe primary challenge in current digital recommendation services is aligning product quality with the economic constraints of the user. This study focuses on the development and implementation of a Food Recommendation System operating on Multi-Criteria, namely Maximum Budget (Price) and Quality (Predicted Rating). The methodology applied is Content-Based Filtering, where the system analyzes nutritional content data and the estimated ingredient cost of each menu to determine the level of compatibility with the user’s preference profile. The processing flow begins with receiving a price limit set by the consumer, followed by a strict filtering phase to exclude menus outside the budget, and subsequently ranking the qualified menus based on the quality score generated by a Machine Learning model. This implementation successfully delivers ordered and cost-efficient menu recommendations, demonstrating its high potential as an effective assistant in supporting food purchasing decisions for consumers facing financial limitations.Keywords: Recommendation System, Multi-Criteria, Budget Constraint, Content-Based Filtering, Predicted Rating.
Co-Authors Adam Maulana Putra Aimar, Juan Sebastian Amelianda Mega Putri Andre Kurniawan Anis Rahmawati Anna Mukhayaroh Anna Mukhayaroh, Anna Arif Ismail Husin Azhar, Raniah Cahyani, Ainun Dea Kartika Aulia Arawi Dhefine Armelsa Dian Yunita sari Endang Zulfikar Fajri Khairullah Fauziah Mk, Noha Noor Feru Adiningrat Findi Ayu Sariasih Findi Ayu Sariasih Galih Khrisna Rangga Ganda Wijaya Hakim, Dimas Lukman Hani Harafani Henny Leidiyana Hulu, Astatia Icuk Maris Kuncoro Imam Suyoto Irfan Rizki Izzudin Ijtara’a Kardinal Novel Khoirun Nisa Lusa Indah Prahartiwi Maulidana Athur Fadilla Minda Septiani Mochammad Imron Awalludin Muhammad Zaky Murni Cahyati Musyaffa, Nu’man Nabilah, Firyal Aufa Nisa , Khoirun Norma Yunita Norma Yunita Norma Yunita Nu’man Musyaffa Prakoso, Azzam Ade Putri, Amelianda Mega Raden Aldhi Rajendra Yusuf Ratna Fitria Reza Reza Rizki Aulianita Rizki Aulianita Rizki Aulianita, Rizki Rizki, Irfan Shidqin, Dhuha Shobiyan Sutikno, Wahyu Sutoyo, Imam Upi Dwi Desi Yanti Widiarina Widyastuty, Wiwiek Yoraeni, Ani Yuni Eka Achyani, Yuni Eka Yunita, Norma Yusuf , Raden Aldhi Rajendra