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All Journal ALGORITMA : JURNAL ILMU KOMPUTER DAN INFORMATIKA Jurnal Pemberdayaan Sosial dan Teknologi Masyarakat JITCoS : Journal of Information Technology and Computer System
Fitra Hidayat Lubis
Universitas Islam Negeri Sumatera Utara
Computer Science & IT Control & Systems Engineering Education Languange, Linguistic, Communication & Media Social Sciences

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Healthy Lifestyle Pattern Classification Using the XGBoost Algorithm on the Lifestyle and Wellbeing Dataset Nur Shafwa Aulia Sitorus; Nada Asmarani Cantika Dewi; Muhammad Haikal Akmal; Fitra Hidayat Lubis; Agung Nugroho
Journal of Information Technology and Computer System Vol. 2 No. 1 (2026): June
Publisher : CV. Multimedia Teknologi Kreatif

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65230/jitcos.v2i1.66

Abstract

Using the Extreme Gradient Boosting (XGBoost) algorithm with the Lifestyle and Wellbeing dataset from the Kaggle platform, this study attempts to categorize healthy lifestyle patterns. The growing incidence of undesirable behaviors that have a direct impact on people's health and well-being, like stress, inactivity, and a poor work-life balance, is what spurred the research. Using 500 randomly chosen data samples that cover a range of behavioral and psychological lifestyle variables, the study uses a quantitative computational method. Data cleaning, standardization, and target label generation using a health index, which is computed as the mean of positive variables less the mean of negative variables, are all included in the preprocessing stage. With n_estimators = 100, learning_rate = 0.1, and max_depth = 4, the XGBoost model was trained using Python in the Google Colab environment. According to the results, the model's accuracy was 97%, and its precision, recall, and F1-score were all balanced. SLEEP_HOURS, DAILY_STRESS, and WORK_LIFE_BALANCE_SCORE are the most significant factors in predicting a healthy lifestyle, suggesting that psychological stability and sufficient rest are important factors in determining general well-being. As a basis for creating adaptive healthy lifestyle recommendation systems and future research incorporating physiological data from wearable devices to improve prediction accuracy, the study concludes that XGBoost successfully classifies lifestyle patterns and offers comprehensible insights into behavioral factors that contribute to health.
IMPLEMENTASI PROGRAM MAHASISWA KKN UINSU: MODERASI BERAGAMA, DIGITALISASI, PENDIDIKAN, LINGKUNGAN, DIKELURAHAN PADANG MAS KEC.KABANJAHE, KAB.KARO Fitra Hidayat Lubis; Anisa Khairani; Raisah Armayanti Nasution; Azizah sofiana; Nadiah Najah
Jurnal Pemberdayaan Sosial dan Teknologi Masyarakat Vol. 5 No. 2 (2025): Desember 2025
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jpstm.v5i2.5081

Abstract

Abstrack :Community Service Program (KKN) is a form of student service to the community through the direct application of knowledge. The KKN activity in Padang Mas Village aims to implement strategic programs covering religious moderation, digitization, education, and environmental preservation. A participatory approach is used to build synergy between students and the local community in resolving social issues and improving the quality of life of residents. The results of this activity show an increase in community understanding of the values of tolerance, the use of digital technology, awareness of education, and concern for the environment. This study uses a qualitative descriptive method with observation, interview, and documentation techniques. These findings are expected to become a model for sustainable CSL implementation that is relevant to local needs.            Keywords: community service program, religious moderation, digitalization, education, environment, padang mas.  Abstrak:Kuliah Kerja Nyata (KKN) merupakan bentuk pengabdian mahasiswa kepada masyarakat melalui penerapan ilmu pengetahuan secara langsung. Kegiatan KKN di Kelurahan Padang Mas bertujuan untuk mengimplementasikan program-program strategis yang mencakup moderasi beragama, digitalisasi, pendidikan, dan pelestarian lingkungan. Pendekatan partisipatif digunakan untuk membangun sinergi antara mahasiswa dan masyarakat lokal dalam menyelesaikan isu-isu sosial dan meningkatkan kualitas hidup warga. Hasil dari kegiatan ini menunjukkan adanya peningkatan pemahaman masyarakat terhadap nilai-nilai toleransi, pemanfaatan teknologi digital, kesadaran pendidikan, serta kepedulian terhadap lingkungan. Penelitian ini menggunakan metode deskriptif kualitatif dengan teknik observasi, wawancara, dan dokumentasi. Temuan ini diharapkan dapat menjadi model implementasi KKN yang berkelanjutan dan relevan dengan kebutuhan lokal. Kata kunci: KKN, moderasi beragama, digitalisasi, pendidikan, lingkungan, padang mas.
Studi Pengelompokan Multimetode Provinsi di Sumatera Utara Menggunakan Pendekatan PCA dan K-Means Fitra Hidayat Lubis; Suthan Farras Ashar; OK Mhd Fahri Al-Faruqy M.S; Ahmad Boby Amari
Algoritma: Jurnal Ilmu Komputer dan Informatika Vol 9, No 2 (2025): November 2025
Publisher : Universitas Islam Negeri Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/algoritma.v9i2.25017

Abstract

This study aims to classify regions in North Sumatra based on a set of social and economic indicators by applying a multi-method clustering approach. Principal Component Analysis (PCA) is employed to reduce data dimensionality and identify the most influential variables, while the K-Means algorithm is used to form clusters based on similarity of characteristics. The results indicate that the combination of PCA and K-Means can cluster provinces or regions more efficiently and interpretably. The resulting clusters reflect patterns of similarity among regions in terms of social and economic development, thus providing a basis for formulating more targeted regional development policies. These findings demonstrate that a multi-method approach can yield more comprehensive results in spatial data clustering.Keywords: Clustering, Principal Component Analysis (PCA), K-Means, multi-method, North Sumatra.
Co-Authors Agung Nugroho Ahmad Boby Amari Anisa Khairani Azizah sofiana Muhammad Haikal Akmal Nada Asmarani Cantika Dewi Nadiah Najah Nur Shafwa Aulia Sitorus OK Mhd Fahri Al-Faruqy M.S Raisah Armayanti Nasution Suthan Farras Ashar