Article Per Year (5 Year)
p-Index From 2021 - 2026
0.61
P-Index
This Author published in this journals
All Journal JURNAL TEKNOLOGI DAN OPEN SOURCE Jurnal Teknisi JITCoS : Journal of Information Technology and Computer System
Nur Shafwa Aulia Sitorus
Universitas Islam Negeri Sumatera Utara
Computer Science & IT Control & Systems Engineering

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
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.
Transfer Learning Implementation with MobileNetV2 for Cassava Leaf Disease Detection Muhammad Fathir Aulia; M. Khalil Gibran; Nur Shafwa Aulia Sitorus; Agung Nugroho; Nayla Faiza; Hervilla Amanda R. Siregar
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 8 No. 1 (2025): Jurnal Teknologi dan Open Source, June 2025
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v8i1.4442

Abstract

Cassava (Manihot esculenta) is one of Indonesia’s key agricultural commodities but is vulnerable to various leaf diseases, such as Cassava Bacterial Blight (CBB) and Cassava Mosaic Disease (CMD). These diseases often exhibit similar visual symptoms, making it challenging for farmers to accurately identify them through manual observation. This study aims to develop an automatic cassava leaf disease detection system based on transfer learning, utilizing the MobileNetV2 architecture. The dataset used consists of 1,500 images, evenly distributed across three categories: CBB, CMD, and healthy leaves. The data underwent preprocessing, augmentation, and model training, including fine-tuning of the last 20 layers of the MobileNetV2 model. Evaluation results indicated that the model achieved an accuracy of 67% on the test set, with the highest performance in detecting Cassava Mosaic Disease, reflected by an F1-score of 0.75. These results demonstrate the potential of MobileNetV2 as a lightweight and efficient solution for detecting cassava leaf diseases, particularly when supported by a larger and more diverse dataset. This research serves as a foundation for developing mobile-based diagnostic tools to help farmers make faster and more accurate decisions in the field.
APPLICATION OF WEIGHTED AVERAGE ALGORITHM IN RECREATIONAL PARK TOURIST DESTINATION RECOMMENDATION SYSTEM BASED ON GOOGLE MAPS USER RATINGS Nayla Faiza; Hervilla Amanda R. Siregar; Nur Shafwa Aulia Sitorus; Agung Nugroho; Muhammad Fathir Aulia; Mhd Furqan
JURNAL TEKNISI Vol. 5 No. 2 (2025): Agustus 2025
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/teknisi.v5i2.3790

Abstract

Abstract: The development of digital technology has changed the behavior patterns of tourists in choosing travel destinations. Google Maps is now not only used to find restaurant locations but has also become the main source for searching nearby tourist destinations based on user ratings and reviews. This research aims to build a recommendation system for recreational park tourist destinations in Medan City by applying the Weighted Average algorithm using Google Maps user rating data. The data used comes from reviews by five users of five popular recreational parks in Medan City during the period from January 1, 2025, to April 30, 2025. The Weighted Average algorithm was chosen because it can provide a more objective and fair assessment by taking into account the weight of each rating given by users. As a result, this system can recommend the best recreational parks based on user experiences related to cleanliness, parking facilities, toilets, security, running paths, and accessibility. It is hoped that this system can help tourists choose destinations that meet their needs and preferences, as well as provide a more enjoyable and satisfying travel experience.Keywords : digital technology; google maps; recommendation system; weighted average algorithmAbstrak: Perkembangan teknologi digital telah mengubah pola perilaku wisatawan dalam memilih destinasi wisata. Google Maps kini tidak hanya digunakan untuk mencari lokasi restoran, tetapi juga menjadi sumber utama dalam mencari destinasi wisata terdekat berdasarkan rating dan ulasan pengguna. Penelitian ini bertujuan untuk membangun sistem rekomendasi destinasi wisata taman rekreasi di Kota Medan dengan menerapkan algoritma Weighted Average menggunakan data rating pengguna Google Maps. Data yang digunakan berasal dari lima ulasan pengguna terhadap lima taman rekreasi populer di Kota Medan selama periode 1 Januari 2025 hingga 30 April 2025. Algoritma Weighted Average dipilih karena mampu memberikan penilaian yang lebih objektif dan adil dengan memperhatikan bobot setiap rating yang diberikan pengguna. Hasilnya, sistem ini dapat merekomendasikan taman rekreasi terbaik berdasarkan pengalaman pengguna terkait aspek kebersihan, fasilitas parkir, toilet, keamanan, lintasan lari, dan aksesibilitas. Diharapkan sistem ini dapat membantu wisatawan dalam memilih destinasi yang sesuai dengan kebutuhan, preferensi, dan memberikan pengalaman wisata yang lebih menyenangkan dan memuaskan. Kata Kunci: google maps; sistem rekomendasi; teknologi digital; weighted average algorithm
Co-Authors Agung Nugroho Fitra Hidayat Lubis Hervilla Amanda R. Siregar M. Khalil Gibran Mhd Furqan Muhammad Fathir Aulia Muhammad Haikal Akmal Nada Asmarani Cantika Dewi Nayla Faiza