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All Journal Jurnal Fitopatologi Indonesia Kontinu: Jurnal Penelitian Didaktik Matematika Jurnal Matematika UNAND APTEK Jurnal Ilmiah Wahana Pendidikan PUCUK : Jurnal Ilmu Tanaman Prosiding Konferensi Nasional PKM-CSR Sungkai STATISTIKA Al-Gafari: Manajemen dan Pendidikan International Conference on Digital Advanced Tourism, Management, and Technology
Yuliana Susanti
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Religion Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Languange, Linguistic, Communication & Media Mathematics Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Penerapan Algoritma K-Nearest Neighbors dalam Sistem Rekomendasi Makanan Berdasarkan Kebutuhan Nutrisi dengan Content-Based Filtering Luqyana Zakiya Almas; Yuliana Susanti; Sri Sulistijowati Handajani
Statistika Vol. 24 No. 1 (2024): Statistika
Publisher : Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Islam Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/statistika.v24i1.3558

Abstract

ABSTRAK Pemenuhan nutrisi merupakan sebuah keharusan untuk menjaga kesehatan tubuh. Memahami manfaat nutrisi dan cairan dalam tubuh dapat mendukung pertumbuhan dan perkembangan serta mencegah berbagai penyakit yang disebabkan oleh kekurangan nutrisi atau bisa disebut malnutrisi. Malnutrisi merujuk pada kelebihan atau kurangnya nutrisi, tidak seimbangnya nutrisi, atau masalah pemanfaatan nutrisi. Seiring berjalannya waktu, hal ini dapat meningkatkan risiko terjadinya beberapa penyakit dan gangguan kesehatan lainnya, seperti kelebihan berat badan atau obesitas, diabetes, atau beberapa penyakit tidak menular. Tujuan dari penelitian ini untuk membuat rekomendasi makanan berdasarkan kebutuhan nutrisi menggunakan algoritma K-Nearest Neighbor dengan content-based filtering. Pada penelitian ini K-Nearest Neighbor digunakan untuk menghasilkan rekomendasi makanan terdekat sesuai dengan keinginan dan kebutuhan pengguna dengan menghitung Euclidean distance. Dengan memanfaatkan algoritma K-Nearest Neighbor dalam content-based filtering, penelitian ini berhasil menciptakan suatu sistem rekomendasi makanan yang dapat disesuaikan dengan kebutuhan nutrisi individu, termasuk pengguna yang sehat, memiliki alergi, dan riwayat diabetes. Hasil evaluasi model menunjukkan bahwa metode ini mampu memberikan rekomendasi makanan dengan tingkat kesalahan yang rendah, dengan melihat nilai Root Mean Square Error 18.75. Dengan demikian, penelitian ini memberikan kontribusi dalam mendukung pemenuhan nutrisi yang tepat, serta memberikan arahan praktis bagi individu untuk menjaga kesehatan tubuh melalui pola makan yang sesuai dengan kebutuhan pengguna. ABSTRACT Fulfilling nutrition is a must to maintain a healthy body. Understanding the benefits of nutrition and fluids in the body can support growth and development and prevent various diseases caused by nutritional deficiencies or what can be called malnutrition. Malnutrition refers to excess or lack of nutrition, imbalance in nutrition, or problems with nutrient utilization. Over time, this can increase the risk of several diseases and other health problems, such as being overweight or obese, diabetes, or several non-infectious diseases. This research aims to make food recommendations based on nutritional needs using the K Nearest Neighbor algorithm with content-based filtering. In this research, K-Nearest Neighbor produces recommendations for the closest food according to the user's desires and needs by calculating Euclidean distance. By utilizing the deep K-Nearest Neighbor algorithm and content-based filtering, this research succeeded in creating a food recommendation system that can be customed by individual nutritional needs, including users who are healthy, have allergies, and have a history of diabetes. The model evaluation results show that this method can provide food recommendations with a low error rate by looking at the values Root Mean Square Error of 18.75. Thus, this research contributes to supporting the fulfillment of proper nutrition and providing practical direction for individuals to maintain body health through a diet that suits the user's needs.
Comparative Analysis Of Performance Levels Of Svm And Naïve Bayes Algorithm For Lifestyle Classification On Twitter Social Media Fadlila Nurwanda; Winita Sulandari; Yuliana Susanti; Zakya Reyhana
International Conference On Digital Advanced Tourism Management And Technology Vol. 1 No. 1 (2023): International Conference on Digital Advanced Tourism, Management, and Technolog
Publisher : Sekolah Tinggi Ilmu Ekonomi Pariwisata Indonesia Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56910/ictmt.v1i1.65

Abstract

Lifestyle is how individuals express themselves through their activities and interests and utilize their financial resources and available time. Twitter is a social network platform that allows people to express opinions and directly criticize various topics, including the recently widely discussed lifestyle topics. Topic classification on Twitter is central in facilitating the search, recommendation, and management of relevant content for users. This research aims to analyze public sentiment regarding lifestyle using 11,000 pieces of data with the keywords "concert", "watching films", "smoking", and others related to lifestyle. Research data is labeled according to the sentiment of public opinion towards lifestyle. Negative polarity for data that has the context of "underestimating", "insulting", "sarcastic", and "feeling sad". Positive polarity for data that has the context of "grateful", "praying", "feeling happy", and "encouraging". Neutral polarity for data that has the contexts “ask”, “predict”, and “feel surprised”. Next, the data enters the pre-processing stage, which consists of case-folding, tokenization, stopword removal, and lemmatizing. The analysis continues by dividing the data into training and test data with a ratio of 70%:30%. Sentiment analysis uses an algorithm Support Vector Machine (SVM) and Naïve Bayes Classifier (NBC). The analysis results show that the SVM algorithm provides better classification than NBC. In this case, the SVM algorithm produces accuracy, precision, recall, and value F1-Score the same, namely 61%.
Melatih Kemampuan Komunikasi Anak Melalui Program Bahasa Arab di Lembaga Kesejahteraan Sosial Anak (LKSA) Dato’ Rahmat Dasan Baru Bengkel: Training Children's Communication Skills Through the Arabic Language Program at the Dato' Rahmat Dasan Baru Bengkel Child Welfare Institution (LKSA). Yuliana Susanti; Irwan Hadi; Abdullah
Al-Gafari : Manajemen dan Pendidikan Vol. 3 No. 3 (2025): DESEMBER
Publisher : Pendidikan Al-Gafari

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66886/algafari-mp.v3i3.240

Abstract

Arabic, as one of the international languages ​​and the main religious language in the Islamic world, requires good mastery to be able to be used actively in various situations, both in social, academic and professional contexts. This study aims to determine the impact of the Arabic language program on the communication skills of children at the Dato' Rahmat Dasan Baru Bengkel Child Welfare Institution (LKSA), as well as the supporting and inhibiting factors in the program's implementation. This study used a qualitative approach with a case study method. Data collection techniques were carried out through observation, interviews, and documentation. The results showed that the Arabic language program can increase children's confidence in communicating, strengthen religious understanding, and encourage them to be more active. Supporting factors for the program's success include the children's enthusiasm, teacher commitment, and internal support from the institution. Inhibiting factors include limited facilities and infrastructure, limited learning time, and minimal parental involvement. In conclusion, the Arabic language program at LKSA Dato' Rahmat plays an important role in training children's communication skills effectively.
Co-Authors Abdullah Al Muzafri Aslamiah, Suhaibatul Budi Irwansyah Budi Tjahjono Defidelwina Dina Rohmah Edward Bahar Fadlila Nurwanda hasih pratiwi Husni Zakiah Ikhsan Gunawan Irwan Hadi Januar Kaswari Khusnu Abdillah Siregar Kiki Ferawati Kikin H Mutaqin Laily Fitriana Laksono Trisnantoro Lufita Nur Alfiah Luqyana Zakiya Almas MEITY SURADJI SINAGA Monaluvy Septiningrum Muhammad Alfatih Muhammad Bayu Nirwana NISWATUL QONA’AH Pada Lumba Purwo Subekti Respatiwulan Respatiwulan Riswandi Riswandi Sri Sulistijowati Handajani Syarifatul Munawarah Wahyudi, Tino Winita Sulandari Zakya Reyhana Zukhronah, Etik