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Penguatan Kesadaran Perbaikan Lingkungan melalui Participatory Action Research Dusun Gedok Wisata B29 Farida, Laili Nur; Chomsa, Diah Agustina; Hidayati, Nurul; Munir, Sirojum; Asegaf, Muhammad Maulana; Junjunan, Mochammad Ilyas
El-Mujtama: Jurnal Pengabdian Masyarakat Vol 4 No 2 (2024): El-Mujtama: Jurnal Pengabdian Masyarakat
Publisher : Intitut Agama Islam Nasional Laa Roiba Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47467/elmujtama.v4i2.5127

Abstract

One of the interesting tourist destinations in Lumajang Regency is B29 Tourism Village in Argosari Village, Senduro District. From here, visitors can enjoy views of Mount Bromo, the highest peak in the Sand Sea area, which is located about 40 kilometers from Lumajang City and has a height of about 2900 meters above sea level. Argosari Village also has amazing natural beauty, making it an attraction for tourists. The Lumajang Regency Government has a policy that regulates the tourism sector through the cultural tourism office. Garbage is a common problem for the wider community, especially in the people of Gedok Hamlet, Argosari Village, Senduro District, Lumajang Regency. The unavailability of trash bins has led to the habit of burning garbage which can cause air pollution, and throwing garbage in vacant land or ravines which can cause environmental damage. This service focuses on the waste problems that occur in Gedok Hamlet, to reduce littering we provide several trash cans that can be useful for Gedok Hamlet residents to dispose of waste in its place. This service uses the PAR (Participatory Action Research) method, which involves community participation through interviews and observations of the community around Gedok Hamlet as a data source. This service aims to provide benefits and good impacts on waste problems that occur both in Gedok Hamlet and in the wider community, so that they understand the impact of waste on the environment and health.
Klasifikasi Gagal Jantung menggunakan Metode SVM (Support Vector Machine) Farida, Laili Nur; Bahri, Saiful
Komputika : Jurnal Sistem Komputer Vol. 13 No. 2 (2024): Komputika: Jurnal Sistem Komputer
Publisher : Computer Engineering Departement, Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputika.v13i2.11330

Abstract

Gagal jantung merupakan penyakit mematikan nomor satu di dunia. Menurut data WHO (World Health Organization) dan WHF (World Heart Federation), pada tahun 2025 diperkirakan penyakit jantung akan menjadi penyebab utama kematian di negara-negara Asia. Tahun ini, setidaknya 78% angka kematian global disebabkan oleh penyakit jantung yang terjadi pada orang miskin dan kelas menengah. Data RisKesDas (Riset Kesehatan Dasar) KemenKes (Kementerian Kesehatan) RI tahun 2018, prevalensi gagal jantung di Indonesia berdasarkan diagnosis dokter diperkirakan mencapai 5%, di mana lebih sering terjadi pada pria yaitu sebanyak 66% dibandingkan wanita yang hanya 34%. Tujuan dari penelitian ini adalah melakukan klasifikasi penyakit gagal jantung menggunakan metode Support Vector Machine. Proses uji coba menghasilkan akurasi tertinggi pada kernel linear, RBF dan polynomial masing-masing sebesar 85.96%, 85.84%, dan 84.50%. Kernel yang menghasilkan akurasi paling tinggi, yaitu kernel linear dengan cost 0.1. Proses pengujian menggunakan parameter tersebut menghasilkan akurasi, presisi, recall, dan F1-score berturut-turut sebesar 89.13%, 86.21%, 96.15%, dan 90.91%. Berdasarkan hasil penelitian, diperoleh kesimpulan bahwa metode Support Vector Machine cukup baik dalam melakukan klasifikasi pada penyakit gagal jantung.
Identifikasi Tipe Penyakit Anemia dengan Menggunakan Metode Random Forest Farida, Laili Nur
JOINTER : Journal of Informatics Engineering Vol 6 No 01 (2025): JOINTER : Journal of Informatics Engineering
Publisher : Program Studi Teknik Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53682/jointer.v6i01.386

Abstract

Anemia is a blood disorder caused by a lack of red blood cells and hemoglobin levels. Based on a survey conducted by the World Health Organization (WHO), anemia affects 1.62 billion people worldwide. As many as 93% of chronic disease patients experience anemia. Anemia can be diagnosed using Complete Blood Count (CBC) which aims to evaluate the total number and characteristics of cell components in the blood. The purpose of this study was to perform Random Forest (RF) classification of anemia. This study used numerical data in the form of blood cell characteristics, consisting of 269 Normocytic Normochromic Anemia patients, 189 Iron Deficiency Anemia patients, and 336 Healthy patients. In this study, the classification process used RF which was then evaluated using the Confusion Matrix, so that the classification evaluation results were obtained in the form of accuracy, sensitivity, and specificity. This study obtained the best results at k of 5 with parameters n estimators, max depth, and min samples leaf of 10, 90, and 4, respectively. The accuracy, sensitivity, and specificity values ​​produced were each 100%.