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Air Bersih, Hidup Sehat : Edukasi Sanitasi Untuk Masyarakat Sehat di Kelurahan Selumit Pantai Kota Tarakan Novita Ranti Muntiari; Fathul Khair Tabri; Syamsiah; Muhammad Aris; Muliady; Asma; Lily Herawati
Jurnal Pengabdian Masyarakat - PIMAS Vol. 5 No. 1 (2026): Februari
Publisher : LPPM Universitas Harapan Bangsa Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35960/pimas.v5i1.2111

Abstract

Access to safe water and adequate sanitation is a crucial factor in maintaining public health, particularly in coastal areas with high population density such as Selumit Pantai Subdistrict, Tarakan City. Various challenges, including environmental conditions, limited supporting infrastructure, and suboptimal sanitation practices among the community, have the potential to increase the risk of environment-related diseases. In response to these conditions, this Community Service activity was conducted as an educational effort through the provision of information and assistance related to clean water and sanitation. The activity involved resource persons from the Tarakan City Water Supply Company (PDAM) and Health Promotion lecturers, who delivered materials on water quality and utilization, clean and healthy living behaviors, and the prevention of environmental-based diseases. The implementation employed an interactive approach through lectures, discussions, and question-and-answer sessions to ensure the material was easily understood by the community. The objective of this activity was to improve community knowledge, awareness, and attitudes regarding the importance of proper clean water management and sanitation, thereby encouraging the sustainable adoption of healthy behaviors. Through synergy between practitioners and academics, this activity is expected to support improvements in the health status of coastal communities and contribute to health development in Tarakan City
Pengabdian Sebagai Dewan Juri Bidang Teknik Desain Laman Lomba Kompetensi Siswa (LKS) SMK Tingkat Provinsi Kalimantan Utara Tahun 2025 Novita Ranti Muntiari; Denis Prayogi
Jurnal Pengabdian Masyarakat - PIMAS Vol. 4 No. 4 (2025): November
Publisher : LPPM Universitas Harapan Bangsa Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35960/pimas.v4i4.2186

Abstract

Student Competency Competition is an annual competition between students at the vocational high school level according to the areas of expertise taught at participating vocational schools. This LKS is equivalent to the OSN (National Science Olympiad) held in junior high schools/high schools. This activity is one part of a series of selections to get the best students from all over Indonesia who will be further guided by their respective competition field teams and will be included in international level expertise competitions. The purpose of the community service activities carried out through this LKS competition activity is for the Community Service Team to contribute as a jury in the Page Design Technique competition. The role of the jury is very important as the determinant of the final results of the provincial level LKS, so that the best students can represent North Kalimantan Province to compete at the national level. The final result of this community service activity is an official decision from the jury on the competition assessment process in determining the winner of the 2025 North Kalimantan Provincial Level SMK LKS.
Penanganan Ketidakseimbangan Data Pada Klasifikasi Penyakit Campak Menggunakan Kombinasi Smote Dan Xgboost Novita Ranti Muntiari; Kharis Hudaiby Hanif; Muliyadi; Mufida
Jurnal Ilmu Komputer dan Sistem Komputer Terapan (JIKSTRA) Vol. 8 No. 1 (2026): Edisi April
Publisher : Universitas Harapan Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Data imbalance is one of the main challenges in developing disease classification models, as it can cause algorithms to recognize the majority class more dominantly and perform less optimally in detecting positive cases. This study aims to analyze the application of the combination of Synthetic Minority Over-sampling Technique (SMOTE) and XGBoost in measles disease classification. The data used consisted of 1,000 records with clinical features including age, immunization history, fever, cough, runny nose, conjunctivitis, skin rash, and measles status. The research data were divided into two subsets, namely 80% for the model training process and 20% for testing. The SMOTE technique was applied to the training data to address class distribution imbalance, while the XGBoost algorithm was used to build the classification model. Model performance was then evaluated using a confusion matrix and the metrics of accuracy, precision, recall, and F1-score. The results showed that XGBoost without SMOTE achieved an accuracy of 94.0%, precision of 83.3%, recall of 50.0%, and F1-score of 62.5%. After applying SMOTE, the performance improved, with an accuracy of 97.0%, precision of 79.2%, recall of 95.0%, and F1-score of 86.4%. These results indicate that the combination of SMOTE and XGBoost is more effective in improving the detection capability of positive measles cases in imbalanced data..
Penerapan Algoritma YOLOv8 Dalam Indentifikasi Wajah secara Real-Time menggunakan CCTV untuk Presensi Siswa Novita Ranti Muntiari; Indah Chairun Nisa; Ana Sriekaningih; Andri Yogi Adyatma Prasetyo; Muhammad Yusril
Decode: Jurnal Pendidikan Teknologi Informasi Vol. 4 No. 3: NOVEMBER 2024
Publisher : Program Studi Pendidikan Teknologi Infromasi UMK

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51454/decode.v4i3.847

Abstract

Sistem presensi siswa di SMK N 4 saat ini masih dilakukan secara manual, rentan terhadap manipulasi data dan inefisiensi. Penelitian ini bertujuan untuk mengembangkan sistem presensi siswa yang lebih akurat dan efisien dengan memanfaatkan algoritma YOLOv8 untuk melakukan deteksi wajah secara real-time. Melalui studi kasus di SMK Negeri 4 Tarakan, penelitian ini menggunakan metode eksperimental dengan mengumpulkan dataset wajah siswa dan melatih model YOLOv8. Menggunakan Algoritma YOLOv8 dalam mengidentifikasi wajah secara real-time. Berdasarkan dataset dari 30 Siswa SMK Negeri 4 Tarakan dengan pengambilan data  menggunakan foto wajah, 120 foto data wajah dari 30 siswa. Dengan data training yaitu 84 gambar, data valid yaitu 24 gambar, dan data testing 12 gambar. Hasil performa model yaitu, nilai mAP yaitu 88,1%, precision 76,1%, dan recall 82,8% untuk pengolahan dataset siswa. Hasil penelitian berdasarkan performa menunjukkan bahwa model yang dibuat mampu mendeteksi dengan baik.