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Ratnasari Ratnasari

Departemen Rekayasa Perangkat Lunak, Fakultas Teknologi Dan Informatika Universitas Aisyah Pringsewu, Lampung - Indonesia

Author-ID : 5903592

Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Social Sciences Other

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Deteksi Dini Stunting Pada Anak Berdasarkan Indikator Antropometri dengan Menggunakan Algoritma Machine Learning Ratnasari, Ratnasari; Wahidin, Ahmad Jurnaidi; Andika, Tahta Herdian
Jurnal Algoritma Vol 21 No 2 (2024): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.21-2.2122

Stunting sebagai dampak dari kekurangan gizi kronis, memiliki dampak yang signifikan terhadap kesehatan dan perkembangan anak di Indonesia. Penelitian ini mengembangkan model deteksi dini stunting pada anak dengan memanfaatkan indikator antropometri menggunakan pendekatan Machine Learning. Berbeda dengan metode tradisional yang bergantung pada penilaian manual atau pemeriksaan klinis yang memakan waktu, pendekatan ini menawarkan keunggulan berupa deteksi yang lebih cepat dan akurat. Data antropometri, seperti tinggi badan, berat badan, usia, dan jenis kelamin, digunakan dalam algoritma Machine Learning: Random Forest, K-Nearest Neighbors (KNN), Naive Bayes, dan Support Vector Machine (SVM). Setiap model dievaluasi berdasarkan akurasi, confusion matrix, dan ROC Analysis untuk menentukan performa terbaik. Hasil penelitian menunjukkan bahwa Random Forest memiliki akurasi tertinggi sebesar 92,70%, lebih unggul dibandingkan dengan KNN yang memiliki akurasi 91,40%. Algoritma Random Forest dipilih sebagai model terbaik untuk deteksi dini stunting karena kemampuannya yang tinggi dalam meminimalkan kesalahan prediksi. Penerapan model ini diharapkan dapat meningkatkan efektivitas skrining cepat stunting di lapangan, memungkinkan deteksi lebih awal bagi anak-anak berisiko tinggi, dan mendukung intervensi yang lebih tepat sasaran dalam program kesehatan masyarakat.

Deteksi Penyakit Daun Padi Menggunakan Deep Learning untuk mendukung Produktivitas dan Pertanian Berkelanjutan Ratnasari; Feriyanto, Dwi
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2900

Rice is a major food commodity that is susceptible to leaf diseases, such as blast, bacterial leaf blight, and tungro, which can significantly reduce productivity if not detected early. This study aims to develop an early detection method for rice leaf diseases using a deep learning approach with a VGG16-based Convolutional Neural Network (CNN) architecture. The data used came from the Rice Leaf Dataset (Kaggle) and field images in Pringsewu Regency. The training process was carried out through transfer learning. The results showed that the model was able to achieve an accuracy of 99.75% on the training data, 96.08% on the validation data, and 100% on the test data. Field tests also proved the model's ability to generalize to real conditions, although there were still some cases with prediction confidence levels that were close between classes. These findings confirm that VGG16-based CNNs are effective for accurate and efficient detection of rice leaf diseases. The application of this model has the potential to support faster decision-making, reduce pesticide use, and encourage environmentally friendly sustainable agricultural practices.

Deteksi Penyakit Daun Padi Menggunakan Deep Learning untuk mendukung Produktivitas dan Pertanian Berkelanjutan Ratnasari; Feriyanto, Dwi
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2900

Rice is a major food commodity that is susceptible to leaf diseases, such as blast, bacterial leaf blight, and tungro, which can significantly reduce productivity if not detected early. This study aims to develop an early detection method for rice leaf diseases using a deep learning approach with a VGG16-based Convolutional Neural Network (CNN) architecture. The data used came from the Rice Leaf Dataset (Kaggle) and field images in Pringsewu Regency. The training process was carried out through transfer learning. The results showed that the model was able to achieve an accuracy of 99.75% on the training data, 96.08% on the validation data, and 100% on the test data. Field tests also proved the model's ability to generalize to real conditions, although there were still some cases with prediction confidence levels that were close between classes. These findings confirm that VGG16-based CNNs are effective for accurate and efficient detection of rice leaf diseases. The application of this model has the potential to support faster decision-making, reduce pesticide use, and encourage environmentally friendly sustainable agricultural practices.

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