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All Journal Jurnal Pengabdian Informatika (JUPITA) Jurnal Nasional Teknologi Informasi dan Aplikasinya
I Nyoman Adi Mahendra Putra
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Sosialisasi Rancangan Aplikasi Prediksi Beban Puncak Sistem Kelistrikan Nusa Penida I Nyoman Adi Mahendra Putra; Cokorda Rai Adi Pramartha; I Wayan Santiyasa
Jurnal Pengabdian Informatika Vol. 4 No. 1 (2025): JUPITA Volume 4 Nomor 1, November 2025
Publisher : Jurusan Informatika, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Udayana

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Abstract

Nusa Penida, sebagai destinasi wisata populer, mengalami lonjakan permintaan energi listrik akibat tingginya jumlah wisatawan. Beban listrik mencapai 12,26 MW pada puncaknya, sedangkan kapasitas yang tersedia hanya 14,45 MW, menunjukkan potensi kekurangan pasokan energi. PT PLN UID Bali membutuhkan solusi prediksi beban listrik guna memitigasi risiko kelebihan beban. Sebagai rancangan baru yang menjadi solusi, dikembangkan platform berbasis website dengan algoritma Prophet untuk memberikan pandangan prediksi baru terhadap data historis yang digunakan. Platform Streamlit yang digunakan menawarkan visualisasi interaktif, impor dataset yang fleksibel, dan komponen prediksi yang lebih detail. Pengembangan aplikasi diharapkan adanya peningkatan efisiensi operasional yang mendukung pengelolaan dan prediksi data beban puncak sistem kelistrikan yang lebih baik dan terstruktur.
Optimasi Hyperparameter Algoritma Support Vector Machine dalam Klasifikasi Penyakit β-Thalassemia I Nyoman Adi Mahendra Putra; Cokorda Pramartha
Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 3 No. 2 (2025): JNATIA Vol. 3, No. 2, Februari 2025
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JNATIA.2025.v03.i02.p07

Abstract

Beta-thalassemia, a type of thalassemia disease caused by genetic variations, forces sufferers to receive regular blood transfusions for survival. Therefore, classification of this disease is important to reduce the number of births of beta-thalassemia patients in the future. 5066 beta-thalassemia carrier patient data from the Punjab Thalassemia Prevention and Program (PTPP) case study was taken as the source in this study which was accessed through the github.com website. Preprocessing is done for class alignment to avoid data imbalance, feature selection to streamline model performance, and normalization of feature values to a certain scale. In this research, the main focus is on applying the performance of the Support Vector Machine (SVM) algorithm to obtain classification results. Before entering the final model, hyperparameter tuning is required to obtain suitable parameter values to be entered into the model. hyperparameter tuning that will be carried out include "C" (regularization parameter), "kernel" (kernel type), "gamma" (kernel parameter for non-linear kernels), and "degree" (polynomial degree for polynomial kernels), carried out before the model is evaluated. The accuracy results were evaluated using confusion matrix, resulting in precision of 99.81%, recall of 99.62%, f1-score of 99.71%, and accuracy of 99.71% after hyperparameter tuning where the best parameters are "'C': 1, 'gamma': 100, 'kernel': 'rbf'" with an average test score of 0.993494149 
Co-Authors Cokorda Pramartha Cokorda Rai Adi Pramartha I WAYAN SANTIYASA