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All Journal Format : Jurnal Imiah Teknik Informatika Journal Innovations Computer Science
Hasma, Nur Amalia
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Computer Science & IT

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Pengembangan Model Prediksi Diabetes Melitus Menggunakan Metode Stochastic Gradient Boosting Sah, Andrian; Niesa, Chaeroen; Damuri, Amat; Hasma, Nur Amalia
FORMAT Vol 14, No 1 (2025)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/format.2025.v14.i1.002

Abstract

Diabetes mellitus is one of the global health issues with a continuously increasing prevalence. Its high prevalence significantly impacts economic burdens and healthcare systems, as it often leads to severe complications such as cardiovascular diseases and kidney failure. Therefore, early prediction and detection of diabetes mellitus are crucial in mitigating its adverse effects. Data mining and machine learning technologies offer innovative solutions for processing complex medical data, providing deeper insights, and supporting data-driven decision-making. This study aims to develop a diabetes mellitus prediction model using the Stochastic Gradient Boosting (SGB) algorithm. The model utilizes a dataset comprising clinical variables such as glucose levels, blood pressure, body mass index (BMI), and genetic history to identify diabetes risk. The results indicate that the developed prediction model demonstrates high performance across various dataset splitting ratios: 70:30, 80:20, and 90:10. The model achieved the highest accuracy of 95.50% at the 70:30 ratio, with an AUC (Area Under the Curve) value of 0.9862, showcasing its ability to effectively differentiate between positive (diabetes) and negative (non-diabetes) classes. At the 80:20 and 90:10 ratios, the model achieved accuracies of 92.75% and 92.31%, with AUC values of 0.9767 and 0.9777, respectively, indicating consistent performance. The model’s high accuracy is attributed to the iterative boosting approach in the SGB algorithm, which adaptively corrects prediction errors at each iteration. Additionally, regulatory mechanisms such as learning rate and subsampling help prevent overfitting, making the algorithm effective for datasets with complex patterns.
Implementation of Edge Computing for Optimizing Sensor Data Collection in Smart Buildings Fajri, T. Irfan; Ningsih, Liasulistia; Octiva, Cut Susan; Hakim, Muhammad Lukman; Hasma, Nur Amalia
Journal Innovations Computer Science Vol. 4 No. 2 (2025): November
Publisher : Yayasan Kawanad

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56347/jics.v4i2.369

Abstract

The development of the Internet of Things (IoT) has driven the implementation of smart buildings that rely on real-time sensor data collection and analysis. However, cloud computing-based systems often face problems of high latency and large network loads. This research implements an edge computing architecture to optimize sensor data collection in smart buildings. A prototype was built using edge nodes (Raspberry Pi) that process data from temperature, humidity, light, and motion sensors locally before sending it to the cloud. Test results show that edge computing can reduce latency by up to 45% and reduce data traffic to the cloud by 60%, while also improving the energy efficiency of sensor devices. Thus, edge computing has been proven to effectively improve the performance and efficiency of data collection systems in smart buildings
Co-Authors Damuri, Amat Muhammad Lukman Hakim, Muhammad Lukman Mutasar Niesa, Chaeroen Ningsih, Liasulistia Octiva, Cut Susan Sah, Andrian