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All Journal Jurnal Edukasi dan Penelitian Informatika (JEPIN) ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika JOURNAL OF ELECTRICAL AND SYSTEM CONTROL ENGINEERING TELKA - Telekomunikasi, Elektronika, Komputasi dan Kontrol JMECS (Journal of Measurements, Electronics, Communications, and Systems) eProceedings of Engineering Jurnal INFOTEL
Nor Kumalasari Caecar Pratiwi
Universitas Telkom
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology

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Journal : Jurnal INFOTEL

 1 
Early Detection of Deforestation through Satellite Land Geospatial Images based on CNN Architecture Nor Kumalasari Caecar Pratiwi; Yunendah Nur Fu'adah; Edwar Edwar
JURNAL INFOTEL Vol 13 No 2 (2021): May 2021
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v13i2.642

Abstract

This study has developed a CNN model applied to classify the eight classes of land cover through satellite images. Early detection of deforestation has become one of the study’s objectives. Deforestation is the process of reducing natural forests for logging or converting forest land to non-forest land. The study considered two training models, a simple four hidden layer CNN compare with Alexnet architecture. The training variables such as input size, epoch, batch size, and learning rate were also investigated in this research. The Alexnet architecture produces validation accuracy over 100 epochs of 90.23% with a loss of 0.56. The best performance of the validation process with four hidden layers CNN got 95.2% accuracy and a loss of 0.17. This performance is achieved when the four hidden layer model is designed with an input size of 64 × 64, epoch 100, batch size 32, and learning rate of 0.001. It is expected that this land cover identification system can assist relevant authorities in the early detection of deforestation.
In-Depth Exploration and Comparison of Machine Learning Performances for Early-Stage Diabetes Risk Prediction Pratiwi, Nor Kumalasari Caecar
JURNAL INFOTEL Vol 16 No 2 (2024): May 2024
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v16i2.1117

Abstract

Abstract — Diabetes mellitus is distinguished by an inability of the human system to produce insulin on an ongoing basis, as well as by the inefficient utilization of the insulin hormone, resulting in an elevated level of blood glucose. Global diabetes rates have nearly doubled since 1980, reaching 9.3% among adults. Alarmingly, of the 463 million individuals with diabetes, 50.1% are unaware of their condition. Indonesia ranks seventh globally with 10.7 million diabetes cases. In 2019, it was fifth globally for adults (20–79 years) with undiagnosed diabetes. This silent epidemic demands urgent attention and comprehensive strategies for early detection and management. In recent years, researchers have increasingly studied machine learning for early diabetes recognition. In this study, we aim to predict early-stage diabetes risk by utilizing 16 health condition features. We explore 12 distinct machine learning algorithms, applying a hyperparameter grid to tune each algorithm. This involves systematically testing combinations of hyperparameters to identify the optimal settings for achieving the most accurate and reliable predictive models. The results indicate that the Light GBM algorithm achieved the highest accuracy of 0.9692. By contrast, the logistic regression and Naive Bayes algorithms demonstrated the lowest performance, each with an accuracy of 0.8923. The implications of these results underline the capability of employing machine learning algorithms to precisely and effectively detect individuals susceptible to diabetes, enabling the implementation of individualized healthcare approaches.
Co-Authors Adhi Irianto Mastur, Adhi Irianto AGUSTINA, REGITA Dwi Hardina Aprilia Sari Edwar Efri Suhartono Fadlil Azimi Syafli Febriani Ruming Sari Fitya Nur Fadhilah HANAFI, FANIESA SAUFANA Hanif Chandra Mulya Heri Syahrian, Heri Hurianti Vidya Iwan Iwut Tritoasmoro Jangkung Raharjo KHAERUDIN SALEH Koredianto Usman LESTARY, GITA AYU Marnelius, Chelsya Dwi Michael Binson Situmeang Muhamad Rokhmat Isnaini Muthia Syafika Haq, Muthia Syafika Nur Alyyu Nur Ibrahim Olyvia Fernanda Soedradjat Pellokila, Victor Aji Admaja PERDANI, WAHYUNI RIZKY Putra, Akbar Trisnamulya R. Yunendah Nur Fu’adah Ratna Sari Rita Magdalena Sari, Febriani Ruming Sayidina Ariq Farhan Sofia Sa'idah SOFIA SAIDAH Syamsul Rizal Syamsul Rizal Vidya, Hurianti Vitria Puspitasari Rahadi