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All Journal Dinamika Informatika: Jurnal Ilmiah Teknologi Informasi Journal of Information System Exploration and Research
Anisa, Devi Nurul
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Computer Science & IT Decision Sciences, Operations Research & Management Engineering

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KLASIFIKASI PENYAKIT DIABETES MENGGUNAKAN ALGORITMA NAIVE BAYES Anisa, Devi Nurul; Jumanto, Jumanto
Dinamika Informatika : Jurnal Ilmiah Teknologi Informasi Vol 14 No 1 (2022)
Publisher : Fakultas Teknologi Informasi Universitas Stikubank (Unisbank) Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (678.905 KB) | DOI: 10.35315/informatika.v14i1.9135

Abstract

Penyakit diabetes adalah suatu penyakit gangguan metabolik yang di tandai oleh tingginya gula darah yang melebihi nilai normal. Terdapat banyak faktor yang menjadi penyebab penyakit diabetes, faktor-faktor tersebut diantaranya seperti faktor keturunan, berat badan, usia, dan faktor lainnya. Banyak yang tidak menyadari bahwa dirinya terkena penyakit diabetes, sehingga angka kematian yang disebabkan oleh penyakit diabetes ini semakin banyak dan setiap tahunnya diperkirakan akan terus meningkat angka kasus kematiannya. Maka dari itu penelitian ini mencoba menerapkan suatu metode klasifikasi untuk memprediksi apakah seseorang terkena diabetes atau tidak. Dataset yang digunakan pada penelitian ini merupakan data yang di dapatkan dari data Kaggle, yaitu Predict diabetes based on diagnostic measure. Metode klasifikasi yang digunakan yaitu dengan menerapkan algoritma Naive Bayes yang mampu menghasilkan akurasi yang baik. Hasil dari penelitian ini di dapati nilai akurasi 92%. Hasil ini lebih baik dibanding dengan penelitian sebelumnya yang menggunakan k-nearest neighbor (KNN) dengan tingkat akurasi sebesar 91%.
Inception ResNet v2 for Early Detection of Breast Cancer in Ultrasound Images Nikmah, Tiara Lailatul; Syafei, Risma Moulidya; Anisa, Devi Nurul; Juanara, Elmo; Mahrus, Zohri
Journal of Information System Exploration and Research Vol. 2 No. 2 (2024): July 2024
Publisher : shmpublisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joiser.v2i2.439

Abstract

Breast cancer is one of the leading causes of death in women. Early detection through breast ultrasound images is important and can be improved using machine learning models, which are more accurate and faster than manual methods. Previous research has shown that the use of the CNN (Convolutional Neural Network) algorithm in breast cancer detection still does not achieve high accuracy. This study aims to improve the accuracy of breast cancer detection using the Inception ResNet v2 transfer learning method and data augmentation. The data is divided into training, validation and testing data consisting of 3 classes, namely Benign, Malignant and Normal. The augmentation process includes rotation, zoom, and rescale. The model trained using CNN and Inception ResNet v2 showed good performance by producing the highest accuracy of 89.72% in the training data evaluation data and getting 90% accuracy in the prediction test stage with data testing. This study shows that the combination of data augmentation and the Inception ResNet v2 architecture can improve the accuracy of breast cancer detection in CNN models.
Optimization of Energy Consumption Prediction with Random Forest Regressor and XGBoost Feature Importance Syafei, Risma Moulidya; Nikmah, Tiara Lailatul; Anisa, Devi Nurul; Kharisma, Sidiq Noor
Journal of Information System Exploration and Research Vol. 4 No. 1 (2026): January 2026
Publisher : shmpublisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joiser.v4i1.653

Abstract

Energy consumption is increasing as industry and technology advance. However, it will have a bad impact if its use is not properly controlled. Therefore, predicting energy consumption is needed to prevent energy waste and to streamline its use across several influencing factors. Predictions are made using the Random Forest Regressor method. Where regression and Random Forest techniques can produce accurate results for continuous values such as total energy consumption. The feature importance method is also used to select the most influential features. Where of the 40 features in the energy consumption dataset in Southern California, only 24 features were selected based on the average threshold of the gain value. The results showed that the use of XGBoost feature importance lowered the Mean Absolute Error (MAE) value of the Random Forest Regressor, which was 16.56 to 16.55. This value is the difference between the actual data and the predicted data. This proves that the model successfully predicts with a small error value. The application of feature importance in energy consumption prediction using Random Forest Regressor is expected to be more efficient in energy consumption, especially in the sectors that most affect the increase in energy consumption.
Co-Authors Elmo Juanara, Elmo Jumanto Unjung Kharisma, Sidiq Noor Mahrus, Zohri Nikmah, Tiara Lailatul Syafei, Risma Moulidya