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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Journal of Soft Computing Exploration Journal of Information System Exploration and Research
Nikmah, Tiara Lailatul
Unknown Affiliation
Aerospace Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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 1 
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.
Segmentasi Pelanggan Berdasarkan Tingkat Loyalitas Menggunakan K-Means dan Seleksi Fitur LRFM pada Toko Online Retail Nikmah, Tiara Lailatul; Harahap, Nur Hazimah Syani; Utami, Gina Cahya; Razzaq, Muhammad Mirza
Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Vol. 7 No. 1 (2023)
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/eltikom.v7i1.648

Abstract

Customer experience is a key component in increasing sales numbers. Customers are important assets that must be kept up for a corporation or firm. Prioritizing customer service is one way to protect client loyalty. To ensure that service priority is right on target, this research was conducted on groups of consumers who are anticipated to have high business prospects. The 2011 retail online shop sales dataset with 379,980 records and eight char-acteristics was used. The length, recency, frequency, and monetary (LRFM) feature selection approach was used in the study process to select features for further segmentation using the K-Means data mining method to define consumer types. Following the completion of the research, clients were divided into four categories: Premium Loyalty, Inertia Loyalty, Latent Loyalty, and No Loyalty. The correct clustering results are displayed in the vali-dation test using the Silhouette Score Index technique, which yielded a score value of 0.943898. Based on the outcomes of this segmentation, business actors may prioritize providing clients with the proper service.
News text classification using Long-Term Short Memory (LSTM) algorithm Triyadi, Indra; Prasetiyo, Budi; Nikmah, Tiara Lailatul
Journal of Soft Computing Exploration Vol. 4 No. 2 (2023): June 2023
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v4i2.136

Abstract

Over the past few years, the classification of texts has become increasingly important. Because knowledge is now available to users through various sources namely electronic media, digital media, print media, and many more. One of them is the development of so much news every day. LSTM is one of the algorithms of deep learning methods that can classify a text. This research proves for the LSTM algorithm on the classification of news text sentences. The data used is the news text from the Kaggle data center set i.e. aggregator news data. The results of the LSTM experiment from 10 epochs obtained with an accuracy value of 93,15% on the classification of texts into four categories, namely entertainment, bussines, science, and health.
Deep Learning Model Implementation Using Convolutional Neural Network Algorithm for Default P2P Lending Prediction Nikmah, Tiara Lailatul; Jumanto, Jumanto; Prasetiyo, Budi; Fitriani, Nina; Muslim, Much Aziz
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 3 (2023): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i3.26366

Abstract

Peer-to-peer (P2P) lending is one of the innovations in the field of fintech that offers microloan services through online channels without intermediaries. P2P  lending facilitates the lending and borrowing process between borrowers and lenders, but on the other hand, there is a threat that can harm lenders, namely default.  Defaults on  P2P  lending platforms result in significant losses for lenders and pose a threat to the overall efficiency of the peer-to-peer lending system. So it is essential to have an understanding of such risk management methods. However, designing feature extractors with very complicated information about borrowers and loan products takes a lot of work. In this study, we present a deep convolutional neural network (CNN) architecture for predicting default in P2P lending, with the goal of extracting features automatically and improving performance. CNN is a deep learning technique for classifying complex information that automatically extracts discriminative features from input data using convolutional operations. The dataset used is the Lending Club dataset from P2P lending platforms in America containing 9,578 data. The results of the model performance evaluation got an accuracy of 85.43%. This study shows reasonably decent results in predicting p2p lending based on CNN. This research is expected to contribute to the development of new methods of deep learning that are more complex and effective in predicting risks on P2P lending platforms.
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 Anisa, Devi Nurul Budi Prasetiyo, Budi Elmo Juanara, Elmo Harahap, Nur Hazimah Syani Jumanto Jumanto, Jumanto Kharisma, Sidiq Noor Mahrus, Zohri Much Aziz Muslim Nina Fitriani, Nina Razzaq, Muhammad Mirza Syafei, Risma Moulidya Triyadi, Indra Utami, Gina Cahya