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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) JUITA : Jurnal Informatika
Siti Sa'adah
Telkom University
Computer Science & IT Electrical & Electronics Engineering

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LSTM Algorithm in Predicting Chronic Kidney Disease Optimized Using Genetic Algorithm Brillyando Magathan Achmad; Siti Sa'adah; Isman Kurniawan
JUITA: Jurnal Informatika JUITA Vol. 12 No. 2, November 2024
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v12i2.22965

Abstract

Chronic Kidney Disease is a health condition in which the kidneys experience a progressive decline in function. Kidneys are vital organs that filter waste and excess blood fluids. CKD can lead to excess products in the body and cause various health issues, so early detection of CKD is necessary. While traditional machine learning techniques have performed well in predicting CKD in existing studies, this study investigates the potential of long short-term memory (LSTM) optimized with Genetic Algorithm to enhance predictive accuracy and efficiency by optimizing its hyperparameters, including number of units, hidden layers, activation function, recurrent activation, and dropout rate. The result demonstrates that the optimized LSTM slightly performs better than without optimization, achieving higher precision, recall, accuracy, and f1 score by 100% respectively. This outstanding result can be attributed to several key factors, such as ensuring rigorous data preprocessing and utilizing k-fold cross-validation to make the model more reliable. This indicates the hybrid approach can be a powerful method for the early detection of CKD, leading to better patient outcomes. Despite the promising performance, further research is suggested, specifically using a larger dataset to ensure applicability to more general population and exploring other optimization methods to reduce computational cost.
Export Commodity Price Forecasting in Indonesia Using Decision Tree, Random Forest, and Long Short-Term Memory Shadifa Auliatama Harjanto; Siti Sa'adah; Gia Septiana Wulandari
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 8 No. 4 (2022): Desember
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v8i4.25242

Abstract

Gross Domestic Product (GDP) is an indicator that becomes a benchmark for a country's economic performance. One of the factors that significantly affect GDP is export activity. However, the problem that occurs is that the export value is relatively fluctuating, this is because commodity prices are always changing every time. Therefore, we need a system that can predict commodity prices accurately. It is hoped that this system can help the government to make appropriate export policies based on predictions of commodity prices in the future. The contribution of this study is to compare Decision Tree, Random Forest, and Long Short-Term Memory (LSTM) performance in forecasting several export commodities in Indonesia. In this study, the commodities forecasted are the main commodities from each sector that dominates exports in Indonesia, namely palm oil from the manufacturing sector, coffee from the agricultural sector, and coal from the mining sector. The experiments in this study were conducted by testing several hyperparameters of each method to determine the best model. The performance of models is measured using Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). The results show that LSTM has the lowest error among Decision Tree and Random Forest with MAPE of 0.121, 0.494, and 0.282 in forecasting coal, coffee, and palm oil price respectively. Therefore, LSTM has proven to be the best method among Random Forest and Decision Tree in forecasting export commodity prices in Indonesia.
Measuring and Mitigating Bias in Bank Customers Data with XGBoost, LightGBM, and Random Forest Algorithm Berliana Shafa Wardani; Siti Sa'adah; Dade Nurjanah
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 1 (2023): March
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i1.25768

Abstract

To retain its clients, the Portuguese banking institution conducts direct marketing in the form of phone calls to conduct marketing so that clients subscribe to the bank's term deposit. The data used is named bank customers data. Important client features are considered in the acquisition process. This research was conducted with bank customers data from Portuguese banking institution which implements agent acquisition. With a large number of data on bank customers, it can lead to a diversity of data which allows the results of agent acquisition to be unfair. With this, a bias detection and mitigation algorithm are needed to achieve fairness. AI fairness 360 (AIF 360) is a toolkit that provided a bias detection and mitigation algorithm. The bias mitigation algorithm in AIF 360 is divided into three processes, namely reweighing and learning fair representation at the pre-processing stage, prejudice remover and adversarial debasing at the in-processing stage, and equalized odds and reject option classification at the post-processing stage. The output of this study is a comparison of the calculation of bias detection with disparate impact (DI) and statistical parity differences (SPD) before and after mitigation. The adversarial debiasing algorithm performed best than others with 0.943 of DI, -0.004 of SPD, and also increased the 0.015% of the AUC score. Conducting this research can help the prediction of client’s term deposits in Portuguese banking institution more fairly.
Co-Authors Berliana Shafa Wardani Brillyando Magathan Achmad Dade Nurjanah Gia Septiana Wulandari Isman Kurniawan Shadifa Auliatama Harjanto