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All Journal Jurnal Ilmu Komputer dan Informasi Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika Indonesian Journal on Computing (Indo-JC) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Indonesian Journal of Artificial Intelligence and Data Mining Building of Informatics, Technology and Science eProceedings of Engineering Telkatika: Jurnal Telekomunikasi Elektro Komputasi & Informatika
Prasti Eko Yunanto
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science

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Analysis of Random Forest, Multiple Regression, and Backpropagation Methods in Predicting Apartment Price Index in Indonesia I NYM Yoga Saputra; Siti Saadah; Prasti Eko Yunanto
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 7, No 2 (2021): August
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v7i2.20997

Abstract

This study focuses on predicting the apartment price index in Indonesia using property survey data from Bank Indonesia. In the era of the Covid-19 pandemic, accurately predicting the sale and purchase price of apartments is essential to minimize the impact of losses, thus making apartment prices attractive to predict. The machine learning approach used to predict the apartment price index are the Random Forest method, the Multiple Regression method, and the Backpropagation method. This study aims to determine which method is more effective in predicting small amounts of data accuracy. The data used is apartment price index data from 2012 to 2019 in the JABODEBEK area. The research will produce prediction accuracy that will determine the effectiveness of the application of the method. The Random Forest method with parameters n_estimators=100 and max_features=”log2” produces an R2 accuracy of 0.977. The Multiple Regression method with a correlation between the selling price and rental price variables is 0.746, and the rental inflation variable is 0.042 produces an R2 accuracy of 0.559. The Backpropagation method with a 1000-4000-1 hidden scheme and 20000 iterations produces an R2 accuracy of 0.996. Therefore, the Backpropagation method is more suitable in this study compared to the other two methods. The Backpropagation method is suitable because it gets almost perfect accuracy, so this method will minimize losses in investing in buying and selling apartments in the Covid-19 pandemic era.
Crude Oil Price Forecasting Using Long Short-Term Memory Muhamad Fariz Maulana; Siti Sa’adah; Prasti Eko Yunanto
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 7, No 2 (2021): August
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v7i2.21086

Abstract

Crude oil has an important role in the financial indicators of global markets and economies. The price of crude oil influences the income of a country, both directly and indirectly. This includes affecting the prices of basic needs, transportation, commodities, and many more. Therefore, understanding the future price of crude oil is essential in helping to budgeting and planning for a better economy. The contribution of this research is in finding the best hyperparameters and using early stopping methods in the LSTM model to predict oil prices. This research implemented Long Short-Term Memory (LSTM), an artificial neural network that can handle long-term dependencies and the problems of time series data. The LSTM method will be used to predict Brent oil prices on daily and weekly time frames. The experiment has been conducted by tuning some parameters to obtain the best result. From the daily time frame experiment, the model obtained RMSE and MAE of 1.27055 and 0.92827, respectively, while the weekly time frame has RMSE and MAE of 3.37817 and 2.60603, respectively. The results show that the LSTM model can improve to the trends that occur in the original data.
Blood Glucose Prediction Using Convolutional Long Short-Term Memory Algorithms Redy Indrawan; Siti Saadah; Prasti Eko Yunanto
Khazanah Informatika Vol. 7 No. 2 October 2021
Publisher : Department of Informatics, Universitas Muhammadiyah Surakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v7i2.14629

Abstract

Diabetes Mellitus is one of the preeminent causes of death to date. Effective procedures are necessary to prevent diabetes and avoid complications that may cause early death. A common approach is to control patient blood glucose, which necessitates a periodic measurement of blood glucose concentration. This study developed a blood glucose prediction system using a convolutional long short-term memory (Conv-LSTM) algorithm. Conv-LSTM is a variation of LSTM algorithms that are suitable for use in time series problems. Conv-LSTM overcomes the lack in the LSTM algorithm because the latter algorithm cannot access the content of previous memory cells when its output gate has closed. We tested the algorithm and varied the experiment to check the effect of the cross-validation ratio between 70:30 and 80:20. The study indicates that the cross-validation using a ratio of 70:30 data split is more stable compared to one with 80:20 data split. The best result shows a measure of 21.44 in RMSE and 8.73 in MAE. With the application of conv-LSTM using correct parameters and selected data split, our experiment attains accuracy comparable to the regular LSTM.
Column-Level Database Encryption Using Rijndael Algorithm and Dynamic Key on Learning Management System Ariva Syam Mursalat; Ari Moesriami Barmawi; Prasti Eko Yunanto
Indonesia Journal on Computing (Indo-JC) Vol. 7 No. 1 (2022): April, 2022
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/INDOJC.2022.7.1.609

Abstract

The course management system’s goal is to help learning activities. The system helps tomanage tasks, the grading process, and user communications. To avoid unauthorized dataaccess, the course management system needs a mechanism to protect the password that isused in the system’s login process. Database encryption using Rijndael algorithm is proposedby Francis Onodueze et al. to protect the data. A key is needed for the encryption process,and the key has to be kept secret. Thus, when the key is static, it is vulnerable against keyguessing attacks. To overcome the static key’s drawback, a dynamic key generation usingHash Messages Authentication Code - Deterministic Random Bit Generator (HMAC-DRBG)is proposed because it can generate keys periodically. Based on the evaluation, the probabilityof success key guessing attack using the proposed method is less than using the previousmethod, while the time complexity of those methods is similar.
Gold price prediction using Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) I Wayan Krisna Gita Santika; Siti Sa'adah; Prasti Eko Yunanto
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vo. 6, No. 3, August 2021
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v6i3.1253

Abstract

Gold has an important role in worldwide economics. Gold is not only used in jewelry but also can be a good deal for investment however several factors can affect the fluctuation in gold which can make the risk of investing in gold is bigger for many people. Therefore, is very important to predict the gold price for people who invest in gold in order to help reduce the investment risk. This study will implement a hybrid method from Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). CNN can extract useful knowledge and learn the internal representation of time-series data, and LSTM networks will identify short-term and long-term dependencies effectively. This research will use daily time frame data and weekly time frame data. This research also tried some experiments to find the best hyperparameters of batch size and epochs in ratio data 60:40 and 80:20. The best result obtained in the daily time of ratio data 60:40 with RMSE 13.67953 and MAE 9,40998, while in ratio data 80:20 has RMSE 15,53199 and MAE 12,78120. In weekly time has obtained the RMSE 38,01949 and MAE 28,32035 for ratio data 60:40 while in ratio data 80:20 the result was RMSE 32,61283 and MAE 22,74638. Those results shows that CNN-LSTM model can predict the trend of daily time frame gold price.
Bimodal Keystroke Dynamics-Based Authentication for Mobile Application Using Anagram Prasti Eko Yunanto; Ari Moesriami Barmawi
Jurnal Ilmu Komputer dan Informasi Vol. 15 No. 2 (2022): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Informatio
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21609/jiki.v15i2.1015

Abstract

Currently, most of the smartphones recognize uses based on static biometrics, such as face and fingerprint. However, those traits were vulnerable against spoofing attack. For overcoming this problem, dynamic biometrics like the keystroke and gaze are introduced since it is more resistant against spoofing attack. This research focuses on keystroke dynamics for strengthening the user recognition system against spoofing attacks. For recognizing a user, the user keystrokes feature used in the login process is compared with keystroke features stored in the keystroke features database. For evaluating the accuracy of the proposed system, words generated based on the Indonesian anagram are used. Furthermore, for conducting the experiment, 34 participants were asked to type a set of words using the smartphone keyboard. Then, each user’s keystroke is recorded. The keystroke dynamic feature consists of latency and digraph which are extracted from the record. According to the experiment result, the error of the proposed method is decreased by 23.075% of EER with FAR and FRR are decreased by 16.381% and 10.41% respectively, compared with Kim’s method. It means that the proposed method is successful increase the biometrics performance by reducing the error rates
Automatic Feature Reduction Framework For Identification Process In Palm Vein Recognition Prasti Eko Yunanto; Hertog Nugroho; Tjokorda Agung Budi Wirayuda
eProceedings of Engineering Vol 2, No 2 (2015): Agustus, 2015
Publisher : eProceedings of Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Abstract Feature or dimensionality reduction has become one of fundamental problem in the field of pattern recogni- tion such as biometrics. The choosing number of fea- ture or dimension has become one challenge. Instead choosing number of feature manually, in this paper, we proposed an automatic feature reduction by using a cas- caded feature reduction schemes based on variance or- der of the DCT feature space and eigenvalue of k-PCA in the palm vein recognition. Based on experiment re- sults, our proposed scheme can achieve recognition rate above 0.92 accuracy which uses fewer features and can reduce time process significantly until 99.5% comparing with traditional manual feature reduction method.
Pengenalan Bentuk Tangan Dengan Ekstraksi Ciri Pyramid Histogram Of Oriented Gradient (phog) Dan Klasifikasi Support Vector Machine (Svm) Riski Novanda; Kurniawan Nur Ramadhani; Prasti Eko Yunanto
eProceedings of Engineering Vol 7, No 2 (2020): Agustus 2020
Publisher : eProceedings of Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Abstrak Terdapat banyak kegunaan yang dapat dilakukan oleh Gesture atau bentuk tangan, salah satunya ialah sebagaialatkomunikasiyangkemudiankitakenaldenganBahasaIsyarat. DalamBahasaIsyaratterdapat banyak bentuk tangan yang mewakili suatu arti seperti angka, huruf, kata, dan lain sebagainya. Dengan tujuanmempermudahpengenalanbentuktangan, dilakukanpengembangansistemyangdapatmengenali artidaribentuktangansebagaibahasaisyarat. Sistemyangdikembangkanmenggunakanmetodeekstraksi ciri Pyramid Histogram of Oriented Gradient (PHOG) dan Klasifikasi Support Vector Machine (SVM). Datasetyangdigunakanpadasistemberupa3800gambardanterdiridari6label/class,kemudiandataset akandigunakansebagaibahanTrainingdanTestingpadasistemsehinggasistemdapatmengenaliartidari tiap gambar yang menjadi masukan. Pengukuran kinerja sistem menggunakan F1 Score dengan akurasi sebesar86%Katakunci: bentuktangan,HandFormRecognition,ComputerVision,PHOG,SVM,bahasaisyarat.
Implementation of Hyperparameters to the Ensemble Learning Method for Lung Cancer Classification Ridlo Yanuar; Siti Sa’adah; Prasti Eko Yunanto
Building of Informatics, Technology and Science (BITS) Vol 5 No 2 (2023): September 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i2.4096

Abstract

Lung cancer is the most common cause of death in someone who has cancer. This happens because of remembering the importance of lung function as a breathing apparatus and oxygen distribution throughout the body. Early identification of lung cancer is crucial to reduce its mortality rate. Accuracy is crucial since it indicates how accurately the model or system makes the right predictions. High levels of accuracy show that the model can produce trustworthy and accurate findings, essential for making effective decisions based on available data. In this research, ensemble learning approaches, namely bagging and boosting methods, were employed for classifying lung cancer. Hyperparameters, a class of parameters, are crucial to this model's effectiveness. In order to increase the lung cancer classification model's accuracy, a thorough investigation was conducted to identify the best hyperparameter combination. In this study, the dataset used is a medical dataset that contains a history of patients who have been diagnosed with lung cancer or not. The dataset is taken from Kaggle mysarahmadbhat and cancerdatahp from data world. To evaluate the model's accuracy, this study used the confusion matrix method which compares the model's prediction results with the ground truth. the study findings revealed that employing a dataset split ratio of 70:30 produced the best results, with the Random Forest, CatBoost, and XGBoost models achieving an impressive 98% accuracy, 0.98 precision, 0.98 recall, and 0.98 f1-score. but for AdaBoost, the best results were obtained on a dataset with a ratio of 80:20 with an accuracy of 96%, 0.97 precision, 0.96 recall, and 0.96 f1-score
Deteksi Risiko Kredit dalam Peer-to-Peer Lending Menggunakan CatBoost Fadhlurrahman Akbar Nasution; Siti Saadah; Prasti Eko Yunanto
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 5 (2023): October 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i5.5139

Abstract

P2P lending (Peer-to-peer lending) is widely used by private borrowers, small businesses, and MSMEs because P2P lending allows individuals and businesses to be able to lend money directly from lenders without the stringent requirements and criteria of traditional banks and financial institutions. However, P2P lending has a credit risk problem characterized by a high failure rate for borrowers to repay their loans. Therefore, a system was necessary to detect credit risk to minimize the risk of P2P lending. In this study, a system had been built using the CatBoost method; the dataset used was taken from the Bondora loan dataset. To measure the performance of the CatBoost algorithm, an evaluation matrix was performed using ROC (Receiver Operating Characteristics) curves and AUC (Area Under Curve) was performed. The experiment consists of three scenarios, of which the best result regards Scenario 2 with a data splitting of 90:10. It was caused by the result of AUC value 0.80329 compared to scenario 1 with a data split of 80:20 with the AUC value around 0.789583, and scenario 3 with a data split of 70:30 with the AUC value around 0.781066, respectively.
Co-Authors Alhafidz, Bagas Millen Ari Moesriami Barmawi Ariva Syam Mursalat Azzam, Muhammad Abdullah Ema Rachmawati Fadhlurrahman Akbar Nasution Faturahman, Farhan Febryanti Sthevanie Hertog Nugroho Hutomo, Ardityo Cahyo Putro Hutomo I NYM Yoga Saputra I Wayan Krisna Gita Santika Kurniawan Nur Ramadhani Larasati Larasati Mahmud Dwi Sulistiyo Maulana, Didit Johar Muhamad Fariz Maulana Muhammad Rahman Ali Redy Indrawan Ridlo Yanuar Rifki Wijaya Riski Novanda Siti Sa'adah Siti Saadah Siti Saadah Siti Sa’adah Siti Sa’adah Tjokorda Agung Budi Wirayuda