Articles
<![CDATA[Analysis of Random Forest, Multiple Regression, and Backpropagation Methods in Predicting Apartment Price Index in Indonesia ]]>
<![CDATA[I NYM Yoga Saputra]]>;
<![CDATA[Siti Saadah]]>;
<![CDATA[Prasti Eko Yunanto]]>
<![CDATA[ Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 7, No 2 (2021): August ]]>
Publisher : <![CDATA[Universitas Ahmad Dahlan ]]>
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DOI: 10.26555/jiteki.v7i2.20997
<![CDATA[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=â€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.]]>
<![CDATA[Crude Oil Price Forecasting Using Long Short-Term Memory ]]>
<![CDATA[Muhamad Fariz Maulana]]>;
<![CDATA[Siti Sa’adah]]>;
<![CDATA[Prasti Eko Yunanto]]>
<![CDATA[ Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 7, No 2 (2021): August ]]>
Publisher : <![CDATA[Universitas Ahmad Dahlan ]]>
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DOI: 10.26555/jiteki.v7i2.21086
<![CDATA[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.]]>
<![CDATA[Blood Glucose Prediction Using Convolutional Long Short-Term Memory Algorithms ]]>
<![CDATA[Redy Indrawan]]>;
<![CDATA[Siti Saadah]]>;
<![CDATA[Prasti Eko Yunanto]]>
<![CDATA[ Khazanah Informatika Vol. 7 No. 2 October 2021 ]]>
Publisher : <![CDATA[Department of Informatics, Universitas Muhammadiyah Surakarta, Indonesia ]]>
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DOI: 10.23917/khif.v7i2.14629
<![CDATA[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.]]>
<![CDATA[Column-Level Database Encryption Using Rijndael Algorithm and Dynamic Key on Learning Management System ]]>
<![CDATA[Ariva Syam Mursalat]]>;
<![CDATA[Ari Moesriami Barmawi]]>;
<![CDATA[Prasti Eko Yunanto]]>
<![CDATA[ Indonesia Journal on Computing (Indo-JC) Vol. 7 No. 1 (2022): April, 2022 ]]>
Publisher : <![CDATA[School of Computing, Telkom University ]]>
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DOI: 10.34818/INDOJC.2022.7.1.609
<![CDATA[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.]]>
<![CDATA[Gold price prediction using Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) ]]>
<![CDATA[I Wayan Krisna Gita Santika]]>;
<![CDATA[Siti Sa'adah]]>;
<![CDATA[Prasti Eko Yunanto]]>
<![CDATA[ Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vo. 6, No. 3, August 2021 ]]>
Publisher : <![CDATA[Universitas Muhammadiyah Malang ]]>
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DOI: 10.22219/kinetik.v6i3.1253
<![CDATA[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.]]>
<![CDATA[Bimodal Keystroke Dynamics-Based Authentication for Mobile Application Using Anagram ]]>
<![CDATA[Prasti Eko Yunanto]]>;
<![CDATA[Ari Moesriami Barmawi]]>
<![CDATA[ Jurnal Ilmu Komputer dan Informasi Vol 15, No 2 (2022): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information ]]>
Publisher : <![CDATA[Faculty of Computer Science - Universitas Indonesia ]]>
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DOI: 10.21609/jiki.v15i2.1015
<![CDATA[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]]>
<![CDATA[Automatic Feature Reduction Framework For Identification Process In Palm Vein Recognition ]]>
<![CDATA[Prasti Eko Yunanto]]>;
<![CDATA[Hertog Nugroho]]>;
<![CDATA[Tjokorda Agung Budi Wirayuda]]>
<![CDATA[ eProceedings of Engineering Vol 2, No 2 (2015): Agustus, 2015 ]]>
Publisher : <![CDATA[eProceedings of Engineering ]]>
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<![CDATA[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.]]>
<![CDATA[Pengenalan Bentuk Tangan Dengan Ekstraksi Ciri Pyramid Histogram Of Oriented Gradient (phog) Dan Klasifikasi Support Vector Machine (Svm) ]]>
<![CDATA[Riski Novanda]]>;
<![CDATA[Kurniawan Nur Ramadhani]]>;
<![CDATA[Prasti Eko Yunanto]]>
<![CDATA[ eProceedings of Engineering Vol 7, No 2 (2020): Agustus 2020 ]]>
Publisher : <![CDATA[eProceedings of Engineering ]]>
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<![CDATA[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 Klasiï¬kasi 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.]]>
<![CDATA[Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) Methods to Forecast Daily Turnover at BM Motor Ngawi ]]>
<![CDATA[Larasati Larasati]]>;
<![CDATA[Siti Saadah]]>;
<![CDATA[Prasti Eko Yunanto]]>
<![CDATA[ Indonesian Journal of Artificial Intelligence and Data Mining Vol 7, No 1 (2024): March 2024 ]]>
Publisher : <![CDATA[Universitas Islam Negeri Sultan Syarif Kasim Riau ]]>
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DOI: 10.24014/ijaidm.v7i1.27643
<![CDATA[The number of motorcycles on the report of Indonesian BPS statistics from the Indonesian State Police between 2019 to 2021 by its type has increased annually. Routine motorcycle checks, services, and maintenance are essential to keep a motorcycle in good condition and more durable; therefore, buying spare parts is enlarged in line with the growth of public motorcycle ownership. The necessity of buying spare parts increases with the growth of public motorcycle ownership. Numerous stores in Ngawi offer motorcycle spare parts and check services for routine motorcycle maintenance. One of these stores is BM Motor. To develop an effective product-selling strategy, it is essential to forecast the daily turnover of the shop. To achieve this, the present research aims to analyze the daily turnover using Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM). These methods were applied to a time-series dataset, allowing for an in-depth examination of the patterns and trends in the shop's turnover. The research compares several hyperparameter tunings and scenarios to optimize the models that forecast daily turnover data at the store. The outcomes presented that the LSTM model achieved a lesser MAE score of 0.087, while the RNN model scored 0.092. These findings proved that the LSTM model achieved lower MAE than the RNN model, it means LSTM is more accurate than the RNN model.]]>
<![CDATA[Deteksi Risiko Kredit dalam Peer-to-Peer Lending Menggunakan CatBoost ]]>
<![CDATA[Fadhlurrahman Akbar Nasution]]>;
<![CDATA[Siti Saadah]]>;
<![CDATA[Prasti Eko Yunanto]]>
<![CDATA[ Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 5 (2023): October 2023 ]]>
Publisher : <![CDATA[Ikatan Ahli Informatika Indonesia (IAII) ]]>
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DOI: 10.29207/resti.v7i5.5139
<![CDATA[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.]]>
