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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.
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.
Implementation of Verification and Matching E-KTP with Faster R-CNN and ORB Muhammad Muttabi Hudaya; Siti Saadah; Hendy Irawan
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 4 (2021): Agustus 2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (936.392 KB) | DOI: 10.29207/resti.v5i4.3175

Abstract

needs a solid validation that has verification and matching uploaded images. To solve this problem, this paper implementing a detection model using Faster R-CNN and a matching method using ORB (Oriented FAST and Rotated BRIEF) and KNN-BFM (K-Nearest Neighbor Brute Force Matcher). The goal of the implementations is to reach both an 80% mark of accuracy and prove matching using ORB only can be a replaced OCR technique. The implementation accuracy results in the detection model reach mAP (Mean Average Precision) of 94%. But, the matching process only achieves an accuracy of 43,46%. The matching process using only image feature matching underperforms the previous OCR technique but improves processing time from 4510ms to 60m). Image matching accuracy has proven to increase by using a high-quality dan high quantity dataset, extracting features on the important area of EKTP card images.
Prediksi Harga Bitcoin Menggunakan Metode Random Forest : (Studi Kasus: Data Acak Pada Masa Pandemic Covid-19) Siti Saadah; Haifa Salsabila
Jurnal Komputer Terapan  Vol. 7 No. 1 (2021): Jurnal Komputer Terapan
Publisher : Politeknik Caltex Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (439.575 KB) | DOI: 10.35143/jkt.v7i1.4618

Abstract

During this pandemic, virtual financial transactions increased sharply. Because the storage of assets and forms of buying and selling transformed using digital services. Bitcoin as one of the cryptocurrencies that is currently widely used and in demand by the people of the world, but there is no specialized financial institution responsible for bitcoin buying and selling transactions, requires a bitcoin price prediction system to know the status of the value of bitcoin. Referring to the ever-fluctuating characteristics of bitcoin data, the Random Forest Regression method is used to predict the price of bitcoin. This algorithm is one of the modeling that can produce good performance in terms of prediction. Using Random Forest Regression modeling, MAPE value was obtained by 1.50% with accuracy of 98.50%. That value is the value that produces the best performance among all bitcoin prediction attempts.
Support Vector Regression (SVR) Dalam Memprediksi Harga Minyak Kelapa Sawit di Indonesia dan Nilai Tukar Mata Uang EUR/USD: Support Vector Machine (SVM) To Predict Crude Oil Palm in Indonesia and Exchange Rate of EUR/USD Siti Saadah; Fakhira Zahra Z; Hasna Haifa Z
Journal of Computer Science and Informatics Engineering (J-Cosine) Vol 5 No 1 (2021): June 2021
Publisher : Informatics Engineering Dept., Faculty of Engineering, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jcosine.v5i1.403

Abstract

Support Vector Machine merupakan algoritma pembelajaran mesin yang banyak digunakan untuk melakukan prediksi. Salah satunya dengan menggunakan vector kernel radial basis. Dengan karakteristik regresi pada kernel RBF maka metode ini berhasil melakukan prediksi untuk permasalahan seasoning. Mengacu kepada hal tersebut, maka pada penelitian ini akan digunakan pendekatan RBF untuk prediksi forex exchange rate atau minyak kelapa sawit. Karakteristik dua data ini jauh memiliki kesamaan, yakni cenderung ke arah trend seasonal. Mengingat pentingnya dilakukan prediksi untuk kedua studi kasus tersebut, maka kedua permasalahan ini dikaji pada penelitian ini untuk diuji menggunakan algoritma SVR. Hasil yang diperoleh menunjukkan bahwa presentase akurasi untuk exchange rate yaitu 99.97%. Sementara, akurasi pada saat memprediksi minyak kelapa sawit yaitu pada kisaran 98%.
Implementation of BERT, IndoBERT, and CNN-LSTM in Classifying Public Opinion about COVID-19 Vaccine in Indonesia Siti Saadah; Kaenova Mahendra Auditama; Ananda Affan Fattahila; Fendi Irfan Amorokhman; Annisa Aditsania; Aniq Atiqi Rohmawati
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 4 (2022): Agustus 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (557.978 KB) | DOI: 10.29207/resti.v6i4.4215

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COVID-19 was classified as a pandemic in March 2020, and then in July 2021, this virus had its variance that spreads all over the world including Indonesia. The probability of the detrimental of its effect cannot be avoided, because this virus has a huge transmission risk during daily activity. To prevent suffering from COVID-19, people certainly need to be vaccinated. In responding to its vaccine, the citizen of Indonesia become expressive, so they try to express opinions, for example by uploading text on Twitter. Those expressions can be learned using deep learning frameworks which are BERT, CNN-LSTM, and IndoBERTweet to get knowledge about negative speech categories such as anxiety, panic, and emotion, or positive speech such as vaccines whether worked well. By then, these three methods accomplish in carrying out the prediction of sentiments about vaccination using dataset tweets on Twitter from January-2021 to March-2022, for instance using IndoBERT succeeds to classify sentiments as positive sentiment at around 80%, and then IndoBERTweet at 68%, in addition using CNN-LSTM reach 53% with the total of using 2020 dataset from Twitter. According to these results, a lesson learned for continued improvement for Indonesia's Government or authorities can be acquired in ending the COVID-19 pandemic.
Analisis dan Implementasi Jaringan Syaraf Tiruan–Propagasi Balik Dalam Memprediksi Produksi dan Konsumsi Minyak Bumi, Gas Bumi, dan Batu Bara di Indonesia Anggit Nourislam; Jondri Jondri; Siti Saadah
eProceedings of Engineering Vol 1, No 1 (2014): Desember, 2014
Publisher : eProceedings of Engineering

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Abstract

ndonesia adalah salah satu negara penghasil sumber energi yang terbentuk dari fosil ataupun non fosil. Sumber energi yang tercipta dari fosil bukanlah sesuatu yang dapat dengan mudah didaur ulang yang mengakibat terjadinya krisis energi di masa mendatang. Kondisi krisis energi ini perlu diprediksi kapan terjadinya karena dapat mempengaruhi kondisi perekonomian Indonesia. Prediksi krisis energi fosil di masa mendatang dapat dilakukandengan melihat pola dari produksi dan konsumsi energi tersebut di Indonesia. Untuk mengetahui pola tersebut, dibutuhkan sebuah model yang cukup stabil terhadap perubahan karena naik turunnya produksi dan konsumsi bisa terjadi dengan cepat.Oleh sebab itu dibutuhkan algoritma jaringan syaraf tiruan yang merupakanmodel pembelajar an yang stabil terhadap perubahan pola dalam kurun waktu yang cepat. Model ini menghasilkan keluaran berupa nilai prediksi dari produksi dan konsumsi di masa mendatang yang nantinya dapat dikelompokkan apakah indeks tersebut tergolong krisis atau tidak. Kata Kunci: krisis energi, jaringan syaraf tiruan.
Prediksi Harga Dogecoin Berdasarkan Sentimen dari Twitter Menggunakan LSTM Ecky Prasetyo Nugroho; Siti Saadah; Farah Afianti
eProceedings of Engineering Vol 10, No 5 (2023): Oktober 2023
Publisher : eProceedings of Engineering

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Abstract

Abstrak— Dogecoin adalah mata uang kripto yang diciptakan oleh Billy Markus dan Jackson Palmer, tetapi mereka membuat Dogecoin hanya untuk dibuat sebagai bahan candaan di dunia mata uang kripto. Tugas akhir ini menganalisis sentimen dan prediksi terhadap Doge dengan melakukan korelasi antara harga Doge terhadap data yang dikumpulkan dari media social Twitter mengenai Doge. Penelitian ini dilakukan menggunakan pendapat-pendapat yang disampaikan oleh pengguna jejaring sosial yang menggunakan bahasa Inggris. Metode yang digunakan adalah LSTM dengan mengacu pada penelitian-penelitian sebelumnya yang menunjukkan bahwa LSTM memberikan akurasi tertinggi. Data yang digunakan pada penelitian ini adalah harga doge dan tweet pada periode januari-april 2021. Menentukan korelasi antara doge dan tweet dilakukan dengan korelasi pearson dimana hasil korelasi tersebut menentukan korelasi positif, korelasi negatif dan tidak berkorelasi, setelah itu dilakukan prediksi harga doge close dengan LSTM. Harga Doge Close berkorelasi dengan sentimen, namun tidak kuat tidak juga lemah. Tidak ada peningkatan akurasi hasil prediksi dibandingkan pengujian pertama yang dimana pada pengujian pertama nilai RMSE sebesar 0,003 dan pengujian kedua nilai RMSE sebesar 0,008.Kata kunci— analisis sentimen, LSTM, prediksi, korelasi
Forecasting of GPU Prices Using Transformer Method Risyad Faisal Hadi; Siti Saadah; Diditq Adytia
eProceedings of Engineering Vol 10, No 5 (2023): Oktober 2023
Publisher : eProceedings of Engineering

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Abstract

Abstract— GPU or VGA (graphic processing unit) is a vital component of computers and laptops, used for tasks such as rendering videos, creating game environments, and compiling large amounts of code. The price of GPU/VGA has fluctuated significantly since the start of the COVID19 pandemic in 2020. This research aims to forecast future GPU prices using deep learning-based time series forecasting using the Transformer model. We use daily prices of NVIDIA RTX 3090 Founder Edition as a test case. We use historical GPU prices to forecast 8, 16, and 30 days. Moreover, we compare the results of the Transformer model with two other models, RNN and LSTM. We found that to forecast 30 days; the Transformer model gets a higher coefficient of correlation (CC) of 0.8743, a lower root mean squared error (RMSE) value of 34.68, and a lower mean absolute percentage error (MAPE) of 0.82 compared to the RNN and LSTM model. These results suggest that the Transformer model is an effective and efficient method for predicting GPU prices.Keywords— GPU, Transformer, Forecasting, Time Series Forecasting