Article Per Year (5 Year)
p-Index From 2020 - 2025
0.817
P-Index
This Author published in this journals
All Journal Seminar Nasional Teknologi Informasi Komunikasi dan Industri Malcom: Indonesian Journal of Machine Learning and Computer Science PREDATECS: Public Research Journal of Engineering, Data Technology and Computer Science
Putri, Amanda Iksanul
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mathematics

Published : 4 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 4 Documents
Search

 1 
Implementation of K-Nearest Neighbors, Naïve Bayes Classifier, Support Vector Machine and Decision Tree Algorithms for Obesity Risk Prediction Putri, Amanda Iksanul; Husna, Nur Alfa; Cia, Neha Mella; Arba, Muhammad Abdillah; Aisyi, Nasywa Rihadatul; Pramesthi, Chintya Harum; Irdayusman, Abidaharbya Salsa
Public Research Journal of Engineering, Data Technology and Computer Science Vol. 2 No. 1: PREDATECS July 2024
Publisher : Institute of Research and Publication Indonesia (IRPI).

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/predatecs.v2i1.1110

Abstract

An abnormal or excessive build-up of fat that can negatively impact one's health as a result of an imbalance in energy between calories consumed and burnt is known as obesity. The majority of ailments, such as diabetes, heart disease, cancer, osteoarthritis, chronic renal disease, stroke, hypertension, and other fatal conditions, are linked to obesity. Information technology has therefore been the subject of several studies aimed at diagnosing and treating obesity. Because there is a wealth of information on obesity, data mining techniques such as the K-Nearest Neighbors (K-NN) algorithm, Naïve Bayes Classifier, Support Vector Machine (SVM), and Decision Tree can be used to classify the data. The 2111 records and 17 characteristics of obesity data that were received from Kaggle will be used in this study. The four algorithms are to be compared in this study. In other words, using the dataset used in this study, the Decision Tree algorithm's accuracy outperforms that of the other three algorithms K-NN, Naïve Bayes, and SVM. Using the Decision Tree algorithm, the accuracy was 84.98%; the K-NN algorithm came in second with an accuracy value of 83.55%; the Naïve Bayes algorithm came in third with an accuracy rate of 77.48%; and the SVM algorithm came in last with the lowest accuracy value in this study, at 77.32%.
Implementation of Gated Recurrent Unit, Long Short-Term Memory and Derivatives for Gold Price Prediction Putri, Amanda Iksanul; Syarif, Yulia; Aisyi, Nasywa Rihadatul; Waeyusoh, Nuralisa
Public Research Journal of Engineering, Data Technology and Computer Science Vol. 2 No. 2: PREDATECS January 2025
Publisher : Institute of Research and Publication Indonesia (IRPI).

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/predatecs.v2i2.1609

Abstract

Gold is a precious metal with high resale value, often considered a safe investment as its price typically rises with inflation, attracting investors. However, even slight changes in gold prices can have significant impacts. To build an accurate forecasting model, this study applies and compares Gated Recurrent Unit (GRU), Bidirectional Gated Recurrent Unit (Bi-GRU), Long Short-Term Memory (LSTM), and Bidirectional Long Short-Term Memory (Bi-LSTM) algorithms on global gold prices. GRU and LSTM are recurrent neural networks designed to capture patterns in sequential data, where GRU uses a simplified gating mechanism to retain essential information, and LSTM, with its more complex gates, helps manage long-term dependencies in data. Bi-GRU and Bi-LSTM process data bidirectionally, capturing context from both past and future sequences for better prediction accuracy. This research uses data from Yahoo Finance (01-01-2014 to 12-06-2024) and experiments with optimization techniques (Adam, AdamW, Adamax, and Nadam), batch sizes (8, 16, and 32), time steps (10, 20, and 30), and a learning rate of 0.0001, trained for 1000 epochs with checkpoints and early stopping. Bi-GRU with Nadam, batch size 8, and 20 time steps proved most effective, with MSE of 4.1153, RMSE of 2.0286, MAE of 1.5881, and MAPE of 0.8857%. Forecasts using this model predict a 20-day decline in gold prices.
Pengelompokan Data Pelayanan Berbasis Density Based Learning Putri, Amanda Iksanul; Marsal, Arif; Muttakin, Fitriani
Seminar Nasional Teknologi Informasi Komunikasi dan Industri 2023: SNTIKI 15
Publisher : UIN Sultan Syarif Kasim Riau

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

Abstract

Pelayanan publik sebagaimana dimaksud dalam Undang-Undang RI Nomor 25 Tahun 2009 adalah setiap tindakan yang dilakukan dalam rangka memenuhi kebutuhan mendasar yang sejalan dengan hak asasi setiap warga negara dan penduduk atas produk, jasa, dan pelayanan administratif yang ditawarkan oleh penyedia layanan terkait dengan pelayanan publik. Kepuasan masyarakat yang tepat diperlukan untuk meningkatkan kualitas pelayanan. Dalam penelitian ini jawaban dari daerah yang mempunyai pola kasus yang sebanding dikelompokkan dengan menggunakan teknik clustering guna dijadikan sebagai tolak ukur standar pelayanan publik yang diberikan.  Algoritma DBSCAN diterapkan pada penelitian ini menggunakan 102 data dari masyarakat yang melakukan pelayanan di Kantor Camat Rumbai Timur untuk mendapatkan hasil klasterisasi. Dengan nilai Eps berkisar antara 3,0 hingga 4,0 dan nilai Minpts 7 dan 8, total uji coba dilakukan sebanyak 22 kali. Pada penelitian ini diperoleh satu cluster disetiap percobaan, dimana cluster tersebut mengelompokkan masyarakat puas terhadap pelayanan yang dilakukan di Kantor Camat Rumbai Timur. Hal tersebut terbukti melalui perhitungan sekala likert dimana semua variable pada data mendapatkan sekala puas dari masyarakat.
Implementasi Algoritma Decision Tree dan Support Vector Machine (SVM) untuk Prediksi Risiko Stunting pada Keluarga: Implementation of Decision Tree and Support Vector Machine (SVM) Algorithm for Stunting Risk Prediction Putri, Amanda Iksanul; Syarif, Yulia; Jayadi, Puguh; Arrazak, Fadlan; Salisah, Febi Nur
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 3 No. 2 (2023): MALCOM October 2023
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v3i2.1228

Abstract

Kondisi kekurangan gizi kronis yang disebabkan oleh asupan makanan yang tidak mencukupi sebagai akibat dari kebiasaan makan yang tidak tepat sesuai dengan gizi yang diperlukan disebut juga dengan stunting. Stunting dapat membuat fisik anak menjadi lebih pendek, selain itu dapat menghambat pertumbuhan dan perkembangan organ lain seperti ginjal, jantung, dan otak pada anak. Meningkatnya kasus stunting pada anak memerlukan upaya pencegahan secara dini. Pada penelitian ini menggunakan 18 atribut dan 5021 record data dari 10 kelurahan Kota Dumai dimana salah satu diantaranya dijadikan sebagai kelas. Pada penelitian ini menerapkan Algoritma Decision Tree dan Support Vactor Machine (SVM) untuk mengetahui algoritma mana yang tepat memproses data tersebut. Hasil prediksi dengan menggunakan Decision Tree pada penelitian ini mendapatkan nilai akurasi sebesar 96.15%, nilai recall Tidak sebesar  92.06% serta Ya sebesar 97.34% dan nilai presisi Tidak sebesar 90.99% serta Ya sebesar 97.68%. Sedangkan dengan menggunakan Algoritma SVM mendapatkan nilai akurasi sebesar 62.48%, nilai recall Tidak sebesar 99.12% serta Ya sebesar 51.80% dan nilai presisi Tidak sebesar 37.49% serta Ya sebesar 99.51%. Berdasarkan penelitian menggunakan data  tersebut dapat disimpulkan bahwa akurasai algoritma Decision Tree jauh lebih baik dibandingkan dengan algoritma SVM.
Co-Authors Aisyi, Nasywa Rihadatul Arba, Muhammad Abdillah Arif Marsal Arrazak, Fadlan Cia, Neha Mella Febi Nur Salisah, Febi Nur Husna, Nur Alfa Irdayusman, Abidaharbya Salsa Jayadi, Puguh Muttakin, Fitriani Pramesthi, Chintya Harum Syarif, Yulia Waeyusoh, Nuralisa