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All Journal International Journal of Electrical and Computer Engineering Harmonia: Journal of Research and Education JOIN (Jurnal Online Informatika) JOIV : International Journal on Informatics Visualization International Journal of Artificial Intelligence Research Knowledge Engineering and Data Science ILKOM Jurnal Ilmiah Journal of Applied Data Sciences Bulletin of Social Informatics Theory and Application
Nafalski, Andrew
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Arts Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Social Sciences

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LSTM-based Multivariate Time-Series Analysis: A Case of Journal Visitors Forecasting Saputra, Anggie Wahyu; Wibawa, Aji Prasetya; Pujianto, Utomo; Putra Utama, Agung Bella; Nafalski, Andrew
ILKOM Jurnal Ilmiah Vol 14, No 1 (2022)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v14i1.1106.57-62

Abstract

Forecasting is the process of predicting something in the future based on previous patterns. Forecasting will never be 100% accurate because the future has a problem of uncertainty. However, using the right method can make forecasting have a low error rate value to provide a good forecast for the future. This study aims to determine the effect of increasing the number of hidden layers and neurons on the performance of the long short-term memory (LSTM) forecasting method. LSTM performance measurement is done by root mean square error (RMSE) in various architectural scenarios. The LSTM algorithm is considered capable of handling long-term dependencies on its input and can predict data for a relatively long time. Based on research conducted from all models, the best results were obtained with an RMSE value of 0.699 obtained in model 1 with the number of hidden layers 2 and 64 neurons. Adding the number of hidden layers can significantly affect the RMSE results using neurons 16 and 32 in Model 1.
Generating Javanese Stopwords List using K-means Clustering Algorithm Wibawa, Aji Prasetya; Fithri, Hidayah Kariima; Zaeni, Ilham Ari Elbaith; Nafalski, Andrew
Knowledge Engineering and Data Science
Publisher : citeus

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

Abstract

Stopword removal necessary in Information Retrieval. It can remove frequently appeared and general words to reduce memory storage. The algorithm eliminates each word that is precisely the same as the word in the stopword list. However, generating the list could be time-consuming. The words in a specific language and domain must be collected and validated by specialists. This research aims to develop a new way to generate a stop word list using the K-means Clustering method. The proposed approach groups words based on their frequency. The confusion matrix calculates the difference between the findings with a valid stopword list created by a Javanese linguist. The accuracy of the proposed method is 78.28% (K=7). The result shows that the generation of Javanese stopword lists using a clustering method is reliable.
Co-Authors Aji Prasetya Wibawa Andini, Nurul Fajriah Anusua Ghosh, Anusua Atmaja, Nimas Hadi Darwis, Herdianti Denis Eka Cahyani Dewandra, Aderyan Reynaldi Fahrezza Dewi, Popy Maulida Didik Dwi Prasetya Dwiyanto, Felix Andika Erna Daniati Fithri, Hidayah Kariima Haviluddin Haviluddin Hery Widijanto Izdihar, Zahra Nabila Langlang Gumilar Marida, Tyas Agung Cahyaning Muladi Nedic, Zorica Ningtyas, Yana Nurfadila, Piska Dwi Purnawansyah Purnawansyah Putra Utama, Agung Bella Raja, Roesman Ridwan Saputra, Anggie Wahyu Saputra, Irzan Tri Triyanna Widiyaningtyas Utama, Agung Bella Putra Utomo Pujianto Wahyu Sakti Gunawan Irianto Zaeni, Ilham Ari Elbaith