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All Journal Jurnal Infra Agora Jurnal Ekonomi & Studi Pembangunan Jurnal Psikologi Ulayat: Indonesian Journal of Indigenous Psychology JRSI (Jurnal Rekayasa Sistem dan Industri) JOURNAL OF APPLIED INFORMATICS AND COMPUTING TESLA: Jurnal Teknik Elektro Jurnal Psikologi Ulayat: Indonesian Journal of Indigenous Psychology SULTANIST: Jurnal Manajemen dan Keuangan Journal of Applied Information, Communication and Technology Journal of Applied Data Sciences International Journal of Social Service and Research Jurnal Ilmiah MEA (Manajemen, Ekonomi, dan Akuntansi) Jurnal Manajemen Sistem Informasi Nexus Synergy: A Business Perspective The International Journal of Medical Science and Health Research Jurnal Pengabdian Masyarakat Trimedika
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Program Studi Teknik Informatika
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Nursing Public Health Social Sciences Veterinary

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Journal : Journal of Applied Data Sciences

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Improving Publishing: Extracting Keywords and Clustering Topics Soekamto, Yosua Setyawan; Maryati, Indra; Christian, Christian; Kurniawan, Edwin
Journal of Applied Data Sciences Vol 5, No 2: MAY 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i2.199

Abstract

Humans, by nature, are inclined to share knowledge across various platforms, such as educational institutions, media outlets, and specialized research publications like journals and conferences. The consistent oversight and evaluation of these publications by ranking bodies serve to maintain the integrity and quality of scholarly discourse on a global scale. However, there has been a decline in the proliferation of such publications in recent times, partly attributed to ethical misconduct within specific segments of the scholarly community. Despite implementing systems such as the Open Journal System (OJS), publishers grapple with the formidable task of managing editorial and review processes. Compounding the multifaceted nature of scholarly content, manual review procedures often lead to considerable time investment. Thus, a pressing need exists for advanced technological solutions to streamline the article selection process, empowering publishers to prioritize articles for review based on topical relevance. This study advocates adopting a comprehensive framework integrating advanced text analysis techniques such as keyword extraction, topic clustering, and summarization algorithms. These tools can be implemented and integrated by connecting with the database of the existing system. By leveraging these tools with the expertise of editorial and review teams, publishers can significantly expedite the initial assessment of submitted articles. Given the rapid technological advancements, publishers must embrace robust systems that enhance efficiency and effectiveness, particularly in reviewer assignments and article prioritization. This research employs the neural network approach of BERT and K-Means clustering to perform keyword extraction and topic clustering. Furthermore, using BERT facilitates accurate semantic understanding and context-aware representation of textual data. Additionally, BERT's pre-trained models enable its fine-tuning capability to allow customization to specific domains or tasks. By harnessing the power of BERT, publishers can gain deeper insights into the content of scholarly articles, leading to more informed decision-making and improved publication outcomes.
Gold Prices Time-Series Forecasting: Comparison of Statistical Techniques Maryati, Indra; Christian, Christian; Paramita, Adi Suryaputra
Journal of Applied Data Sciences Vol 4, No 4: DECEMBER 2023
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v4i4.135

Abstract

The fluctuation of gold prices throughout the year makes it difficult for both investors and regular individuals to predict the future value. The goal of this research is to utilize various statistical techniques, such as linear regression, naive bayes, and various types of smoothing algorithms, to predict the price of gold. The data used in this study was obtained from Kaggle and is from a 70-year time period. The results showed that using a single exponential smoothing method had the highest accuracy and precision, with a good MAPE score of 7.12%. This study is unique in that it compares multiple algorithms using data over a long time period, and it can be useful for investors and traders in making decisions related to gold prices. Additionally, it can also serve as a reference for future research studies.
Co-Authors Adi Suryaputra Paramita Al-aytami, Abdul Musyfiq Amirudin, Dede Anthony Anthony Ariesa, Yeni Christian, Natalis Clara Moningka Darmasaputra, Albert Edwin Kurniawan Endah Setyaningsih Felim, Nicholas Gonardi, Jesslyn Hang Suharto I Eng, Kho Indra Maryati Ipung, Heru Purnomo Johanna, Felita Laksmi, Antin Tri Lira Panduwaty Mahardieni, Karlina Mario, Benedictus Nazma, Diani Nurseha, Samsudin Pina, Pina Purnomo, Kevin Rusdianto Roestam Rustam, Rinaldi Sembiring, Rinawati Silvana, Versia Silvia Rostianingsih Surya, Vincent Alain Trianggoro Wiradinata Wijaya, Evelyn William Onnyxiforus Purnomo, William Onnyxiforus Wiratama, Stefani Gisella Yosua Setyawan Soekamto