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All Journal Semantik JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Sains dan Informatika JOURNAL OF APPLIED INFORMATICS AND COMPUTING JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Technomedia Journal Journal of Information System,Graphics, Hospitality and Technology Zonasi: Jurnal Sistem Informasi Jurnal Leverage, Engagement, Empowerment of Community (LeECOM) Jurnal Informatika dan Sistem Informasi Jurnal Teknik Informatika (JUTIF) Teknika Journal of Applied Data Sciences Jurnal Sistem Informasi Widya Teknik
Indra Maryati
Universitas Ciputra Surabaya
Humanities Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Mathematics Social Sciences Other

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Journal : journal of applied data sciences

 1 
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.
Implementation of the K-Nearest Neighbor Algorithm for the Classification of Student Thesis Subjects Paramita, Adi Suryaputra; Maryati, Indra; Tjahjono, Laura Mahendratta
Journal of Applied Data Sciences Vol 3, No 3: SEPTEMBER 2022
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v3i3.66

Abstract

Students who have studied for a considerable amount of time and will complete a lecture process must complete the necessary final steps. One of them is writing a thesis, a requirement for all students who wish to graduate from college. Each student's choice of topic or specialization will be enhanced if it not only corresponds to their interests but also to their skills. K-Nearest Neighbor is one of the classification techniques used. K-Nearest Neighbor (KNN) operates by determining the shortest distance between the data to be evaluated and the K-Nearest (neighbor) from the training data. K-Nearest Neighbor is utilized to classify new objects based on the learning data closest to the new object. Therefore, KNN is ideally suited for classifying data to predict student thesis topics. This research concludes that optimizing the k value using k-fold cross-validation yields an accuracy rate of 79.37% using k-fold cross-validation = 2 and the K-5 value. Based on the K-Nearest Neighbor Algorithm classification results, 45 students are interested in computational theory thesis (RPL) topics, 32 students are interested in artificial intelligence (AI) thesis topics, and 21 students are interested in software development topics.
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.
Enhancing Online Batik Shopping Experience through Live Streaming Commerce and the LYFY Application Wiradinata, Trianggoro; Wibowo, Wilbert Bryan; Oktian, Yustus Eko; Maryati, Indra; Soekamto, Yosua Setyawan
Journal of Applied Data Sciences Vol 6, No 1: JANUARY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i1.504

Abstract

Online batik shopping often results in buyer dissatisfaction due to discrepancies between product descriptions and the actual items received. Static images and text on e-marketplace platforms are insufficient to convey the intricate details of batik designs, leading to mismatches in customer expectations. To mitigate this issue, Live Streaming Commerce (LSC) features, such as those on Shopee Live, allow sellers to showcase products in real-time, providing more accurate representations. However, sellers face challenges in managing overwhelming volume of comments during live streams, making it difficult to prioritize important queries. LYFY, a comment management app developed to streamline these interactions, aims to address this problem by improving the quality of interaction between live streamers and prospective buyers through filtering important comments. This study examines the determinants affecting the adoption of LYFY by online batik vendors. The research integrates the Task-Technology Fit (TTF), Technology Acceptance Model (TAM), and Expectation-Confirmation Model (ECM) frameworks to evaluate LYFY's performance in fulfilling user requirements. Data were collected from 243 respondents with LSC experience, and the research model underwent evaluation through Partial Least Squares Structural Equation Modeling (PLS-SEM). The measurement model exhibited high reliability and validity, with values surpassing the suggested thresholds, thereby providing solid support for subsequent analysis. Key factors such as TTF, confirmation, perceived usefulness, ease of use, and satisfaction were examined to determine their impact on user adoption. The analysis revealed that TTF has the strongest influence on confirmation, perceived usefulness, satisfaction, and individual performance. Additionally, perceived ease of use and confirmation substantially influence continuance intentions and satisfaction. These results suggest that enhancing LYFY's task-technology fit and simplifying its user interface are crucial for improving user satisfaction and adoption. By addressing these areas, LYFY can better support live stream sellers, reduce product expectation discrepancies, and improve overall customer experience, particularly in the online batik market.
Interpretable Temporal Risk Modeling for Contributor Inactivity Prediction: A Comparative Study of Tree-Based Ensembles Paramita, Adi Suryaputra; Maryati, Indra; Christian, Christian; Witanto, Elizabeth Nathania; Tileubaevna, Auezova Raya; Onn, Choo Wou
Journal of Applied Data Sciences Vol 7, No 2: May 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i2.1311

Abstract

This study aims to develop an interpretable temporal risk modeling framework for predicting contributor inactivity in collaborative development environments, thereby supporting sustained participation and improving productivity. The research focuses on contributor activity data collected from a collaborative software development platform, in which participation histories are represented by temporal engagement features that capture activity recency, participation intensity, and contribution patterns over time. To model inactivity risk, several tree-based ensemble learning algorithms, including Random Forest, XGBoost, LightGBM, and a stacking ensemble, are employed and evaluated under imbalanced classification conditions. Experimental results demonstrate strong predictive performance across models, with Random Forest achieving the highest AUC of 0.9401, while XGBoost obtains the best Matthews Correlation Coefficient (0.7353). The novelty of this study lies in prioritizing structured temporal behavioral representation through normalized temporal engagement features rather than increasing model complexity, enabling more interpretable inactivity risk modeling. The findings provide practical implications for collaborative platform managers by enabling early identification of contributor disengagement, supporting sustained participation, improving productivity, and facilitating continuous product innovation.
Co-Authors . Gunawan . Gunawan . Gunawan . Gunawan Adi Suryaputra Paramita Adnyana, I Wayan Bayu Budianto, Herman Calvin Leonardo christian christian Citra, Caecilia Citra Daniel Martomanggolo Wonohadidjojo Devi Dwi Purwanto Edwin Kurniawan Elizabeth Nathania Witanto Esther Irawati S. Gunawan Gunawan Hendrawan Armanto Henry Kurniawan Wibowo Herman Budianto Indrasanti, Zefanya Oktaviana Ivana, Angeline Joan Santoso Joshua Theopilus Laura Mahendratta Tjahjono Lie, Kristian Malvin Patrick Kurniawan, Mochammad Machlul Alamin Nugroho, Elisabeth Inez Onn, Choo Wou Permana, Belinda Putri Adi Rinabi Tanamal Rossy P. C. Satriani, Putu Denisa Florence Setiawan, Eugene Abigail Sudjana, Alvin Cahya Suhatati Tjandra Tileubaevna, Auezova Raya Trianggoro Wiradinata Wibowo, Wilbert Bryan Yaurentius, Francois Verrel Yosua Setyawan Soekamto Yustus Eko Oktian