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All Journal Jupiter TELKOMNIKA (Telecommunication Computing Electronics and Control) Seminar Nasional Informatika (SEMNASIF) Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal ULTIMA InfoSys ComTech: Computer, Mathematics and Engineering Applications Indonesian Journal of Electrical Engineering and Computer Science MANDAR: Management Development and Applied Research Journal Jurnal Teknik Informatika (JUTIF) Jurnal Teknimedia: Teknologi Informasi dan Multimedia Engineering, Mathematics and Computer Science Journal (EMACS) International Journal of Electronics and Communications Systems
Tanty Oktavia, Tanty
Unknown Affiliation
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Library & Information Science Mathematics Social Sciences

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DSS Using MABAC,MOORA For Selection of Majors According to Students' Interests Sari, Ayulita Purnama; Oktavia, Tanty
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 2 (2023): Research Article, Volume 7 Issue 2 April, 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i2.12335

Abstract

In the current digital era, individual abilities are needed to be more creative and innovative in various fields, so that vocational students must better prepare their competencies. In this case the competence is related to the major they choose. On average, students take the wrong major about 35%, follow friends around 50%, for students who really choose the right major 15%. For this, the MABAC and MOORA decision support system methods are needed in terms of determining majors according to student interests and talents. System development uses the Waterfall method. The purpose of this study is to design a decision support system that can be used for selecting majors according to student interests by utilizing the results of a comparison of the MABAC and MOORA methods. The results of this study illustrate the MOORA calculation for major selection, so prospective students get the decision to choose the Multimedia major because it has the highest score. From the MABAC calculations for the selection of majors, prospective students get the decision to choose the Accounting major because it has the highest score. The comparison of the mabac and moora methods is where mabac has the highest decision outcome value compared to the decision outcome value of the moora method so that the mabac method is used to assist decision making in selecting majors according to interests.
Integrasi Business Analytics dalam Manajemen Performa Jaringan Seluler 4G Eka Kosasih; Oktavia, Tanty
JUPITER (Jurnal Penelitian Ilmu dan Teknologi Komputer) Vol 16 No 2 (2024): Jurnal Penelitian Ilmu dan Teknologi Komputer (JUPITER)
Publisher : Teknik Komputer Politeknik Negeri Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.13207799

Abstract

 Business analytics plays a crucial role in optimizing telecommunication network performance. This research focuses on using data mining methodologies, specifically the Knowledge Discovery Database (KDD) approach, combined with machine learning algorithms, to predict and enhance 4G signal quality for a mobile operator in Indonesia. The study leverages real-time data from a new site project of an Indonesian telecom operator. Employing SQL ClickHouse for data processing, Python for machine learning, and Tableau for visualization, the research develops a comprehensive data analysis model using hidden Markov models (HMM). The selection of this topic stems from the increasing demand for reliable and high-quality mobile networks, which necessitates advanced analytical techniques to monitor and improve network performance. By implementing HMM, the research aims to provide accurate predictions and actionable insights into signal quality improvements. Preliminary results demonstrate the model's effectiveness in identifying key performance indicators such as accuracy, precision, and recall, as well as trends in network signal quality. The model achieved an accuracy of 93,06%, precision of 96,97%, recall of 95,52%, and F1-score of 96,24%. This study highlights the significant potential of integrating business analytics and machine learning in the telecommunications sector, offering valuable contributions to the industry by improving service quality and operational efficiency.   Keywords—Business Analytics, Network Optimization, Hidden Markov Model, Data Mining, Signal Quality Prediction.
Design of An Intelligent Tutoring System – Student Model: Predicting Learning Style Hawari, Nubli; Oktavia, Tanty
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 6 No. 1 (2024): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v6i1.10938

Abstract

Education is very important for everyone, not only for acquiring knowledge but also for improving quality of life and well-being. An Intelligent Tutoring System (ITS) is a computer system that can provide personalized and adaptive learning assistance and support to students. This system is designed to offer effective guidance to students based on their individual abilities and learning styles. ITS utilizes artificial intelligence (AI) technology to understand students' abilities and provide guidance tailored to their needs. Recently, there have been methods to predict learning styles, such as through questionnaires on the EducationPlanner website, but these determinations are often too general. This study aimed to predict the learning styles used by specific students for specific subjects. Researchers conducted this study at XYZ University to determine the learning styles of certain students or groups. With this information, instructional materials and methods can be uniquely designed to cater to the needs of these groups. Based on the evaluation results, the study found that the Logistic Regression model was the best, with a precision of 0.5653 and a hamming loss value of 0.3468. This research demonstrates that information from six selected subjects (English, Religion, Civics, Arts, Physics, and Geography) can be used to determine students' learning styles.
Unveiling critical factors of test automation adoption in software testing Al Fath, Miftahul Kahfi; Oktavia, Tanty
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i3.pp1826-1836

Abstract

This paper aims to observe the adoption of test automation in Indonesia and examine the determining factors that influence the use of this technology in organizations. The study focuses on five critical factors: technology acceptance model, task-technology fit, managerial support (MS), individual performance, and organizational performance. A survey of 109 QA community members was conducted to collect data, and partial least squares structural equation modeling was used for data processing. Based on the study, Selenium is the top test automation framework used for organizations in Indonesia, followed by Appium and Postman. The result showed that out of twelve (12) examined relationships, nine (9) of them were accepted. This data indicates the strong influence of task technology fit (TTF), computer self-efficacy (CSE), perceived ease of use, perceived usefulness, and MS towards behavioral intention and actual use of test automation. Additionally, the actual use of test automation was found to have a positive impact on individual and organizational performance. The study contributes valuable insights for decision-makers by identifying critical factors influencing automation adoption and offers a replicable methodology for evaluating similar technologies.
ANALYSIS OF FACTORS INFLUENCING CONTINUANCE INTENTION OF DIGITAL BANK USERS IN JABODETABEK: A STUDY ON USERS OF JENIUS, NEOBANK, DIGIBANK, AND TMRW APPLICATIONS Stephani Elmanta Ratri; Tanty Oktavia
TEKNIMEDIA: Teknologi Informasi dan Multimedia Vol. 7 No. 1 (2026): June 2026
Publisher : Badan Penelitian dan Pengabdian Masyarakat (BP2M) STMIK Syaikh Zainuddin NW Anjani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46764/teknimedia.v7i1.393

Abstract

The rapid development of digital technology has accelerated the transformation of the banking industry, particularly through the emergence of digital banking applications that enable customers to conduct financial transactions more efficiently and conveniently. This study aims to analyze the factors influencing the continuance intention of digital banking users by examining the roles of perceived ease of use, perceived usefulness, customer service quality, perceived security, and perceived risk in shaping customer satisfaction and customer trust. This research employs a quantitative approach using the Partial Least Squares Structural Equation Modeling (PLS-SEM) method. Data were collected through questionnaires distributed to active users of several digital banking applications. The analysis results indicate that perceived ease of use and customer service quality play important roles in increasing customer satisfaction. In addition, perceived risk is identified as a crucial factor in shaping customer trust toward digital banking services. Furthermore, customer satisfaction is found to be a major determinant in encouraging users to continue using digital banking applications, while customer trust also contributes positively to the intention to maintain long-term usage. The structural model demonstrates strong explanatory capability in explaining users’ continuance intention. These findings highlight that improving user experience, strengthening service quality, and managing security and risk effectively are essential strategies for digital banking providers to maintain customer satisfaction, build trust, and encourage sustainable usage of digital banking services in Indonesia.
Understanding Telegram User Continuance: Extending the ECM with Trust and Perceived Risk Aryaguna, Arvin; Oktavia, Tanty
International Journal of Electronics and Communications Systems Vol. 6 No. 1 (2026): International Journal of Electronics and Communications System
Publisher : Universitas Islam Negeri Raden Intan Lampung, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/ijecs.v6i1.29979

Abstract

Sustaining user engagement has become a critical challenge for encrypted instant messaging platforms, where continued usage depends not only on functional performance but also on users’ perceptions of trust and security. This study examines the determinants of Telegram continuance intention in Indonesia by extending the Expectancy-Confirmation Model (ECM) with trust and perceived risk. A quantitative survey was conducted among 410 active Telegram users with at least three months of experience, and the proposed model was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings confirm that expectation confirmation enhances both perceived usefulness and user satisfaction, while satisfaction emerges as the strongest determinant of continuance intention. Trust also plays a pivotal role by directly strengthening satisfaction and users’ intention to continue using Telegram. Interestingly, higher trust is associated with greater awareness of potential risks, revealing a trust–risk paradox in highly encrypted communication platforms. However, perceived risk does not directly discourage continued use, suggesting that strong trust can offset the negative influence of perceived security concerns. In addition, perceived usefulness no longer directly predicts continuance intention, indicating that functional benefits have become baseline expectations on mature messaging platforms. This study extends the ECM by demonstrating that long-term user retention is primarily driven by the interplay between trust and satisfaction rather than functional usefulness alone, offering practical implications for designing trust-centered communication platforms.
Comparative Analysis of Temporal Fusion Transformer and Long Short-Term Memory Architecture Resilience in Predicting Solana Price Volatility Across Different Market Phases Putra, Mahdy Eka; Oktavia, Tanty
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.3.5894

Abstract

Abstract must be written in English. The high volatility of cryptocurrency markets, particularly for altcoins like Solana (SOL), presents a significant challenge for predictive modeling. Traditional deep learning architectures often struggle to adapt to sudden market regime shifts. Therefore, this study aims to provide a comparative analysis of the resilience between the Temporal Fusion Transformer and Long Short-Term Memory architectures in predicting Solana price volatility across three distinct market phases: the bull market of 2024, the bear market of 2025, and the recovery phase of 2026. We utilized hourly historical price and volume data combined with technical indicators such as Relative Strength Index (RSI). The models were evaluated using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and a specific performance degradation rate formula. The results demonstrate that while LSTM performs adequately during stable trends, its accuracy degrades massively by 1575.69% during high-volatility regime changes due to memory inertia causing a severe lagging effect. Conversely, the TFT model exhibited superior resilience, limiting its performance degradation to only 218.53% during the extreme bear market phase. The inherent attention mechanism and skip connections in TFT allow it to dynamically adapt to sudden structural breaks in real-time without delay. Furthermore, the implementation of the TFT architecture proved to be 62% more computationally efficient than LSTM. This research significantly contributes to the field of computer science and informatics, specifically in adaptive time-series forecasting, by proving that attention mechanisms and skip connections can efficiently solve the memory inertia problem in recurrent networks during real-time structural breaks.
Co-Authors Al Fath, Miftahul Kahfi Aryaguna, Arvin Eka Kosasih Hawari, Nubli Liang Miladi Budhaye Putra, Mahdy Eka Sari, Ayulita Purnama Stephani Elmanta Ratri Suroto Adi, Suroto Warnars, Harco Leslie Hendric Spits