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All Journal Bulletin of Electrical Engineering and Informatics JOIV : International Journal on Informatics Visualization Asean International Journal of Business JITEEHA: Journal of Information Technology Applications in Education, Economy, Health and Agriculture
Ahmad, Sharifah Sakinah Syed
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

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Analysis of Restaurant Ordering Patterns Using Apriori Algorithm Marisa, Fitri; Badrussalam, Nanda; Ahmad, Sharifah Sakinah Syed; Vitianingsih, Anik Vega; Maukar, Anastasia L
Journal of Information Technology application in Education, Economy, Health and Agriculture Vol. 2 No. 2 (2025): June
Publisher : Lumina Infinity Academy Foundation

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

Abstract

This study implements the Apriori algorithm to analyze ordering patterns in home-based restaurants, specifically Dapur Mb Yani. Sales transaction data for three weeks shows that the Geprek Sambal Merah, Geprek Sambal Ijo, and Ayam Crispy menus are the most frequently ordered items, both individually and in combination. The combination of Geprek Sambal Merah, Ayam Crispy, and Es Teh has a high association value, making it a candidate for bundling promotions, while the strong relationship between Geprek Sambal Merah and Geprek Sambal Ijo opens up opportunities for special offers involving both menus. These results help restaurant managers design more effective promotional strategies, manage ingredient stocks efficiently, and improve customer experience. The application of the Apriori algorithm proves its relevance in supporting data-based decisions, especially for small businesses, as well as opening up opportunities for further development in the culinary industry.
Unsupervised outlier detection in high-dimensional text data: a comparative analysis Sidek, Zuleaizal; Ahmad, Sharifah Sakinah Syed; Teo, Noor Hasimah Ibrahim
Bulletin of Electrical Engineering and Informatics Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i4.9573

Abstract

Outlier detection in user reviews is a critical task for identifying anomalous and potentially valuable insights within large datasets. This study presents a comparative analysis of three different algorithms for outlier detection in user reviews: isolation forest, local outlier factor (LOF), and latent dirichlet allocation (LDA). The performance of each algorithm was evaluated using accuracy and silhouette score for outlier detection and clustering quality. LDA performed best with 0.98 accuracy and a silhouette score of 0.13. Isolation forest followed with 0.90 accuracy and a score of 0.11. LOF had lower results with 0.42 accuracy and a score of -0.05 due to its sensitivity to neighbors. The study contributes by systematically exploring the impact of parameter variations on algorithm performance, providing valuable insights for high-dimensional text data analysis. Despite the promising results, limitations include the dependence on preprocessing and specific parameter settings. Future work will explore hybrid approaches and broader datasets to enhance scalability and adaptability.
Modeling the civil servant discipline in Indonesia: partial least square-structural equation modeling approach Soelaiman, Nur Fauzi; Ahmad, Sharifah Sakinah Syed; Mohd, Othman; Al Hakim, Rosyid Ridlo; Hidayah, Hexa Apriliana
Asean International Journal of Business Vol. 1 No. 1 (2022)
Publisher : Asosiasi Dosen Peneliti Ilmu Ekonomi dan Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1471.965 KB) | DOI: 10.54099/aijb.v1i1.72

Abstract

Purpose – This paper seeks to discover the factors that influence the supervisor to give the punishment level to civil servant staff—the data being used is a questionnaire to several civil servants in public academic institutions. Methodology/approach – This research used computational tools to classify transgressions into punishment categories (light, medium, or severe) with the model using the data science technique based on the partial least square-structural equation modeling (PLS-SEM) approach. Findings – It was found that the model of civil servant discipline in Indonesia is based on 14 hypotheses from bootstrapping technique and by using data science technique to support the result analysis of PLS-SEM. Novelty/value – This research contributed to providing civil servant supervisors to understand factors that influence the discipline of their staff, so it can be used to determine the punishment categorization.
Strategic Recommendations in Increasing Gen Z User Engagement towards Gamification Elements with Fuzzy AHP and Octalysis Approaches Marisa, Fitri; Istiadi, -; Ahmad, Sharifah Sakinah Syed; Handajani, Endah Tri Esti; NoerTjahyana, Agustinus; Maukar, Anastasia L
JOIV : International Journal on Informatics Visualization Vol 9, No 6 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.6.3324

Abstract

Generation Z (Gen Z), often referred to as the "digital native" generation, interacts extensively with digital technology and social media. E-commerce companies need to adopt the right strategies, such as gamification, to increase user engagement among Gen Z. However, there is limited research evaluating which gamification elements are most effective in engaging Gen Z users. This study addresses this gap by identifying the most impactful gamification elements that enhance Gen Z user engagement and providing strategic recommendations for e-commerce designers and developers. Using the Fuzzy AHP method and Octalysis approach, this study evaluates five gamification elements: Point, Reward, Referral, Leaderboard, and Level across four key parameters: Motivation, Engagement, User Experience, and Retention. The Fuzzy AHP results indicate that the "Reward" element ranks highest with a score of 1.0, followed by "Level" with a score of 0.829. "Leaderboard" comes in third with a score of 0.669, while "Point" and "Referral" score 0.606 and 0.220, respectively. The low score of "Referral" suggests its limited effectiveness in fostering social connectedness among Gen Z users. The Octalysis analysis reveals that "Reward" has the most significant influence on core drives such as "Development and Accomplishment" and "Scarcity and Impatience," with an average score of 7.25, followed by "Level" with a score of 7.125. These findings underscore the importance of prioritizing "Reward" and "Level" to optimize user engagement for Gen Z. The practical implications of this study suggest that e-commerce platforms should integrate these gamification elements to create more engaging and interactive shopping experiences for Gen Z users, aligning with their preferences and motivations.
Co-Authors Agustinus Noertjahyana Anik Vega Vitianingsih Badrussalam, Nanda Handajani, Endah Tri Esti Hexa Apriliana Hidayah Istiadi MARIFANI FITRI ARISA Maukar, Anastasia L Mohd, Othman Nur Fauzi Soelaiman Rosyid Ridlo Al-Hakim Sidek, Zuleaizal Teo, Noor Hasimah Ibrahim