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All Journal Jurnal Manajemen dan Organisasi Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Rekayasa Sistem Industri The Asian Journal of Technology Management (AJTM) JRSI (Jurnal Rekayasa Sistem dan Industri) Jurnal Teknik Industri: Jurnal Keilmuan dan Aplikasi Teknik Industri Jurnal Teknik Action Research Literate (ARL) INDONESIAN JOURNAL OF URBAN AND ENVIRONMENTAL TECHNOLOGY Logistic and Operation Management Research Journal of Research in Industrial Engineering and Management
Kadarsah Suryadi
Institut Teknologi Bandung
Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Social Sciences Other

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Analysis Of Factors Which Affect Cafe Customer Loyalty Post Covid-19 Pandemic Using Structural Equation Modeling Lutecia, Ekacandra; Suryadi , Kadarsah
Action Research Literate Vol. 8 No. 4 (2024): Action Research Literate
Publisher : Ridwan Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46799/arl.v8i4.294

Abstract

One of the MSME business sectors that is most popular and frequently visited by Indonesian people is cafes. In December 2019, the global health crisis caused by the Coronavirus disease (COVID-19) began to hit, causing the world to be caught unprepared and to disrupt business activities, one of which is the cafe sector. To encourage people to visit cafes and increase revenue, further research is needed regarding factors that can increase customer purchasing power through customer loyalty. This study took data from online respondents to see their perceptions of the factors that influence customer loyalty for cafe visitors in the Greater Jakarta area. The results of distributing this questionnaire were then processed using Structural Equation Modeling (SEM). From this study it was found that customer satisfaction has the most influence on customer loyalty, which is then followed by trust and customer engagement. It was also found that service quality has a direct effect on customer loyalty. Atmosphere & environment has an indirect effect on customer loyalty. However, brand image, employee attitude, price and product quality were found to have no effect on customer loyalty.
Analisis Sentimen Data Ulasan Pengguna MyPertamina di Twitter dengan Metode Text Mining Hutabarat, Andita Widya Valencia; Adnyani, Ni Luh Saddhwi Saraswati; Suryadi, Kadarsah
Jurnal Rekayasa Sistem Industri Vol. 13 No. 1 (2024): Jurnal Rekayasa Sistem Industri
Publisher : Universitas Katolik Parahyangan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26593/jrsi.v13i1.6958.145-154

Abstract

To ensure that the distribution process of subsidized fuel is more well-targeted, PT Pertamina has developed an application called MyPertamina. The increasing number of MyPertamina users has led to an increasing number of reviews related to the use of MyPertamina. Reviews of MyPertamina fill various social media channels, including Twitter. However, the analysis of user perceptions through social media has not been optimal. Therefore, a better user sentiment mapping is needed. This study was conducted to answer this need by building a text mining model and designing a prototype that can extract and analyze sentiments from tweets related to MyPertamina. This research adopts the CRISP-DM methodology, which consists of business understanding, data understanding, data preparation, modeling, evaluation, and deployment. The data obtained for model development reached 6,920 tweet data. Each data was classified into one of three sentiment categories, namely positive, negative, and neutral. After data preparation, 2,057 data were used for model development. The models tested in this study consist of Support Vector Machine (SVM), Multinomial Naïve Bayes, Gaussian Naïve Bayes, Long Short-Term Memory (LSTM), and Bidirectional Long Short-Term Memory (Bi-LSTM) algorithms. The model that produced the best evaluation score and was selected for prototype development is the SVM model with an accuracy score of 83.74%, weighted precision of 83.96%, weighted recall of 83.74%, and weighted F1-score of 83.72%. The prototype is used for extracting and predicting sentiment for new datasets, which can then be visualized in the form of graphs and word clouds according to the user's needs.
Leveraging Time Series Analytics for Sustainable Urban and Environmental Development: A Global SDG Trajectory Framework Gama Harta Nugraha Nur Rahayu; Kadarsah Suryadi; Titah Yudhistira; Ferani Eva Zulvia; Rohollah Ghasemi; Muhammad Rizki
INDONESIAN JOURNAL OF URBAN AND ENVIRONMENTAL TECHNOLOGY VOLUME 9, NUMBER 1, APRIL 2026
Publisher : Universitas Trisakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25105/urbanenvirotech.v9i1.24113

Abstract

Achieving the Sustainable Development Goals (SDGs), including the environmental goals (7, 12, 13, 14, and 15), requires analytical methods that capture long-term national trajectories. Existing studies have not widely used approaches that integrate similarity measurement, clustering, and forecasting. Aims: This study proposes a hybrid framework of similarity measurement, clustering, and forecasting for global SDG trajectories. By comparing cluster structures from historical SDG data with those generated using historical and forecasted trajectories, the study identifies how countries’ development patterns may shift over time. Methodology and results: The framework integrates time-series clustering and predictive modeling. Clustering utilizes both historical data from 2000 to 2025 and a combination of historical and forecasted values, employing DTW variants to measure similarities across 167 countries. K-Means, Agglomerative, and Spectral clustering algorithms are evaluated to identify the most coherent grouping. ARIMA, LSTM, GRU, and Prophet forecasting algorithms are assessed to determine the most accurate SDG score projections for 2026 to 2030. Results show that Soft DTW with K-Means produces the most coherent clusters, and ARIMA yields the lowest forecasting errors. The clustering reveals three groups representing different development pathways: strong SDG index but uneven environmental performance; strong environmental scores despite low SDG index performance; and high SDG performance with moderate environmental outcomes. These patterns highlight diverse sustainability trajectories and the multidimensional nature of global development progress. Conclusion, significance, and impact study: The study validates elastic similarity measures integrated with clustering and forecasting and provides a data-driven decision support framework to improve policy coherence and strengthen international cooperation.
Systematic Review of Sustainable Competitive Advantage Factors of SMEs in The Creative Industry Triwulandari Satitidjati Dewayana; Akbar Gunawan; Kadarsah Suryadi; Iveline Anne Marie
Logistic and Operation Management Research (LOMR) Vol. 4 No. 1 (2025): Logistic and Operation Management Research (LOMR)
Publisher : Research Synergy Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31098/lomr.v4i1.3331

Abstract

A sustainable competitive advantage refers to a combination of characteristics and capabilities that allow a business to fulfill customer needs more effectively than its competitors. It encompasses elements that enable a company to produce goods or services of superior quality or at a lower cost than others. These advantages help businesses achieve higher sales or profit margins in the market. This literature review aims to examine and identify various factors contributing to the development of sustainable competitive advantage in the creative industry, a topic that has gained significant importance and widespread attention. This research employs a systematic literature review (SLR) approach to investigate these factors in the context of creative industries. Using the SLR approach and the PRISMA framework, this research identified, evaluated, and synthesized 27 relevant articles from the Scopus and IEEE Xplore databases, published between 2014 and 2024. These articles contain results about the factors and problems that can influence sustainable competitive advantage in creative industries with relevant fields, such as social, economic, and technical. A total of 19 factors were found that influence sustainable competitive advantage.  All these factors are important, but production quality, environmental friendliness, finance, innovation, consumer behavior, and human resources are the most prominent. This meta-analysis provides valuable insights and serves as a foundation for advancing efforts to promote the implementation of competitive advantage practices.
Co-Authors Adnyani, Ni Luh Saddhwi Saraswati Andi Cakravastia Anis Fuad Bram Andryanto Erlin Trisyulianti Ferani Eva Zulvia Gama Harta Nugraha Nur Rahayu Gunawan, Akbar Hilmi Aulawi Hilmi Aulawi Hutabarat, Andita Widya Valencia Iman Sudirman Iman Sudirman Iveline Anne Marie Khuria Amila Khuria Amila Lutecia, Ekacandra Muhammad Ilham Muhammad Rizki Oktri Mohammad Firdaus Prihantoro, Budi Putri Mety Zalynda Rajesri Govindaraju Rifki Firdaus, Rifki Rohollah Ghasemi Sigit Pramudyo, Cahyono T. M. A. Ari Samadhi T.M.A. Ari Samadhi Triwulandari Satitidjati Dewayana Yudhistira, Titah