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All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Ilmu Komputer dan Informasi Bulletin of Electrical Engineering and Informatics Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL INSTEK (Informatika Sains dan Teknologi) Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Jurnal Informasi dan Komputer JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Tedc Jurnal Sistem Informasi The Indonesian Journal of Computer Science
Indra Budi
Fakultas Ilmu Komputer Universitas Indonesia
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Customer Satisfaction Evaluation in Online Food Delivery Services: A Systematic Literature Review Adimas Fiqri Ramdhansya; Shella Maria Vernanda; Indra Budi; Prabu Kresna Putra; Aris Budi Santoso
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 2 (2025): April 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i2.6205

Abstract

The rapid growth of online food delivery services has heightened the need for effective customer satisfaction measurement. This systematic literature review examines 476 papers, selecting 15 key studies to identify prevailing evaluation approaches. Findings reveal that sentiment analysis and PLS-SEM are the most frequently used analytical methods, each appearing in six studies. Satisfaction measurement relies on sentiment polarity scores in five studies and SERVQUAL frameworks in three studies. Data collection primarily involves surveys in seven studies and user-generated content in six studies, but limited demographic diversity reduces generalizability. Three key future research directions emerge. Advanced analytical techniques appear in 5 of 11 future works in the analysis methods domain. Expanding evaluation metrics is mentioned in 6 of 12 proposals in the evaluation domain. Exploring demographic context is highlighted in 10 of 25 recommendations in the dataset’s domain, with dataset development receiving twice the attention of methodological advancements. These results provide researchers with a structured framework for customer satisfaction evaluation while guiding food delivery platforms in refining service quality. By systematically mapping current methodologies and future priorities, this study bridges gaps between academia and industry, ensuring more effective customer satisfaction assessments.
DOES PERSONALIZATION MATTER IN PROMPTING? A CASE STUDY OF CLASSIFYING PAPER METADATA USING ZERO-SHOT PROMPTING Lesmana, Chandra; Muhammad Okky Ibrohim; Indra Budi
Jurnal INSTEK (Informatika Sains dan Teknologi) Vol 10 No 1 (2025): APRIL
Publisher : Department of Informatics Engineering, Faculty of Science and Technology, Universitas Islam Negeri Alauddin, Makassar, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/instek.v10i1.57445

Abstract

Systematic Literature Review (SLR) is one way for researchers to obtain information on research developments on a topic in a structured manner. This makes SLR a preferred method by researchers because the process involves systematic, objective analysis and focuses on answering research questions. In general, there are three stages to conducting SLR, namely planning, implementation, and reporting. However, compiling an SLR takes a long time because it goes through all the stages one by one. To overcome this problem, an automation process is needed so that it can speed up the SLR compilation process. Previous studies have carried out an automation process in the form of SLR document classification by utilizing several machine learning models that require a lot of training data like Naïve Bayes, Support Vector Machine, and Logistic Model Tree. In this study, the authors conducted an automation process by utilizing open-source Large Language Model (LLM) namely Mistral-7B-Instruct-v0.2 and LLaMA-3.1–8B to classify title and abstract of SLR documents. We compared the effect of using personalization on zero-shot prompting. By using LLM with zero-shot prompting, the classification process no longer requires training data, so that it does not need data annotation cost. Experiment results showed that personalization improved classification performance, getting the best results with Macro F1 0.5538 using the Llama 3.1 model.
Co-Authors Achmad Nizar Hidayanto Adimas Fiqri Ramdhansya Agus Widodo Agus Widodo Andina Septiani Ari Sudrajat Ario Santoso Ario Santoso Ario Santoso Aris Budi Santoso Aris Budi Santoso Astria Kurniawan Sumantri Cahyanti, Rhoma Chandra Lesmana, Chandra Chandra Sukma Christina Deni Rumiarti Dea Annisayanti Putri Desiana Nurul Maftuhah Dhenda Rizky Pradiptyo Dian Adiputro Dwi Retnoningrum Hafid Mukhlasin Handayani, Putu Wuri Heri Kurniawan Imbenay, Joash Lorenzo Irfanda Husni Sahid J.W. Saputro J.W. Saputro J.W. Saputro Muhammad Okky Ibrohim Mukhlasin, Hafid Novita Lestari Anggreini Prabu Kresna Putra Prabu Kresna Putra Rizal Fathoni Aji Rizal Fathoni Aji Rizal Mulyadi Rumiarti, Christina Deni Ryan Randy Suryono Shella Maria Vernanda Sri Widiastuti