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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) Jurnal Nasional Teknik Elektro dan Teknologi Informasi Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Tedc Jurnal Sistem Informasi The Indonesian Journal of Computer Science Scientific Journal of Informatics
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.
A Systematic Review and Bibliometric Study of Climate Change Sentiment Analysis: Trends and Approaches Kusumawati, Karisma Vinda Nissa; Indra Budi; Amanah Ramadiah; Aris Budi Santoso; Prabu Kresna Putra
Scientific Journal of Informatics Vol. 12 No. 4: November 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i4.34947

Abstract

Purpose: This study aims to map research trends in sentiment analysis on the climate change topic from the beginning of 2020 to the middle of 2025 by utilizing a Systematic Literature Review (SLR) method, along with bibliometric analysis. Climate change represents a worldwide challenge that profoundly affects both the environment and human social interactions, making it essential to comprehend public perceptions of this issue thoroughly. The escalating use of social media is driving an increase in research related to sentiment analysis, which is utilized to gain insights into public opinions and emotions. Methods: Data was collected from six leading databases such as Scopus, ScienceDirect, Taylor and Francis, IEEE Xplore, Sage Journals, and ProQuest, resulting in 3,326 articles. After a screening process using the PRISMA 2020 framework, 42 articles were selected for further analysis.   Result: The findings suggest that Twitter is the predominant platform for climate change sentiment analysis, referenced in 32 articles, while Sina Weibo is mentioned in nine articles, Reddit in two articles, and both Facebook and YouTube in one article each. Of the four approaches assessed, the leading approaches identified in this research are Machine Learning and Deep Learning. In the Machine Learning category, Naïve Bayes is the predominant approach, appearing in 18 articles, followed by Naïve Bayes, cited in 17 articles. Furthermore, Logistic Regression and Random Forest are each mentioned in 13 articles. In the field of Deep Learning methodologies, 10 articles used Convolutional Neural Networks (CNNs), nine articles featured Bi-LSTMs, six articles featured LSTMs, and 13 articles referenced Transformer-based models, particularly BERT. Furthermore, model validation primarily used cross-validation techniques, and the most referenced evaluation metrics were accuracy, recall, and F1-score in 33 articles and precision in 32 articles.   Novelty: The novelty of this research lies in the time of information collection for research on climate change sentiment analysis, spanning 2020 to the middle of 2025. The latest research on a related issue was conducted from 2008 to 2022. Furthermore, this study provides insights into research trends and includes the distribution of articles by country, separating them into Single-Country Publications (SCPs) and Multi-Country Publications (MCPs). This research also presents information on social media platforms, classification approaches, and commonly employed validation and evaluation tools, which differentiate it from prior studies. This analysis is conducted on six leading databases, producing valuable findings for researchers and policymakers.
Entity dan Relation Linking untuk Knowledge Graph Question Answering Menggunakan Pencarian Berjenjang Adila Alfa Krisnadhi; Mohammad Yani; Indra Budi
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 13 No 2: Mei 2024
Publisher : This journal is published by the Department of Electrical and Information Engineering, Faculty of Engineering, Universitas Gadjah Mada.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jnteti.v13i2.9184

Abstract

Knowledge graph question answering (KGQA) systems have an important role in retrieving data from a knowledge graph (KG). With the system, regular users can access data from a KG without the need to construct a formal SPARQL query. KGQA systems receive a natural language question (NLQ) and translate it into a SPARQL query through three main tasks, namely, entity and relation detection, entity and relation linking, and query construction. However, the translation is not trivial due to lexical gaps and entity ambiguity that may occur during entity or relation linking. This research proposed an approach based on multiclass classification of NLQ whose entity occurrences are detected into categories based on KG relations to address the lexical gap challenge. Next, to solve the entity ambiguity challenge, this research proposed a three-stage searching procedure to determine appropriate KG entities associated with the NLQ entities, given the correspondence between the NLQ and a particular KG relation. This three-stage searching consisted of text-based searching, vector-based searching, and entity and relation pairing. The proposed approach was evaluated on the SimpleQuestions and LC-QuAD 2.0 datasets. The experiments demonstrated that the proposed approach outperformed the state-of-the-art baseline. For the relation linking task, the proposed approach reached 89.87% and 74.83% recall for the SimpleQuestions and LC-QuAD 2.0, respectively. This approach also achieved 91.74% and 61.96% recall on the entity linking tasks for the SimpleQuestions and LC-QuAD 2.0, respectively.
Co-Authors Achmad Nizar Hidayanto Adila Alfa Krisnadhi Adimas Fiqri Ramdhansya Agus Widodo Agus Widodo Amanah Ramadiah 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 Kusumawati, Karisma Vinda Nissa Mohammad Yani 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