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All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Ilmu Komputer dan Informasi Bulletin of Electrical Engineering and Informatics Sistemasi: Jurnal Sistem Informasi 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 Keperawatan Widya Gantari Indonesia 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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Klasifikasi Ticket Service Desk Perusahaan Asuransi Jiwa Berbasis Machine Learning Imbenay, Joash Lorenzo; Indra Budi
The Indonesian Journal of Computer Science Vol. 13 No. 4 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i4.4142

Abstract

This study focuses on developing a ticket classification model for the Service Desk at an insurance company to enhance operational efficiency. Manual ticket classification is time-consuming and prone to errors, so the research aims to compare the performance of various classification algorithms to determine the best model. The methodology involves text mining and machine learning techniques using four main algorithms: Random Forest, Decision Tree, Support Vector Machine (SVM), and Naïve Bayes. The data comes from Service Desk tickets processed through text preprocessing stages. Findings indicate that the Random Forest model with a combination of TF-IDF Unigram features in the Access context performs best in classifying IT Support tickets, with a Precision of 0.76%, Recall of 0.66%, F-Score of 0.70%, and Accuracy of 0.54%. Implementing this model is expected to improve operational efficiency and user satisfaction with IT services, speeding up ticket handling, reducing administrative workload, and enhancing user satisfaction with IT services.
Twitter Sentiment Analysis Towards Candidates of the 2024 Indonesian Presidential Election Cahyanti, Rhoma; Desiana Nurul Maftuhah; Aris Budi Santoso; Indra Budi
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 4 (2024): August 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i4.5839

Abstract

Long before the elections were held, the topic related to elections was widely discussed on news portals and social media, including Twitter. A few studies related to the Indonesian election have tried to predict candidates who will run for the presidential election, but there has been no research that examines public sentiment on social media towards each of the potential candidates. The main objective of this study is to analyze the public sentiment in Twitter towards potential candidates for the 2024 Indonesian presidential election. This research seeks to fill the gaps in previous research and become a reference for further research regarding sentiment analysis for election prediction using Twitter. The presidential candidates used in the research are the top 3 candidates based on the Poltracking survey, namely Ganjar Pranowo, Prabowo Subianto, and Anies Baswedan. The data were taken from January until October 2022, more than a year before the general election began. To predict the sentiment, four different machine-learning methods were used and compared to each other. There are Naïve Bayes, Support Vector Machines, Random Forests, and Neural Networks. Based on the sentiment results of each candidate, the highest sentiment towards Prabowo is neutral (55.49%), the highest sentiment towards Ganjar is positive (61.34%), and the highest sentiment towards Anies is neutral (44.84%). Results from the study also show that Anies was the presidential candidate who received more negative sentiment than the other two (56.63%). Meanwhile, Ganjar Pranowo got the most positive sentiment of all (42,69%). For the neutral sentiment, Anies Baswedan also got the most results (39,87%), followed by Prabowo (38.99%) and Ganjar Pranowo (21.14%). The result of the study also discovered that random forest and neural networks have the best performance for sentiment analysis.
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 Abdul Gofur Achmad Nizar Hidayanto Adimas Fiqri Ramdhansya Agus Widodo Agus Widodo Andina Septiani Ani Widiastuti 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 Elly Nurachmah 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 Rita Sekarsari Rizal Fathoni Aji Rizal Fathoni Aji Rizal Mulyadi Rumiarti, Christina Deni Ryan Randy Suryono Shella Maria Vernanda Sri Widiastuti Yandi Dwi Permana