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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Ilmu Komputer dan Informasi Bulletin of Electrical Engineering and Informatics Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JOURNAL OF APPLIED INFORMATICS AND COMPUTING JURNAL INSTEK (Informatika Sains dan Teknologi) Jiko (Jurnal Informatika dan komputer) Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Jurnal Informasi dan Komputer JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Media Jurnal Informatika Jurnal Impresi Indonesia Jurnal Nasional Teknik Elektro dan Teknologi Informasi Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Tedc Eduvest - Journal of Universal Studies 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.
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.
KLASIFIKASI TINGKAT PEMROSESAN MAKANAN BERBASIS TEKS KOMPOSISI MENGGUNAKAN STRATEGI WEIGHTED ENSEMBLE LARGE LANGUAGE MODELS Khairul Hudha Nasution; Indra Budi
Jurnal INSTEK (Informatika Sains dan Teknologi) Vol 11 No 1 (2026): 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.v11i1.64305

Abstract

Klasifikasi tingkat pemrosesan makanan merupakan langkah krusial dalam mitigasi risiko kesehatan global akibat konsumsi makanan ultra-proses. Meskipun label komposisi tersedia pada kemasan, penulisan yang kecil, tidak terstruktur, typo serta terminologi kimia yang kompleks memiliki kecenderungan menyulitkan penilaian manual oleh konsumen. Pemanfaatan Large Language Models (LLM) menawarkan potensi efisiensi deteksi otomatis, namun mengandalkan satu arsitektur model tunggal memiliki risiko tinggi akibat variabilitas performa. Penelitian ini bertujuan untuk mengevaluasi efektivitas strategi Weighted Ensemble Learning dibandingkan model tunggal dalam memprediksi skor pemrosesan makanan (FPro) pada dataset GroceryDB. Eksperimen dilakukan menggunakan lima arsitektur LLM dengan skala parameter kecil hingga sedang (Gemma-3-4B, Llama-3.2-3B, Qwen3-4B, R1-Distill-1.5B, dan Phi-2) melalui pendekatan Weighted Voting berbasis kinerja historis. Hasil evaluasi menunjukkan adanya disparitas ekstrem pada kinerja model tunggal, di mana model berkapasitas rendah (Phi-2) mengalami kegagalan penalaran dengan F1-score hanya 10%, sementara model dengan kemampuan instruksi tinggi (Gemma) mencapai 68%. Penerapan strategi Ensemble berhasil memitigasi kelemahan model individual melalui mekanisme koreksi silang, meningkatkan akurasi F1-score menjadi 70% dan menghasilkan prediksi yang lebih stabil serta robust dibandingkan jika hanya mengandalkan satu model terbaik sekalipun.
SENTIMENT AND TREND ANALYSIS OF PUBLIC OPINION ON BANK BTN MORTGAGE SERVICES VIA INSTAGRAM Putri Dian Zara; Veny Anggraini; Indra Budi; Aris Budi Santoso; Prabu Kresna Putra
Jurnal INSTEK (Informatika Sains dan Teknologi) Vol 11 No 1 (2026): 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.v11i1.64333

Abstract

Bank BTN is the leading provider of mortgage (KPR) services in Indonesia, yet customer complaints on social media remain frequent. According to the OJK 2023 Annual Report, the banking sector recorded 10,848 consumer complaints, an increase from 7,425 complaints in 2022. This trend highlights the need for a systematic analysis of public opinion on KPR services. This study analyzes public sentiment expressed on Bank BTN’s official Instagram account during 2023. User comments were classified into positive and negative sentiments using text mining techniques. Four machine learning algorithms were compared: K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree (DT), and Naive Bayes (NB). A total of 1,507 comments were obtained after data cleaning, preprocessing, and filtering from 28,745 raw comments. SMOTE was applied to address the class imbalance. Decision Tree achieved the highest F1-score (0.9348) on the imbalanced test set, followed by SVM (0.9259). Trend analysis was conducted using Pearson, Spearman, and Kendall correlation tests. The correlation between daily sentiment and KPR disbursement was weak and not statistically significant (Pearson: 0.082, p = 0.187; Spearman: 0.099, p = 0.112; Kendall: 0.077, p = 0.102; N = 259). These results indicate that sentiment fluctuations on Instagram cannot be used as a direct predictor of same-day mortgage disbursement. There may be a time lag, a non-linear relationship structure, or the influence of more dominant fundamental factors.
SENTIMENT ANALYSIS OF PUBLIC HEALTH APP REVIEWS USING INDOBERT AND XLM-ROBERTA: A STUDY ON SATUSEHAT MOBILE APP Dimas Ananda; Indra Budi; Aris Budi Santoso; Ali Adil Qureshi
JIKO (Jurnal Informatika dan Komputer) Vol 8 No 3 (2025)
Publisher : Program Studi Teknik Informatika Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v8i3.10083

Abstract

Sentiment analysis is a key method for deriving insights from user-generated content, particularly in evaluating public satisfaction with digital health services. This study conducts a comparative analysis of sentiment polarity classification models on 34,178 Indonesian-language reviews from SATUSEHAT Mobile, a national health application by the Indonesian Ministry of Health. The dataset was manually annotated into positive, neutral, and negative classes. Three model categories were evaluated: classical machine learning (Support Vector Machine, XGBoost), baseline neural networks (Multilayer Perceptron, Convolutional Neural Network), and pretrained transformer-based models (IndoBERT, XLM-RoBERTa). All models were trained using stratified 5-fold cross-validation and tested on a held-out set. Results show that transformer-based models significantly outperform others in all metrics. IndoBERT achieved the highest weighted F1-score (0.8555), followed closely by XLM-RoBERTa (0.8552). Despite the similar average performance, XLM-RoBERTa exhibited the lowest performance variance across folds, making it the most stable and effective model overall. Statistical validation using Friedman and Nemenyi tests confirmed these differences as significant. However, all models struggled with neutral sentiment detection due to data imbalance. Although computationally more expensive than IndoBERT, XLM-RoBERTa offers superior robustness for sentiment classification in Indonesian health-related text. These findings support the integration of transformer-based sentiment monitoring into public health dashboards to enable timely, data-driven service improvements
Comparative Analysis of Black-Box and White-Box Machine Learning Model in Explainable Phishing Detection Abdullah Fajar; Setiadi Yazid; Indra Budi
Media Jurnal Informatika Vol 18 No 1 (2026): Media Jurnal Informatika
Publisher : Universitas Suryakancana Cianjur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35194/mji.v18i1.6501

Abstract

Explainability in phishing detection model can support a further solution of phishing attack mitigation by increasing trust and understanding how phishing can be detected.  The aims of this study to determine and best recommendation to apply an approach which has several components with abilities to fulfil the critical needs A methodology starting with analyzing both black-box and white-box models to get the pros and cons specifically in phishing detection. The conclusion of the analysis will be validated by experiment using a set of well-known algorithms and public phishing datasets. Experimental metrics covers 3 measurements such as predictive accuracy and explainability metrics. Both models are comparable in terms of interpretability and consistency, with room for improvement in diverse datasets. EBM as an example of white-box model is generally better suited for applications requiring explainability and actionable insights. Finally, each model, white-box and black-box model has positive and negative aspects both for performance metric and for explainable metric. It is important to consider the objective of model usage.
The Role of Social Media in Shaping Social Movements: A Case Study of #Daruratreformasi In Indonesia Using Text Mining and Network Analytics Alifdaffa Nurfahmi Dekatama; Devin Prayogo; Indra Budi; Prabu Kresna Putra; Aris Budi Santoso
Eduvest - Journal of Universal Studies Vol. 5 No. 7 (2025): Eduvest - Journal of Universal Studies
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/eduvest.v5i7.51376

Abstract

The social movement #DaruratReformasi emerging in Indonesia since July 2024 has attracted widespread attention both nationally and internationally. This study aims to analyze the communication dynamics and interaction patterns within the social media network of the movement using text mining and network analytics approaches. Topic modeling identifies the dominant key issues in public discourse, while social network analysis reveals the main actors and influencers involved in information dissemination and public opinion formation. A modularity approach is employed to detect naturally formed discussion communities within the network, and temporal analysis illustrates the phases of the movement’s development from initiation to its peak in November 2024. The results indicate that social media serves as a strategic platform for social mobilization and political advocacy, with key actors distributed across interconnected communities. Additionally, the involvement of government institutions as central actors highlights the two-way communication dynamics within the digital public sphere. These findings underscore the urgency of understanding social network structures in the context of modern digital social movements and provide implications for public communication management and mass mobilization strategies in the digital era.
Klasifikasi Stance dan Pemodelan Topik Komentar YouTube Terhadap Narasi Risiko Penyakit Menular di Indonesia Yudha Dwika Sandya; Indra Budi
Jurnal Impresi Indonesia Vol. 5 No. 1 (2026): Jurnal Impresi Indonesia
Publisher : Riviera Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58344/jii.v5i1.7421

Abstract

asca berakhirnya status darurat kesehatan global COVID-19, komunikasi risiko tetap penting karena potensi kemunculan varian baru dan penyakit menular lain masih terus terjadi. Namun efektivitas komunikasi risiko menghadapi tantangan akibat kelelahan pandemi dan infodemi, sehingga diperlukan pemetaan sikap yang terukur untuk penyesuaian strategi komunikasi di masa pasca pandemi. Penelitian ini memetakan respons publik terhadap narasi risiko penyakit menular COVID-19 dan human metapneumovirus (HMPV) pada komentar YouTube melalui stance detection dan pemodelan topik LDA. Data dikumpulkan dari komentar video bertopik COVID-19/HMPV pada kanal media resmi. Dataset berisi 19.172 komentar dan 9.586 sampel (50%) dianotasi. Penelitian menerapkan klasifikasi dua tahap, yaitu klasifikasi relevansi, diikuti klasifikasi stance pada komentar relevan. Eksperimen membandingkan IndoBERT dan IndoBERTweet dengan Stratified 5-Fold Cross Validation pada skenario tanpa oversampling dan Random Oversampling (ROS). Hasil menunjukkan IndoBERTweet memberikan performa terbaik dengan skor F1-macro 0.8541 pada klasifikasi relevansi dan 0.8474 pada klasifikasi stance. Hasil LDA menunjukkan bahwa respons masyarakat pada COVID-19 dan HMPV memperlihatkan pola serupa yang didominasi penolakan. Studi ini menunjukkan analisis stance dan pemodelan topik dengan LDA pada komentar YouTube dapat mendukung formulasi strategi komunikasi risiko, serta mengindikasikan IndoBERTweet cenderung sesuai dengan  karakteristik teks komentar YouTube yang cenderung informal dan bervariasi.
Unraveling Insights from User Reviews of Sapawarga – Jabar Super Apps Through Topic Modeling Basrah Nasution; Indra Budi; Aris Budi Santoso; Prabu Kresna Putra
Journal of Applied Informatics and Computing Vol. 10 No. 4 (2026): August 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i4.12624

Abstract

This study conducted topic modeling on user reviews of the Sapawarga – Jabar Super App application on the Google Play Store. The dataset comprised user reviews submitted since the date application released until January 7, 2026 yielded 5.082 user reviews. Collected data processed through a series of data processing pipeline namely case folding, remove punctuation, normalization, tokenization, stop word removal and stemming. Topic modeling was conducted using Latent Dirichlet Allocation (LDA) algorithm grouped based on year of data. Topic distribution was visualized using the PyLDAvis to facilitate analysis and interpretation. This study found there are 9 topic after analysing year by year data namely Appreciation of the usefulness of the application in helping residents (1), Technical issues in terms of how to log in to the application (2), Application improvement proposal (3), Ease of information and citizen empowerment (4), General expressions of appreciation and criticism from citizens towards the application (5), Technical issues with payment features (6), Application error problem (7), User appreciation for the ease of vehicle tax payments from the application (8) and Criticism of application constraints in vehicle tax payments (9). The interpretation results indicate that the application is widely used by residents for online tax payments, particularly vehicle tax services, compared to other features or uses of the application. The findings of this study are expected to serve as a valuable insight for relevant stakeholders to improve the quality, performance, and overall effectiveness of the application as a public service platform.
Co-Authors Abdullah Fajar Achmad Nizar Hidayanto Adi Hanif Sedar Adila Alfa Krisnadhi Adimas Fiqri Ramdhansya Agus Widodo Agus Widodo Ali Adil Qureshi Alifdaffa Nurfahmi Dekatama Amanah Ramadiah Andina Septiani Ari Sudrajat Ario Santoso Ario Santoso Ario Santoso Aris Budi Santoso Aris Budi Santoso Astria Kurniawan Sumantri Basrah Nasution Cahyanti, Rhoma Chandra Lesmana Chandra Sukma Christina Deni Rumiarti Dea Annisayanti Putri Desiana Nurul Maftuhah Devin Prayogo Dhenda Rizky Pradiptyo Dian Adiputro Dimas Ananda Dwi Retnoningrum Hafid Mukhlasin Handayani, Putu Wuri Heri Kurniawan Imbenay, Joash Lorenzo Irfanda Husni Sahid J.W. Saputro J.W. Saputro J.W. Saputro Khairul Hudha Nasution Lukman Yudokusumo Mohammad Yani Muhammad Okky Ibrohim Mukhlasin, Hafid Novita Lestari Anggreini Prabu Kresna Putra Prabu Kresna Putra Prabu Kresna Putra Prabu Kresna Putra Putri Dian Zara Rizal Fathoni Aji Rizal Fathoni Aji Rizal Mulyadi Rumiarti, Christina Deni Ryan Randy Suryono Setiadi Yazid, Setiadi Shella Maria Vernanda Sri Widiastuti Veny Anggraini Yudha Dwika Sandya