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All Journal Tekno : Jurnal Teknologi Elektro dan Kejuruan Journal of Information Systems Engineering and Business Intelligence Jurnal Teknik Informatika (JUTIF)
Lazuardy Syahrul Darfiansa
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Computer Science & IT Engineering

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Journal : Jurnal Teknik Informatika (JUTIF)

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Sentiment Analysis Of Indihome Service Based On Geo Location Using The Bert Model On Platform X Siregar, Robiatul Adawiyah; Fitriyani, Fitriyani; Darfiansa, Lazuardy Syahrul
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.4993

Abstract

The rapid growth of internet usage in Indonesia has led more people to express their feelings, whether positive or negative, about online services, including IndiHome, through social media platforms such as X (formerly Twitter). This study aims to analyze public sentiment toward IndiHome services based on geographic location using the IndoBERT natural language processing model. The data consists of 3.307 Indonesian tweets that are geo-tagged and categorized into three sentiment groups: good, okay, and bad. The research process involves collecting the data, cleaning it (organizing and splitting words), and testing the IndoBERT model with a confusion matrix and classification scores. The findings reveal that negative feelings are more prevalent in most locations, especially in Java. The IndoBERT model achieved its highest accuracy of 80% in detecting negative sentiment. However, there is still room for improvement in distinguishing between positive and neutral sentiments, possibly due to data imbalance. The study shows how combining sentiment analysis with geo-location can provide strategic insights to service providers. In practical terms, these insights can help IndiHome prioritize infrastructure upgrades, improve customer support in areas with high dissatisfaction, and assist policymakers in promoting fairer digital access across regions. Beyond these practical implications, this study also contributes to the field of informatics by demonstrating the application of a transformer-based NLP model (IndoBERT) combined with geo-tagged data for regional sentiment mapping- a relatively unexplored approach in the Indonesian context. The integration of geospatial analysis with sentiment classification offers methodological advances for NLP-based service evaluation beyond business applications.
Validation of Question Classification Using Support Vector Machine and Intraclass Correlation Coefficient Based on the Revised Bloom’s Taxonomy Darfiansa, Lazuardy Syahrul; Larasti, Sza Sza Amulya
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 6 (2025): JUTIF Volume 6, Number 6, Desember 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.6.4728

Abstract

The assessment process must be carried out accurately as it is a crucial aspect of identifying cognitive abilities in students. Cognitive ability identification needs to be done by providing exam questions that refer to the Revised Bloom's Taxonomy for difficulty-level classification to ensure students' understanding of what has been taught. The traditional manual classification process carried out by educators often requires significant time and is susceptible to subjective variability. The classification of questions from levels C1 to C6 based on the Revised Bloom's Taxonomy shows an imbalance in the data distribution for each level, leading to inaccurate classification results. The automatic classification technique using the SVM algorithm allows educators to quickly classify questions based on their difficulty levels. The automated classification technique needs to be validated to what extent the difficulty levels classified by the machine align with the perceptions of educators and students. This research will validate the results of question classification generated from the SVM algorithm, supplemented by the oversampling technique to address data imbalance. The validation method used is ICC. Applying the SMOTE oversampling technique to handle a class imbalance in the training data shows improvement, with an accuracy rate of 91% when using SMOTE compared to 83% without it. Results of the classification suitability test with the SVM algorithm by educators and students indicate a high level of agreement. The ICC Average Measures values are as follows: SVM classification is 0,979, assessment by non-science subject educators is 0,956, assessment by science subject educators is 0,991, assessment by non-science subject students is 0,982, and assessment by science subject students is 0,984. ICC testing consistently yields excellent results in non-science and science subjects, indicating that the assessments conducted by educators and students have a very high level of agreement.
Co-Authors Fitriyani Fitriyani Fitriyani Larasati, Sza Sza Amulya Larasti, Sza Sza Amulya Siregar, Robiatul Adawiyah