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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Informatics and Communication Technology (IJ-ICT) Jurnal Mantik Insyst : Journal of Intelligent System and Computation
Samuel Indra Gunawan Situmeang
Institut Teknologi Del
Computer Science & IT Economics, Econometrics & Finance Engineering Languange, Linguistic, Communication & Media

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Survey on Ditenun Application Utilization Through Independent Learning – Independent Campus Program (Merdeka Belajar – Kampus Merdeka) Humasak Tommy Argo Simanjuntak; Arlinta Christy Barus; Samuel Indra Gunawan Situmeang; Arie Satia Dharma
Jurnal Mantik Vol. 5 No. 4 (2022): February: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

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Abstract

The policy of Independent Learning - Independent Campus (Merdeka Belajar - Kampus Merdeka: MBKM) by the Ministry of Education, Culture, Research, and Technology provides opportunities for students to gain real work experience in an industrial or professional environment to prepare students in social, cultural, work and technological changes. DiTenun (Digital Tenun Nusantara) responds to this challenge by organizing an independent learning program to accelerate student work readiness while increasing the competitiveness of DiTenun’s industry and products. This study aims to evaluate the successful implementation of MBKM in the development of the DiTenun application. The implementation was analyzed from the perspective of students and application users. This study used a survey research method and a saturated sampling technique. Hypothesis testing showed that the implementation of MBKM program positively affects the development of DiTenun application.
Impact of Text Preprocessing on Named Entity Recognition Based on Conditional Random Field in Indonesian Text Samuel Situmeang
Jurnal Mantik Vol. 6 No. 1 (2022): May: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

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Abstract

The text preprocessing stage within a natural language processing application framework helps eliminate parts that are not helpful in the text analysis process or particular noise. Despite having a potential impact on the final performance of the application, text preprocessing has not received attention in the text analysis application literature, especially in the named entity recognition application in Indonesian texts. This paper aims to comprehensively examine the impact of text preprocessing in the Indonesian named entity recognition based on a baseline model, namely Conditional Random Field, to find the fittest preprocessing procedures for a NER model compelling performance. Various forms of text preprocessing contribute to the successful recognition of named entities assessed comparatively across three categories: people, places, and organizations. Experimental analysis of the data set reveals that several combinations of preprocessing text forms are useful. Rather than enabling or disabling them all, several combinations can significantly improve the accuracy of Indonesian named entity recognition depending on the entity category.
A cluster and association analysis visualization using Moodle activity log data Andri Reimondo Tamba; Krista Lumbantoruan; Aulia Pakpahan; Samuel Situmeang
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 12, No 2: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v12i2.pp150-161

Abstract

The course activity log is where a learning management system (LMS) like Moodle keeps track of the various learning activities. In order to conduct a quicker and more in-depth examination of the students' behaviors, the instructor may either directly examine the log or make use of more complex methodologies such as data mining. The majority of the proposed methods for analyzing this log data center mostly on predictive analysis. In this research, cluster analysis and association analysis, two separate data mining functions, are investigated in order to analyze the log. The students' activities are used in the cluster analysis performed with K-Means++, and the association analysis performed with Apriori is used to investigate the connections between the students' various activities. A dashboard presentation of the findings is provided in order to facilitate clearer comprehension. Based on the findings of the analysis, it can be concluded that the structure of the student cluster is medium, whereas the association between the activities undertaken by students is positively correlated and well-balanced. The subjective review of the dashboard reveals that the visualization is already sufficient, but there are some recommendations for making it even better.
Indonesian automated short-answer grading using transformers-based semantic similarity Situmeang, Samuel; Tambunan, Sarah Rosdiana; Ginting, Lidia; Simamora, Wahyu Krisdangolyanti; ButarButar, Winda Sari
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 14, No 3: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v14i3.pp1034-1043

Abstract

Automatic short answer grading (ASAG) systems offer a promising solution for improving the efficiency of reading literacy assessments. While promising, current Indonesian artificial intelligence (AI) grading systems still have room for improvement, especially when dealing with different domains. This study explores the effectiveness of large language models, specifically bidirectional encoder representations from transformers (BERT) variants, in conjunction with traditional hand-engineered features, to improve ASAG accuracy. We conducted experiments using various BERT models, hand-engineered features, text pre-processing techniques, and dimensionality reduction. Our findings show that BERT models consistently outperform traditional methods like term frequency-inverse document frequency (TF-IDF). IndoBERTLite-Base-P2 achieved the highest quadratic weighted kappa (QWK) score among the BERT variants. Integrating handengineered features with BERT resulted in a substantial enhancement of the QWK score. Utilizing comprehensive text pre-processing is a critical factor in achieving optimal performance. In addition, dimensionality reduction should be carefully used because it potentially removes semantic information.
Automated Data Extraction from Aircraft Fuel Invoices Using PaddleOCR Hutapea, Reinaldy; Harwanto, Vanessa; Situmeang, Samuel
Intelligent System and Computation Vol 7 No 1 (2025): INSYST: Journal of Intelligent System and Computation
Publisher : Institut Sains dan Teknologi Terpadu Surabaya (d/h Sekolah Tinggi Teknik Surabaya)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52985/insyst.v7i1.427

Abstract

This study presents an automated data extraction system for aircraft fuel invoice documents using PaddleOCR, a deep learning-based optical character recognition (OCR) technology. The system is designed to address the challenges of extracting information from complex and unstructured document formats, which traditionally require extensive manual processing. To enhance performance, the system incorporates image pre-processing techniques and artificial intelligence-based validation methods, ensuring higher accuracy in recognizing aviation-specific details such as flight identifiers and fuel data. Evaluation of the system demonstrates notable improvements in both time efficiency and accuracy. On average, documents can be processed in under 60 seconds with high recognition rates for clean, standard-quality inputs. While performance decreases with noisy or small-text documents, results indicate that accuracy can be further improved through deep learning-based denoising and training with aviation-specific datasets. The system also proves scalable, successfully handling up to 640 documents without compromising performance, suggesting its feasibility for large-scale industrial deployment. Beyond technical efficiency, the system delivers tangible economic benefits by reducing operational costs, minimizing transaction discrepancies, and enabling staff to focus on higher-value strategic tasks. Furthermore, it establishes a foundation for future enhancements, including integration with ERP systems, multilingual OCR support, and handwriting recognition. Overall, this research highlights the potential of PaddleOCR-based automation to significantly transform document management in the aviation industry and offers promising opportunities for adoption across other data-intensive sectors.
Transformer and text augmentation for tourism aspect-based sentiment analysis Situmeang, Samuel; Tambunan, Sarah Rosdiana; Jevania, Jevania; Simanjuntak, Mastawila Febryanti; Sinaga, Sandro
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp4614-4622

Abstract

The 36.98% growth in the quantity of electronic word of mouth (e-WOM) over the past five years presents opportunities for the tourism industry to understand tourists' needs and desires better when analyzed effectively. Aspect-based sentiment analysis (ABSA) is proposed as a solution, as it can identify the sentiment at a more detailed aspect level. Prior research revealed two issues in ABSA: imbalanced datasets and poor performance in representing implicit aspects and opinions. The authors proposed a combination of the bidirectional and auto-regressive transformer (BART) and bidirectional encoder representations from transformers (BERT) models. Leveraging BART capability in modeling context and BERT expertise in modeling text semantics and nuances, the author proposed an ABSA model that combines BART and BERT using the ensemble method. The experimental results reveal that combining these models significantly enhances the performance of the ABSA model, with an F1-score reaching 70%. Furthermore, text augmentation and preprocessing did not bring improvements in ABSA performance.
Co-Authors Andri Reimondo Tamba Arie Satia Dharma Arlinta Christy Barus Aulia Pakpahan ButarButar, Winda Sari Ginting, Lidia Harwanto, Vanessa Humasak T. A. Simanjuntak Hutapea, Reinaldy Jevania, Jevania Krista Lumbantoruan Sarah Rosdiana Tambunan Simamora, Wahyu Krisdangolyanti Simanjuntak, Mastawila Febryanti Sinaga, Sandro