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All Journal Sistemasi: Jurnal Sistem Informasi INOVTEK Polbeng - Seri Informatika
Ibrahim, Ichsan
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Computer Science & IT Electrical & Electronics Engineering

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Application of Artificial Intelligence using K-Means for Programming Question Assessment Waliyyudin, Waliyyudin; Ibrahim, Ichsan
Sistemasi: Jurnal Sistem Informasi Vol 14, No 4 (2025): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v14i4.5360

Abstract

The manual assessment of programming assignments remains a significant challenge in educational settings due to its time-consuming nature and susceptibility to human error. Observational studies of course instructors reveal that over 40% have made grading mistakes, often due to fatigue or inconsistent evaluation standards. This study aims to develop an automated assessment system using artificial intelligence to enhance both objectivity and efficiency in the evaluation process. The method employed is the K-Means clustering algorithm, chosen for its ability to group answers based on similarities in logic and code structure rather than mere textual similarity. Five assessment categories were used as clustering standards: Logic and Algorithm, Data Structures, Object-Oriented Programming (OOP), Implementation, and Error Handling. The system was developed using an Agile Development approach and evaluated with student responses from programming courses. System performance was validated quantitatively by comparing cluster results against ground truth labels from manual grading. The system achieved 87% clustering accuracy, reduced the average grading time to 4.5 seconds per answer (compared to 13 seconds manually—representing a 65% efficiency gain), and decreased the inter-rater score standard deviation from 7.5 to 2.8 points. The results indicate that the system can deliver accurate real-time feedback. This study focused on programming questions ranging from easy to hard difficulty levels. In the future, the system could be enhanced by integrating advanced syntax analysis and expanding the evaluation criteria to support large-scale deployment.
Sentiment Analysis on the PT Pertamina Corruption Case using IndoBERT and RCNN Methods Kusoema, Wildan Jaya; Ibrahim, Ichsan
Sistemasi: Jurnal Sistem Informasi Vol 14, No 5 (2025): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v14i5.5392

Abstract

This study aims to evaluate the performance of a hybrid IndoBERT-RCNN model in classifying public sentiment toward the PT Pertamina corruption case, with a focus on how different hyperparameter combinations affect model accuracy. The dataset consists of 10,078 YouTube comments collected via the YouTube Data API, which were then preprocessed, automatically labeled using an Indonesian-language RoBERTa model, and balanced through class distribution techniques including undersampling and contextual embedding-based augmentation with IndoBERT. The model architecture integrates IndoBERT as a feature extractor and RCNN as the classifier, and was tested using various combinations of learning rates and batch sizes. Experimental results show that the optimal configuration was achieved with a learning rate of 2e-5 and a batch size of 16, resulting in an accuracy of 84% and an F1-score of 83%. While the model demonstrated strong performance in classifying negative comments, accuracy for neutral and positive classes was relatively lower due to semantic overlap and ambiguity in user expressions. This study contributes to Indonesian-language sentiment analysis by: 1. Integrating the IndoBERT-RCNN architecture for social-political issues, 2. Systematically evaluating hyperparameter combinations for three-class public opinion data, and 3.Utilizing YouTube comments as a relevant source of informal public discourse. The findings have potential applications in real-time digital public opinion monitoring systems for strategic national issues.
Implementation of Machine Learning in Business Intelligence for Customer Segmentation and Loyalty at PT. Inti Group Galuh Pandu Siwi Ambarsari; Ibrahim, Ichsan
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 2 (2025): July
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/5xwns554

Abstract

This study addresses the need for integrated data analytics and machine learning in PT Inti Group’s BI dashboard by implementing an unsupervised K‑Means clustering method on historical training data (January 2021–May 2025) extracted directly from a PostgreSQL database and analyzed using Python. The analysis process includes data preprocessing and feature engineering to create key variables: number of participants, training‑type frequency, recency (days since the last training), and engagement duration. Cluster determination was evaluated using the Elbow method (4 clusters), Silhouette score (2 clusters), and Davies–Bouldin index (9 clusters). Based on business interpretation and the balance between cluster compactness and separation, four clusters were selected: Loyal & High‑Value Customers, Inactive, Growing/Potential, and New/Sporadic. Customers who attended training more than ten times were classified as loyal. The segmentation results are visualized in a Power BI dashboard integrated directly with the data source, supporting rapid data‑driven managerial decisions. This study demonstrates that integrating unsupervised learning with BI effectively enhances understanding of customer characteristics and serves as a basis for designing more targeted marketing strategies. A limitation of this study is that the data cover only up to May 2025.
Co-Authors Galuh Pandu Siwi Ambarsari Kusoema, Wildan Jaya Waliyyudin, Waliyyudin