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INOVTEK Polbeng - Seri Informatika
Published by Politeknik Negeri Bengkalis
ISSN : 25279866     EISSN : -     DOI : https://doi.org/10.35314
Core Subject : Science,
Computer Science & IT
The Journal of Innovation and Technology (INOVTEK Polbeng—Seri Informatika) is a distinguished publication hosted by the State Polytechnic of Bengkalis. Dedicated to advancing the field of informatics, this scientific research journal serves as a vital platform for academics, researchers, and practitioners to disseminate their insightful findings and theoretical developments. Scope and Focus: INOVTEK Polbeng - Seri Informatika focuses on a broad spectrum of topics within informatics, including but not limited to Web and Mobile Computing, Image Processing, Machine Learning, Artificial Intelligence (AI), Intelligent Systems, Information Systems, Databases, Decision Support Systems (DSS), IT Project Management, Geographic Information Systems, Information Technology, Computer Networks and Security, and Wireless Sensor Networks. By covering such a wide range of subjects, the journal ensures its relevance to a diverse readership interested in both the practical and theoretical aspects of informatics.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 543 Documents
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Sentiment Analysis of Indonesia's Economic Acceleration Program 2025 Using Support Vector Machine Kamelia Lestari; Ermatita
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 11 No. 3 (2026): August (Inpress)
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/r2tz5h34

Abstract

Indonesia's Economic Acceleration Program 2025 is a government policy to accelerate national economic growth in response to the global economic slowdown. The implementation of this program has generated a variety of public responses on social media. This study aims to analyze public sentiment towards the Indonesian Economic Acceleration Program in the launch and implementation phases and identify differences in sentiment distribution in both phases. Data in the form of TikTok comments was collected through web scraping and then processed through preprocessing, lexicon-based sentiment labeling validated using manually labeled samples, TF-IDF feature representation, and classification using the Support Vector Machine. Model evaluation was carried out using 10-fold cross-validation. The results of the study showed that SVM provided superior performance to the comparison model. In the launch phase, SVM achieved an accuracy of 81%, while in the implementation phase it achieved an accuracy of 79.5% with superior performance in all evaluation metrics. The distribution of sentiment in the launch phase was dominated by neutral sentiment by 67.1%, while in the implementation phase the proportion of negative sentiment increased to 46.38%. These results show that there is a difference in the distribution of sentiment between the launch and implementation phases, so they can be an input in understanding the public's response to the Indonesian Economic Acceleration Program.
Implementation Of The Naïve Bayes Method For Skincare Product Recommendations According To Skin Type At Dermakila Clinic Afilda Maharani; Supriyono; Zainur Romadhon
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 11 No. 3 (2026): August (Inpress)
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/krks4h07

Abstract

Advances in information technology have encouraged the implementation of recommendation systems in various fields, including beauty and skincare. Selecting skincare products that are not suitable for an individual's skin type and condition may reduce treatment effectiveness and potentially lead to skin problems. Dermakila Clinic offers a wide range of skincare products with diverse characteristics, creating a need for a system that can assist users in selecting products that best suit their skin needs. This study aims to implement the Naïve Bayes method in developing a skincare product recommendation system based on user characteristics, including age range, gender, skin type, and skin concerns. The research applies a data mining approach using the Naïve Bayes classification algorithm. The dataset consists of 960 skincare product records that have undergone preprocessing and data transformation stages. The system was developed as a web-based application to provide users with fast and accurate product recommendations. The experimental results demonstrate that the Naïve Bayes method achieved an accuracy of 88%, with a precision of 89%, a recall of 88%, and an F1-score of 88%. These findings indicate that the Naïve Bayes method is effective for implementing a skincare product recommendation system at Dermakila Clinic.
Random Forest and LightGBM Comparison for Acute Pain Diagnosis Using SMOTE on an Expert-Labeled Dataset Wayan Andre Pratama; I Made Gede Sunarya; Putu Hendra Suputra
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 11 No. 3 (2026): August (Inpress)
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/x2getn56

Abstract

Limited healthcare personnel may delay early pain assessment and encourage self-medication, increasing medication-error risk. However, evidence remains limited regarding whether bagging or boosting is more suitable for multiclass acute pain classification using imbalanced, expert-system-derived symptom data and whether SMOTE improves performance. This study compared Random Forest as a bagging approach and LightGBM as a boosting approach for classifying nine acute pain diagnostic classes without SMOTE and with SMOTE using k_neighbors=1 and 5. The dataset comprised 2,722 records and 36 discrete symptom features. Of 125 representative symptom combinations reviewed by a medical expert, 115 were considered appropriate; the remaining records were synthetically generated using the same expert-system knowledge base and inference mechanism. Data were divided using stratified 80:20 sampling, while model configuration was evaluated using five-fold cross-validation. SMOTE was applied only to training data within each fold. LightGBM without SMOTE achieved the best performance, with 83.49% accuracy, a macro F1-score of 0.81, and a weighted F1-score of 0.83, compared with 80.18%, 0.77, and 0.80 for Random Forest. With SMOTE, Random Forest achieved 78.35% and 77.61% accuracy, while LightGBM achieved 81.10% and 82.39%. Thus, LightGBM without SMOTE performed best for this dataset. Validation using real clinical data and multiple experts is required

Page 55 of 55 | Total Record : 543

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