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All Journal Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer JIKO (Jurnal Informatika dan Komputer) JOURNAL OF APPLIED INFORMATICS AND COMPUTING KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vocatech : Vocational Education and Technology Journal TECHSI - Jurnal Teknik Informatika Lok Seva: Journal of Contemporary Community Service Journal Of Artificial Intelligence And Software Engineering Jurnal Pengabdian Sosial
Hamdhana, Defry
Universitas Malikussaleh
Religion Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Law, Crime, Criminology & Criminal Justice Mechanical Engineering Public Health Social Sciences Other

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Journal : JOURNAL OF APPLIED INFORMATICS AND COMPUTING

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Few-Shot Learning for Classifying Genuine and Bot Comments on YouTube Using Transformer Models Fikriah Nst, Nahdah; Hamdhana, Defry; Qamal, Mukti
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.10023

Abstract

This study aims to develop a comment classification system on the YouTube platform to distinguish between real accounts and bot accounts, addressing the challenge of limited labeled data through a few-shot learning approach. The issue of bot accounts masquerading as real users in comment sections is becoming increasingly prevalent and has the potential to spread spam, misinformation, and influence public opinion. In this study, a Transformer-based model, DistilBERT, is used, which is known for its efficiency in understanding natural language context. The model is trained in a few-shot scenario (N5 to N50) using a very limited amount of training data. Testing results show that the model maintains high and stable performance even with minimal data (N5), achieving an F1-score above 0.90. In addition, this system is implemented into a web application using Flask to enable direct and interactive comment detection. The main contribution of this research is the proof that the combination of few-shot learning and the DistilBERT model can provide a practical and efficient solution for classifying YouTube bot account comments even with limited data conditions, as well as providing a replicable approach for similar problems on other digital platforms.
Implementation of Ant Colony Optimization (ACO) Algorithm for Route Optimization of Tourist Paths in Takengon Suryana, Fitra; Nurdin, Nurdin; Hamdhana, Defry
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.9706

Abstract

This study aims to design and implement a system for determining the shortest route between tourist destinations in Takengon using the Ant Colony Optimization (ACO) algorithm. The system is developed to assist travelers in obtaining efficient visitation routes based on distance and travel time. Experiments were conducted on 20 tourist locations, resulting in an optimized route with a total travel distance of 40.40 km and an estimated travel time of 81 minutes. The computation process took only 0.024001 seconds with a memory usage of 20.23 KB. The ACO algorithm was executed using 10 ants with key parameters set to alpha (α) = 1, beta (β) = 2, and rho (ρ) = 0.5. ACO demonstrated high effectiveness in exploring route combinations and iteratively generating near-optimal solutions. The chosen parameters were determined through experimentation to balance solution quality and convergence speed. In addition to generating the optimal visitation sequence, the system also provides complete turn-by-turn navigation instructions, including major roads such as Jalan Lintas Tengah Sumatera and Jalan Lebe Kader. The actual estimated travel route based on the generated navigation covers a distance of 97.4 km with a travel duration of approximately 2 hours and 42 minutes. The results indicate that ACO is an effective and efficient approach for solving medium- to large-scale tourist route optimization problems. The developed system can serve as a practical tool in the tourism sector and has the potential to be adapted and implemented in other tourist regions with similar routing challenges.
Classification Analysis of Single Tuition Fees Using the Random Forest Method with K-Fold Cross Validation Khaidar, Al; Nurdin, Nurdin; Fajriana, Fajriana; Taufiq, Taufiq; Hamdhana, Defry
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.11798

Abstract

Classification is the process of grouping data into specific categories based on their characteristics or features, which plays a crucial role in the analysis, decision-making, and prediction of new data. In academic settings, classification is used to determine the Single Tuition Fee to place students according to their economic ability. Lhokseumawe State Polytechnic has implemented the UKT system since 2020 with eight categories, but some students are still placed in UKT groups that do not match the results of the manual process, which has limited accuracy. This study uses the Random Forest method as a technology-based solution to improve the accuracy and objectivity of UKT classification. The dataset used consists of 10,000 student data with 10 variables, covering economic and social information. The research process includes data preprocessing, Random Forest model training, performance evaluation using accuracy, precision, recall, and F1-score, and model stability testing through 10-fold K-Fold Cross Validation. The results show that Random Forest is able to classify most UKT classes well, especially classes 0–5 and 7. Class 6 has lower performance with a recall of 0.39 and an F1-score of 0.56 due to the limited number of samples. The overall accuracy of the model reaches 96%, while K-Fold Cross Validation produces an average accuracy of 95.50% with a standard deviation of 0.66%, indicating the model is stable and able to generalize to new data. This study proves that Random Forest is effective in UKT classification, producing an objective, fair, and efficient system. This implementation model supports data-driven decision-making in higher education and increases transparency in UKT determination.
Co-Authors Arliansyah, Arliansyah Dahlan Abdullah Fadhilah Fadhilah Fadlisyah Fadlisyah Fajriana, Fajriana Fanny Nailufar Fikriah Nst, Nahdah Fitri Maghfirah Ismail, Baharuddin Kembaren, Emmia Tambarta Khaidar, Al Kusworo, Zulfikar Aji Mardiaton, Mardiaton Martin Martin Martin Martin Maulidawati, Maulidawati Mauliza Mauliza Mauliza, Mauliza Muhammad Fikry Muhammad Iqbal Muhammad Johan Setiawan Mukti Qamal Mukti Qamal Nur Afni Yunita Nurdin Nurdin Pangestu, Aridho Putri, Rany Gesta Rio Pratomo Rizal Rizal Rizal Rizal Rizki Suwanda Safira Adani Sari, Cut Putri Mellita Sayed Fachrurrazi Siregar, Widyana Verawaty Sri Mulyati Suryana, Fitra Taufiq Taufiq Utomo, Muhammad Fikri Yusra, Muhammad