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All Journal Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Compiler Jurnal Pengabdian Informatika (JUPITA)
Ardiansyah, Ricy
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Aerospace Engineering Humanities Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Other

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PELATIHAN PENGENALAN DAMPAK POSITIF DAN NEGATIF DALAM PENGGUNAAN ARTIFICIAL INTELLIGENCE PADA BIDANG PENDIDIKAN Wala, Jihan; Nahdli, Muhammad Fahmi Mubarok; Ardiansyah, Ricy; Umar, Rusydi; Yuliansyah, Herman
Jurnal Pengabdian Informatika Vol. 2 No. 4 (2024): JUPITA Volume 2 Nomor 4, Agustus 2024
Publisher : Jurusan Informatika, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Udayana

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Artificial Intelligence (AI) merupakan kecerdasan yang ditunjukan dengan suatu objek buatan. AI memiliki potensi untuk mengubah pendidikan dengan mempersonalisasi pengalaman belajar, menyediakan bimbingan belajar yang cerdas, mengintegrasikan teknologi yang mendalam, dan mengotomatiskan pembuatan konten. Dampak positif AI mencakup peningkatan personalisasi pembelajaran, penghematan waktu bagi tenaga pendidik, serta peningkatan aksesibilitas dan kualitas pendidikan. Dampak negatif penggunaan AI yaitu kurangnya sentuhan manusia, risiko ketergantungan pada teknologi, mengurangi kemampuan berpikir kritis dan pemecahan masalah secara mandiri. Oleh karena itu, diperlukan pelatihan yang bertujuan untuk mengedukasi siswa SMK 2 Al-Hikmah 1 Sirampog, Brebes, Jawa Tengah, tentang dampak penggunaan AI dalam pendidikan. Peningkatan pengetahuan siswa diukur melalui pre-test dan post-test. Kegiatan ini mencakup serangkaian sesi yang dirancang untuk memberikan pemahaman mendalam kepada peserta mengenai pengaruh teknologi AI melalui berbagai aktivitas interaktif, diskusi, dan presentasi dengan total peserta sebanyak 30 siswa. Hasil dari pengabdian ini menghasilkan peningkatan pada kategori pengetahuan "Sangat Paham" meningkat dari 50% menjadi 80%.
Multi-Label Classification for Opinion Mining in The Presidential Election using TF-IDF with NB And SVM Ardiansyah, Ricy; Yuliansyah, Herman; Yudhana, Anton
Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Vol. 9 No. 1 (2025)
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/eltikom.v9i1.1432

Abstract

Public opinion plays a crucial role in presidential elections, shaping voter choices and influencing outcomes. Most sentiment analysis studies focus on binary (positive vs. negative) or multiclass (positive, negative, neutral) classification, which limits their ability to capture opinions that express multiple sentiments simultaneously. In presidential elections, a single opinion may support one candidate while criticizing another. This study proposes a MultiLabelBinarizer model to classify candidate and sentiment labels simultaneously—an approach that remains underexplored. The model combines Naïve Bayes (NB) and Support Vector Machine (SVM) for opinion mining using public data and TF-IDF for feature extraction, applying Multinomial and Linear kernels. Performance is evaluated using Accuracy, Precision, Recall, and F1-score. The study is conducted in two stages: developing a multi-label analysis model for presidential candidates and testing the effectiveness of cross-validation. Results show that multi-label classification is effective for both candidate and sentiment categories. Cross-validation with NB and SVM yields high accuracy. NB achieves 0.89 for candidate labels and 0.86 for sentiment labels. SVM performs better, with 0.93 for candidate labels and 0.94 for sentiment labels. While SVM provides higher accuracy, NB offers faster implementation with still competitive results.
Multi-Label Opinion Mining Based on Random Forest with SMOTE and ADASYN Ardiansyah, Ricy; Yuliansyah, Herman; Yudhana, Anton
Compiler Vol 14, No 2 (2025)
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/compiler.v14i2.3185

Abstract

Multi-label classification is essential to categorize data into multiple labels simultaneously. However, data imbalance poses a challenge, where some labels have much less representation, thus reducing the model performance. This study aims to propose a candidate-based sentiment analysis model on the 2024 Jakarta Presidential and Gubernatorial Election review. The SMOTE and ADASYN oversampling methods are applied to handle class imbalance. Both oversampling methods are compared with the Random Forest machine learning method. The experimental results show that. The experimental results show that in the classification of Presidential candidates, Random Forest achieves an accuracy of 0.947 with SMOTE and 0.948 with ADASYN. For sentiment labels, the accuracy of Random Forest remains high with a result of 0.989 for both SMOTE and ADASYN. In the classification of Jakarta Gubernatorial candidates, Random Forest + SMOTE produces an accuracy of 0.975, while with ADASYN it decreases slightly to 0.973. For sentiment labels, both SMOTE and ADASYN have the highest accuracy of 0.993. The application of SMOTE and ADASYN helps to improve the distribution of the minority class without decreasing the overall accuracy, as well as improving the stability in recognizing various multi-label classes in a balanced manner.
Co-Authors Anton Yudhana Herman Yuliansyah, Herman Muhammad Fahmi Mubarok Nahdli Rusydi Umar Wala, Jihan