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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika JURTEKSI Jurnal PkM MIFTEK
Rohman, Fauza
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Humanities Computer Science & IT Economics, Econometrics & Finance Languange, Linguistic, Communication & Media Social Sciences Other

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A Comparative Study of Alternative Automatic Labeling Using AI Assistant Julianto, Indri Tri; Kurniadi, Dede; Balilo Jr, Benedicto B.; Rohman, Fauza
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 4 (2024): Article Research Volume 8 Issue 4, October 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i4.13950

Abstract

The development of AI assistants has become increasingly sophisticated, as evidenced by their growing adoption in assisting humans with various tasks. In particular, AI assistants have demonstrated potential in the field of sentiment analysis, where they can automate the labeling of text data. Traditionally, this labeling process has been performed manually by humans or using tools like the VADER Lexicon. This study is imperative to evaluate the performance of AI Assistants in sentiment labeling, as compared to traditional human-based labeling and the application of the VADER sentiment analysis algorithm. The methodology involves comparing the labeling results of Gemini and You AI with those of human labeling and VADER. Performance is evaluated using the Naive Bayes and K- Nearest Neighbour algorithms, and K-Fold Cross Validation is employed for evaluation. The results indicate that the performance of both AI assistants can closely approximate the performance of human labeling. Gemini's best accuracy is achieved with the k-NN algorithm at 53.71%, while You AI's best accuracy is achieved with the Naive Bayes algorithm at 48.30%. These results are close to the accuracy of human labeling (61.12%) using the k-NN algorithm and VADER (54.29%) using the Naive Bayes algorithm. This suggests that AI assistants can serve as an alternative for text data labeling, as the differences in performance are not statistically significant.
PENGEMBANGAN UMKM MELALUI PENINGKATAN PENGETAHUAN MASYARAKAT TERHADAP PROSES DIGITAL MARKETING Yogaswara, Dendi; Asidqi, Ayu Aliyah; Faza, Zhilan Muhammad; Muhamad, Yusup; Amanah, Mala Hasanatul; Sulaeman, Ade Saep; Soleh, Soleh; Syauqi, Naufal Azhar; Khoirotunnisa, Siti Luthfiah; Rahman, Muhamad Ridwan; Aripin, Muhamad; Maulana, Rif’at; Bunga, Silvia; Kamila, Hazna Tiara; Alwi, Muhamad; Firmasyah, Denny; Khautsar, Ferdinand Al; Fauzi, M Ripki; Rohman, Fauza; Alawiyah, Siti Rahmah
Jurnal PkM MIFTEK Vol 5 No 1 (2024): Jurnal PkM MIFTEK
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/miftek/v.5-1.1479

Abstract

Mayoritas pelaku UMKM di Desa Panembong sebagian besar berpendidikan SD dan SMP sehingga dapat dikategorikan berpendidikan rendah. Hal ini berdampak terhadap peningkatan kapasitas produksi dan pemasaran. Permasalahan yang utama dalam pengetahuan pemasaran dimulai dari pelaku UMKM terutama rajut dan rengginang yang hanya memasarkan produk tanpa dukungan iklan baik itu secara tradisional maupun digital. Program workshop pelatihan pemasaran digital mengenai peningkatan pemasaran UMKM ini bertujuan untuk meningkatkan kapasitas pelaku UMKM dalam memasarkan produk dan meningkatkan kualitas pengetahuan tentang pemasaran. Metodologi yang digunakan adalah metodologi Integrasi Relawan TIK, dimana dalam metode pelaksanaannya menggunakan metode offline. Hasil yang dicapai yaitu dengan meningkatnya pengetahuan pelaku UMKM dalam pemasaran produk sehingga membantu peningkatan kapasitas pemasaran UMKM rajut dan rengginang.
THE ROLE OF FEATURE SELECTION IN ENHANCING THE ACCURACY OF AI ASSISTANT AUTO-LABELING Julianto, Indri Tri; Kurniadi, Dede; B. Balilo Jr, Benedicto; Rohman, Fauza
JURTEKSI (Jurnal Teknologi dan Sistem Informasi) Vol 11, No 1 (2024): Desember 2024
Publisher : Universitas Royal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v11i1.3364

Abstract

Abstract: The development of AI assistants such as Gemini and ChatGPT can significantly assist in daily human tasks. In the field of Sentiment Analysis, AI assistants can be utilized as an automated labeling alternative to provide positive, negative, or neutral sentiments within a dataset. This research aims to enhance the performance of AI assistants in automated labeling processes by employing the Feature Selection algorithm, specifically Forward Selection. The methodology involves utilizing the Naïve Bayes and K-NN algorithms, and subsequently improving accuracy through the Feature Selection algorithm. The evaluation is conducted using K-Fold Cross Validation. Research findings indicate an improvement in the accuracy of the best model, which is ChatGPT, when using the Naïve Bayes algorithm and Shuffled Sampling technique. The initial accuracy of 79.09% increased to 87.18% after Feature Selection was applied. This demonstrates the effectiveness of Feature Selection, particularly Forward Selection, in enhancing the accuracy performance of the model.            Keywords: ai; assistant; chat gpt; feature selection; gemini.  Abstrak: Pekembangan Asisten AI seperti Gemini dan Chat GPT dapat membantu pekerjaan manusia sehari-hari. Dalam bidang Analisis Sentimen, Asisten AI dapat digunakan sebagai alternatif pelabelan otomatis untuk memberikan sentimen positif, negatif atau netral dalam suatu dataset. Penlitian ini bertujuan untuk meningkatkan performa yang dihasilkan oleh Asisten AI dalam proses pelabelan otomatis menggunakan Algortima Feature Selection yaitu Forward Selection. Metode yang digunakan adalah dengan menggunakan Algoritma Naïve Bayes dan K-NN kemudian hasil akurasi akan ditingkatkan menggunkan Algoritma Feature Selection. Evaluasi yang digunakan adalah K-Fold Cross Validation. Hasil penelitian menunjukkan peningkatan akurasi model terbaik berada pada Chat GPT dengan menggunakan Algoritma Naïve Bayes dan Teknik Shuffled Sampling, dari nilai akurasi awal sebesar 79.09%, setelah ditingkatkan menggunakan Feature Selection, maka nilai akurasinya meningkat menjadi 87.18%. Hal ini membuktikan peran Feature Selection, dimana yang digunakan adalah Forward Selection dalam meningkatkan akurasi ternyata memang efektif dalam meningkatkan performa akurasi model. Kata kunci: ai; assisten; chat gpt; feature selection; gemini 
THE ROLE OF FEATURE SELECTION IN ENHANCING THE ACCURACY OF AI ASSISTANT AUTO-LABELING Julianto, Indri Tri; Kurniadi, Dede; B. Balilo Jr, Benedicto; Rohman, Fauza
JURTEKSI (jurnal Teknologi dan Sistem Informasi) Vol. 11 No. 1 (2024): Desember 2024
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Royal Kisaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v11i1.3364

Abstract

Abstract: The development of AI assistants such as Gemini and ChatGPT can significantly assist in daily human tasks. In the field of Sentiment Analysis, AI assistants can be utilized as an automated labeling alternative to provide positive, negative, or neutral sentiments within a dataset. This research aims to enhance the performance of AI assistants in automated labeling processes by employing the Feature Selection algorithm, specifically Forward Selection. The methodology involves utilizing the Naïve Bayes and K-NN algorithms, and subsequently improving accuracy through the Feature Selection algorithm. The evaluation is conducted using K-Fold Cross Validation. Research findings indicate an improvement in the accuracy of the best model, which is ChatGPT, when using the Naïve Bayes algorithm and Shuffled Sampling technique. The initial accuracy of 79.09% increased to 87.18% after Feature Selection was applied. This demonstrates the effectiveness of Feature Selection, particularly Forward Selection, in enhancing the accuracy performance of the model.            Keywords: ai; assistant; chat gpt; feature selection; gemini.  Abstrak: Pekembangan Asisten AI seperti Gemini dan Chat GPT dapat membantu pekerjaan manusia sehari-hari. Dalam bidang Analisis Sentimen, Asisten AI dapat digunakan sebagai alternatif pelabelan otomatis untuk memberikan sentimen positif, negatif atau netral dalam suatu dataset. Penlitian ini bertujuan untuk meningkatkan performa yang dihasilkan oleh Asisten AI dalam proses pelabelan otomatis menggunakan Algortima Feature Selection yaitu Forward Selection. Metode yang digunakan adalah dengan menggunakan Algoritma Naïve Bayes dan K-NN kemudian hasil akurasi akan ditingkatkan menggunkan Algoritma Feature Selection. Evaluasi yang digunakan adalah K-Fold Cross Validation. Hasil penelitian menunjukkan peningkatan akurasi model terbaik berada pada Chat GPT dengan menggunakan Algoritma Naïve Bayes dan Teknik Shuffled Sampling, dari nilai akurasi awal sebesar 79.09%, setelah ditingkatkan menggunakan Feature Selection, maka nilai akurasinya meningkat menjadi 87.18%. Hal ini membuktikan peran Feature Selection, dimana yang digunakan adalah Forward Selection dalam meningkatkan akurasi ternyata memang efektif dalam meningkatkan performa akurasi model. Kata kunci: ai; assisten; chat gpt; feature selection; gemini 
Co-Authors Alawiyah, Siti Rahmah Amanah, Mala Hasanatul Aripin, Muhamad Asidqi, Ayu Aliyah B. Balilo Jr, Benedicto Balilo Jr, Benedicto B. Bunga, Silvia Dede Kurniadi Fauzi, M Ripki Faza, Zhilan Muhammad Firmasyah, Denny Indri Tri Julianto Kamila, Hazna Tiara Khautsar, Ferdinand Al Khoirotunnisa, Siti Luthfiah Maulana, Rif’at Muhamad Alwi Muhamad, Yusup Rahman, Muhamad Ridwan Soleh, Soleh Sulaeman, Ade Saep Syauqi, Naufal Azhar Yogaswara, Dendi