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All Journal Pixel : Jurnal Ilmiah Komputer Grafis Elkom: Jurnal Elektronika dan Komputer RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Jurnal Pengabdian Bersama Masyarakat Indonesia Jurnal Informatika Dan Tekonologi Komputer
Arif Fitra Setyawan
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Languange, Linguistic, Communication & Media Public Health Other

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Emotion Detection Using Contextual Embeddings for Indonesian Product Review Texts on E-commerce Platform Ariyanto, Amelia Devi Putri; Fari Katul Fikriah; Arif Fitra Setyawan
Pixel :Jurnal Ilmiah Komputer Grafis Vol 17 No 1 (2024): Vol 17 No 1 (2024): Jurnal Ilmiah Komputer Grafis
Publisher : UNIVERSITAS STEKOM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/pixel.v17i1.2010

Abstract

The advancement of e-commerce has changed the way people shop. However, there is a mismatch between the actual quality of a product and the seller’s description. Product reviews are an important source of information for making purchasing decisions. However, processing large numbers of reviews manually is difficult. This research aims to detect emotions in Indonesian language product review texts using contextual embeddings. The public dataset used was PRDECT-ID, which comprises five emotion labels. The methods used include data preprocessing, feature extraction using contextual embeddings such as Bidirectional Encoder Representations from Transformers (BERT), and classification using Decision Tree, Naïve Bayes, and k-Nearest Neighbors (KNN). Among the compared models, the KNN model demonstrated the highest improvement, achieving a 15.09% enhancement over the decision tree results. This research provides insights into the effectiveness of contextual embeddings in detecting emotions in Indonesian language product review texts.
Analisis Sentimen Ulasan iPhone di Amazon Menggunakan Model Deep Learning BERT Berbasis Transformer Arif Fitra Setyawan; Amelia Devi Putri Ariyanto; Fari Katul Fikriah; Rozaq Isnaini Nugraha
Elkom: Jurnal Elektronika dan Komputer Vol. 17 No. 2 (2024): Desember : Jurnal Elektronika dan Komputer
Publisher : STEKOM PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/elkom.v17i2.2150

Abstract

This study aims to analyze the sentiment of iPhone product reviews fromAmazon using the BERT (Bidirectional Encoder Representations from Transformers) model to classify reviews as either positive or negative. The dataset, sourced from Kaggle, includes text reviews and star ratings, where high ratings indicate positive sentiment and low ratings indicate negative sentiment. After text preprocessing steps, including data cleaning, tokenization, and sentiment labeling, the BERT model was fine-tuned for sentiment classification, with the data split into training, validation, and test sets. Evaluation results demonstrate that the BERT model achieves a high classification accuracy, with an accuracy rate of 93.9% and a balanced F1 score between precision and recall. Confusion matrix evaluation also indicates that the model consistently identifies both positive and negative sentiments. This study shows that Transformer-based models like BERT are highly effective in understanding customer opinions in e-commerce, with broad application potential for data-driven decision-making in marketing strategies and product development.
Emotion Detection Using Contextual Embeddings for Indonesian Product Review Texts on E-commerce Platform Ariyanto, Amelia Devi Putri; Fari Katul Fikriah; Arif Fitra Setyawan
Pixel :Jurnal Ilmiah Komputer Grafis Vol. 17 No. 1 (2024): Pixel :Jurnal Ilmiah Komputer Grafis dan Ilmu Komputer
Publisher : UNIVERSITAS STEKOM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/pixel.v17i1.2010

Abstract

The advancement of e-commerce has changed the way people shop. However, there is a mismatch between the actual quality of a product and the seller’s description. Product reviews are an important source of information for making purchasing decisions. However, processing large numbers of reviews manually is difficult. This research aims to detect emotions in Indonesian language product review texts using contextual embeddings. The public dataset used was PRDECT-ID, which comprises five emotion labels. The methods used include data preprocessing, feature extraction using contextual embeddings such as Bidirectional Encoder Representations from Transformers (BERT), and classification using Decision Tree, Naïve Bayes, and k-Nearest Neighbors (KNN). Among the compared models, the KNN model demonstrated the highest improvement, achieving a 15.09% enhancement over the decision tree results. This research provides insights into the effectiveness of contextual embeddings in detecting emotions in Indonesian language product review texts.
Canva-education transform: Akselerasi kompetensi digital guru dalam merancang media pembelajaran interaktif berbasis web Wening Nur Habibah Alif; Arif Fitra Setyawan; Chandra Yogatama; Dyah Ratna Kusuma Mayang Sari; Muhammad Bhayu Bramantyo; Nur Hidayat Arief; Thomas Tri Wibowo
Jurnal Pengabdian Bersama Masyarakat Indonesia Vol. 4 No. 2 (2026): April : Jurnal Pengabdian Bersama Masyarakat Indonesia
Publisher : CV. Aksara Global Akademia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59031/jpbmi.v4i2.855

Abstract

Transformasi digital menuntut tenaga pendidik menjadi fasilitator yang adaptif terhadap teknologi. Menghadapi tuntutan tersebut, guru di SMPIP Nihadul Qulub mengalami hambatan kompetensi dalam menciptakan media pembelajaran berbasis digital. Selain itu, kurangnya sumber referensi praktis yang dapat diakses secara mandiri membuat guru kesulitan untuk meningkatkan kompetensi digitalnya di tengah padatnya aktivitas mengajar. Pengabdian ini bertujuan untuk meningkatkan kompetensi digital melalui pelatihan Canva guna merancang media interaktif berbasis web. Diikuti sebanyak 20 guru, kegiatan pengabdian berupa pelatihan ini dilaksanakan secara langsung dan luar jaringan (luring) di SMPIP Nihadul Qulub, Ungaran. Metode pelaksanaan pengabdian meliputi perencanaan, analisis kebutuhan mitra, implementasi workshop praktik langsung, serta evaluasi. Hasil pengabdian menunjukkan peningkatan signifikan, di mana 90% guru berhasil mempublikasikan modul ajar interaktif dalam format situs web tanpa keahlian pemrograman. Pelatihan ini terbukti efektif meningkatkan peran guru menjadi desainer pengalaman belajar yang kreatif, sejalan dengan tuntutan pendidikan abad ke-21.
KLASIFIKASI HASIL MRI TUMOR OTAK DENGAN EKTRAKSI FITUR GRAY LEVEL CO-OCCURANCE MATRIX (GLCM) Fari Katul Fikriah; Amelia Devi Putri Ariyanto; Arif Fitra Setyawan
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 9 No 2 (2024): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v9i2.4793

Abstract

Bagian penting dari tubuh adalah otak yang mana menjadi sumber dari semua alat tubuh yang terletak dalam rongga tengkorak, tumor otak merupakan salah satu penyakit yang dapat menyerangnya. Pendeteksian tumor otak adalah salah satu aspek yang dinilai penting dalam diagnosa medis. Pada penelitian ini memiliki tujuan melakukan implementasi ekstraksi fitur GLCM (Gray Level Co-occurence Matrix) pada citra MRI tumor otak serta mencari performa algoritma yang paling baik dari deteksi tumor otak menggunakan citra MRI ini. Data yang dipakai pada penelitian ini merupakan data public yang berasal dari kaggle.com. Proses ekstraksi fitur pada citra digunakan pada penelitian ini GLCM yang mana memiliki fungsi menghitung frekuensi dari nilai intensitas piksel yang berjarak antar citra dengan menggunakan parameter 0o, 45o, 90o, 135o. Tahap selanjutnya pada penelitian ini adalah dengan melakukan langkah preprocessing dengan selanjutnya mencari nilai klasifikasi dari hasil MRI menggunakan algoritma Naïve Bayes, C4.5 dan Neural Network. Hasil yang didapatkan memperlihatkan bahwa Naïve Bayes memiliki performa algoritma paling baik dibandingkan C4.5 dan Neural Network yaitu dengan akurasi algoritma Naïve Bayes sebesar 96.8%, sedangkan untuk algoritma C4.5 sebesar 41.5% dan Neural Network sebesar 38.25%. selain hal tersebut pada penelitian ini membuktikan bahwa dengan ekstraksi fitur GLCM terbukti efektif dalam menangkap informasi tekstur dari citra MRI yang sangat penting pada klasifikasi tumor otak.
Perancangan Sistem Informasi Penelitian Pengabdian BIMA Internal LPPM UWHS Rozaq Isnaini Nugraha; Arif Fitra Setyawan; Dwi Retnaningsih; Rohadi Jaka Raharja
Jurnal Informatika Dan Tekonologi Komputer (JITEK) Vol. 5 No. 3 (2025): November : Jurnal Informatika dan Tekonologi Komputer
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jitek.v5i3.8430

Abstract

SIPPMASBIMA UWHS stands for Sistem Informasi Internal Penelitian Pengabdian BIMA Internal UWHS (Internal Information System for Research and Community Service of UWHS), which is designed as a system architecture for uploading research and community service data. The urgency of this research lies in the fact that the previous system used for managing research and community service data at the UWHS LPPM had limitations in data uploading, and lecturers were not accustomed to the system used by the Ministry of Education, Culture, Research, and Technology (Kemendikbudristek). Therefore, the objective of this research is to develop a new system that is more integrated and user-friendly, with an interface and methods resembling the BIMA system of Kemendikbudristek, to support lecturers in adapting their data input practices in accordance with the ministry’s system. The method applied is Agile Development, ensuring flexibility in feature development based on user needs. The targeted outcomes include an information system, a journal publication (at least Sinta 4), and a system prototype.
Co-Authors Amelia Devi Putri Ariyanto Amelia Devi Putri Ariyanto Chandra Yogatama Dwi Retnaningsih Dyah Ratna Kusuma Mayang Sari Fari Katul Fikriah Fari Katul Fikriah Muhammad Bhayu Bramantyo Nur Hidayat Arief Rohadi Jaka Raharja Rozaq Isnaini Nugraha Thomas Tri Wibowo Wening Nur Habibah Alif