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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Teknik Komputer AMIK BSI Jurnal Khatulistiwa Informatika Bina Insani ICT Journal Jurnal Pilar Nusa Mandiri Faktor Exacta Jurnal Teknik Informatika STMIK Antar Bangsa Jurnal Mantik Penusa JISICOM (Journal of Information System, Infomatics and Computing) Journal of Information System, Applied, Management, Accounting and Research EVOLUSI : Jurnal Sains dan Manajemen INTI Nusa Mandiri JATI (Jurnal Mahasiswa Teknik Informatika) SPEED - Sentra Penelitian Engineering dan Edukasi Jurnal Cendekia Ilmiah
Elly Indrayuni
Universitas Bina Sarana Informatika
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Library & Information Science Social Sciences

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Eksplorasi Pola Penjualan Produk di E-Commerce menggunakan Web Scraping Deigo Anugrah Pratama; Kevin Rafael Manuela Feltya; Ibnu Rizal Mutaqin Muyasar; Belsana Butar Butar; Elly Indrayuni
J-CEKI : Jurnal Cendekia Ilmiah Vol. 5 No. 1: Desember 2025
Publisher : CV. ULIL ALBAB CORP

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56799/jceki.v5i1.12966

Abstract

Penelitian ini membahas tentang pola penjualan produk dan data ulasan pengunjung pada platform e‑commerce Shopee, dengan memanfaatkan teknik web scraping pada data ulasan pelanggan sebagai proxy tanggal transaksi. Tujuan penelitian meliputi eksplorasi pola penjualan produk, identifikasi lonjakan penjualan menurut waktu, dan penyajian visualisasi interaktif sebagai alat dukung keputusan. Data dikumpulkan melalui web scraping berbasis JavaScript yang dijalankan pada developer console untuk mengekstrak ulasan pelanggan. Setelah itu data diproses pada tahap pra pemrosesan data menggunakan alat google colab dan library python, untuk selanjutnya diunggah ke aplikasi Looker Studio sebagai bentuk pembuatan dashboard interaktif. Visualisasi yang dihasilkan lewat looker studio mendukung optimasi strategi bisnis seperti: digital marketing, dan perencanaan stok Studi ini menegaskan web scraping pada ulasan pelanggan dapat menjadi pendekatan praktis untuk pengambilan keputusan bisnis dan analisa kompetitor.
OPTIMASI HYPERPARAMETER INDOBERT UNTUK ANALISIS SENTIMEN KENDARAAN LISTRIK DENGAN KOMPARASI ALGORITMA MACHINE LEARNING Elly Indrayuni; Acmad Nurhadi
INTI Nusa Mandiri Vol. 21 No. 1 (2026): INTI Periode Agustus 2026
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v21i1.8588

Abstract

The presence of electric vehicles has generated diverse public opinions on social media, creating the need for an automated approach to sentiment identification. Transformer-based models such as IndoBERT can capture semantic context more effectively than conventional machine learning methods that rely on feature representations such as TF-IDF, which are less effective in modeling word relationships, particularly in imbalanced datasets. This study aims to analyze public sentiment toward electric vehicles using an IndoBERT model optimized with Grid Search and compare its performance with Naive Bayes and Support Vector Machine (SVM). An experimental method was applied to a dataset of 1,517 Indonesian-language opinions. IndoBERT was fine-tuned using Grid Search by evaluating hyperparameter combinations of epochs (3, 4, and 5), learning rates (2e-5 and 3e-5), and a batch size of 16. Model performance was evaluated using accuracy, precision, recall, F1-score, and ROC-AUC. The best IndoBERT configuration was obtained with 5 epochs, a learning rate of 3e-5, and a batch size of 16, achieving 73% accuracy. Although its accuracy matched that of SVM, IndoBERT produced more balanced results, with a macro F1-score of 0.62 and the highest average AUC (0.780), outperforming SVM (0.758) and Naive Bayes (0.736). The novelty of this study lies in optimizing IndoBERT using Grid Search and comparing it with Naive Bayes and SVM based on ROC-AUC for Indonesian-language electric vehicle sentiment analysis. The findings demonstrate that Grid Search optimization enhances IndoBERT's contextual understanding, resulting in superior overall performance.
OPTIMASI NAIVE BAYES BERBASIS PSO UNTUK ANALISA SENTIMEN PERKEMBANGAN ARTIFICIAL INTELLIGENCE DI TWITTER Elly Indrayuni; Acmad Nurhadi
INTI Nusa Mandiri Vol. 18 No. 1 (2023): INTI Periode Agustus 2023
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v18i1.4282

Abstract

At present the development of Artificial Intelligence technology is progressing rapidly. There are many new artificial intelligence technologies available in various fields. Artificial Intelligence is an artificial intelligence program that can study data, perform processes of thinking and acting like humans. The presence of Artificial Intelligence technology has many positive impacts, especially in increasing work effectiveness and efficiency. However, AI is also a threat to human resources because slowly human work is being replaced by Artificial Intelligence. Various opinions about the development of Artificial Intelligence are widely discussed on social media such as Twitter. Sentiment analysis is a computational study to automatically categorize opinions into positive or negative categories. In this study, the Naive Bayes algorithm was used to analyze sentiment or public opinion regarding the development of Artificial Intelligence for Twitter users. The data collection method used is crawling data on Twitter. The results of the sentiment classification test for the development of Artificial Intelligence using Naive Bayes yield an accuracy value of 86.42%. Meanwhile, the results of the sentiment classification test using Naive Bayes based on Particle Swarm Optimization (PSO) increased with an accuracy value of 87.55%. Based on the results of this study, the use of PSO as an optimization technique for the Naive Bayes algorithm is proven to be the best algorithm model in sentiment analysis for the development of Artificial Intelligence for English text.
IMPLEMENTASI TEKNIK SMOTE UNTUK MENGATASI IMBALANCE CLASS DALAM KLASIFIKASI SENTIMEN MENGENAI CHATGPT Elly Indrayuni; Acmad Nurhadi
INTI Nusa Mandiri Vol. 19 No. 1 (2024): INTI Periode Agustus 2024
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v19i1.5595

Abstract

ChatGPT is a chatbot or computer program in the form of a virtual robot that can simulate human-like conversations. ChatGPT is widely used in various fields in academia. The impact of the use of ChatGPT on academia and public perception of this technology is significant. Sentiment analysis can be used to determine the polarity of a text or opinion that is positive or negative. In this research, social media is used as a data source to collect public opinion regarding ChatGPT instantly. The methods used in this reserach are the KNN algorithm and Naive Bayes algorithm. The aim of this research is to find the best algorithm model for sentiment classification in terms of public opinion for ChatGPT which contains English text. Before testing the algorithm model, a text processing stage was carried out which included the processes of case folding, tokenizing, stopword removal, and stemming. Word weighting using TF-IDF was carried out before the data was ready to be processed. Splitting data used in this research includes 80% of the dataset as training data and 20% of the dataset as testing data. The application of the SMOTE technique to the KNN and Naive Bayes algorithms to overcome the imbalance class of the public opinion dataset regarding ChatGPT. The research results show that combining SMOTE and Naive Bayes algorithm gives the best results with an accuracy value of 85.00%, a precision value of 87.64%, a recall value of 84.78% and an f1-score of 86.18%.
ANALISIS SENTIMEN APLIKASI TIKTOK SHOP SELLER CENTER MENGGUNAKAN NAIVE BAYES, SVM DAN LOGISTIC REGRESSION Elly Indrayuni; Acmad Nurhadi
INTI Nusa Mandiri Vol. 20 No. 1 (2025): INTI Periode Agustus 2025
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v20i1.6851

Abstract

The rapid growth of e-commerce has driven the emergence of new platforms such as TikTok Shop Seller Center, which is now integrated with Tokopedia. Increasing competition among digital platforms has made service quality and user experience key success factors. In this context, user reviews and feedback serve as crucial data sources that reflect satisfaction, complaints, and expectations toward the application. However, the large and diverse volume of reviews renders manual analysis inefficient. Therefore, an automated approach such as sentiment analysis is required to classify user opinions quickly and accurately. This study aims to perform sentiment analysis on TikTok Shop Seller Center user reviews using Naïve Bayes, Support Vector Machine (SVM), and Logistic Regression algorithms to determine the best-performing model. The dataset was obtained from the Kaggle platform and underwent preprocessing, including case folding, tokenization, stemming, and TF-IDF weighting. Model evaluation was conducted using confusion matrix and ROC curve, along with performance metrics such as accuracy, precision, recall, and F1-score. The results show that the SVM algorithm outperformed Naïve Bayes and Logistic Regression, achieving 93.75% accuracy, 93.78% precision, 95.65% recall, 94.70% F1-score, and an AUC of 0.98, categorized as Excellent Classification. Thus, SVM proved to be the most effective algorithm for classifying user review sentiments on TikTok Shop Seller Center.
Co-Authors Achmad Nurhadi Achmad Nurhadi Acmad Nurhadi Belsana Butar Butar Deigo Anugrah Pratama Dinar Ajeng Kristiyanti Ibnu Rizal Mutaqin Muyasar Kevin Rafael Manuela Feltya