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All Journal Indonesian Journal of Electrical Engineering and Computer Science SKANIKA: Sistem Komputer dan Teknik Informatika Jurnal Riset Informatika dan Teknologi Informasi (JRITI)
Pratama Yusuf, Ajif Yunizar
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Computer Science & IT Control & Systems Engineering Engineering

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CHATBOT BERBASIS NLP UNTUK REKOMENDASI PRODUK SKINCARE LOKAL PADA TELEGRAM Agustin, Syafira Cessa; Syafina, Prilia Hashifah; Rachmatin, Nida; Pratama Yusuf, Ajif Yunizar
SKANIKA: Sistem Komputer dan Teknik Informatika Vol 7 No 1 (2024): Jurnal SKANIKA Januari 2024
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/skanika.v7i1.3141

Abstract

This research applies a Natural Language Processing (NLP)-based chatbot to provide recommendations for local skincare products. Telegram, as a widely used communication platform, is an ideal medium to present this innovative solution to consumers looking for appropriate skincare products. The chatbot is designed to understand the user's needs regarding skin type, and skin concerns. By utilizing artificial intelligence, the chatbot can provide personalized recommendations of suitable local skincare products, improving consumers' access to product information and facilitating the process of selecting the right product. This research is expected to make chatbot an effective tool in finding skincare products that suit the skin, as well as increasing consumer participation in supporting the local skincare industry through instant messaging platform, telegram.
Improving the MSMEs data quality assurance comprehensive framework with deep learning technique Sadikin, Mujiono; Katidjan, Purwanto S.; Dwiyanto, Arif Rifai; Nurfiyah, Nurfiyah; Pratama Yusuf, Ajif Yunizar; Trisnojuwono, Adi
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 1: January 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i1.pp613-626

Abstract

In the year of 2022 the ministry of cooperatives and small and medium enterprises (SMEs) executed a complete data collection program for the cooperatives and micro small and medium enterprises (MSMEs) profile. As the complexity of the process and the uniqueness of the data characteristics, plenty of risks must be mitigated. The most challenging risk is the possibility of reduced data quality. This study is performed to validate the proposed comprehensive framework to ensure the quality data of cooperatives and MSME. The proposed framework aims to prevent, detect, repair, and recover dirty data to achieve the required data quality minimum standard. We investigated many techniques namely rule-based, selection-based, and deep learning-based. By applying the framework, 6,850,000 missing values are found and corrected, whereas the number of instant data containing attribute values that do not follow the domain constraints or integrity rule is 4,082,630. The first deep learning task applied in the framework is MSME activity image description (image captioning) generated by the convolutional neural network-recurrent neural network (CNN-RNN) model. By using 1000 MSME images as data training, the model’s performance is quite good, achieving the average BLEU score of Culinary 0,3149, Fashion 0,4868, and creative products 0,5086. So far, the proposed framework can contribute to supporting MSME one data as the Indonesian government program.
Analisa Sentimen terhadap Twitter Pemilu 2024 menggunakan Perbandingan Algoritma Naïve Baiyes rahmaddyan, reyhan tri; Damara, Rian; Pratama Yusuf, Ajif Yunizar; Munandar, Tb Ai
Jurnal Riset Informatika dan Teknologi Informasi Vol 2 No 3 (2025): April - Juli 2025
Publisher : Jejaring Penelitian dan Pengabdian Masyarakat (JPPM)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58776/jriti.v2i3.157

Abstract

In the digital era, sentiment analysis has become essential for understanding public opinion on various issues, including general elections. In the context of the 2024 General Election (Pemilu), this study aims to analyze sentiments expressed on the Twitter platform regarding the event. A primary classification algorithm, Naïve Bayes, was used to classify sentiments into positive, negative, and neutral categories and compare its performance. Twitter data was collected using a crawling technique during the 2024 election campaign period and used as the dataset. The data was then processed to remove noise and underwent text preprocessing, including tokenization, stemming, and stop word removal. Subsequently, the Naïve Bayes algorithm was applied to classify the sentiment of the collected tweets. Naïve Bayes, with its probabilistic approach and feature independence assumption, offers a fast and straightforward solution for classification tasks. The analysis results show that the algorithm was able to classify sentiments effectively. In tests using a separate test set, Naïve Bayes achieved an accuracy of approximately 82%. However, this algorithm has strengths and weaknesses that must be considered in the context of sentiment analysis on Twitter related to the 2024 election. For example, Naïve Bayes is more efficient in terms of time and resources. The study concludes that although Naïve Bayes produced accurate results, selecting the best algorithm depends on specific analysis needs, such as processing speed and resource availability. Further research is recommended to explore hybrid methods and deep learning techniques to enhance the accuracy and efficiency of sentiment analysis on social media platforms. The processed data consisted of 1,500 tweets. This study shows that the classification of Twitter data using the Naïve Bayes algorithm achieved an accuracy of 80%.
Co-Authors Agustin, Syafira Cessa Arif Rifai Dwiyanto Damara, Rian Mujiono Sadikin Nurfiyah Purwanto S. Katidjan, Purwanto S. Rachmatin, Nida rahmaddyan, reyhan tri Syafina, Prilia Hashifah Tb Ai Munandar, Tb Ai Trisnojuwono, Adi