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All Journal Jurnal Transformatika Journal of Natural Sciences and Mathematics Research Vision: Journal for Language and Foreign Language Learning Phenomenon : Jurnal Pendidikan MIPA SINTECH (Science and Information Technology) Journal Jurnal Sisfokom (Sistem Informasi dan Komputer) JSiI (Jurnal Sistem Informasi) IJID (International Journal on Informatics for Development) JURIKOM (Jurnal Riset Komputer) EDUMATIC: Jurnal Pendidikan Informatika Aiti: Jurnal Teknologi Informasi TEPIAN Jurnal Teknik Informatika (JUTIF) Walisongo Journal of Information Technology
Wenty Dwi Yuniarti, Wenty Dwi
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Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Materials Science & Nanotechnology Mathematics Physics

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Implementasi Algoritma Random Forest dalam Klasifikasi Ulasan Pengunjung Mall Semarang untuk Pengambilan Keputusan Layanan Maizaliyanti, Annisa; Umam, Khothibul; Yuniarti, Wenty Dwi; Handayani, Maya Rini
Jurnal Pendidikan Informatika (EDUMATIC) Vol 9 No 2 (2025): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v9i2.30379

Abstract

Visitor preferences for malls in Semarang are not optimal because bold reviews have not been utilized optimally in decision making. Our research aims to classify the sentiment of Google Maps reviews from 13 malls in Semarang with a total of 2,600 reviews. Labeling is done manually based on ratings, where ratings 1–3 are considered negative reviews and 4–5 as positive reviews. The classification method used is Random Forest because the ensemble approach (bagging) provides optimal results. The research process includes data collection, labeling, cleaning, data sharing, classification, and model evaluation. The data used is unbalanced and dominated by positive reviews, so the Synthetic Minority Over-sampling Technique (SMOTE) technique was applied. The overall accuracy before and after SMOTE remained the same at 84%. However, the model's performance in detecting negative reviews increased from 27% to 44% in recall and F1-score from 0.40 to 0.52, but these values ​​are still relatively low. Java Supermall Semarang is the mall with the best reviews, with a classification accuracy reaching 90%. This model is better at recognizing positive reviews, but less reliable for negative reviews. Therefore, its use as a decision-making preference needs to be done with caution. This research opens up opportunities for further development, including the use of other models such as BERT which are superior in understanding context and language in reviews.
Klasifikasi sentimen pada ulasan pengguna aplikasi Cryptocurrency di Google Play Store menggunakan algoritma Decision Tree Tsuroyya, Kamiliya; Umam, Khothibulu; Yuniarti, Wenty Dwi; Handayani, Maya Rini
AITI Vol 22 No 2 (2025)
Publisher : Fakultas Teknologi Informasi Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24246/aiti.v22i2.279-293

Abstract

Cryptocurrency has become a trend in digital investment. The Pintu application exemplifies the use of digital technology for trading cryptocurrency assets. Reviews from the Google Play Store serve as an important source of data to understand the opinions of Pintu application users. This study focuses on investigating the sentiment analysis of Pintu application users sourced from the Google Play Store by implementing the Decision Tree and Random Forest algorithms. The approach used involves collecting data from the Google Play Store, which contains user reviews and ratings. The data is then labeled as positive or negative and cleaned, processed, and analyzed using Decision Tree and Random Forest algorithms. The results of the study showed that the accuracy of the Decision Tree reached 0.90, while the Random Forest achieved an accuracy of 0.88. From these results, it can be concluded that the Decision Tree is superior in classifying text mining with high accuracy. The difference between the two methods is insignificant in terms of accuracy, specifically for Decision Tree, with an accuracy of 0.90, Precision of 0.91, and recall of 0.95, and Random Forest, with an accuracy of 0.88, precision of 0.87, and recall of 0.95. User sentiment analysis of the Pintu application provides a positive response to using the Pintu application.
Analisis Sentimen Ulasan Mobile Legends di Google Play Store dan YouTube Menggunakan Pelabelan Otomatis Roberta dan Klasifikasi Random Forest Muhammad Rafid Pratama; Handayani, Maya Rini; Yuniarti, Wenty Dwi; Khothibul Umam
Jurnal Sistem Informasi Vol. 12 No. 2 (2025)
Publisher : Universitas Serang Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30656/jsii.v12i2.10459

Abstract

Perkembangan industri game mobile telah mendorong meningkatnya jumlah pengguna dan ulasan terhadap berbagai judul populer, salah satunya Mobile Legends: Bang Bang. Penelitian ini bertujuan untuk menganalisis persepsi pengguna terhadap aplikasi Mobile Legends melalui ulasan yang diperoleh dari Google Play Store dan YouTube. Metode yang digunakan meliputi pengambilan data secara crawling, pelabelan otomatis menggunakan model RoBERTa untuk klasifikasi sentimen (positif, negatif, dan netral), serta pemodelan menggunakan algoritma Random Forest. Dataset terdiri dari 1.400 data dari Google Play Store dan ratusan data dari YouTube yang telah melalui proses pra-pemrosesan. Evaluasi model menggunakan metrik precision, recall, dan f1-score. Hasil pengujian menunjukkan bahwa model mampu mengklasifikasikan ulasan dengan cukup baik, dengan akurasi sebesar 80% pada data Google Play Store dan 82% pada data YouTube. Model menunjukkan performa tinggi dalam mendeteksi ulasan negatif dan positif, meskipun akurasi untuk kelas netral masih rendah. Secara keseluruhan, model berbasis Random Forest cukup andal dalam mengolah data ulasan pengguna, dan memberikan wawasan mengenai persepsi masyarakat terhadap Mobile Legends di berbagai platform.
Identifikasi Polaritas Sikap Pengguna Aplikasi X terhadap Coretax di Indonesia Menggunakan Algoritma Naïve Bayes Prasilda, Dina Rahma; Yuniarti, Wenty Dwi; Handayani, Maya Rini; Umam, Khothibul
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 3 (2025): Juni 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i3.8548

Abstract

The Core Tax Administration System (Coretax) was launched by the Directorate General of Taxes (DGT) in January 2025 as a technology-based integrated tax system. While its initial goal was to improve tax efficiency and compliance, Coretax faced technical challenges, including system errors, slow processing speed, and criticism from the public. The main platform used to address these challenges is the X app (formerly known as Twitter). This research aims to understand the public's views and responses to Coretax's services by analyzing user sentiment patterns seen on social media. The research identifies the polarity of user attitudes by utilizing natural language processing (NLP) and Naïve Bayes algorithms, applied to a dataset of 1,628 tweets collected between January and March 2025. The analyzed data reflects a wide range of public reactions that include both positive and negative opinions towards the Coretax implementation, both in terms of functionality and ease of use. The results show that the model has an accuracy rate of 93.07%, a precision value of 95%, a recall value of 96%, and an F1-Score value of 96%. The results of this study are expected to be able to provide precise mapping related to changes in public opinion towards Coretax, so that it can be a valuable source of information for application developers, policy makers in the field of taxation, and analysis in the technology sector in responding to the needs and expectations of society in the digital era.
Medicinal Plants Recommendation System using ROC and MOORA Widians, Joan Angelina; Tejawati, Andi; Yuniarti, Wenty Dwi
TEPIAN Vol. 5 No. 2 (2024): June 2024
Publisher : Politeknik Pertanian Negeri Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51967/tepian.v5i2.3019

Abstract

Kalimantan has extraordinary biodiversity, including medicinal plants. Medicinal plants are a type of plant that certain parts, such as roots, leaves, bark, stems, and the results of their excretions. However, people sometimes need help choosing plants that suit their needs because of the many types of medicinal plants and the need for knowledge regarding their use. Decision support systems (DSS) combine computer capabilities with data processing or manipulation that utilizes unstructured models or solution rules. Furthermore, the method of documenting knowledge of traditional medicine is through the media of information systems. This system helps select medicinal plants according to user needs. This research developed a DSS using Rank Order Centroid (ROC) and Multi-Objective Optimization by Ratio Analysis (MOORA) methods to select medicinal plants for fungal and skin infections, including Furuncles, Tinea corporis, Tinea versicolor, and Acne. ROC method for determining criteria weight values. This research has four criteria: plant part, processing method, use method, and habitus. Determining recommendations for alternative ranking results using the MOORA method. This study aims to help the public get recommendations for medicinal plants in human skin disease treatment. This study aims to increase the preservation of biodiversity, particularly sustainable medicinal plants in the tropical rainforest of East Kalimantan.
Co-Authors Andi Fadllan Bagus Setiawan Damayanti, Laili Zanura Faiz, Achmad Nur Joan Angelina Widians, Joan Angelina Khoirul Adib Kholifah, Khusnul Khothibul Umam Khotibul Umam Maizaliyanti, Annisa Maya Rini Handayani Muhadzib Al-Faruq, Muhammad Naufal Muhammad Rafid Pratama Nur'Aini, Siti Prasilda, Dina Rahma Retantyo Wardoyo Safitri, Sindy Eka Tejawati, Andi Tsuroyya, Kamiliya Umam, Khothibulu