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All Journal ComEngApp : Computer Engineering and Applications Journal JSI: Jurnal Sistem Informasi (E-Journal) Annual Research Seminar JITK (Jurnal Ilmu Pengetahuan dan Komputer) Jurnal Sisfokom (Sistem Informasi dan Komputer) JURTEKSI Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal Jurnal Pengabdian Masyarakat Bumi Raflesia Jurnal Generic Jurnal Altifani Penelitian dan Pengabdian kepada Masyarakat AKM: Aksi Kepada Masyarakat Indonesian Journal of Innovation Studies Jurnal Algoritma Jurnal Pengabdian Kolaborasi dan Inovasi IPTEKS Jurnal Pengabdian dan Pemberdayaan Masyarakat Indonesia The Indonesian Journal of Computer Science
Mira Afrina
Department Of Information Systems, Faculty Of Computer Science, Universitas Srwijaya, Indonesia
Religion Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Nursing Public Health Social Sciences Other

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Analisis Sentimen Ulasan Aplikasi Pembelajaran Bahasa Menggunakan Metode VADER Leonardi, Veronica Hertensia; Ibrahim, Ali; Kurnia, Rizka Dhini; Afrina, Mira
Jurnal Algoritma Vol 22 No 1 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-1.2285

Abstract

Perkembangan teknologi saat ini mempermudah proses belajar bahasa melalui aplikasi seperti Duolingo. Penelitian ini bertujuan untuk memahami persepsi pengguna terhadap Duolingo dengan menggunakan analisis sentimen berbasis VADER (Valence Aware Dictionary and Sentiment Reasoner). Ulasan pengguna dari Google Play Store diproses menggunakan Google Collaboratory, menghasilkan 1.831 data yang dikelompokkan sebagai netral, negatif, dan positif. Hasil analisis menunjukkan akurasi keseluruhan sebesar 98 persen. Model ini efektif dalam mengidentifikasi sentimen netral (presisi 100 persen, recall 97 persen, F1-score 99 persen) dan positif (presisi 99 persen, recall 82 persen, F1-score 99 persen). Namun, model kurang efektif dalam mendeteksi emosi negatif, dengan F1-score 74 persen, recall 82 persen, dan presisi 67 persen, yang menunjukkan adanya kesalahan klasifikasi pada beberapa emosi negatif. Awan kata menunjukkan kata-kata positif seperti "good," "helpful", dan "fun," serta kata-kata negatif seperti "technical problems" dan "learning limitations." Tantangan dalam penggunaan VADER termasuk ketidakmampuan menangani konteks bahasa yang kompleks dan nuansa emosional yang mendalam. Untuk meningkatkan klasifikasi sentimen, penelitian ini merekomendasikan penggunaan VADER bersama Deep-Translator. Kombinasi ini dapat membantu mengidentifikasi sentimen negatif dengan lebih baik dan menangani data dengan berbagai bahasa secara lebih efisien. Tujuan penelitian ini adalah untuk memahami sudut pandang pengguna dan meningkatkan akurasi analisis sentimen, sehingga berkontribusi pada pengembangan aplikasi pembelajaran bahasa yang lebih baik.
The Effect of Chatbot Usage on Customer Satisfaction: A Quantitative Study of Shopee, Tokopedia, and Lazada Using SmartPLS Afrina, Mira; Gumay, Naretha Kawadha Pasemah; Ariani, Ardina; Febriady, Mukhlis
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 14 No. 1 (2025): JANUARY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v14i1.2312

Abstract

With the increasing growth of e-commerce, it is important to identify the features available in e-commerce applications that can provide customer satisfaction. One of the features in e-commerce is the chatbot. Chatbots in e-commerce can provide various services to users, such as assistance in product search, ordering, product information, payment processing, customer support, and more. This research aims to analyze and understand how the response quality of each chatbot in e- commerce platforms such as Shopee, Tokopedia, and Lazada affects e-commerce user satisfaction. This study employs a quantitative methodology, integrating data analysis conducted through the SmartPLS 4.1 software. The research results show that the chatbot in Shopee platform has a impact on customer satisfaction. The same goes for chatbot in Tokopedia platform, but there are two variables that do not have a direct impact, there are information quality and waiting time. Meanwhile, chatbot in Lazada platform does not affect customer satisfaction. The findings of this research should reveal new strategies for leveraging chatbot technology to better satisfy customers in e- commerce environments, as well as lay the groundwork for further research on how artificial intelligence can shape customer experiences in the future.
Identification of Indonesian Authors Using Deep Neural Networks Firdaus; Fahreza, Irvan; Nurmaini, Siti; Darmawahyuni, Annisa; Sapitri, Ade Iriani; Rachmatullah, Muhammad Naufal; Lestari, Suci Dwi; Fachrurrozi, Muhammad; Afrina, Mira; Putra, Bayu Wijaya
Computer Engineering and Applications Journal (ComEngApp) Vol. 11 No. 1 (2022)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Author Name Disambiguation (AND) is a problem that occurs when a set of publications contains ambiguous names of authors, i.e. the same author may appear with different names (synonyms) in other published papers, or author (authors) who may be different who may have the same name (homonym). In this final project, we will design a model with a Deep Neural Network (DNN) classifier. The dataset used in this final project uses primary data sourced from the Scopus website. This research focuses on integrating data from Indonesian authors. Parameters accuracy, sensitivity and precision are standard benchmarks to determine the performance of the method used to solve AND problems. The best DNN classification model achieves 99.9936% Accuracy, 93.1433% Sensitivity, 94.3733% Precision. Then for the highest performance measurement, the case of Non Synonym-Homonym (SH) has 99.9967% Accuracy, 96.7388% Sensitivity, and 97.5102% Precision.
COMPARATIVE ANALYSIS OF MACHINE LEARNING ALGORITHMS FOR COSMETIC SALES PREDICTION ON TOKOPEDIA Sahira, Mutia; Tania, Ken Ditha; Afrina, Mira
JURTEKSI (jurnal Teknologi dan Sistem Informasi) Vol. 11 No. 4 (2025): September
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.v11i4.4187

Abstract

Abstract: The rapid growth of the cosmetics industry on e-commerce platforms has intensified competition, creating a critical need for effective, data-driven marketing strategies. This study aims to conduct a comparative analysis of machine learning algorithms to predict the sales categories (High, Medium, Low) of cosmetic products on the Tokopedia marketplace. Four classification models; Random Forest, XGBoost, Logistic Regression, and Naive Bayes were trained and evaluated on data collected via web scraping. The methodology incorporates the Synthetic Minority Over-sampling Technique (SMOTE) to address significant class imbalance and GridSearchCV for hyperparameter optimization to ensure a fair and robust comparison. The experimental results conclusively show that the Random Forest model achieved the best performance, yielding the highest F1-Score Macro Average of 0.75 and an accuracy of 85.3%. The superior model was subsequently implemented in a simple recommendation system to simulate optimal discount strategies, demonstrating its practical utility in providing actionable insights for business decisions. Keywords: classification; comparative analysis; machine learning; sales prediction; SMOTE Abstrak: Pertumbuhan pesat industri kosmetik pada platform e-commerce telah membuat persaingan ketat, sehingga menciptakan kebutuhan krusial akan strategi pemasaran yang efektif dan berbasis data. Penelitian ini bertujuan untuk melakukan analisis komparatif terhadap algoritma machine learning untuk memprediksi kategori penjualan (Tinggi, Sedang, Rendah) produk kosmetik di marketplace Tokopedia. Empat model klasifikasi, yaitu Random Forest, XGBoost, Regresi Logistik, dan Naive Bayes, dilatih dan dievaluasi menggunakan data yang dikumpulkan melalui web scraping. Metodologi penelitian ini menerapkan Synthetic Minority Over-sampling Technique (SMOTE) untuk mengatasi ketidakseimbangan kelas yang signifikan dan GridSearchCV untuk optimisasi hyperparameter guna memastikan perbandingan yang adil. Hasil eksperimen menunjukkan bahwa model Random Forest mencapai performa terbaik, dengan menghasilkan F1-Score Macro Average tertinggi sebesar 0,75 dan akurasi 85,3%. Model unggul ini kemudian diimplementasikan dalam sebuah sistem rekomendasi sederhana untuk menyimulasikan strategi diskon yang optimal, yang menunjukkan kegunaan praktisnya dalam memberikan wawasan yang dapat ditindaklanjuti untuk pengambilan keputusan bisnis. Kata kunci: analisis komparatif; klasifikasi; machine learning; prediksi penjualan; SMOTE
Sosialisasi Social Media Security Awareness Pada Warga Desa Cempaka Kab. Oku Hardiyanti, Dinna Yunika; Putra, Pacu; Afrina, Mira; Seprina, Iin; Sevtiyuni, Putri Eka
Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal Vol. 8 No. 2 (2025): April 2025
Publisher : STMIK Royal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurdimas.v8i2.3666

Abstract

Awareness of social media security is very important today, especially due to the increasing number of online security threats that can affect user privacy and data security. Moreover, the condition of rural communities is in dire need of knowledge to be wiser in using social media. This community service was carried out in Cempaka Village, Cempaka District, OKU Timur Regency, with the aim of increasing public understanding of cyber security. The methods used were education and socialization about cyber threats, the importance of maintaining password confidentiality, and personal data privacy. This activity involved the active participation of various levels of the Cempaka Village community. The results of this service showed a significant increase  understanding of social media secutty awareness. People became more aware of the risks of crime on social media and had better knowledge of how to protect themselves.Keywords: cempaka village; security awareness; social media; socialization  Abstrak:  Kesadaran akan keamanan media sosial sangat penting saat ini, terutama karena meningkatnya ancaman keamanan daring yang dapat memengaruhi privasi dan keamanan data pengguna. Apalagi kondisi masyarakat desa yang sangat membutuhkan pengetahuan agar lebih bijaksana dalam menggunakan media sosial. Pengabdian masyarakat ini dilaksanakan di Desa Cempaka, Kecamatan Cempaka, Kabupaten OKU Timur, dengan tujuan meningkatkan pemahaman masyarakat mengenai keamanan siber. Metode yang digunakan adalah edukasi dan sosialisasi tentang ancaman siber, pentingnya menjaga kerahasiaan kata sandi, dan privasi data pribadi. Kegiatan ini melibatkan partisipasi aktif dari berbagai lapisan masyarakat Desa Cempaka. Hasil dari pengabdian ini menunjukkan peningkatan yang signifikan dalam pemahaman masyarakat mengenai keamanan media sosial. Masyarakat menjadi lebih sadar akan risiko kejahatan di media sosial dan memiliki pengetahuan yang lebih baik tentang cara melindungi diri mereka.Kata kunci: desa cempaka; security awareness; sosial media; sosialisasi
Sentiment Analysis of Zalora Products on Google Play Store Using Random Forest Method: Analisis Sentimen terhadap Produk Zalora di Google Play Store Menggunakan Metode Random Forest Cendikiawan, Rizky Saputra; Ibrahim, Ali; Afrina, Mira; Kurnia, Rizka Dhini 
Indonesian Journal of Innovation Studies Vol. 26 No. 3 (2025): July
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/ijins.v26i3.1401

Abstract

General Background: The rapid growth of e-commerce platforms has intensified the need to understand consumer sentiment to improve service quality and competitiveness. Specific Background: ZALORA, as a leading online fashion retailer in Southeast Asia, has accumulated vast user-generated feedback, particularly on platforms like the Google Play Store. Knowledge Gap: Despite the availability of such data, limited studies have analyzed consumer sentiment using machine learning methods specifically tailored to ZALORA’s mobile platform. Aims: This study aims to examine consumer sentiment toward ZALORA products and assess the effectiveness of the Random Forest algorithm in classifying sentiment. Results: Utilizing a quantitative approach, 1,200 user reviews were analyzed, with 63.5% expressing positive sentiment. Word cloud visualization supported this finding, revealing frequently mentioned terms such as “product,” “goods,” and “shopping.” The Random Forest model achieved an accuracy of 80%, with precision, recall, and F1-score values for positive sentiment all exceeding 0.80. Novelty: This research integrates TF-IDF-based preprocessing with Random Forest classification to enhance sentiment analysis performance specifically for mobile commerce reviews. Implications: The findings highlight the potential of machine learning in extracting actionable insights from user reviews, offering practical implications for improving customer experience and guiding strategic development in digital retail platforms. Highlights:   High accuracy (80%) achieved using Random Forest for sentiment classification. TF-IDF preprocessing significantly improved model performance. Word cloud analysis revealed key satisfaction indicators from users. Keywords: Sentiment Analysis, Random Forest Method, Zalora Consumers 
Co-Authors Abdiansah, Abdiansah Ade Iriani Sapitri Ahmad Fali Oklilas Ahmad Fali Oklilas Ahmad Fali Oklilas Ahmad Fali Oklilas Ahmad Rifai Akbar Al Zaini Al Farissi Ali Ibrahim Ali Ibrahim Ali Ibrahim Annisa Darmawahyuni Apriansyah Putra - Ariani, Ardina Bayu Wijaya Putra Beriadi Agung Nur Rezqe Cendikiawan, Rizky Saputra Damayanti, Risma Darmawahyuni, Annisa Dedeng Zamawi Dicha Pratiwi Dinna Yunika Hardiyanti Dyah Paramita P Endang Lestari Ruskan Ermatita - Fahreza, Irvan Fathoni - Febriady, Mukhlis Firdaus Firdaus - Firdaus Firdaus Firdaus Firdaus Firmansyah, M. Daffa Gumay, Naretha Kawadha Pasemah Gustin Saputri Hadini Novianti Hafiiz Kresna Prasetya Hardini Novianti Hardini Novianti Hardini Novianti Hedi Yunus Iredho Fani Reza Irvan Fahreza Islamiansyah, Wira Ken Dihta Tania Ken Ditha Tania Kesuma, Lucky Indra Kodri, Lay Kurnia, Rizka Dhini  Lay Kodri Lay Kodri Leonardi, Veronica Hertensia M. Aris Garniardi Meitisari, Nia Muhammad Anshori Muhammad Fachrurrozi Muhammad Fachrurrozi Muhammad Farisan Zhafiri Muhammad Naufal Rachmatullah Nabila Hidayati Nurullah Marina Kelana Oky Budiyarti Opi Hernayanti Ovi Dyantina Pacu Putra Purwita Sari Putri Eka Sevtiyuni Rahmat Izwan Heroza Rezqe, Beriadi Agung Nur Risma Damayanti Rizka Dhini Rizka Dhini Kurnia Rizka Dhini Kurnia Rizka Dhini Kurnia Sahira, Mutia Sapitri, Ade Iriani Seprina, Iin Septiani Aulia Putri Sevtiyuni, Putri Eka Siti Nurmaini Sri Desy Siswanti Suci Dwi Lestari Suci Dwi Lestari Tasmi Tasmi Tasmi Tasmi Tumpol S Simarmata Welly Nailis Winda Kurnia Sari Wira Islamiansyah Wita Farla WK Wiwik Handayani Yadi Utama Yadi Utama Yadi Utama Yadi Utama Yadi Utama Yadi Utama Yadi Utama, Yudha Pratomo Yunus, Hedi Zaini, Akbar Al Zhafiri, Muhammad Farisan