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All Journal ComEngApp : Computer Engineering and Applications Journal JSI: Jurnal Sistem Informasi (E-Journal) Annual Research Seminar Sistemasi: Jurnal Sistem Informasi Journal of Information Technology and Computer Science JITK (Jurnal Ilmu Pengetahuan dan Komputer) JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Sisfokom (Sistem Informasi dan Komputer) JURTEKSI Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal JSAI (Journal Scientific and Applied Informatics) Building of Informatics, Technology and Science Journal of Information Systems and Informatics Jurnal Pengabdian Masyarakat Bumi Raflesia Journal Informatic, Education and Management (JIEM) 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 Abdimas Maduma 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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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 2025
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 
Komparasi Klasterisasi Data Historis Gempa Bumi Menggunakan DBSCAN, K-Means, dan Agglomerative Clustering Lakeisyah, Eka Therina; Tania, Ken Ditha; Afrina, Mira
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8426

Abstract

Earthquakes are one of the natural disasters that are prone to occur on the island of Sumatera and pose a serious challenge because they can have a devastating impact on human life, such as loss of life, material losses, and environmental damage. Therefore, earthquake hazard zone mapping is needed to provide information about the potential and history of disasters and is an important tool for disaster mitigation efforts. This study aims to map earthquake vulnerability in Sumatra by comparing three clustering algorithms, namely DBSCAN, K-Means, and Agglomerative Clustering, based on earthquake data in Sumatra from 1973 to 2023. This is to find the best algorithm so that it can provide recommendations for appropriate earthquake risk mitigation strategies. The results show that the K-Means algorithm is the best because it obtained the highest Silhouette Coefficient value, namely 0.3948 with a total of 3 clusters. It is hoped that this research can improve understanding of earthquake hazard zones on the island of Sumatra and provide practical contributions in the form of mitigation strategy recommendations tailored to the characteristics of each cluster to support the application of this research for the government and local communities.
Analysis of E-Commerce and Fintech Trends in the Digital Economy Ecosystem Redha Bayu Anggara; Asyrof Fitrah; Ali Ibrahim; Mira Afrina
Journal Informatic, Education and Management (JIEM) Vol 8 No 1 (2026): FEBRUARY (CALL FOR PAPERS)
Publisher : STMIK Indonesia Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61992/jiem.v8i1.249

Abstract

The rapid expansion of e-commerce and fintech has significantly shaped the digital economy ecosystem in Indonesia. This study analyzes key trends, behavioral patterns, and the evolving dynamics within these sectors as digital adoption continues to accelerate. The increasing volume and complexity of digital transactions demand advanced analytical approaches capable of identifying hidden patterns and potential anomalies. To address this need, the study employs a Convolutional Neural Network (CNN) model to extract deep feature representations from transaction data and classify emerging behavioral trends. The proposed method demonstrates strong accuracy, achieving 91.25%, indicating its ability to capture non-linear relationships that traditional methods often overlook. The findings highlight several major trends, including shifting consumer behavior, increasing transaction frequency, and the growing prominence of digital financial services. Practically, this research provides valuable insights for enhancing risk mitigation, fraud detection, and real-time monitoring in digital platforms. Academically, it contributes to the understanding of deep learning applications in digital economic analysis and opens avenues for further research on hybrid models and multi-source data integration within the digital economy ecosystem.
Sentiment Analysis of JMO Application Reviews on the Google Play Store Using BERT Hendi Putra Wijaya; Adhityah Anugrah; Mira Afrina; Ali Ibrahim
Journal Informatic, Education and Management (JIEM) Vol 8 No 1 (2026): FEBRUARY (CALL FOR PAPERS)
Publisher : STMIK Indonesia Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61992/jiem.v8i1.250

Abstract

The development of digital technology has encouraged increased use of online-based public service applications, including the JMO (Jamsostek Mobile) application developed by BPJS Ketenagakerjaan to provide easy access for its participants. This application has received many user reviews on the Google Play Store, reflecting the level of satisfaction and public perception of service quality. However, the large and unstructured volume of comments makes manual analysis difficult. This study aims to conduct sentiment analysis on user comments about the JMO application on the Play Store using the Bidirectional Encoder Representations from Transformers (BERT) model. The research method involves collecting comments through web scraping, text preprocessing (such as data cleaning, normalization, and tokenization), and sentiment labeling (positive, negative, and neutral). Evaluation using precision, recall, and F1-score is employed to describe the results. The study is expected to identify patterns of user sentiment and public perceptions of the JMO application. It is also expected to serve as an evaluation material and input for developers to improve service quality and user experience.
Implementasi Aplikasi E-Arsip Surat Keluar Untuk Meningkatkan Tertib Administrasi di Kelurahan Plaju Darat Naretha Kawadha Pasemah Gumay; Purwita Sari; Ermatita; Mira Afrina; Miftahul Falah; Junia Kurniati; Willy; Iin Seprina; Ari Wedhasmara; Muhammad Ichsan Hadjri
JURNAL ABDIMAS MADUMA Vol. 5 No. 1 (2026): Januari, 2026
Publisher : English Lecturers and Teachers Association (ELTA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52622/jam.v5i1.591

Abstract

Pengelolaan arsip surat keluar di Kelurahan Plaju Darat masih dilakukan secara manual sehingga sering menimbulkan masalah seperti pencarian arsip yang lama, risiko kehilangan dokumen, penomoran tidak tertib, dan keterbatasan ruang penyimpanan. Kondisi ini menghambat pelayanan administrasi. Kegiatan pengabdian ini bertujuan mengimplementasikan aplikasi e-arsip berbasis web untuk meningkatkan ketertiban, efisiensi, dan kecepatan pengelolaan arsip. Mitra kegiatan adalah aparatur kelurahan dan perangkat RT/RW dengan 35 peserta pelatihan. Pelaksanaan meliputi identifikasi masalah, analisis kebutuhan, perancangan, pengembangan, uji coba aplikasi, pelatihan, pendampingan, dan evaluasi. Proses dilakukan secara partisipatif melalui observasi, wawancara, FGD, dan praktik langsung. Hasil menunjukkan aplikasi berhasil diterapkan dan digunakan aktif. Secara kualitatif, sistem mempercepat pencatatan, meningkatkan ketertiban data, serta memudahkan pencarian dan pelaporan. Secara kuantitatif, waktu pencarian surat turun dari ±30 menit menjadi kurang dari 1 menit, kesalahan pencatatan berkurang signifikan, dan setidaknya tiga aparatur telah menguasai seluruh prosedur aplikasi. Kegiatan ini berdampak pada peningkatan kapasitas digital aparatur, modernisasi administrasi kelurahan, mendukung SDGs 9, serta berkontribusi pada pencapaian IKU perguruan tinggi. Kata Kunci : E-arsip; Surat keluar; administrasi kelurahan
Analisis Sentimen Aplikasi MPStore Menggunakan Algoritma Logistic Regression dan LDA Tia Arlin Dita; Ali Ibrahim; Rizka Rahmadhani; Mira Afrina
JSAI (Journal Scientific and Applied Informatics) Vol 9 No 1 (2026): Januari
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v9i1.9557

Abstract

The rapid growth of the digital economy encourages user satisfaction as the key to successful application innovation. Within technopreneurship, understanding user sentiment is essential for sustainable product development. This study aims to analyze sentiment and identify the deter-minants of user satisfaction regarding the MPStore application based on reviews from the Google Play Store. Review data were collected via scraping and analyzed using Logistic Regression (LR) for sentiment classification (positive, negative, neutral) also Latent Dirichlet Al-location (LDA) for satisfaction topic extraction. The result shows that the LR model achieved an accuracy of 88.5%. The LDA analysis also successfully revealed eight main topics, includ-ing ease of use, transaction speed, and technical obstacles (errors, login, balance issues). Over-all, a majority of users hold a positive perception of MPStore's efficiency and ease of transac-tions. This study concludes that the combination of sentiment analysis and topic modeling is effective for explaining the level of user satisfaction and providing a strategic foundation for digital application developers.
Co-Authors Abdiansah, Abdiansah Ade Iriani Sapitri Adhityah Anugrah Ahmad Fali Oklilas Ahmad Fali Oklilas Ahmad Fali Oklilas Ahmad Fali Oklilas Ahmad Rifai Aini Nabilah Al Farissi Ali Ibrahim Ali Ibrahim Ali Ibrahim Annisa Darmawahyuni Apriansyah Putra - Ari Wedhasmara Ariani, Ardina Asyrof Fitrah Baidhawi, Alif Bayu Wijaya Putra Cendikiawan, Rizky Saputra Damayanti, Risma Darmawahyuni, Annisa Dedeng Zamawi Dicha Pratiwi Dinna Yunika Hardiyanti Dwi Rosa Indah 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 Hendi Putra Wijaya Iin Seprina Iredho Fani Reza Irvan Fahreza Islamiansyah, Wira Junia Kurniati Ken Dihta Tania Ken Ditha Tania Kesuma, Lucky Indra Kharisma, Agung Chandra Kodri, Lay Kurnia, Rizka Dhini  Lakeisyah, Eka Therina Lay Kodri Leonardi, Veronica Hertensia M. Aris Garniardi Miftahul Falah Muhammad Anshori Muhammad Fachrurrozi Muhammad Fachrurrozi Muhammad Naufal Rachmatullah Nabila Hidayati Naretha Kawadha Pasemah Gumay Nashiroh Ramadhani, Muthia Nia Meitisari Nurlayli Indah Sari Nurullah Marina Kelana Oky Budiyarti Opi Hernayanti Ovi Dyantina Pacu Putra Purwita Sari Putri Eka Sevtiyuni Rahmat Izwan Heroza Redha Bayu Anggara Rezqe, Beriadi Agung Nur Risma Damayanti Rizka Dhini Rizka Dhini Kurnia Rizka Dhini Kurnia Rizka Dhini Kurnia Rizka Rahmadhani Sabila, Amalia Sahira, Mutia Sapitri, Ade Iriani Saputra, Muhammad Haykal Alfariz Sartika, Widya Seprina, Iin Septiani Aulia Putri Sevtiyuni, Putri Eka Siti Nurmaini Sri Desy Siswanti Suci Dwi Lestari Suci Dwi Lestari Tasmi Tasmi Tasmi Tasmi Tia Arlin Dita Tumpol S Simarmata Welly Nailis Willy Winda Kurnia Sari 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