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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 Jurnal Pengabdian Masyarakat Bumi Raflesia Journal Informatic, Education and Management (JIEM) Jurnal Generic Jurnal Informatika dan Teknologi Komputer ( J-ICOM) Decode: Jurnal Pendidikan Teknologi Informasi 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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PELATIHAN DALAM PENGEMBANGAN MEDIA PEMBELAJARAN BERBASIS ICT UNTUK GURU SEKOLAH YAYASAN AZIZAH KOTA PALEMBANG DALAM MENDUKUNG PROSES PEMBELAJARAN PADA MASA PANDEMI COVID 19 Utama, Yadi; Ibrahim, Ali; Afrina, Mira; Rezqe, Beriadi Agung Nur; Kodri, Lay; Zhafiri, Muhammad Farisan; Islamiansyah, Wira; Yunus, Hedi; Zaini, Akbar Al
Jurnal Pengabdian Masyarakat Bumi Rafflesia Vol. 4 No. 3 (2021): Jurnal Pengabdian Masyarakat Bumi Raflesia
Publisher : Universitas Muhammadiyah Bengkulu

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

Abstract

Proses belajar mengajar di sekolah-sekolah tersebut secara umum telah berjalan dengan baik, tetapi rata-rata hasil belajar siswa masih tergolong rendah. Menurut informasi beberapa guru, rendahnya hasil belajar siswa salah satunya disebabkan karena guru belum memaksimalkan penggunaan media animasi dalam proses pembelajaran. Proses pembelajaran masih berlangsung secara konvensional, dimana aktivitas menulis lebih dominan dilakukan oleh guru dalam mengajar. Alasan utama mengapa para guru belum menggunakan media animasi dalam pembelajaran antara lain karena para guru belum mengerti, belum mamahami bagaimana cara membuat media ajar berbasis ICT dan animasi. Guru adalah pendidik profesional dengan tugas utama mendidik, mengajar, membimbing, mengarahkan, melatih, memberi teladan, menilai dan mengevaluasi peserta didik. Karena guru adalah SDM yang terdidik, potensi tersebut dapat ditingkatkan dengan meningkatkan pengetahuan dan pemahaman serta kemampuan guru dalam pengelolaan bidang computer.Kata Kunci: SDM, ICT, Movie maker
The Influence of Experience-Centric IT Governance on Digital Ethics Perception in Social Commerce Gumay, Naretha Kawadha Pasemah; Afrina, Mira; Indah, Dwi Rosa; Sari, Winda Kurnia; Sartika, Widya
SISTEMASI Vol 15, No 1 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i1.5750

Abstract

The Influence of Knowledge Management and Digital Competence on Employee Performance: Mediating Role of Innovative Behavior Sabila, Amalia; Afrina, Mira; Tania, Ken Ditha
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11529

Abstract

Rapid technological changes in the era of Industry 4.0 and 5.0 have made digital knowledge and skills more important in improving the way employees perform their tasks. Earlier research has given mixed results. This shows there is still a lot to learn. Based on the KBV (Knowledge Based-View) theory, this study looks at how knowledge management and digital competence directly and indirectly affect employee performance through innovative work behavior. Data were obtained using a questionnaire that had been compiled and analyzed with Partial Least Squares-Structural Equation Modeling (PLS-SEM) method with SmartPLS 4.1.1.4. The research sample included all employees in the case study (N = 56), with census sampling method. The study found that KM had a significant impact on IWB (p < 0,05), but did not have a significant direct impact on EP (p > 0,05). DC had a significant impact on EP (p < 0,05), but did not have a significant impact on IWB (p > 0,05). IWB played an important role in improving EP and also mediated the relationship between KM and EP. Theoretically, this study adds value to both the KBV theory by explaining how KM boosts performance through indirect ways, and by showing that digital capital plays a limited role in improving performance. Practically, the findings offer actionable implications for HR practitioners in designing performance systems that reward innovative behaviour, thereby motivating employees to utilize knowledge and digital tools more creatively to enhance productivity and service quality in medium enterprises.
Knowledge Discovery in Sharia Mobile Banking Reviews Using Aspect-Based Sentiment Analysis and Machine Learning Nashiroh Ramadhani, Muthia; Ditha Tania, Ken; Afrina, Mira
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.11753

Abstract

User reviews provide important insights into the quality of digital banking applications; however, their large volume makes manual analysis inefficient. This study applies Aspect-Based Sentiment Analysis (ABSA) to examine user perceptions of the BYOND by BSI application based on three aspects: interface, features and performance, and services. Three classification algorithms were compared: Naïve Bayes, Support Vector Machine (SVM), and Random Forest, evaluated with accuracy, precision, recall, F1-score, and ROC-AUC. The results indicate that SVM and Naïve Bayes achieved the best performance, with an accuracy of 0.95 and an F1-score of 0.92, whereas Random Forest exhibited slightly lower performance with an F1-score of 0.89. Furthermore, sentiment analysis reveals the features and performance aspect exhibits the highest proportion of negative sentiment (39.6%), primarily associated with system reliability issues, login problems, transaction failures, and application instability. These findings demonstrate that ABSA can serve as an effective knowledge discovery approach for identifying critical functional issues and supporting data-driven prioritization in improving digital banking services, particularly within the context of sharia banking applications.
The Sentiment Analysis Of Indonesian Startup Application Reviews Using TF-IDF+SVM and FastText: A Comparative Study Aini Nabilah; Nurlayli Indah Sari; Mira Afrina; Ali Ibrahim
Journal of Information Technology and Computer Science Vol. 10 No. 3: Desember 2025
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jitecs.2025103807

Abstract

The rapid rise of startups in Indonesia makes user reviews on the Google Play Store a valuable data source for understanding user perceptions and satisfaction. These unstructured reviews contain insights supporting product development and business strategies. This study analyzes sentiments in Indonesian startup app reviews and compares two classification methods: TF-IDF + Linear SVM and fastText, implemented using Google Colab. Reviews were collected in September 2025 using google-play-scraper; 4,000 reviews were retrieved and refined into 3,152 unique reviews after cleaning and preprocessing. Sentiment labeling used ratings (1–2 negative, 4–5 positive); because the neutral class was limited, this study focuses on balanced binary classification with 1576 positive and 1576 negative reviews. The process involves data scraping, text preprocessing, model training, and evaluation using accuracy, precision, recall, and F1-score metrics, with Linear SVM chosen as an efficient baseline for high-dimensional sparse TF-IDF features. Results show that fastText achieves 91.88% accuracy and an F1-macro of 0.9184, slightly outperforming TF-IDF + SVM (F1-macro 0.9103), suggesting that the embedding-based approach better captures semantic nuances of Indonesian text. Future work may extend this study to ABSA to assess sentiments toward price, UI/UX, and customer service for deeper technopreneurship insights in Indonesia.
Identifikasi Pola Fraud pada Ekosistem Pembayaran Digital menggunakan Metode Isolation Forest Akbar, M. Willi; Kusuma Ningrum, Septiani; Afrina, Mira; Ibrahim, Ali
Jurnal Informatika dan Teknologi Komputer (J-ICOM) Vol 7 No 01 (2026): Jurnal Informatika dan Teknologi Komputer ( J-ICOM)
Publisher : E-Jurnal Universitas Samudra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55377/j-icom.v7i01.13622

Abstract

Transformasi menuju ekonomi digital di Indonesia dihadapkan pada tantangan krusial berupa meningkatnya serangan fraud yang semakin canggih. Penelitian ini mengajukan sebuah pendekatan unsupervised learning untuk mengenali pola serangan fraud generasi baru sebagai dasar penguatan kapasitas supervisi institusional. Penelitian ini berfokus pada identifikasi anomali tanpa bergantung pada label historis yang ada dengan memanfaatkan algoritma ensemble Isolation Forest. Model berhasil memetakan karakteristik transaksi yang mencurigakan berkat penerapan rekayasa fitur yang mendalam, yang mencakup analisis perilaku, korelasi alamat, dan ekstraksi sinyal dari IP. Hasil evaluasi menunjukkan bahwa pendekatan unsupervised ini mampu mengidentifikasi 18% dari total kasus fraud yang telah dilabelkan, membuktikan relevansinya dalam menangkap sinyal serangan yang sesungguhnya. Lebih penting lagi, analisis kualitatif terhadap anomali yang ditemukan berhasil mengkarakterisasi sebuah Pola Serangan Senyap, yaitu kombinasi multi-faktor risiko yang berpotensi terlewatkan oleh sistem deteksi konvensional. Temuan ini menyajikan sebuah wawasan baru bagi institusi regulator untuk beralih dari supervisi reaktif ke penemuan ancaman proaktif, yang pada akhirnya mendukung terciptanya ekosistem keuangan digital yang aman dan berkelanjutan sejalan dengan Tujuan Pembangunan Berkelanjutan (SDGs).
Penggunaan Metode Multimedia Development Life Cycle (MDLC) Dalam Game Edukasi Virtual Kampus Universitas Sriwijaya Pada Platform Roblox Hakim, Adzka Fahmi Aulia; Meiriza, Allsela; Afrina, Mira; Kurnia, Rizka Dhini; Putra, Pacu
Decode: Jurnal Pendidikan Teknologi Informasi Vol. 6 No. 1: MARET 2026
Publisher : Program Studi Pendidikan Teknologi Infromasi UMK

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51454/decode.v6i1.1485

Abstract

Promosi institusi pendidikan di era digital menuntut inovasi media yang interaktif, di mana platform metaverse seperti Roblox menawarkan potensi besar untuk pengalaman imersif dan partisipatif yang melampaui media konvensional. Penelitian ini bertujuan untuk merancang dan mengembangkan sebuah game edukasi virtual Kampus Universitas Sriwijaya yang berlokasi di Palembang, yang berfungsi sebagai media promosi dan pengenalan lingkungan kampus yang interaktif bagi calon mahasiswa dan mahasiswa baru. Metode penelitian yang digunakan adalah Multimedia Development Life Cycle (MDLC) yang mencakup enam tahapan sistematis: Concept, Design, Material Collecting, Assembly, Testing, dan Distribution. Pengujian produk dilakukan melalui pengujian menggunakan User Experience Questionnaire (UEQ) yang disebarkan kepada 200 responden mahasiswa baru Universitas Sriwijaya. Hasil penelitian ini adalah sebuah game edukasi virtual yang fungsional dan telah berhasil dipublikasikan di platform Roblox, lengkap dengan visualisasi 3D lingkungan kampus, fitur eksplorasi, dan interaksi multipemain. Hasil testing menggunakan UEQ menunjukkan bahwa game ini mendapatkan evaluasi sangat positif pada keenam dimensi (Daya Tarik, Kejelasan, Efisiensi, Ketepatan, Stimulasi, dan Kebaruan), dengan nilai rata-rata tertinggi pada aspek Daya Tarik (1,90). Disimpulkan bahwa metode MDLC berhasil diterapkan secara efektif untuk membangun game edukasi ini, dan produk yang dihasilkan terbukti sangat diterima dengan baik oleh pengguna sebagai media pengenalan kampus yang inovatif dan menarik.
Development of a Flask-based Application for Bank Customer Churn Prediction as a Decision Support Tool Suluh Arif Wibowo; Muhammad Rezky; Ali Ibrahim; Mira Afrina; Fathoni Fathoni
Sistemasi: Jurnal Sistem Informasi Vol 15, No 4 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i4.6257

Abstract

Customer churn prediction is a crucial aspect of the banking industry for maintaining customer loyalty and reducing the cost of acquiring new customers. This study aims to develop a web-based decision support system capable of predicting potential customer churn using the Gradient Boosting Machine (GBM) algorithm. The dataset used is the Bank Customer Churn Dataset, consisting of 10,000 customer records with 14 attributes. The research stages include exploratory data analysis and preprocessing, which involves data cleaning, categorical feature encoding, feature engineering (BalanceSalaryRatio, TenureByAge, CreditScoreGivenAge), and data balancing using SMOTE to address class imbalance. The GBM model was trained on the balanced dataset and evaluated using accuracy, precision, recall, and F1-score metrics. The evaluation results show that the model achieved an accuracy of 83.95%, with a recall of 67.32% for the churn class, indicating a strong capability in identifying customers at risk of churn. Feature importance analysis reveals that Age and NumOfProducts are the most dominant features, contributing approximately 77% to the prediction. The model was then implemented in a Flask-based web application with an HTML and CSS interface, enabling non-technical users to perform real-time churn predictions. This system is expected to assist banking institutions in designing more targeted and data-driven customer retention strategies.
Analysis of User Satisfaction Levels in the Shopee PayLater System using the User Experience Questionnaire (UEQ) Aliyah Khofifah; Apriansyah Putra; Ari Wedhasmara; Mira Afrina
Sistemasi: Jurnal Sistem Informasi Vol 15, No 5 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i5.6461

Abstract

The rapid advancement of information technology in the digital era has triggered significant changes across various aspects of life, including the e-commerce sector. This study aims to analyze the level of user satisfaction with the Shopee PayLater system using the User Experience Questionnaire (UEQ) method. The research was motivated by the increasing use of PayLater services in e-commerce and the importance of evaluating user experience to improve system quality. This study employed a quantitative method using the User Experience Questionnaire (UEQ) approach, which consists of six dimensions: attractiveness, perspicuity, efficiency, dependability, stimulation, and novelty. Data were collected through questionnaires distributed to 100 students from the Faculty of Computer Science at Sriwijaya University and analyzed using the UEQ Data Analysis Tools. The results indicate that Shopee PayLater achieved positive user satisfaction across all UEQ dimensions. The highest scores were obtained in perspicuity (2.03), efficiency (1.89), dependability (1.89), and attractiveness (1.66), indicating that Shopee PayLater is easy to understand, efficient to use, and capable of providing user comfort. Meanwhile, the stimulation (1.65) and novelty (1.56) dimensions still require improvement through feature development and service innovation to create a more engaging user experience. In addition, the benchmark results show that all dimensions fall within the excellent category and are included in the top 10% of benchmark results, indicating a very high-quality user experience. Based on these findings, it can be concluded that Shopee PayLater provides an excellent user experience overall. However, the stimulation and novelty aspects still need enhancement through feature innovation and interface improvements to make the service more attractive and less monotonous for users.
Penilaian Risiko Fraud Transaksi Digital menggunakan Hybrid Machine Learning dengan Clustering dan Klasifikasi Hendra Wijaya; Naek Parulian Hutagalung; Mira Afrina; Ali Ibrahim; Fathoni
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3398

Abstract

Credit card transaction fraud detection is commonly treated as a binary classification problem, whereas operational risk management requires more detailed risk-level information to support investigation prioritization. This study proposes a hybrid machine learning framework for transaction risk stratification. In the first stage, the K-Means algorithm was applied to the training set to discover latent risk structures and generate cluster-based risk labels. Subsequently, a Random Forest model was trained to predict risk levels for new transaction data. To maintain evaluation objectivity, the dataset was divided into training, validation, and testing sets, and data leakage prevention mechanisms were implemented. The testing results show that the model was able to consistently classify two levels of risk with stable precision, recall, and F1-score values. In the binary fraud detection scenario, the model achieved an accuracy of 0.8831. These findings indicate that separating latent risk exploration from predictive classification can produce a more informative risk representation compared to conventional binary approaches. However, this study is still limited to a single public dataset and one classification model. Therefore, the generalizability and potential performance improvements of the model still need to be evaluated by experimenting with other algorithms.
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 Akbar, M. Willi Al Farissi Ali Ibrahim Ali Ibrahim Ali Ibrahim Aliyah Khofifah Allsela Meiriza, Allsela Annisa Darmawahyuni Apriansyah Putra Apriansyah Putra - Ari Wedhasmara Ariani, Ardina Asyrof Fitrah 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 Fathoni - Febriady, Mukhlis Firdaus Firdaus - Firdaus Firdaus Firdaus Firdaus Firmansyah, M. Daffa Gumay, Naretha Kawadha Pasemah Gustin Saputri Hadini Novianti Hafiiz Kresna Prasetya Hakim, Adzka Fahmi Aulia Hardini Novianti Hardini Novianti Hardini Novianti Hendi Putra Wijaya Hendra Wijaya Iin Seprina Iredho Fani Reza Irvan Fahreza Islamiansyah, Wira Junia Kurniati Ken Dihta Tania Ken Ditha Tania Kesuma, Lucky Indra Kodri, Lay Kurnia, Rizka Dhini  Kusuma Ningrum, Septiani Lakeisyah, Eka Therina Lay Kodri Leonardi, Veronica Hertensia M. Aris Garniardi Miftahul Falah Muhammad Anshori Muhammad Fachrurrozi Muhammad Fachrurrozi Muhammad Naufal Rachmatullah Muhammad Rezky Nabila Hidayati Naek Parulian Hutagalung 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 Sartika, Widya Seprina, Iin Septiani Aulia Putri Sevtiyuni, Putri Eka Siti Nurmaini Sri Desy Siswanti Suci Dwi Lestari Suci Dwi Lestari Suluh Arif Wibowo 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