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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Penelitian Pendidikan Matematika dan Sains JIEET (Journal of Information Engineering and Educational Technology) JOURNAL OF APPLIED INFORMATICS AND COMPUTING PengabdianMu: Jurnal Ilmiah Pengabdian kepada Masyarakat Journal of Electronics, Electromedical Engineering, and Medical Informatics Journal of Robotics and Control (JRC) Jurnal Abdi: Media Pengabdian Kepada Masyarakat Indonesian Journal of Electrical Engineering and Computer Science Abdimas: Papua Journal Of Community Service Insyst : Journal of Intelligent System and Computation Jurnal Nasional Teknik Elektro dan Teknologi Informasi Journal of Informatics and Computer Science (JINACS) Ranah Research : Journal of Multidisciplinary Research and Development Serambi Engineering Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Journal of Emerging Information Systems and Business Intelligence (JEISBI)
YUNI YAMASARI
Teknik Informatika, Universitas Negeri Surabaya
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Public Health Social Sciences Transportation Other

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Hybrid Autoencoder Architectures with LSTM and GRU Layers for Bitcoin Price Prediction Yamasari, Yuni; Nafisah, Nurun; Yohannes, Ervin
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 4 (2025): November
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v7i4.132

Abstract

The high volatility of cryptocurrency markets, particularly Bitcoin, poses significant challenges for accurate price forecasting. To address this issue, this study evaluates the performance of four autoencoder-based deep learning architectures: AE-LSTM, AE-GRU, AE-LSTM-GRU, and AE-GRU-LSTM. The models were developed and tested using a univariate approach, where only the closing price was used as input, and two different window sizes (30 and 60) were applied to analyse the effect of historical sequence length on prediction accuracy. Several parameter configurations, including the number of epochs, dropout rate, and learning rate, were explored to determine the optimal model performance. The dataset comprises Bitcoin’s daily closing prices from 2018 to 2025, encompassing diverse market phases, including both bullish and bearish trends. Model performance was assessed using four evaluation metrics: Root Mean Square Error (RMSE), Mean Absolute Error (MAE), the coefficient of determination (R²), and Mean Absolute Percentage Error (MAPE). The results indicate that the AE-LSTM-GRU consistently achieved the best overall performance across all configurations. For a window size of 30, it achieved an RMSE of 1.53067 and a MAPE of 1.98%, while for a window size of 60, the best performance recorded was an RMSE of 1.55217 and a MAPE of 2.09%. The hybrid structure combining LSTM’s capability to capture long-term dependencies with GRU’s efficiency in information decoding demonstrated strong robustness in modelling highly volatile time series. This study contributes to financial time series forecasting by presenting hybrid autoencoder architectures that strike a balance between predictive accuracy and computational efficiency, providing practical insights for researchers and practitioners in financial technology and cryptocurrency analytics
Otomatisasi Klasifikasi Tingkat Urgensi Keluhan E-Layanan Unesa Berbasis TF-IDF dan Logistic Regression Alpiana, Intan; Yustanti, Wiyli; Yamasari, Yuni
Ranah Research : Journal of Multidisciplinary Research and Development Vol. 8 No. 1 (2025): Ranah Research : Journal Of Multidisciplinary Research and Development
Publisher : Dinasti Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38035/rrj.v8i1.1912

Abstract

Perkembangan teknologi digital menuntut perguruan tinggi untuk menghadirkan layanan akademik yang cepat, tepat, dan responsif. Universitas Negeri Surabaya (Unesa) melalui platform E-Layanan memberikan sarana bagi civitas akademika untuk menyampaikan keluhan terkait kendala penggunaan sistem informasi dan jaringan. Namun, proses klasifikasi tingkat urgensi keluhan masih dilakukan secara manual oleh admin, yang berpotensi menyebabkan keterlambatan penanganan, inkonsistensi penilaian, serta meningkatnya beban kerja. Penelitian ini bertujuan untuk mengembangkan sistem otomatisasi klasifikasi tingkat urgensi keluhan dengan memanfaatkan Term Frequency-Inverse Document Frequency (TF-IDF) sebagai representasi fitur teks, serta Logistic Regression berbobot (class_weight) sebagai model klasifikasi utama. Dataset yang digunakan terdiri dari 79.303 keluhan, dibagi menjadi data latih (70%), validasi (15%), dan uji (15%). Evaluasi model dilakukan menggunakan metrik akurasi, presisi, recall, F1-score, dan confusion matrix. Hasil penelitian menunjukkan bahwa kombinasi TF-IDF dan Logistic Regression berbobot mampu memberikan kinerja yang baik dengan akurasi 92,54% pada data uji. Selain itu, model menunjukkan kemampuan yang tinggi dalam mendeteksi keluhan kritis secara akurat, memastikan prioritas penanganan terjaga secara optimal. Temuan ini menegaskan bahwa penerapan model berbasis pembelajaran mesin dapat meningkatkan efisiensi operasional dan konsistensi klasifikasi dibandingkan pendekatan manual. Sistem yang dikembangkan diharapkan dapat diintegrasikan lebih lanjut ke dalam platform E-Layanan Unesa, mendukung proses penanganan keluhan secara otomatis dan real-time, serta membantu administrasi fokus pada resolusi masalah yang paling mendesak.
PELATIHAN MEDIA PEMBELAJARAN MENGGUNAKAN CANVA UNTUK GURU MI AL AHMAD, KRIAN, SIDOARJO Naim Rochmawati; Yamasari, Yuni; Yustanti, WIyli; Qoiriah, Anita; Aviana, Anisah Nurul
Jurnal ABDI: Media Pengabdian Kepada Masyarakat Vol. 9 No. 1 (2023): JURNAL ABDI : Media Pengabdian Kepada masyarakat
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/abdi.v9i1.19853

Abstract

Dalam proses belajar mengajar, media pembelajaran berperan penting. Media pembelajaran yang interaktif membantu para siswa lebih mudah dalam memahami konten materi yang disampaikan para guru. Dengan kesadaran untuk meningkatkan kemampuan dalam membuat media pembelajaran agar kualitas pembelajaran semakin meningkat, para guru MI Al Ahmad, Krian, Sidoarjo, meminta pelatihan pembuatan media pembelajaran. banyak tool yang bisa digunakan, salah satunya adalah Canva. Dalam Canva, disediakan banyak fasilitas menu untuk membuat media pembelajaran yang interaktif. Untuk itu, pelatihan kali ini adalah memberikan pelatihan Canva bagi para guru MI Al Ahmad untuk meningkatkan kemampuan digital para guru MI Al Ahmad dalam membuat media pembelajaran yang interaktif. Metode kegiatan adalah dengan model ceramah dilanjutkan dengan praktikum menggunakan Canva. Hasil dari pelatihan ini adalah kemampuan para guru MI Al Ahmad dalam membuat media pembelajaran interaktif menggunakan Canva. Dari hasil evaluasi kegiatan disimpulkan bahwa pelatihan ini dapat dikatakan berhasil meskipun masih perlu penyempurnaan dalam kegiatan yang dilakukan. Hal ini diindikasikan dengan respon yang diberikan oleh guru MI Al Ahmad, sebagai peserta pelatihan, pada angket online yang dibagikan setelah selesai pelatihan.
Pelatihan Pemanfaatan Internet untuk Menunjang Kreativitas Guru dalam Penyampaian Materi secara Daring Yamasari, Yuni; Qoiriah, Anita; Yustanti, Wiyli; Rochmawati, Naim; Nurhidayat, Andi Iwan; Kurniawan, Ari
Abdimas: Papua Journal of Community Service Vol. 6 No. 1 (2024): Januari
Publisher : Lembaga Pengembangan dan Pengabdian Masyarakat Universitas Muhammadiyah Sorong

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33506/pjcs.v6i1.2749

Abstract

   
Deteksi Validitas Berita pada Media Sosial Twitter dengan Algoritma Naive Bayes Setiawan, Esther Irawati; Johanes, Sugiharto; Hermawan, Arya Tandy; Yamasari, Yuni
Intelligent System and Computation Vol 3 No 2 (2021): INSYST: Journal of Intelligent System and Computation
Publisher : Institut Sains dan Teknologi Terpadu Surabaya (d/h Sekolah Tinggi Teknik Surabaya)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52985/insyst.v3i2.164

Abstract

Banyaknya berita-berita online sering menarik minat masyarakat untuk membacanya, tetapi kadang dengan terlalu banyaknya berita tersebut membuat orang susah mendapatkan informasi yang terpercaya. Berita palsu merupakan kumpulan kata atau kalimat yang mengandung informasi yang tidak benar yang berupaya untuk membohongi atau mengarahkan pembaca atau pendengarnya agar mendukung atau percaya dengan isi beritanya. Penyebar berita palsu umumnya mengetahui bahwa berita yang disebarkan tidak benar. Tujuan penelitian ini adalah mendeteksi berita palsu yang tersebar pada media sosial. Dalam mengklasifikasi berita palsu, deteksi validitas berita digunakan algoritma naïve bayes sebagai kategorisasi teks berbasis pembelajaran mesin. Penelitian ini juga membangun website yang menyediakan fitur web service, pencarian berita yang ada di Twitter, dan klasifikasi berita secara manual. User interface merupakan website berbasis PHP dimana pengguna dapat melakukan interaksi secara langsung sepeti komentar, login, atau melihat artikel-artikel yang sudah diklasifikasi. Sedangkan back-end dari website ini adalah program klasifikasi teks berbasis Python. Dari percobaan yang telah dilakukan ternyata algoritma Naïve Bayes dapat digunakan untuk mengklasifikasi berita palsu. Berdasarkan eksperimen, penggunaan metode naive bayes untuk deteksi validitas berita dengan data uji media social Twitter dapat mencapai nilai akurasi dengan persentase terbaik yaitu 92% pada data ujicoba sebesar 309 artikel.
Rule-Based Adaptive Chatbot on WhatsApp for Visual, Auditory, and Kinesthetic Learning Style Detection Rahulil, Muhammad; Yamasari, Yuni; Putra, Ricky Eka; Suartana, I made; Qoiriah, Anita
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 8 No 1 (2026): January
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v8i1.1215

Abstract

Adapting learning methods to individual learning styles remains a major challenge in digital education due to the static nature of traditional questionnaires and the absence of adaptive feedback mechanisms. This study aimed to develop a rule-based adaptive WhatsApp chatbot capable of automatically identifying users’ learning styles, visual, auditory, and kinesthetic, through a weighted questionnaire enhanced with probabilistic refinement. The proposed system introduces an adaptive decision framework that dynamically manages conversation flow using score dominance evaluation, early termination, and selective question expansion. Bayesian posterior probability estimation is employed to strengthen decision confidence in borderline cases, ensuring consistent and interpretable results even when user responses are ambiguous. The chatbot was implemented using WhatsApp-web.js and MongoDB, supported by session validation and activity log monitoring to ensure operational reliability and data integrity. System validation involved white-box testing using Cyclomatic Complexity to verify logical accuracy and 20-fold cross-validation using a Support Vector Machine (SVM) to evaluate classification performance. The adaptive model achieved an accuracy of 80.2% and an AUC of 0.902, supported by a balanced precision (0.738), recall (0.662), and F1-score (0.698). These results demonstrate stable discriminative capability and confirm that the adaptive scoring mechanism effectively reduces redundant questioning, lowers cognitive load, and improves interaction efficiency without compromising reliability. In conclusion, the study successfully achieved its objective of developing an adaptive, efficient, and mathematically transparent learning style detection system. The findings confirm that adaptive rule-based logic reinforced by probabilistic reasoning can significantly enhance the efficiency and reliability of digital learning assessments. Future research will extend this framework by incorporating multimodal behavioral indicators and personalized learning content to further strengthen adaptive learning support
Deep Learning-Based Detection of Online Gambling Promotion Spam in Indonesian YouTube Comments Ammar, Muhammad Zhafran; Putra, Ricky Eka; Yamasari, Yuni
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.v9i6.11240

Abstract

Online gambling promotion has increasingly penetrated social media platforms, with YouTube comments becoming a frequent target for spam-based advertising. Such activities not only violate platform policies but also expose users to harmful content. Addressing this issue requires automated detection systems capable of handling noisy, informal, and highly imbalanced text data. This study investigates the effectiveness of four recurrent neural architectures LSTM, GRU, BiLSTM, and BiGRU for detecting gambling promotion comments in Indonesian YouTube data. To address class imbalance, multiple experimental scenarios were explored, including the original distribution, undersampling, oversampling, and class weighting. Model performance was evaluated using accuracy, precision, recall, F1-score, ROC-AUC, and confusion matrix analysis. The results show that bidirectional models outperformed their unidirectional counterparts, with BiGRU achieving the best overall performance. When combined with class weighting, BiGRU reached 98% accuracy, 0.83 F1-score, and 0.971 ROC-AUC, demonstrating a superior ability to detect minority-class instances. Oversampling improved recall substantially but increased false positives, while undersampling reduced accuracy; class weighting provided the most balanced performance across metrics. These findings confirm that BiGRU with class weighting offers the most practical balance between accuracy, recall, and computational efficiency, making it well-suited for real-time moderation systems. The study provides a strong foundation for future research on transformer-based architectures and cross-platform spam detection in Indonesian social media environments.
MD-ViT: Multidomain Vision Transformer Fusion for Fair Demographic Attribute Recognition Putri, Rezky Arisanti; Putra, Ricky Eka; Yamasari, Yuni
JIEET (Journal of Information Engineering and Educational Technology) Vol. 9 No. 2 (2025)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jieet.v9n2.p64-79

Abstract

Demographic attribute recognition particularly race and gender classification from facial images, plays a critical role in applications ranging from precision healthcare to digital identity systems. However, existing deep learning approaches often suffer from algorithmic bias and limited robustness, especially when trained on imbalanced or non-representative data. To address these challenges, this study proposes MD-ViT, a novel framework that leverages multidomain Vision Transformer (ViT) fusion to enhance both accuracy and fairness in demographic classification. Specifically, we integrate embeddings from two task-specific pretrained ViTs: ViT-VGGFace (fine-tuned on VGGFace2 for structural identity features) and ViT-Face Age (trained on UTKFace and IMDB-WIKI for age-related morphological cues), followed by classification using XGBoost to model complex feature interactions while mitigating overfitting. Evaluated on the balanced DemogPairs dataset (10,800 images across six intersectional subgroups), our approach achieves 89.07% accuracy and 89.06% F1-score, outperforming single-domain baselines (ViT-VGGFace: 88.61%; ViT-Age: 78.94%). Crucially, fairness analysis reveals minimal performance disparity across subgroups (F1-score range: 87.38%–91.03%; σ = 1.33), indicating effective mitigation of intersectional bias. These results demonstrate that cross-task feature fusion can yield representations that are not only more discriminative but also more equitable. We conclude that MD-ViT offers a principled, modular, and ethically grounded pathway toward fairer soft biometric systems, particularly in high-stakes domains such as digital health and inclusive access control.
A Performance Comparison of LSTM and GRU Architectures for Forecasting Daily Bitcoin Price Volatility Nafisah, Nurun; Yamasari, Yuni; Yohannes, Ervin
JIEET (Journal of Information Engineering and Educational Technology) Vol. 9 No. 2 (2025)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jieet.v9n2.p156-167

Abstract

The highly volatile movement of Bitcoin prices necessitates the use of prediction methods capable of accurately capturing complex and rapidly changing patterns. This study aims to compare the performance of two recurrent neural network architectures, namely Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), in forecasting Bitcoin prices based on historical time series data. The analysis was conducted using daily closing price data, with several parameter configurations applied, including dropout value, learning rate, and number of epochs at a window size of 30. The training process was carried out using a univariate approach to assess the fundamental ability of each model to learn temporal patterns without the influence of external variables. The results indicate that the GRU model consistently outperforms LSTM across most experimental settings. The best performance was achieved with 30 epochs, dropout 0.1, and a learning rate of 0.001, producing RMSE 1478.333, MAE 1000.900, R² 0.996081, and MAPE 1.973072. These metrics demonstrate a lower error level and a stronger fit to actual Bitcoin price movements. Moreover, a paired t-test confirmed that the performance gap between the two models is statistically significant. Overall, the findings suggest that the Gated Recurrent Unit architecture is more efficient in capturing nonlinear patterns and responding to the volatile dynamics of cryptocurrency price fluctuations, making it a promising approach for future predictive modeling in financial time series.
Analisis Sentimen Ulasan Google Maps Menggunakan Long Short-Term Memory (LSTM) (Studi Kasus: Kafe di Surabaya) Khusna, Asmaul; Yuni Yamasari
Journal of Informatics and Computer Science (JINACS) Article In Press(1)
Publisher : Universitas Negeri Surabaya

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

Abstract

Abstrak – Pertumbuhan pesat kafe di Surabaya telah menjadikan ulasan Google Maps sebagai sumber informasi krusial bagi konsumen, namun volume ulasan yang besar menyulitkan untuk analisis secara manual. Penelitian ini bertujuan melakukan analisis sentimen pada ulasan kafe tersebut menggunakan metode Long Short-Term Memory (LSTM) yang diperkuat dengan mekanisme Attention. Data ulasan dikumpulkan melalui teknik scraping menggunakan SerpAPI (114.960 ulasan dari 218 kafe) dan dibagi secara sekuensial (Sequential split) untuk pelatihan dan pengujian. Ulasan diklasifikasikan ke dalam tiga kelas sentimen: positif, netral, dan negatif. Model dibangun dengan arsitektur spesifik: embedding layer berdimensi 128, LSTM (64 unit), attention layer, dropout (0,5), dan dense layer dengan tiga output neuron. Evaluasi kinerja model, yang diukur menggunakan akurasi, presisi, recall, dan F1-score, menunjukkan hasil terbaik dengan akurasi 92,45%, presisi 91,84%, recall 92,45%, dan F1-score 92,01%. Model terbukti cukup efektif dalam mendeteksi sentimen ekstrem (positif dan negatif), tetapi kesulitan dalam mengklasifikasikan kelas netral akibat ketidakseimbangan distribusi data. Hal ini menunjukkan bahwa penyeimbangan data atau penyederhanaan kelas sentimen diperlukan untuk meningkatkan performa model secara keseluruhan pada penelitian mendatang. Kata Kunci – Analisis Sentimen, LSTM, Google Maps, Ulasan Kafe, Deep Learning.
Co-Authors Aditya Prapanca Agnes, Rifa Zaini Agus Prihanto Agus Prihanto Agus Setiawan Agustin Tjahyaningtijas, Hapsari Peni Alhakiim, Thomi Aditya Alpiana, Intan Ammar, Muhammad Zhafran Amrina Rosyada Andi Iwan Nurhidayat Anggraini, Lusiana Anita Qoiriah Anita Qoiriah ANITA QOIRIAH Anjani, Ayu Annisa Nur Hidayati ARI KURNIAWAN Arya Tandy Hermawan Asma Johan Asmunin Asmunin Atik Wintarti Atik Wintarti Aviana, Anisah Nurul Azalia, Virna Hari Nur Chindy Ayudia Sri Fastaf Eka Putra, Ricky Ervin Yohannes Esther Irawati Setiawan Esti, Esti Yogiyanti Fani Fadillah Hermawan Farid Baskoro Fatimah Nur Alifiah Firdaus Bagus Wicaksono Firdaus, Mohamad Adi Putra Hani Nafisah Amaliya Hanik Badriyah Hidayati,* Mohammad Hasan Machfoed,* Kuntoro,** Soetojo,*** Budi Santoso,**** Suroto,***** Budi Utomo****** Hapsari Peni Agustin Tjahyaningtijas Harahap, Satria Baladewa I Made Suartana Ika Putri Arisanti Iqbaal Januar Eka Firmansyah Ismail Johanes, Sugiharto Khahar, Abdul Khusna, Asmaul M Dzikri Hisyam Ilyasa M. Aziz Rizaldi Mas Arya Bhisma Rangga Douval Saputra Mauridhi H. Purnomo Mediana, Prissely Pravasstifany Muhamad Azis Thohari Muhamad Khafidhun Alim Muslim Muhammad Rifki Agustian Muhammad Zakia Avlach Muttaqin, Aziz Fiqri Nafisah, Nurun Naim Rahmawati Naim Rochmawati Naim Rochmawati Nanda Ade Handaya Nugroho, Supeno M. S. Praptama, Ervan Putri Alvina Putri, Rezky Arisanti Raden Mohamad Herdian Bhakti Rafli Aditya Pramana Raharko, Natasha Isnaeni Rahayu, Siskawati Rahmawati, Naim Rahulil, Muhammad Ramadhan, Dani Ricky Eka Putra Rina Harimurti Rochmawati, Naim S., Rahma Aziz Sadewa, Bagas Ahmad Salahuddin, Muhammad Rico Saputra, Ivan Rangga Shahputri, Vira Arum Solihin, Aziz Suartana, I Made Sukrisna Surya, Arum Ayu Suyatno, Dwi F. Syarif Hidayatulloh Tazki Yatun Niyah Tohari Ahmad Widi Aribowo Wiyli Yustanti Wiyli Yustanti Wiyli Yustanti Yogiyanti, Esti Yoyok Prastyo, Yoyok