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All Journal Jurnal Ilmu Komputer dan Informasi Jurnal Buana Informatika Register: Jurnal Ilmiah Teknologi Sistem Informasi Indonesian Journal of Artificial Intelligence and Data Mining SENTIA 2016 Journal of Intelligent Decision Support System (IDSS) Idealis : Indonesia Journal Information System GUYUB: Journal of Community Engagement Journal of Electrical Engineering and Computer (JEECOM) Journal of Technology and Informatics (JoTI) Kontribusi: Jurnal Penelitian dan Pengabdian Kepada Masyarakat Internet of Things and Artificial Intelligence Journal Jurnal Ilmiah Teknik Informatika dan Komunikasi COREAI: Jurnal Kecerdasan Buatan, Komputasi dan Teknologi Informasi Abdimas Terapan: Jurnal Pengabdian Kepada Masyarakat Terapan JOKI: Journal of Computing and Informatics
Ratri Enggar Pawening
Universitas Nurul Jadid
Religion Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Mechanical Engineering Public Health Social Sciences Transportation Other

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Sentiment Analysis of User Reviews on the NU Online Application Using the Long Short-Term Memory (LSTM) Model Muthmainnah, Atik; Sya'roni, Wahab; Pawening, Ratri Enggar
Journal of Electrical Engineering and Computer (JEECOM) Vol 7, No 2 (2025)
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/jeecom.v7i2.12137

Abstract

This study aims to analyze user sentiment toward the NU Online application by applying a Long Short-Term Memory (LSTM) model. The research was based on 13,576 user reviews collected from the Google Play Store, which underwent preprocessing, sentiment labeling, and Exploratory Data Analysis (EDA). To ensure balanced classification, undersampling was used, resulting in 6,612 reviews equally divided into positive and negative classes. The text data was processed using tokenization and padding before being input into the LSTM model. Model training involved the use of binary crossentropy, Adam optimizer, EarlyStopping, and ReduceLROnPlateau techniques. The model achieved 93% precision, recall, and F1-score, with low error rates and strong generalization ability. EDA results showed that positive feedback mainly focused on worship features like salat schedules, while negative reviews addressed technical issues such as the azan sound. User review peaks occurred during religious periods and major updates. A Gradio-based web interface was also developed to display results and enable user-friendly access to visual sentiment insights. This implementation proves the practical potential of integrating LSTM with an interactive platform for effective sentiment analysis
Klasifikasi Penyakit Jagung Berdasarkan Citra Batang Menggunakan  Metode Convolutional Neural Network (CNN) pada Arsitektur Mobilenet Berbasis Web Kusdatul Komariyah; Ratri Enggar Pawening; Moh Furqan
JOKI: Jurnal Komputasi dan Informatika Vol 2 No 2 (2025)
Publisher : Laskar Karya

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Abstract

Penelitian ini membahas tantangan klasifikasi penyakit batang jagung menggunakan model convolutional neural network MobileNetV3-Small. Penyakit batang jagung seperti busuk fusarium dan busuk gibberella dapat menurunkan hasil panen secara signifikan apabila tidak terdeteksi sejak dini. Penelitian dimulai dengan pengumpulan 720 citra batang jagung yang terbagi menjadi tiga kategori: batang sehat, busuk fusarium, dan busuk gibberella. Tahap praproses meliputi resize, normalisasi, ekstraksi fitur, penghapusan latar belakang, serta augmentasi citra untuk meningkatkan variasi data. Pelatihan model dilakukan menggunakan MobileNetV3-Small dengan transfer learning, dan evaluasi kinerja dilakukan menggunakan dataset validasi terpisah untuk meminimalkan overfitting. Hasil penelitian menunjukkan bahwa model mencapai akurasi tinggi dan efektif dalam mengklasifikasi jenis penyakit pada citra batang jagung. Temuan ini menunjukkan bahwa pendekatan deep learning, khususnya arsitektur CNN ringan, dapat diimplementasikan pada platform sumber daya terbatas seperti aplikasi mobile dan web untuk mendukung deteksi dini penyakit tanaman. Penelitian ini memberikan referensi bagi pengembangan lebih lanjut sistem deteksi penyakit tanaman berbasis kecerdasan buatan dengan arsitektur jaringan saraf yang efisien.
Co-Authors Abu Tholib Agmelita Dwi Jianika Agus Zainal Arifin Agus Zainal Arifin Anny Yuniarti Arum Puji Rahayu Ayu Basirotul Muzayyanah Darlis Herumurti Deddy Junaedi Fadillah Nurul Imami Faiqatul Hikmah Fajaratul Lailiah Fakhrina Kamila Fatimatus Zahro Fiqih Humaidah Fitriyah Hidayati Gufairoh, Siti Gufairoh Gulpi Qorik Oktagalu Pratamasunu Hasanah, Syarifatul Honainah Honainah Kusdatul Komariyah Kusuma, Selvia Ferdiana Meliana Eka Puteri Miftahul Hasanah Moh Furqan Mohammad Furqan Muthmainnah, Atik Muzayyanah, Ayu Basirotul Nadhiroh, Anis Yusrotun Nurul Kamilatul Mahmuda Nurul Lihayati Nurul Wasilatul Laili Oktagalu Pratamasunu, Gulpi Qorik Putri Mar’atu Sholehah Raudhah Lailatul Mufidah Rizqa Raaiqa Bintana Rofiatul Hasanah Rohman Dijaya Shudiq, Wali Ja'far Sofi Magfiroh Sya'roni, Wahab Tio Darmawan Ummi Kholifah wahyuni wahyuni Wulandari, Uky Novita Zainal Arifin Zaskiya, Karina Desy