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All Journal JUTI: Jurnal Ilmiah Teknologi Informasi Jurnal Edukasi dan Penelitian Informatika (JEPIN) Building of Informatics, Technology and Science Jurnal Informasi dan Teknologi Journal of Applied Data Sciences Kalbiscientia Jurnal Sains dan Teknologi ABDIMAS Jurnal Pengabdian Kepada Masyarakat JITSI : Jurnal Ilmiah Teknologi Sistem Informasi Jurnal Ipteks Terapan : research of applied science and education AJAD : Jurnal Pengabdian kepada Masyarakat Jurnal Pengabdian Masyarakat : Pemberdayaan, Inovasi dan Perubahan Jurnal Sistem Informasi Bisnis (JUNSIBI) DEVICE : JOURNAL OF INFORMATION SYSTEM, COMPUTER SCIENCE AND INFORMATION TECHNOLOGY Jurnal Abdimas: Pengabdian dan Pengembangan Masyarakat Proceeding Applied Business and Engineering Conference KALBISIANA Jurnal Sains, Bisnis dan Teknologi Nusantara Journal of Artificial Intelligence and Information Systems
Fryonanda, Harfebi
Jurusan Teknologi Informasi, Politeknik Negeri Padang
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ACLM Model: A CNN-LSTM and Machine Learning Approach for Analyzing Tourist Satisfaction to Improve Priority Tourism Services Arsyah, Ulya Ilhami; Pratiwi, Mutiana; Fryonanda, Harfeby; Anam, M. Khairul; Munawir, Munawir
Journal of Applied Data Sciences Vol 6, No 4: December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i4.974

Abstract

Tourist satisfaction is a key proxy for destination service quality, yet automatic sentiment analysis of online reviews still faces class imbalance, overfitting, and limited deployability. This study proposes ACLM, a hybrid sentiment classification pipeline that learns semantic and temporal features with a CNN-LSTM backbone and evaluates three classifier heads (Softmax, Logistic Regression, XGBoost) on a three-class corpus (neutral, satisfied, dissatisfied). The objective is to deliver an accurate and operational model for decision support in tourism services. The idea combines Word2Vec embeddings, a compact CNN for local patterns, an LSTM for sequence dependencies, and a training workflow with text cleaning, SMOTE based balancing, and regularization to curb overfitting; outputs are exposed through a simple Streamlit interface. Results show that CNN-LSTM with a Softmax head attains accuracy 0.89, macro precision 0.89, macro recall 0.84, and macro F1 0.86, outperforming Logistic Regression (accuracy 0.87, macro precision 0.84, macro recall 0.82, macro F1 0.82) and XGBoost (accuracy 0.85, macro precision 0.80, macro recall 0.82, macro F1 0.80). The findings indicate that deep sequence features paired with a simple Softmax head provide the best tradeoff between accuracy and stability for three-way sentiment classification. The contribution is a reusable, end to end blueprint from preprocessing and balanced training to quantitative evaluation and an inference GUI, and the novelty lies in testing interchangeable classifier heads on a single CNN-LSTM feature extractor while explicitly addressing data imbalance and deployment constraints. The GUI is implemented using the highest accuracy model, namely CNN-LSTM with Softmax.
Web-Based Junior High School Student Attendance System with Face Recognition Feature using the Prototyping Method Kaesmetan, Yampi R; Rosid, Achmat; Fryonanda, Harfebi
Nusantara Journal of Artificial Intelligence and Information Systems Vol. 1 No. 2 (2025): December
Publisher : Faculty of Engineering and Computer Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47776/nuai.v1i2.1873

Abstract

Technology is increasingly developing and innovating rapidly. Among them is the use of technology in various fields, especially in education. Teachers and students at the school still carry out attendance activities manually, namely with a conventional system that requires data recording for each attendance on paper. This system is vulnerable to damage or loss of data because the attendance results there still use paper. The attendance system that utilizes face recognition technology is the system proposed for the formulation of the problem that will be used for the research. The development method of this research uses the Prototyping method which prioritizes speed and time efficiency so that it is very suitable for use considering the current needs for a system that requires speed and accuracy. The framework used in the development of the system is Codeigniter 4. The process of working on the system is system requirements analysis, display design, coding, and testing. The results of the study are to create a website-based attendance application at SMP Daarus Sa'adah by utilizing face recognition technology which is carried out by auto-detecting faces so that it can facilitate users in carrying out attendance activities accurately and quickly.
Co-Authors A'yunin, Qurhata Anam, M Khairul Andre Febrian Kasmar Andrew Kurniawan Vadreas Arsyah, Ulya Ilhami Bellia Putri Alfianki Defni Defni Defni Driantama, Dhani Fajar Edwar Rosman Fajar Rachmat Saputra Ganiyu, Ahmad Rizki Akbar Gatc, Julend Hafizun, Altaf Hendra Rotama Ideva Gaputra Irene Hasian Irvan Fadil Haris Jonitra, Farhan Juli Lidia Mariani Kaesmetan, Yampi R Kevin kurnia, rahmi putri Laia, Basmida Mardiah , Ainil Maulana, Raihan Efel Meri, Rozi Muhammad Rusli Munawir Munawir Mutia Rahmi Dewi Nasution, Muhammad Ibrahim Nefriardo Armaz, Ilham Nirad, Dwi Welly Sukma Nissa, Wirda Novi Noviardi, Refli Nuke Sairah Syabrina Percikjernih Riak Telaga Pratama, Fikri Wilya Pratiwi, Mutiana rahmi putri kurnia Ronal Hadi Rosid, Achmat Roy Ibrahim Rozali Ilham Sekar Arum Sevti Try Astuti Sokoco, Heru Syah Rany, Erwin Syawaldipa, Ardi Taruma Leo Wijaya Veithzal Rivai Zainal Widianti , Nina Wijaya, Teddy Wirda Nissa Yani Nurhadryani Yori Adi Atma Yosse, Sofia Yulia Jihan Sy Yunika Lestari