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All Journal Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal Jurnal Teknologi Journal of Applied Data Sciences Majalah Ilmiah UPI YPTK Jurnal Ipteks Terapan : research of applied science and education Jurnal Pustaka Mitra : Pusat Akses Kajian Mengabdi Terhadap Masyarakat Journal of Information Systems and Technology Research Jurnal Sains Informatika Terapan (JSIT) Jurnal Komtekinfo Indonesian Journal of Informatic Research and Software Engineering Sistem Pendukung Keputusan dengan Aplikasi Jurnal Andalas: Rekayasa dan Penerapan Teknologi Jurnal Abdimas: Pengabdian dan Pengembangan Masyarakat Proceeding Applied Business and Engineering Conference Innovative: Journal Of Social Science Research Journal Of Indonesian Social Society The Indonesian Journal of Computer Science
Arsyah, Ulya Ilhami
Politeknik Negeri Padang
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Digitalisasi Tulisan Adat: Pengembangan E-Book Berbasis Teknologi untuk Pelestarian Budaya Lokal di Nagari Lasi Ilhami Arsyah, Ulya; Atma, Yori Adi; Jihan SY, Yulia; Pratiwi, Mutiana; Kurnia, Rahmi Putri; Arsyah, Rahmatul Husna
Jurnal Pustaka Mitra (Pusat Akses Kajian Mengabdi Terhadap Masyarakat) Vol 5 No 5 (2025): Jurnal Pustaka Mitra (Pusat Akses Kajian Mengabdi Terhadap Masyarakat)
Publisher : Pustaka Galeri Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55382/jurnalpustakamitra.v5i5.1283

Abstract

Tim pengabdian Politeknik Negeri Padang bekerja sama dengan Kerapatan Adat Nagari (KAN) Lasi di Nagari Lasi, Kecamatan Canduang, Kabupaten Agam, melaksanakan program pelestarian budaya melalui digitalisasi tulisan adat. Aktivitas ini mengubah karya tulis adat yang sebelumnya berbentuk cetak menjadi e-book interaktif, memadukan tradisi dan teknologi modern agar warisan budaya dapat diakses dengan lebih mudah dan menarik. Sebanyak 15 dokumen adat berhasil didigitalisasikan dalam program ini sebagai langkah konkret pelestarian dan dokumentasi budaya lokal. Selain itu, dilaksanakan pula pelatihan sosialisasi yang diikuti oleh 20 orang peserta, terdiri dari ketua KAN Nagari Lasi dan perangkat adat, guna meningkatkan pemahaman serta keterampilan dalam memanfaatkan teknologi digital untuk menjaga keberlanjutan budaya. Inisiatif ini menekankan pentingnya inovasi dalam menjaga identitas budaya lokal sekaligus merespons tantangan era digital. Dengan demikian, program ini diharapkan menjadi model pelestarian budaya yang adaptif dan dapat menginspirasi nagari-nagari lain di Sumatera Barat untuk melakukan transformasi digital serupa. Melalui pendekatan ini, nilai-nilai adat tidak hanya terjaga keberlanjutannya, tetapi juga mampu memperkuat daya saing dan eksistensi budaya masyarakat di era globalisasi.
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.
Co-Authors A'yunin, Qurhata Abulwafa Muhammad Aggy Pramana Gusman Ainil Mardiah Akmal, Aulia Razak Aminuyati Anam, M Khairul Andrew Kurniawan Vadreas Ardian Firosha Arsyah, Rahmatul Husna Devia Kartika Dinul Akhiyar Edwar Rosman Elfia Sukma, Elfia Fajrul Islami Fryonanda, Harfebi Ganiyu, Ahmad Rizki Akbar Gema, Rima Liana Ideva Gaputra Jihan Sy, Yulia Kris Hiburan Laoli, Vince kurnia, rahmi putri Liga Mayola Lusinia, Shary Armonitha Mardhiah, Putri Maulana, Raihan Efel Muhammad, Abulwafa Munawir Munawir Mutia Rahmi Dewi Mutiana Pratiwi Mutiana Pratiwi Nasution, Anggi Refachriati Nirad, Dwi Welly Sukma Novi Novi Pratiwi, Mutiana Pratiwi, Mutiana Rahmatul Husna Arsyah Rima Liana Gema Shary Lusinia Sweety, Miana SY, Yulia Jihan Tahmid, Riskian Toar, Miftahul Jannah Yori Adi Atma