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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 Ulya Ilhami Arsyah; Yori Adi Atma; Yulia Jihan SY; Mutiana Pratiwi; Rahmi Putri Kurnia; Rahmatul Husna Arsyah
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.
Twitter Sentiment Analysis of Public Space Opinions using SVM and TF-IDF Methods Arsyah, Ulya Ilhami; Pratiwi, Mutiana; Muhammad, Abulwafa
The Indonesian Journal of Computer Science Vol. 13 No. 1 (2024): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i1.3594

Abstract

Public space opinion reviews are currently a source of information for interested parties and decision-makers. Twitter is a social media that is a means of expressing themselves for people to express their opinions and criticize the current situation. This becomes information for readers. Information published on Twitter contains elements of commentary on a situation or object Sentiment analysis of public space opinion on Twitter using Machine Learning with the Support Vector Machine (SVM) method with the data weighting process using the Term Frequency-Inverse Document Frequency (TF-IDF) method. Dataset obtained by scraping using the Twitter API as much as 5000 data then labeled where the goal is to get accuracy on positive, negative, or neutral sentiment. The results of research conducted experiments on three Machine Learning algorithms with the extraction function "TF-IDF" obtained an accurate training model with good classification capabilities, especially SVM of 91,6% on data distribution 70: 30; SVM is 92.8% in the case of data distribution of 80: 20; the SVM is 91,8% in the case of 90:10 decomposition data.
Co-Authors A'yunin, Qurhata Abulwafa Muhammad Aggy Pramana Gusman Ainil Mardiah Akmal, Aulia Razak Aminuyati Anam, M Khairul Andrew Kurniawan Vadreas Ardian Firosha Devia Kartika Dinul Akhiyar Edwar Rosman Elfia Sukma, Elfia Fajrul Islami Fryonanda, Harfebi Ganiyu, Ahmad Rizki Akbar Gema, Rima Liana Husna Arsyah, Rahmatul Ideva Gaputra 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 Mutiana Pratiwi Nasution, Anggi Refachriati Nirad, Dwi Welly Sukma Novi Novi Pratiwi, Mutiana Pratiwi, Mutiana Rahmi Putri Kurnia Shary Lusinia Sweety, Miana SY, Yulia Jihan Tahmid, Riskian Toar, Miftahul Jannah Yori Adi Atma Yulia Jihan Sy