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All Journal Teknika Jurnal Sains dan Teknologi Jurnal Simetris JSI: Jurnal Sistem Informasi (E-Journal) International Journal of Advances in Intelligent Informatics IJCIT (Indonesian Journal on Computer and Information Technology) Jurnal Pilar Nusa Mandiri SINTECH (Science and Information Technology) Journal Jurnal Informatika Universitas Pamulang Jurnal Nasional Komputasi dan Teknologi Informasi WIDYA LAKSANA Jurnal Informatika Kaputama (JIK) EVOLUSI : Jurnal Sains dan Manajemen JTIK (Jurnal Teknik Informatika Kaputama) Jurnal Ilmu Teknik dan Komputer Jurnal Tekinkom (Teknik Informasi dan Komputer) Infotek : Jurnal Informatika dan Teknologi JUSTIAN - Jurnal Sistem Informasi Akuntansi J-Intech (Journal of Information and Technology) Ilmu Komputer untuk Masyarakat Jurnal Pengabdian Masyarakat Bidang Sains dan Teknologi Jurnal Ilmu Komputer Dan Informatika Jurnal Abdimas Le Mujtamak Mestaka: Jurnal Pengabdian Kepada Masyarakat TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi Dedikasi Saintek Jurnal Pengabdian Masyarakat SOROT: Jurnal Pengabdian Kepada Masyarakat Madani: Jurnal Pengabdian Masyarakat dan Kewirausahaan Jurnal Komputer dan Teknologi (JUKOMTEK) DEDIKASI SAINTEK Jurnal Pengabdian Masyarakat Indonesian Community Service Journal of Computer Science (IndoComs)
Riski Annisa
Universitas Bina Sarana Informatika
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SOSIALISASI PEMANFAATAN ARTIFICIAL INTELLIGENCE UNTUK MEDIA PROMOSI PADA LEGEND AUTO PART PONTIANAK: SOSIALISASI PEMANFAATAN ARTIFICIAL INTELLIGENCE UNTUK MEDIA PROMOSI PADA LEGEND AUTO PART PONTIANAK Panny Agustia Rahayuningsih; Riski Annisa; Anna Anna; Monikka Nur Winnarto
Indonesian Community Service Journal of Computer Science Vol. 2 No. 1 (2025): Periode Januari 2025
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/indocoms.v2i1.7779

Abstract

Perkembangan teknologi Artificial Intelligence (AI) membuka peluang baru bagi strategi pemasaran digital, terutama bagi usaha kecil dan menengah. Pengabdian masyarakat ini bertujuan mensosialisasikan pemanfaatan AI untuk media promosi pada Legend Auto Part Pontianak. Metode pelaksanaan meliputi analisis kebutuhan, pelatihan penggunaan AI, implementasi teknologi, serta monitoring dan evaluasi. Kegiatan difokuskan pada penggunaan chatbot di platform media sosial, iklan digital berbasis AI, dan analisis sentimen pelanggan. Hasil menunjukkan peningkatan signifikan dalam strategi pemasaran, termasuk layanan pelanggan otomatis, target iklan yang lebih presisi, dan pemahaman mendalam tentang perilaku konsumen. Implementasi AI memungkinkan Legend Auto Part Pontianak mengoptimalkan anggaran pemasaran, meningkatkan visibilitas produk, dan memberikan pengalaman pelanggan yang lebih baik, sehingga meningkatkan daya saing bisnis di era digital.
Meningkatkan Daya Saing UMKM Desa Punggur Besar Melalui Strategi Pemasaran Digital Berbasis Kecerdasan Buatan (AI) Anna, Anna; Annisa, Riski; Rahayuningsih, Panny Agustia; Nugraha, Wahyu
Mestaka: Jurnal Pengabdian Kepada Masyarakat Vol. 5 No. 1 (2026): Februari 2026
Publisher : Pakis Journal Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58184/mestaka.v5i1.838

Abstract

The Free Nutritious Meals Program is a social initiative managed by micro, small, and medium enterprises (MSMEs) in Punggur Besar Village to support the nutritional needs of schoolchildren. However, program managers face challenges in increasing visibility, transparency, and community engagement due to limited digital technology capabilities. This activity aims to empower program managers through AI-based digital marketing training that is easily accessible and relevant to their social context. The training was conducted offline at the Punggur Besar Village Office using participatory learning methods and hands-on practice using participants' own devices. Twenty program managers participated in the entire activity. All participants successfully created program-specific social media accounts, produced educational content, and documented activities with the help of AI tools. Eighty percent of participants were able to independently develop content narratives, and seventy-two percent began utilizing basic analytics to understand community responses to their content. The main benefits gained were increased digital communication capacity, strengthened program accountability, and increased confidence in interacting with the public online. This activity demonstrated that the use of AI in digital marketing can be adapted inclusively to strengthen community-based social programs, with the active involvement of managers as key actors in digital transformation at the village level.
Perbandingan Kinerja Naïve Bayes, Support Vector Machine, dan K-Nearest Neighbor dalam Analisis Sentimen Mobile Legends Alvin Zikirlah, Hikmawan; Fazilla, Muhammad; Paula, Iltavera; Annisa, Riski; Fitriana, Lady Agustin
TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi Vol 5 No 2 (2025): TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/tamika.Vol5No2.pp228-235

Abstract

The rapid advancement of information and communication technology has significantly increased the popularity of online games in Indonesia, one of which is Mobile Legends: Bang Bang (MLBB) with millions of active users. The abundance of user reviews on digital platforms provides valuable data for analysis using text mining and natural language processing (NLP) approaches. Sentiment analysis is applied to classify user opinions into positive, negative, and neutral categories, offering insights into player satisfaction and perceptions of game quality. This study compares the performance of three classification algorithms Naïve Bayes (NB), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN) in analyzing sentiment from Mobile Legends user reviews on the Google Play Store. A total of 5,000 reviews were collected using the web scraping technique and processed through the Knowledge Discovery in Databases (KDD) framework, which includes cleaning, case folding, tokenization, normalization, and stopword removal. Sentiment labeling was performed using a lexicon-based approach with the InSet sentiment lexicon. The dataset was divided into training and testing sets with an 80:20 ratio and evaluated using accuracy, precision, recall, and f1-score metrics. The results show that the SVM algorithm achieved the highest accuracy of 88.1%, followed by KNN at 65.1% and NB at 62.6%. Thus, SVM is recommended as the most effective model for sentiment analysis of Mobile Legends user reviews.
Co-Authors Agnes, Veronika Agung Sasongko Agustin Fitriana, Lady Alvin Zikirlah, Hikmawan Anna Anna Anna Anna Anna Anna Anna Anna, Anna Anna, Anna - Arvin Hidayat Waluya Baihaqi, Muhammad Rivaldi Chania Lista Zepani Chika Suhendra Detylia Detylia Dewi Ayu Nur Wulandari Dharmawan, Weiskhy Steven Didi Rosiyadi Dwiza Riana Eliska, Puspa Eva Meilinda Fadilah, Aldi Famela Jessica Fazilla, Muhammad Firly Annisa Luthfi Fitriana, Lady Agustin Hidayana, Reymond Syahputra Iltavera Paula Lady Agustin Fitriana Lestari Yusuf Marelita Marelita Maulana, Reza - Mohammad Kamal Reza Monikka Nur Winnarto Muhammad Eka Fadillah Muhammad Fazilla Murdiansyah Murdiansyah Mutia Sari, Elsa Nur W, Monikka Nurfia Oktaviani Syamsiah Panny Agustia Rahayuningsih Parwandar, Robi Paula, Iltavera Rahayuningsih, Agustia Raja Sabaruddin Raja Sabaruddin Raja Sabarudin Ramadhani, Zulfikar Ismaya Regina, Fransiska Rendi Rendi, Rendi Robi Parwandar Sendi Yustisio Siti Nurdiani Sri Murni Suhendra, Chika Wahyu Nugraha Welly, Welly Winnaarto, Monikka Nur Winny Christiani Thomas Yulia Yulia Yustisio, Sendi Zikirlah, Hikmawan Alvin