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All Journal International Journal of Evaluation and Research in Education (IJERE) Jurnal Simetris Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) OPSI Scientific Journal of Informatics Seminar Nasional Informatika (SEMNASIF) Annual Research Seminar InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Syntax Literate: Jurnal Ilmiah Indonesia Jurnal Ilmiah Matrik Prosiding Seminar Nasional Sains dan Teknologi Terapan Building of Informatics, Technology and Science Jurnal Manajemen Informatika dan Sistem Informasi Journal of Information Systems and Informatics Teknomatika (Jurnal Teknologi dan Informatika) Journal of Information Technology Ampera Journal of Computer and Information Systems Ampera Journal of Software Engineering Ampera Jurnal Pengembangan Sistem Informasi dan Informatika Jurnal Ilmiah Sistem Informasi dan Teknik Informatika (JISTI) Jurnal Bina Komputer International Journal of Scientific and Professional Jurnal Maklumatika Journal of Artificial Intelligence and Digital Business Jurnal Informatika: Jurnal Pengembangan IT E-Amal: Jurnal Pengabdian Kepada Masyarakat Indonesia Bergerak: Jurnal Hasil Kegiatan Pengabdian Masyarakat International Journal of Artificial Intelligence and Science Journal of Information Technology
Ari Muzakir
Universitas Bina Darma
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Enhanced Detection of Indonesian Online Gambling Advertisements Using Multimodal Ensemble Deep Learning Alfiansyah, M Ihksan; Muzakir, Ari
International Journal of Artificial Intelligence and Science Vol. 2 No. 2 (2025): September
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/IJAIS.v2i2.49

Abstract

The rapid growth of online gambling promotion on Indonesian social media creates significant challenges for automated moderation systems, particularly because the content often appears in multimodal forms, uses slang expressions, and disguises promotional intent. The purpose of this study is to improve the accuracy and robustness of gambling advertisement detection by proposing a multimodal ensemble deep learning framework that integrates information from text, images, and audio. The method combines three independent feature streams, namely native text, OCR-extracted text from images, and ASR-generated speech transcripts. These inputs are processed using three classifiers, namely CNN, BiLSTM, and IndoBERT, which are then fused using a weighted soft-voting ensemble strategy. A dataset consisting of 12,000 multimodal samples collected from Facebook, Instagram, TikTok, and YouTube was used for evaluation. The results show that the ensemble model achieves an accuracy of 95.42 percent, outperforming each individual classifier, with substantial improvements in handling noisy OCR and ASR outputs as well as implicit gambling slang. Compared with single-model baselines, the proposed approach reduces false positives by 18.6 percent and false negatives by 22.3 percent. The novelty of this study lies in the integration of multimodal feature streams with an optimized ensemble mechanism, enabling more reliable detection of concealed gambling promotional patterns. The findings provide a strong foundation for future research on adaptive moderation systems and real-time harmful content detection in Indonesian social media.
From algorithms to classrooms: a decade of artificial intelligence in education research Seong Pek, Lim; Akma Ahmad, Nahdatul; Zulkifli, Faiz; Dev Prasad, Rabindra; Muzakir, Ari; S. Camara, Jun
International Journal of Evaluation and Research in Education (IJERE) Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijere.v15i1.34427

Abstract

The education industry has seen a substantial transformation thanks to artificial intelligence (AI), which has improved administrative effectiveness, accessibility, and individualized learning. However, issues like moral dilemmas, digital justice, and policy inconsistencies still exist. From 2015 to 2024, this bibliometric research explores how AI is revolutionizing education. Personalized learning, improved accessibility, and expedited administrative procedures have all been made possible by AI; yet, issues with cost, digital equity, and ethics still exist. We used the Web of Science (WoS) database to conduct a comprehensive bibliometric analysis of 291 peer-reviewed articles that were indexed in the Social Sciences Citation Index (SSCI). The PRISMA methodology was used in the study to find and filter pertinent material. Thematic trends, citation patterns, and co-authorship networks were examined using bibliometric tools like VOSviewer. The progress of generative AI tools like ChatGPT, the importance of AI in democratizing education, and the integration of AI into curriculum building are some of the key discoveries. The report identifies significant nations, organizations, and researchers in AI education and emphasizes global research relationships. Our research raises ethical governance issues while shedding light on AI’s potential to promote individualized learning and increase student engagement. These findings support sustainable development goal (SDG) 4 on quality education by highlighting the need for responsible AI use to address the digital divide. This paper offers useful suggestions for academics, educators, and legislators to maximize AI’s promise while tackling its drawbacks.
Perancangan Aplikasi Akuntansi Laba Rugi Berbasis Web (Studi Kasus: Depot Kayu Fajar Jaya Palembang) Rachman, Yoga Fathur; Muzakir, Ari
RIGGS: Journal of Artificial Intelligence and Digital Business Vol. 5 No. 1 (2026): Februari - April
Publisher : Prodi Bisnis Digital Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/riggs.v5i1.5311

Abstract

Perkembangan teknologi informasi yang semakin pesat memberikan dampak signifikan terhadap berbagai bidang, termasuk bidang akuntansi. Dalam dunia usaha modern, sistem akuntansi berbasis komputer menjadi kebutuhan utama karena mampu membantu proses pengelolaan data keuangan secara lebih cepat, tepat, dan akurat dibandingkan dengan sistem manual. Pemanfaatan teknologi ini juga memungkinkan perusahaan untuk meningkatkan efisiensi kerja serta mengurangi potensi kesalahan dalam proses pencatatan transaksi keuangan. Depot Kayu Fajar Jaya Palembang merupakan salah satu perusahaan yang masih menggunakan sistem manual dalam melakukan pencatatan transaksi penjualan, pembelian, dan penyusunan laporan keuangan. Proses manual tersebut sering menimbulkan berbagai kendala seperti kesalahan perhitungan, kehilangan data, serta keterlambatan dalam pembuatan laporan laba rugi yang dibutuhkan oleh pihak manajemen. Kondisi ini tentunya dapat mempengaruhi kualitas informasi keuangan yang dihasilkan serta efektivitas pengambilan keputusan perusahaan. Oleh karena itu, dirancanglah sebuah aplikasi akuntansi laba rugi berbasis web menggunakan bahasa pemrograman PHP dan database MySQL untuk mengatasi permasalahan tersebut. Metode penelitian yang digunakan adalah metode Waterfall yang terdiri dari tahapan analisis kebutuhan sistem, perancangan, implementasi, pengujian, dan pemeliharaan. Aplikasi ini mampu mengelola data transaksi penjualan, pembelian, serta penyusunan laporan keuangan secara otomatis dan terstruktur. Hasil implementasi menunjukkan bahwa aplikasi ini dapat meminimalkan kesalahan pencatatan, mempercepat penyusunan laporan keuangan, serta memberikan informasi yang akurat bagi pihak manajemen dalam pengambilan keputusan strategis secara lebih efektif dan efisien.  
FAQ Chatbot for Small Businesses on the Web Using Semantic Search and Response Ranking Armansyah, Abi; Muzakir, Ari; Yulianingsih, Evi
J-INTECH ( Journal of Information and Technology) Vol 14 No 01 (2026): Journal of Information and Technology
Publisher : LPPM Universitas Bhinneka Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v14i01.2252

Abstract

Small businesses often handle customer questions through manual replies via chat applications or phone calls, causing repetitive work, delayed responses, and inconsistent information delivery. This study proposes a web-based FAQ chatbot that answers user questions by performing semantic search over an Indonesian FAQ knowledge base and ranking the most relevant response. The chatbot applies a lightweight information retrieval approach using TF-IDF vectorization and cosine similarity to compute the relevance score between the user query and FAQ entries (question and tags). The system then selects the top-ranked FAQ entry and returns its associated answer, meaning the semantic matching is performed at the question-to-question level, not directly between questions and answers. The top results are ranked, and the chatbot returns the best answer along with a confidence score and the top three candidate questions to increase transparency. If the score is below a predefined threshold, the system provides a fallback response and suggests related topics rather than forcing an incorrect answer. The system is implemented as a PHP–MySQL web application with an administrator dashboard that supports secure login, FAQ CRUD management, chat logging, and usage analytics. Functional verification is conducted using black-box testing across main modules, including authentication, FAQ management, chatbot interaction, logging, and analytics dashboards. The expected contribution of this work is a practical and low-cost chatbot solution that can be deployed by small businesses to reduce repetitive customer service workload, accelerate response time, and provide measurable service insights through log-based analytics. Future improvements include expanding the knowledge base, enhancing Indonesian text normalization, and adopting embedding-based retrieval for better semantic matching.
Sentimen Analisis Pengguna Jasa Layanan Kereta Api dengan Menggunakan Metode CNN (Convolutional Neural Network) Alfikri, Zidan; Muzakir, Ari; Purnamasari, Susan Dian; Amalia, Rahayu
Building of Informatics, Technology and Science (BITS) Vol 7 No 4 (2026): March 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i4.9423

Abstract

Train services are a popular mode of transportation in Indonesia, especially in the Greater Jakarta area. However, the quality of train services is often debated among users. This study aims to analyze the sentiment of train service users using the Convolutional Neural Network (CNN) method with a focus on the DAOP 1 Jakarta area. The data used are reviews or comments of train users taken from Indonesian Railways social media. The results of the study show that the CNN method can classify user sentiment analysis with accurate results or high accuracy. This sentiment analysis shows that train users in DAOP 1 Jakarta have positive sentiments towards aspects such as punctuality, service, comfort and safety. The results of this study can help the railway to understand user needs and complaints so that they can improve service quality with a final value of 89.29% accuracy, 88.73% precision, 90.00% recall, and 89.36% F1-score.
Co-Authors Adha, Laili Adhie Tri Wahyudi Ahmad Syazili Akma Ahmad, Nahdatul Alfian Egi Erlangga Alfiansyah, M Ihksan Alfikri, Zidan Amalia, Rahayu Andri Wijaya Anisa Putri Armansyah, Abi Ch Desi Kusmindari Christofora Desi Kusmindari Dev Prasad, Rabindra Dilla Apriliani Edi Supratman Edo Irawan Eksen Hidiansah Febriyanti Panjaitan Febriyanti Panjaitan Hadi Syaputra Hamim Tezar Rizandi Hutrianto Hutrianto Idrus Salim Ifran Wahyudi Kusmindari, Christofora Desi Ledy Armyoktora Mailan Habibi, Mailan Maria Ulfa Meigi Rahman Moh Hafiz Trisaputra Muhammad Ridho Kurniawan Muhammad Ridwan Nyimas Sopiah Poppy Indriani R.M Nasrul Halim Rachman, Yoga Fathur Rahman, Meigi Rendy Jemi Sahputra Reni Laili Rika Anisa Wulandari, Rika Anisa S. Camara, Jun SANTOSO SANTOSO Seong Pek, Lim Susan Dian Purnamasari, Susan Dian Syaputra, Hadi Uci Suriani Usman Ependi Winda Wulandari Yesi Novaria Kunang Yuli Dwi Astanti Yulianingsih, Evi Yunike, Yunike Zulkifli, Faiz