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All Journal Proceeding of the Electrical Engineering Computer Science and Informatics Jurnal Dinamika Pengabdian (JDP) JURNAL TEKNOLOGI DAN OPEN SOURCE JURIKOM (Jurnal Riset Komputer) Jurnal Tekno Kompak Jurnal Review Pendidikan dan Pengajaran (JRPP) Building of Informatics, Technology and Science SENSITEK Jurnal Informasi dan Teknologi Legalitas: Jurnal Hukum Jurnal Teknologi dan Sistem Tertanam J-SAKTI (Jurnal Sains Komputer dan Informatika) Bulletin of Information Technology (BIT) Innovative: Journal Of Social Science Research Journal Of Technology and Information System (J-TIS) Journal of Community Dedication Indonesian Journal of Education (INJOE)
Alam, Sitti Nur
Universitas Yapis Papua
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Public Health Social Sciences Other

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OPTIMIZE TEXTILE BOOK RECOMMENDATION SYSTEM USING DEEP LEARNING ALGORITHMS Sitti Nur Alam; Asep Saeppani; Iwan Setiawan
Indonesian Journal of Education (INJOE) Vol. 3 No. 2 (2024): Indonesian Journal of Education (INJOE)
Publisher : CV. ADIBA AISHA AMIRA

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Abstract

The research aims to optimize the recommendation system for textile books by applying deep learning algorithms. The textile industry, rich in content and material variation, requires a system of recommendations that can accurately accommodate the diverse needs of its users. Deep learning, with its sophistication in processing large and complex data, offers solutions in improving the quality of recommendations. The study explores the use of deep learning models in interpreting user preferences and book characteristics, with the hope of producing more relevant and personal predictions. Research methods that literature conducts systematically through the collection of data from scientific sources such as journals, conferences, and related articles published in the last decade. The results show that deep learning algorithms such as Convolutional Neural Networks (CNN) and Recurrent Neural Network (RNN) have been successfully applied in improving the accuracy of book recommendation systems, including in textile contexts. These models are able to understand and process textile information and user preferences more deeply than traditional algorithms. The research also revealed important factors that influence model performance, such as data quantity and quality, model architecture, and parameter setting. Although there are limitations associated with resource use and the need for large datasets, the use of deep learning algorithms in recommendation systems for textile books shows significant potential in improving personalization and user satisfaction.
Penentuan Bibit Kelapa Sawit Unggul Dengan Metode ARAS Dan TOPSIS Sitti Nur Alam; Rolly Yesputra; Arridha Zikra Syah; Parini; Andi Ernawati
Bulletin of Information Technology (BIT) Vol 6 No 4 (2025): Desember 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v6i4.2213

Abstract

The Industrial Era 4.0 opens up great opportunities to increase production, efficiency and sustainability of the palm oil industry. The problem faced by farmers is that farmers are often hampered by limited knowledge and lack of guidance in choosing plant seeds. Because seeds are an important factor in supporting satisfactory results. This research was carried out to help farmers who have difficulty in choosing oil palm seeds which could become a problem for farmers in the future. day. This research uses the ARAS and TOPSIS methods to evaluate seeds based on criteria that have been identified and analyzed, to assess 10 types of superior seeds based on 5 criteria: oil potential, pest resistance, seed price, productive planting period, and maintenance costs. It is hoped that this research can help oil palm farmers increase their productivity and profits, as well as support the sustainability of the palm oil industry in the Industry 4.0 era. The ARAS and TOPSIS methods have proven to be effective in helping farmers choose superior oil palm seeds. From the results of research conducted using the ARAS and TOPSIS methods, VIM 1 seeds were recommended as the best choice based on the points obtained.
DEVELOPMENT OF ARTIFICIAL INTELLIGENCE-BASED ADAPTIVE LEARNING MEDIA TO SUPPORT INCLUSIVE EDUCATION FOR PUPILS WITH DIVERSE LEARNING NEEDS Sitti Nur Alam; Moh. Aldrin Akbar
Indonesian Journal of Education (INJOE) Vol. 6 No. 1 (2026): Indonesian Journal of Education (INJOE)
Publisher : CV. ADIBA AISHA AMIRA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.22240554

Abstract

Inclusive education requires the availability of learning resources capable of accommodating the diversity of pupils’ abilities, characteristics, learning styles and individual needs. Conventional learning resources, which tend to be uniform, have not yet been fully able to provide a flexible and personalised learning experience, particularly for pupils with diverse learning needs. This article aims to analyse the concept, design and implementation of Artificial Intelligence (AI)-based adaptive learning resources in support of inclusive education. This study employs a literature review using a descriptive-qualitative approach, analysing various scientific articles, academic books, reports from educational institutions, and relevant policy documents. The findings indicate that AI can help tailor content, difficulty levels, learning pace, presentation formats, activities, assessments, and feedback based on students’ profiles and progress. Adaptive learning media can also support accessibility through text-to-speech, speech-to-text, transcripts, visualisations, language simplification, and personalised learning recommendations. However, its implementation still faces challenges in the form of infrastructure limitations, teachers’ competence readiness, the digital divide, algorithmic bias, data security, and the need for human supervision. Therefore, the development of AI-based adaptive learning tools must be grounded in the principles of inclusive education, Universal Design for Learning, data protection, equity, transparency, and collaboration between teachers, pupils, parents, technology developers, and policymakers. When implemented responsibly, AI can serve as a strategic tool for creating learning experiences that are more personalised, flexible, accessible, and responsive to pupils’ diverse learning needs.
Co-Authors Abdul Rasyid Ahmad Husein Ahmad Rifai Rahawarin Akbar, Mohammad Aldrin Andi Ernawati Andi Muh Akbar Saputra Andik Adi Suryanto Andreas Ariyanto Rangga Andri Irawan Arridha Zikra Syah Asep Saeppani Asy Syifa’a, Fintia Maulid Balabuana, Gaspar Bao Budiman, Edy Bugis, Abu Bakar Devi Sukmawati Edowai, Yermias Edy Winarno Iwan Adhicandra Iwan Setiawan Jufri, Muhamad Taher Kraugusteeliana Kraugusteeliana Lasmaida Gultom Moh. Aldrin Akbar Muhamad Taher Jufri Muhamad Yamin Noch Muhammad Riandi Widiyantoro Mukti Ali Mursalam Salim Mutmainna - Numberi, Lita Chrysti Nur, Hasanah Parini Rasna Rasna Robo, Salahudin Rolly Yesputra Sardjana Orba Manullang Siti Nurhayati Sri Mulyono Sri Sudono Saliro Suryani Suryani Suryo Sasono, Damar Syahrul Syahrul Waja, Afgan Wardani, Megasuciati Widjaja, Warkianto Widjanarko Wijaya, Hamid Zen, Agustian