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All Journal ARRUS Journal of Engineering and Technology Information Technology Education Journal Journal of Deep Learning, Computer Vision and Digital Image Processing Artificial Intelligence in Lifelong and Life-Course Education Journal of Vocational, Informatics and Computer Education Journal of Applied Artificial Intelligence in Education JESI : Journal of Engineering Service and Innovation
Muh. Akbar
Universitas Negeri Makassar
Computer Science & IT Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Library & Information Science Social Sciences Other

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The Pengembangan Aplikasi Notes Berbasis Android dengan Model Waterfall Muh. Akbar; Muh Ikhwan Yusrah Yusuf; Andi Sarifa Sa itri Bachmid; Fitra Yusuf; Nurul Fadli; Ahmad Rifaldi
Information Technology Education Journal Vol. 3, No. 3, September (2024)
Publisher : Jurusan Teknik Informatika dan Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/intec.v3i3.5926

Abstract

Di era digital, aplikasi notes berbasis Android menjadi solusi praktis untuk menyimpan dan mengatur informasi penting, baik dalam konteks personal maupun profesional. Penelitian ini menggunakan metode pengembangan perangkat lunak model Waterfall yang terdiri dari lima tahap: analisis kebutuhan, desain sistem, implementasi, pengujian, dan pemeliharaan. Aplikasi yang dikembangkan memungkinkan pengguna untuk membuat, mengedit, dan menghapus catatan dengan mudah, serta menambahkan fitur pengingat untuk mengatur tugas dan jadwal. Dengan antarmuka yang intuitif dan fitur yang dirancang untuk kemudahan penggunaan, aplikasi ini memberikan kemudahan akses informasi kapanpun dan dimanapun. Hasil pengujian menunjukkan bahwa aplikasi memenuhi kebutuhan pengguna secara efisien dan efektif. Penelitian ini memberikan kontribusi pada pengembangan aplikasi berbasis Android sebagai alat produktivitas yang relevan dalam masyarakat modern.
PKM: Edukasi Strategi Masuk PTN melalui Jalur SNBP dan SNBT bagi Siswa SMA/SMK di Kabupaten Sinjai Muh. Akbar; Kurnia Prima Putra; Syamsurijal Basri; Muhalim; Asmaul Husnah Nasrullah
Journal of Engineering Service and Innovation Volume 1, Issue 2 (Februari) 2026
Publisher : Fakultas Teknik

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Kegiatan Pengabdian Kepada Masyarakat (PKM) ini bertujuan untuk memberikan edukasi kepada siswa kelas XII SMA/SMK di Kabupaten Sinjai mengenai strategi masuk Perguruan Tinggi Negeri (PTN) melalui jalur Seleksi Nasional Berdasarkan Prestasi (SNBP) dan Seleksi Nasional Berdasarkan Tes (SNBT). Kegiatan ini dilatarbelakangi oleh masih rendahnya pemahaman siswa di daerah terhadap mekanisme, persyaratan, dan strategi seleksi masuk PTN yang terus mengalami perubahan kebijakan. Kegiatan dilaksanakan pada tanggal 12 Februari 2026 dengan melibatkan 100 siswa kelas XII dari berbagai SMA/SMK di Kabupaten Sinjai. Metode yang digunakan adalah penyampaian informasi secara langsung melalui sosialisasi edukatif dan diskusi interaktif. Materi yang disampaikan mencakup perbedaan jalur SNBP dan SNBT, persyaratan eligibilitas, mekanisme pendaftaran, strategi pemilihan program studi, serta tips dan trik menghadapi seleksi. Hasil kegiatan menunjukkan tingginya antusiasme peserta yang ditandai dengan keterlibatan aktif dalam sesi diskusi dan tanya jawab. Berbagai pertanyaan yang diajukan peserta menunjukkan adanya kebutuhan informasi yang cukup tinggi terkait mekanisme SNBP dan SNBT. Kegiatan ini memberikan kesempatan kepada siswa untuk memperoleh informasi yang lebih jelas mengenai jalur masuk perguruan tinggi serta strategi yang dapat dipersiapkan sejak dini dalam menghadapi proses seleksi masuk PTN. Kegiatan ini diharapkan dapat menjadi bekal informasi yang bermanfaat bagi siswa dalam merancang strategi yang tepat untuk melanjutkan pendidikan ke jenjang perguruan tinggi.
AI Dependency and Critical Thinking in Higher Education: A Life-Course Perspective on Ethical Awareness and Algorithmic Bias Jabal Nur Popalia; Muh. Al-Habsy; Muh. Akbar; Akhmad Affandi
Artificial Intelligence in Lifelong and Life-Course Education Vol 1 No 1 (2026): Artificial Intelligence in Lifelong and Life-Course Education
Publisher : PT. Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/aillce.v1i1.3

Abstract

Purpose – The rapid adoption of artificial intelligence (AI) in higher education has transformed how students engage with learning tasks, raising concerns about dependency, ethical awareness, and algorithmic bias. From a life-course education perspective, early adulthood represents a critical developmental stage in which patterns of AI use may shape long-term critical thinking and lifelong learning dispositions. However, empirical studies integrating AI dependency, ethical awareness, and algorithmic bias awareness in relation to students’ critical thinking remain limited. This study examines the effects of AI dependency, ethical awareness, and algorithmic bias awareness on university students’ critical thinking skills in the context of Indonesian higher education.Design/methods/approach – A quantitative cross-sectional design was employed. Data were collected from 110 undergraduate students across four universities in South Sulawesi, Indonesia, using purposive sampling. A validated questionnaire measured AI dependency, ethical awareness, algorithmic bias awareness, and critical thinking skills. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS. Findings – The results indicate that all three variables significantly and positively influence students’ critical thinking skills. Algorithmic bias awareness exhibits the strongest effect, followed by AI dependency and ethical awareness. These findings suggest that critical awareness of AI limitations contributes more substantially to critical thinking development than the intensity of AI use alone.Research implications/limitations – The cross-sectional design limits causal interpretation, and the dominance of early-semester STEM students constrains generalizability. Potential moderating factors were not examined. Originality/value – This study contributes to the literature on artificial intelligence in education by integrating ethical awareness and algorithmic bias awareness within a life-course framework, highlighting the central role of critical AI literacy in supporting sustainable critical thinking development in higher education.
Self-Efficacy over Trust: Rethinking Sustainable AI Use among University Students Vina Annisa Sofyan; Mustari S. Lamada; Sanatang; Shabrina Syntha Dewi; Muh. Akbar
Journal of Vocational, Informatics and Computer Education Vol 4, No 2 (2026): June 2026
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/voice.v4i1.402

Abstract

Purpose – Artificial intelligence (AI) is increasingly embedded in higher education, yet sustained use remains insufficiently explained by adoption-centered models that primarily emphasize direct effects.This study examines continuous AI use among university students by integrating perceived enjoyment, effort expectancy, trust in artificial intelligence, and AI self-efficacy within a parallel-mediation framework. Methods – A quantitative cross-sectional design was employed involving 247 undergraduate students who reported varying levels of AI use in academic contexts. Data were collected through an online questionnaire and analyzed using partial least squares structural equation modeling (PLS-SEM) to assess direct and indirect relationships. Findings – Perceived enjoyment (β = 0.386) and effort expectancy (β = 0.363) significantly predicted continuous AI use, with perceived enjoyment showing the stronger direct effect. AI self-efficacy significantly mediated both relationships, particularly the effect of effort expectancy (β = 0.297). By contrast, trust significantly mediated only the perceived enjoyment pathway (β = 0.196), while its mediating role in the effort expectancy pathway was not supported under the conventional significance criterion. Research Implications -The study suggests that higher education institutions should strengthen students’ AI self-efficacy while designing AI-supported learning environments that are both intuitive and engaging. Originality - The study contributes to post-adoption AI research by clarifying the relative mediating roles of self-efficacy and trust, highlighting that AI self-efficacy is a more robust mechanism than trust in sustaining AI use among university students.
How AI Personalization and Feedback Shape Student Engagement: The Mediating Role of Technology Engagement Ahmad Abdullah Aswad; Tegar Angbirah Parerungan; Elma Nurjannah; Muh. Akbar
Journal of Applied Artificial Intelligence in Education Vol 1, No 2 (2026): January 2026
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/jaaie.v1i2.8

Abstract

Higher education is rapidly adopting AI-supported learning systems, yet the effectiveness of these tools depends on how students engage with them psychologically, not merely on their availability. However, mere access to AI tools does not automatically translate into meaningful student engagement, indicating a psychological “adoption gap” between technology availability and learners’ active involvement. This study aims to test how key AI features AI usage, personalization/adaptivity, and feedback/analytics relate to student engagement, while examining technology engagement as a mediating mechanism that explains how AI features become educationally effective. Using a quantitative, non-experimental cross-sectional survey of 71 undergraduate students in Eastern Indonesia, the proposed model was analyzed using PLS-SEM (SmartPLS 4) to estimate direct and indirect effects. The model demonstrated strong predictive power, explaining 74.4% of the variance in technology engagement (R² = 0.744) and 66.4% in student engagement (R² = 0.664). AI personalization/adaptivity emerged as the strongest driver, significantly predicting technology engagement (β = 0.516, p < 0.001) and also exerting a significant direct effect on student engagement (β = 0.310, p = 0.010), whereas AI usage and feedback did not show significant direct effects on student engagement but exhibited significant indirect effects through full mediation by technology engagement. These findings imply that technology engagement functions as a “gatekeeper”: institutions should prioritize adaptive personalization and deliberately cultivate students’ sense of control, competence, and psychological involvement with AI systems, rather than relying on high usage intensity or automated feedback alone to drive engagement.
KLASIFIKASI KUALITAS BUAH MANGGIS BERDASARKAN TEKSTUR DAN FITUR WARNA MENGGUNAKAN JARINGAN SYARAF TIRUAN BERBASIS PENGOLAHAN CITRA DIGITAL Muh Raenaldy; Faldi Firmansyah; Muh Yasin Kadir; Jessica Crisfin Lapendy; Muh. Akbar
Journal of Deep Learning, Computer Vision, and Digital Image Processing Volume 2 Issue 2 September 2024
Publisher : CV. Sakura Digital Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61255/decoding.v2i2.546

Abstract

Masalah dalam proses penyortiran buah manggis di Indonesia adalah masih dilakukan secara manual, mengakibatkan ketidakkonsistenan dan rendahnya akurasi dalam klasifikasi kualitas buah. Penelitian ini bertujuan untuk mengembangkan sistem klasifikasi kematangan buah manggis secara otomatis menggunakan metode Jaringan Syaraf Tiruan (JST) berbasis pengolahan citra digital. Metode yang digunakan mencakup enam tahapan: akuisisi citra, preprocessing, segmentasi, operasi morfologi, ekstraksi fitur, dan klasifikasi. Hasil penelitian menunjukkan bahwa metode JST dapat mengklasifikasikan buah manggis dalam tiga kelas tingkat kematangan (belum matang, setengah matang, dan matang) dengan akurasi 100% baik pada data latih maupun data uji. Fitur terbaik diperoleh dari kombinasi RGB + tekstur (contrast + homogeneity) dengan waktu komputasi paling efisien (136,61 detik untuk data latih dan 28,13 detik untuk data uji). Metode JST terbukti lebih efektif dibandingkan metode klasifikasi lainnya seperti Naive Bayes dan KNN, khususnya dalam menghadapi data berdimensi tinggi. Penelitian ini menyimpulkan bahwa sistem klasifikasi berbasis JST dapat diimplementasikan untuk meningkatkan efisiensi dan akurasi penyortiran buah manggis secara industri.
Klasifikasi tingkat aroma daun jeruk purut menggunakan metode jaringan saraf tiruan backpropagation Muh. Asmar; M. Rizky Kurniawan; Reynaldi Nafzal Ashari; Muh. Akbar; Rezki Nurul Jariah S.Intam
Journal of Deep Learning, Computer Vision, and Digital Image Processing Volume 2 Issue 2 September 2024
Publisher : CV. Sakura Digital Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61255/decoding.v2i2.548

Abstract

Penelitian ini bertujuan untuk mengembangkan metode klasifikasi tingkat aroma pada daun jeruk menggunakan citra daun sebagai input. Dataset yang digunakan terdiri dari 300 lembar daun jeruk yang dibagi menjadi tiga kelas aroma: kuat, sedang, dan rendah. Proses klasifikasi melibatkan tahap preprocessing, ekstraksi fitur, pelatihan model menggunakan Jaringan Saraf Tiruan (JST), dan pengujian model. Tahap preprocessing mencakup ekstraksi channel warna dan segmentasi citra. Fitur-fitur warna dan tekstur diekstraksi untuk digunakan dalam pelatihan model JST. Hasil eksperimen menunjukkan bahwa menggunakan fitur warna RGB memberikan akurasi pelatihan sebesar 91,25% dengan waktu komputasi 5,79 detik per citra, dan akurasi pengujian mencapai 100% dengan waktu komputasi 7,76 detik per citra. Hal ini menunjukkan bahwa metode klasifikasi yang dikembangkan mampu dengan baik dalam menentukan tingkat aroma daun jeruk. Namun, dalam penelitian ini kami menyarankan perbaikan pada proses akuisisi citra dan pengembangan metode klasifikasi tambahan untuk meningkatkan keakuratan dalam menentukan tingkat aroma daun jeruk.
WarungSync: Development of an Android-Based Integrated Warung Management Application Using Kotlin and Firebase Muh. Akbar; Ahmad Dinejad Halik
ARRUS Journal of Engineering and Technology Vol. 6 No. 1 (2026)
Publisher : PT ARRUS Intelektual Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/jetech4924

Abstract

This study aims to design and develop WarungSync, an Android-based integrated warung management application intended to assist micro, small, and medium enterprise (MSME) owners in digitalizing their daily business operations. The majority of traditional warung businesses in Indonesia are still managed manually, resulting in inaccurate stock data, unrecorded transactions, and the absence of systematic financial reporting. This study employs the Waterfall software development methodology, consisting of five sequential phases: requirements analysis, system design, implementation, testing, and deployment. The application was built using the Kotlin programming language with Model-View-ViewModel (MVVM) architecture and Firebase as the cloud backend infrastructure, encompassing Firebase Authentication for user security and Firebase Firestore for real-time data synchronization. The results demonstrate that WarungSync successfully integrates Point of Sale (POS) and inventory management functions within a single mobile platform, featuring a real-time dashboard, smart inventory management with low-stock notifications, a digital cashier system, sales reporting with weekly chart visualization, and automated cloud synchronization. The findings of this study reveal that all 13 functional features of WarungSync passed Black Box Testing, confirming that the application operates in accordance with the specified functional requirements and is capable of replacing conventional manual management practices in warung businesses.
Co-Authors Ahmad Abdullah Aswad Ahmad Dinejad Halik Ahmad Rifaldi Akhmad Affandi Andi Sarifa Sa itri Bachmid Asmaul Husnah Nasrullah Elma Nurjannah Faldi Firmansyah Fitra Yusuf Jabal Nur Popalia Jessica Crisfin Lapendy Kurnia Prima Putra M. Rizky Kurniawan Muh Ikhwan Yusrah Yusuf Muh Raenaldy Muh Yasin Kadir Muh. Al-Habsy Muh. Asmar Muhalim Nurul Fadli Reynaldi Nafzal Ashari Rezki Nurul Jariah S.Intam Sanatang Shabrina Syntha Dewi Syamsu Rijal Tegar Angbirah Parerungan Vina Annisa Sofyan