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All Journal Journal of Applied Science, Engineering, Technology, and Education Mattawang: Jurnal Pengabdian Masyarakat Journal of Digital Technology and Computer Science Innovation and Applied Education Journal
Jannah, Devi Miftahul
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Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Public Health Social Sciences Other

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From Ethics to Impact: Modeling the Role of AI Perception Dynamics in the Relationship Between Ethics AI Practices, AI-Driven Societal Impact, and AI Behavioral Analysis Fakhri, M. Miftach; Jannah, Devi Miftahul; Isma, Andika; Dewantara, Hajar; Nirmala S., Aprilianti
Journal of Applied Science, Engineering, Technology, and Education Vol. 7 No. 1 (2025)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.asci3802

Abstract

The rapid evolution of Artificial Intelligence (AI) has brought significant changes across various sectors, including healthcare, finance, and criminal justice, presenting both remarkable opportunities and complex ethical challenges. As AI becomes increasingly embedded in decision-making processes, concerns about individual rights, social equity, and public trust are growing, especially in high-stakes contexts. These ethical implications underscore the critical need for robust frameworks that emphasize AI transparency, accountability, and fairness to mitigate risks such as bias and ensure responsible usage. Despite the increased focus on ethical AI practices, there remains a considerable gap in understanding how these frameworks impact societal perceptions and behaviors toward AI. This study seeks to address this gap by investigating the effects of ethical AI practices—specifically transparency, accountability, and fairness—on public perceptions and behaviors. The study employs a quantitative approach, using purposive sampling to select a sample of AI-knowledgeable participants and analyzing the data with Partial Least Squares Structural Equation Modeling (PLS-SEM). This methodological approach allows for a detailed exploration of the relationships between ethical AI practices and societal impacts. Additionally, the study examines the mediated pathways through which these ethical practices influence AI’s societal and behavioral impacts, hypothesizing that transparency and accountability foster trust and positive engagement. By developing a framework that aligns ethical AI practices with societal values, this study aims to advance the broader goals of societal trust, public acceptance, and sustainable social integration of AI technologies. These insights contribute to the growing body of knowledge on responsible AI deployment, supporting ethical alignment in diverse AI applications and promoting trustworthiness in AI-driven systems
Portal Digital Berbasis Website dan AR/VR untuk Penguatan Link and Match melalui VocationalFive 4.0 di SMKN 5 Barru Baso, Fadhlirrahman; Prima, Kurnia Wahyu; Nurfauziah, Nurfauziah; Jannah, Devi Miftahul; Nurjannah, Elma
Mattawang: Jurnal Pengabdian Masyarakat Vol. 6 No. 3 (2025)
Publisher : Yayasan Ahmar Cendekia Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.mattawang4303

Abstract

Vocational High School (SMK) graduates in Indonesia face the problem of competency mismatch with the needs of the world of work, reflected in the high open unemployment rate of 9.42% (BPS, 2023). The community service program at SMKN 5 Barru aims to increase the capacity of teachers and students by utilizing the VocationalFive 4.0 portal based on Augmented Reality (AR) and Virtual Reality (VR). A participatory action research approach was used, including needs identification, training, mentoring, and evaluation. The results showed improved skills with an average post-test score of 85.17 and increased teacher confidence in using AR/VR. The portal was also rated as relevant for publication of work, alumni tracking, and career navigation. The findings confirm the importance of digital technology integration in vocational education, which can create interactive learning and align with industry needs. This program has the potential to become a model for developing AR/VR-based learning with an alumni tracer system.
KNN Vs Naive Bayes: An Innovative Comparison in Predictive AI Learning With Association Data Support Jannah, Devi Miftahul; Aprilianti Nirmala S
Journal of Digital Technology and Computer Science Vol. 3 No. 1 (2025): November 2025
Publisher : Lontara Digitech Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61220/

Abstract

This study analyzes how Naive Bayes and K-Nearest Neighbor (KNN) predict learning outcomes based on artificial intelligence (AI). The main focus of this study is the difficulty of algorithms in handling complex learning data and the contribution of Association Rule Mining (ARM) attribute features in improving prediction accuracy. The methods applied include two classification algorithms (KNN and Naive Bayes) in an exploratory-comparative quantitative research design, as well as the application of ARM to uncover hidden patterns among variables using the apriori algorithm. Data for 368 students with prior experience in artificial intelligence technology was collected through an online survey. Although KNN outperforms in recall, the study results show that Naive Bayes has higher precision. By detecting hidden correlation patterns that cannot be identified by conventional classification methods, ARM improves classification results. The discussion emphasizes that the selection of the best algorithm depends on the application's objectives, namely whether the priority is on classification accuracy or the range of relevant results. Based on these findings, a hybrid technique combining KNN, Naive Bayes, and ARM is highly recommended for creating a more efficient and accurate prediction system to support AI-based education.
PENGEMBANGAN DAN VALIDASI INVENTARISASI KECEMASAN UJIAN PADA SISWA BELAJAR ONLINE Elma Nurjannah; Jannah, Devi Miftahul; Annajmi Rauf
Innovation and Applied Education Journal Volume 2, Issue 2, Juni 2025
Publisher : PT. Lontara Digitech Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61220/iaej.v2i2.253

Abstract

Pandemi COVID-19 telah mendorong peralihan mendadak ke pembelajaran daring yang memunculkan tantangan psikologis baru, termasuk kecemasan ujian. Penelitian ini bertujuan untuk mengembangkan dan menguji secara awal instrumen pengukuran kecemasan ujian yang relevan dalam konteks pembelajaran daring. Penelitian menggunakan pendekatan kuantitatif dengan desain cross-sectional dan melibatkan 41 mahasiswa aktif dari Program Studi Teknik Informatika dan Komputer Universitas Negeri Makassar. Instrumen berupa angket skala Likert lima poin yang mencakup aspek kognitif, fisiologis, dan lingkungan. Analisis data dilakukan secara deskriptif menggunakan ukuran mean, median, modus, minimum, maksimum, dan distribusi frekuensi. Hasil menunjukkan bahwa 52,5% mahasiswa mengalami kecemasan sedang, 37,5% kecemasan berat, dan hanya 10% kecemasan ringan. Skor mean tertinggi terdapat pada item kekhawatiran terhadap hasil ujian (mean = 3,475), sedangkan gejala fisik dan tekanan lingkungan juga signifikan. Hasil ini menunjukkan bahwa kecemasan ujian pada pembelajaran daring bersifat multidimensional dan perlu diukur secara kontekstual. Penelitian ini berkontribusi dalam menyediakan instrumen awal yang dapat digunakan untuk deteksi dini dan intervensi psikologis guna mendukung kesehatan mental mahasiswa di era digital.
Analisis Efektivitas Model Blended Learning dengan Penerapan Media Gamifikasi di Universitas Negeri Makassar Jannah, Devi Miftahul; Annajmi Rauf; Elma Nurjannah
Innovation and Applied Education Journal Volume 2, Issue 2, Juni 2025
Publisher : PT. Lontara Digitech Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61220/iaej.v2i2.251

Abstract

Pandemi Covid 19 mendorong perubahan signifikan dalam sistem pendidikan, khususnya pergeseran menuju pembelajaran daring. Sebagai respon terhadap tantangan ini, blended learning berbasis gamifikasi muncul sebagai alternatif inovatif untuk meningkatkan keterlibatan dan hasil belajar siswa. Penelitian ini bertujuan untuk mengeksplorasi persepsi mahasiswa terhadap penggunaan sistem gamifikasi dalam pembelajaran berbasis blended learning. Menggunakan pendekatan kuantitatif deskriptif, data dikumpulkan melalui kuesioner tertutup berbasis skala Likert dari 35 mahasiswa dari berbagai program studi di Kota Makassar. Instrumen dikembangkan berdasarkan empat tipe motivasi pengguna dalam gamifikasi: Achiever, Explorer, Philanthropist, dan Socializer. Analisis dilakukan menggunakan perangkat lunak Jamovi dengan menghitung rata rata, frekuensi, dan simpangan baku. Hasil menunjukkan bahwa mayoritas mahasiswa memiliki persepsi positif terhadap penggunaan gamifikasi dalam blended learning, terutama dalam hal meningkatkan motivasi, kolaborasi, dan keterlibatan dalam proses belajar. Elemen seperti antarmuka yang mudah digunakan, interaksi sosial, serta dorongan untuk belajar secara teratur berkontribusi pada efektivitas metode ini. Temuan ini menegaskan bahwa integrasi gamifikasi dalam model blended learning dapat menjadi strategi yang efektif dalam mencegah learning loss dan menjawab tantangan pembelajaran pascapandemi. Penelitian ini memberikan kontribusi terhadap pengembangan desain pembelajaran berbasis teknologi yang lebih menarik dan partisipatif.
Edukasi Keamanan Digital Berbasis AI Generatif: Studi pada Mahasiswa Teknologi di Indonesia Afrisal Arifin; Jannah, Devi Miftahul
Innovation and Applied Education Journal Volume 1, Issue 2, Juni 2024
Publisher : PT. Lontara Digitech Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61220/iaej.v1i2.241

Abstract

Penelitian ini bertujuan untuk mengevaluasi tingkat literasi keamanan siber dan perilaku mahasiswa dalam mengadopsi langkah-langkah perlindungan digital. Dengan pendekatan kuantitatif berbasis survei dan desain cross-sectional, data dikumpulkan melalui kuesioner daring dari mahasiswa program studi berbasis teknologi di Indonesia, menggunakan purposive sampling untuk menjangkau partisipan yang relevan. Instrumen penelitian mencakup tiga aspek utama: pandangan terhadap penggunaan chatbot berbasis AI Generatif, kebiasaan penggunaan teknologi, dan pemahaman serta sikap terhadap ancaman digital. Analisis statistik dilakukan untuk menilai hubungan antara literasi keamanan digital, kesadaran, dan perilaku mahasiswa. Hasil penelitian menunjukkan bahwa tingkat kesadaran keamanan siber mahasiswa berada pada kategori menengah, dengan kesadaran dasar yang cukup baik namun implementasi langkah-langkah spesifik, seperti autentikasi dua faktor dan pengelolaan privasi, masih memerlukan peningkatan. Pembelajaran berbasis AI Generatif, seperti GPT Chatbot, diidentifikasi sebagai pendekatan potensial untuk meningkatkan literasi keamanan siber secara personal dan relevan. Penelitian ini berkontribusi pada pengembangan strategi edukasi keamanan digital yang lebih efektif, memberikan wawasan tentang perilaku mahasiswa, dan menginformasikan institusi pendidikan untuk mengintegrasikan modul keamanan siber ke dalam kurikulum. Temuan ini menegaskan pentingnya kolaborasi antara institusi pendidikan dan penyedia alat keamanan dalam menciptakan generasi pengguna digital yang lebih aman dan terinformasi.
Co-Authors Afrisal Arifin Andika Isma Annajmi Rauf Aprilianti Nirmala S Baso, Fadhlirrahman Elma Nurjannah Hajar Dewantara Kurnia Wahyu Prima M. Miftach Fakhri Nirmala S., Aprilianti Nurfauziah Nurfauziah, Nurfauziah Nurjannah, Elma