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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Sains dan Teknologi Jurnal Buana Informatika Jurnal Edukasi dan Penelitian Informatika (JEPIN) JUITA : Jurnal Informatika Jurnal Sistem dan Informatika Journal of Information Systems Engineering and Business Intelligence Format : Jurnal Imiah Teknik Informatika JOIV : International Journal on Informatics Visualization Tech-E Jurnal ULTIMATICS ComTech: Computer, Mathematics and Engineering Applications Jurnal Teknologi Komputer dan Sistem Informasi bit-Tech eCo-Buss Jurnal Informatika dan Rekayasa Perangkat Lunak International Journal of Digital Entrepreneurship and Business (IDEB) Algor eCo-Fin Abdi-Dharma SATIN - Sains dan Teknologi Informasi Inovasi Gagasan Abdimas & Kuliah Kerja Nyata RUBINSTEIN JTKSI (Jurnal Teknologi Komputer dan Sistem Informasi)
Aditiya Hermawan
Universitas Buddhi Dharma
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Social Sciences Other

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Multimodal Wearable-Based Stress Detection Using Machine Learning: A Systematic Review of Validation Protocols and Generalization Gaps (2021 – 2025) Pannavira; Hermawan, Aditiya
ULTIMATICS Vol 17 No 2 (2025): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ti.v17i2.4488

Abstract

Stress is a major determinant of mental health and productivity. Consequently, continuous, unobtrusive stress detection using wearable sensors and machine learning (ML) has become a key priority in digital health. This paper presents a Systematic Literature Review (SLR) of 19 peer-reviewed articles, selected from 36 initial papers via structured inclusion/exclusion criteria focusing on studies from 2021-2025 that report quantitative ML performance. We employed a quantitative and qualitative synthesis to analyze and map five key dimensions: sensing modalities, ML/DL algorithms, datasets, validation protocols, and societal feasibility. Findings reveal a clear state-of-the-art: multimodal physiological fusion (notably PPG, EDA, and ACC) paired with hybrid deep models (CNN-LSTM) consistently achieves the highest accuracy (85–96%) on benchmark datasets. Our research reveals a significant lab-to-field gap. Most studies utilize intra-subject or k-fold cross-validation, whereas the more robust Leave-One-Subject-Out (LOSO) validation is hardly employed, constraining model applicability. Furthermore, fewer than 15% of studies explicitly address vital practical constraints such as privacy, computational efficiency (Edge AI), or power consumption. This review methodically quantifies the gap, emphasizing that current models, despite their accuracy, are not yet suitable for real-world implementation. We conclude with actionable directions toward generalizable, lightweight, and privacy-aware stress-aware systems.
Optimization of CNN and Vision Transformer Models in Addressing Long-Tailed Data Imbalance for Satellite Cloud Image Classification Nandivadhano, Revatta Manggala; Aditiya Hermawan; Lidya Lunardi
Tech-E Vol. 9 No. 2 (2026): TECH-E (Technology Electronic)
Publisher : Fakultas Sains dan Teknologi-Universitas Buddhi Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31253/te.v9i2.4256

Abstract

This study investigates long-tailed satellite cloud image classification by comparing CNN and Vision Transformers (ViT) built upon vision–language foundation models. A large-scale satellite cloud dataset with 11 highly imbalanced classes, including a dominant non-phenomenon category, is used to represent realistic atmospheric variability. The data are split using stratified sampling, standardized to a fixed resolution, and used to fine-tune CLIP-based backbones from RemoteCLIP and GeoRSCLIP through parameter-efficient adaptation. Several loss functions Cross Entropy, Logit Adjustment, Focal, Class-Balanced, and label-distribution–aware variants are evaluated, along with experiments examining majority-class removal and adapter bottleneck adjustments. Initial results show that Logit Adjustment causes majority-class collapse under default settings. After optimization, ViT-based models consistently outperform CNN models, achieving higher accuracy and more balanced macro-level performance. Class-Balanced loss emerges as the most effective objective, offering a strong trade-off between overall accuracy and per-class fairness. Increasing the adapter bottleneck dimension further boosts ViT performance, enabling the best configuration to match or exceed prior benchmarks while improving minority-class recognition. The final optimized model is deployed in a web-based prediction system, demonstrating the practical potential of foundation-model approaches for satellite-driven weather analysis.
Penguatan Literasi Digital Pendidik Agama Buddha melalui Pelatihan Terintegrasi ChatGPT dan Canva: Evaluasi Pretest–Posttest pada Komunitas PERGABI Hermawan, Aditiya; Wydiastuty, Lianny; Wijaya, Hartana; Margita, Santa
Abdi Dharma Vol. 6 No. 1 (2026): Jurnal Abdi Dharma (Jurnal Pengabdian Masyarakat)
Publisher : LP3kM Universitas Buddhi Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31253/ad.v6i1.4409

Abstract

Perkembangan pesat kecerdasan buatan generatif (Artificial Intelligence/AI) dan platform desain visual telah mengubah praktik pedagogis; namun, bukti empiris mengenai pelatihan terintegrasi berbasis praktik dalam konteks pendidikan keagamaan masih terbatas. Penelitian ini mengevaluasi program pengabdian kepada masyarakat yang bertujuan meningkatkan literasi digital pendidik agama Buddha yang tergabung dalam PERGABI melalui pelatihan terstruktur penggunaan ChatGPT dan Canva. Intervensi dirancang dengan pendekatan praktik langsung, meliputi teknik prompt engineering untuk penyusunan materi ajar berbantuan AI serta perancangan media pembelajaran visual menggunakan Canva. Desain penelitian menggunakan one-group pretest–posttest untuk mengukur perubahan tingkat familiaritas, kepercayaan diri instruksional, dan persepsi kemudahan penggunaan kedua perangkat tersebut. Data dikumpulkan melalui kuesioner daring sebelum dan sesudah pelatihan selama enam jam. Hasil analisis deskriptif dan komparatif menunjukkan peningkatan konsisten pada seluruh indikator. Familiaritas terhadap AI meningkat dari tingkat sedang menjadi tinggi, dengan lonjakan terbesar terjadi pada kepercayaan diri dalam memanfaatkan AI untuk kegiatan pembelajaran. Peningkatan juga terjadi pada penggunaan Canva, meskipun relatif lebih kecil karena tingkat familiaritas awal yang sudah tinggi. Peserta melaporkan tingkat kepuasan yang tinggi dan menilai pelatihan relevan dengan praktik pengajaran. Meskipun demikian, keterbatasan akses perangkat dan kestabilan internet menjadi hambatan implementasi. Temuan ini menegaskan bahwa pelatihan digital terintegrasi mampu menurunkan persepsi kompleksitas AI dan mempercepat adopsi pedagogis. Dukungan institusional berkelanjutan dan evaluasi longitudinal diperlukan untuk memastikan dampak pembelajaran jangka panjang.
Co-Authors A Damiyati Abidin Abidin Agus Setiawan Alvin Rahayu Amin Suyitno Ananda, Stanley Andre Sahulata Andri Wijaya Andrie Suak Tiwa Anton Halim Anwan Chailes Aprilyanti, Rina Ardiane Rossi Kurniawan Maranto Arvin Lawistra Benny Daniawan Ceng Giap, Yo Daniawan, Benny Dera Susilawati Deviastati Putri Sugiarta Karlim edy Edy Edy Edy Edy Ellysha Dwiyanthi Kusuma Eva Eva Evan - Evien Fernando, Albert Gustayo, Teven Halim Wijaya, Ardie Halim, Ardie Hargiani, Fransisca Xaveria Hartana Wijaya Henry Henry Intan Anjali Putri Jelvin Putra Halawa Jessen Laorenza Suwandi Johan Santoso Jowensen, Indrico JUNAEDI Junaedi Junaedi Junaedi Kevin Ivone Sim Kevin kevin Khanti Kusuma Dewi Kumala, Sonya Ayu Kurniawan Maranto, Ardiane Rossi Leonardo Lianata Lianny Wydiastuty Kusuma Lidya Lunardi Luis Alpianto Lunardi, Lidya Margita, Santa Mariana Purnamasari Mesakh Septiadi Simijaya michael vernannes marpaung Nandivadhano, Revatta Manggala Natalya , Margaretha Nathaniel, Joese Niki Destiandi Oscar Hasan Putra Pannavira Philip Kristy Wijaya Raditya Rimbawan O Raditya Rimbawan Oprasto Rheza Vincentius Riki Riki Riki RIKI RIKI, RIKI Rino Rossi Kurniawan Maranto, Ardiane Rossi Sevtian Ferdian Sutopo, Prihantoro Syahdu Suwitno Tia Nurapriyanti Wicaksono, Baghas Budi Willy Wijaya, Willy Wiyono Wydiastuty Kusuma, Lianny Wydiastuty, Lianny Yance Gusnadi Yanti, Lia Dama Yo Ceng Giap Yo Ceng Giap Yuliastati Putri Sugiarta Karlim Yunia Oktari Yusuf Kurnia Yusuf Kurnia, Yusuf