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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) 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 RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal ULTIMATICS ComTech: Computer, Mathematics and Engineering Applications Jurnal Teknologi Komputer dan Sistem Informasi bit-Tech eCo-Buss Jurnal Informatika dan Rekayasa Perangkat Lunak Jurnal TIKOMSIN (Teknologi Informasi dan Komunikasi Sinar Nusantara) 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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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.
Optimizing Virtual Culinary Tours Using Character-based Interaction and Finite State Machine (FSM) Margaretha Natalya; Aditiya Hermawan
bit-Tech Vol. 7 No. 2 (2024): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v7i2.1886

Abstract

This research focuses on the development of a Virtual Tour application utilizing Finite State Machine (FSM) to enhance the interaction of a Non-Playable Character (NPC) as a tour guide in a 3D virtual environment. The primary issue addressed is the significant impact of the COVID-19 pandemic on the tourism industry, which led to travel restrictions and limited opportunities for visitors to explore tourist destinations physically. The aim of the study is to create a virtual tourism experience as an alternative solution, enabling users to explore historical sites like Pasar Lama Tangerang remotely through Google Cardboard VR. To achieve this, the NPC’s behavior is controlled using FSM, allowing the character to transition between states—idle, walking, and talking—based on user interactions. Data was collected through user testing with a Likert scale questionnaire, evaluating user satisfaction and the effectiveness of the FSM method. The results revealed a 74.35% positive user rating, categorized as Good, demonstrating the potential of FSM to provide an interactive, engaging, and educational virtual tour experience. These findings highlight the effectiveness of FSM in creating a dynamic and user-responsive virtual tour, offering significant benefits to the tourism sector by providing an innovative, accessible, and immersive way for potential visitors to explore destinations during travel restrictions. This research contributes to the growing field of e-tourism, showcasing the potential of virtual reality and FSM to transform the tourism industry in times of crisis.
Enhancing Stock Price Forecasting: Optimizing Neural Networks with Moving Average Data Aditiya Hermawan; Stanley Ananda; Junaedi; Edy
bit-Tech Vol. 7 No. 3 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v7i3.2196

Abstract

This research focuses on optimizing a neural network model for stock price prediction using Particle Swarm Optimization (PSO), considering the inherent risks and potential high returns associated with stock investment. Given the challenges posed by stock price volatility, this study combines Moving Average (MA) a fundamental statistical technique in stock market analysis with advanced data mining approaches, specifically neural networks and PSO, to enhance prediction accuracy. The primary objective is to improve the efficiency of neural networks by minimizing error rates and equipping investors with more reliable tools for financial decision-making. The proposed methodology involves converting historical stock price data into a Simple Moving Average (SMA) over a 5-day period, followed by optimizing a neural network model using PSO. This optimization process fine-tunes key parameters, particularly the weight distributions of various stock market indicators, including Open SMA, High SMA, Low SMA, and Close SMA. Model performance is evaluated using Root Mean Square Error (RMSE) as a validation metric. The findings indicate a significant enhancement in the predictive accuracy of the neural network model after PSO optimization. The optimal configuration is identified in a two-layer neural network with a specific node arrangement. This optimized model not only improves stock price forecasting precision but also has practical implications for investors and financial analysts in risk management and profit maximization.
Evaluating Latent Emotional Structures through Unsupervised Semantic Text Clustering Edy, Edy; Junaedi, Junaedi; Hermawan, Aditiya; Kurnia, Yusuf; Maranto, Ardiane Rossi Kurniawan
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 20, No 2 (2026): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.116764

Abstract

Emotion analysis in textual data is an important topic in natural language processing, as emotions play a crucial role in understanding public opinion, psychological states, and dynamics of digital interaction. However, most existing studies rely heavily on supervised classification approaches based on predefined emotion labels, which may overlook latent semantic structures and emotional overlap inherent in natural language. This study aims to evaluate latent emotional structures in text using an unsupervised semantic clustering approach. The proposed method involves text preprocessing, feature representation using Term Frequency–Inverse Document Frequency (TF–IDF), dimensionality reduction through Singular Value Decomposition (SVD), and clustering using K-Means and Hierarchical Agglomerative algorithms. Both internal and post-hoc external evaluation metrics are employed to assess cluster quality and examine their correspondence with available emotion labels. The results indicate that K-Means clustering produces more compact and interpretable clusters than the hierarchical approach, while both methods reveal substantial emotional overlap across clusters. These findings suggest that emotional expressions in text exhibit a continuous semantic structure rather than discrete categorical boundaries. This study highlights the importance of unsupervised semantic clustering as an analytical tool for gaining deeper insight into latent emotional patterns in textual data.
OPTIMASI SISTEM LAYANAN ASISTEN RUMAH TANGGA BERBASIS MOBILE DENGAN EVALUASI TECHNOLOGY ACCEPTANCE MODEL Daniawan, Benny; Culadi, Rafael Daniel; Hermawan, Aditiya; Ceng Giap, Yo
Jurnal Teknologi Informasi dan Komunikasi (TIKomSiN) Vol 14, No 1 (2026): Jurnal Tikomsin, Vol 14, No.1, April 2026
Publisher : STMIK Sinar Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30646/tikomsin.v14i1.1096

Abstract

In the contemporary era, individuals increasingly face demanding work schedules that often exceed standard working hours, intensifying challenges in balancing professional and domestic responsibilities. Household assistant services have emerged as a practical solution to support daily household activities. This study aims to design and evaluate a mobile-based household assistant service system that enables efficient interaction between service providers and employers, while enhancing accessibility, streamlining recruitment processes, and improving user experience through digital integration. To evaluate user acceptance, the study adopts the Technology Acceptance Model (TAM), focusing on Perceived Ease of Use (PEOU), Perceived Usefulness (PU), Attitude Toward Using (ATU), and Behavioral Intention to Use (BITU). Data were collected from 105 respondents and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings reveal that PEOU significantly influences PU (t = 11.324, p < 0.001), explaining 47.6% of its variance. Moreover, PEOU and PU jointly affect ATU (R² = 0.617), while ATU significantly influences BITU (R² = 0.495). However, PU does not directly affect BITU, indicating that perceived usefulness alone is insufficient to drive user intention without supportive attitudinal factors.
Enhancing digital asset ownership through decentralized non fungible token applications Kurnia, Yusuf; Rino, Rino; Edy, Edy; Junaedi, Junaedi; Hermawan, Aditiya; Kevin, Kevin
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i2.pp1972-1981

Abstract

The rapid expansion of the digital ecosystem has introduced pressing challenges surrounding identity, authenticity, trust, and transparency. The ease with which digital content can be duplicated often undermines creators, whose works are distributed without consent or fair compensation. Blockchain technology offers a transformative solution through its decentralized, transparent, and tamper-resistant structure. Among its innovations, non-fungible tokens (NFTs) provide a mechanism to verify the authenticity and ownership of unique digital assets. This study explores the transformative potential of NFTs in strengthening digital ownership and authenticity while identifying critical challenges such as market concentration, interoperability limitations, and security vulnerabilities within public NFT platforms. Employing the extreme programming (XP) methodology, this research proposes a secure framework for NFT creation outside public marketplaces to enhance the protection of smart contracts and user accounts. The findings demonstrate that this approach grants users’ greater control, minimizes exposure to platform-level risks, and promotes trust in decentralized asset management. Overall, this study underscores NFTs’ pivotal role in reshaping digital ownership models and highlights the need for continued innovation to ensure security, transparency, and equitable value distribution in the evolving digital economy.
A Systematic Review of Machine Learning and Deep Learning Techniques for Deepfake Image Detection: Trends, Challenges, and Future Directions Samuel Rhesa; Aditiya Hermawan
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 8 No. 1 (2026): Maret
Publisher : Universitas Wahid Hasyim

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

Abstract

The rapid development of deep learning based face manipulation techniques has produced synthetic images that are increasingly realistic and visually indistinguishable from authentic ones. The deepfake phenomenon poses serious challenges to digital information authenticity and cybersecurity. This research presents a Systematic Literature Review (SLR) of publications from the 2020–2025 period to map trends, methodological approaches, and key challenges in machine learning and deep learning based image deepfake detection. Through an analysis of 24 empirical studies, this review identifies a shift in research direction from conventional convolutional architectures toward hybrid and attention based approaches that emphasize efficiency, adaptivity, and cross domain generalization. Findings show that although recent models such as Vision Transformer and hybrid CNN–LSTM are capable of achieving high accuracy under controlled conditions, their performance remains limited when tested on new domains. Key challenges identified include limited generalization against new manipulation types, vulnerability to image distortion and compression, and low transparency in model decision-making. This study fills research gaps by providing a comprehensive methodological map of architectural evolution, feature representation strategies, and evaluation metrics. Theoretically, this research expands the understanding of deepfake detection research dynamics, while practically, the results provide direction for developing adaptive, transparent, and efficient detection systems for real-time implementation.
Impact of Dataset Background on Deep Learning-Based Waste Classification Nazzua Azzahra; Aditiya Hermawan; Junaedi; Yusuf Kurnia; Edy
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 3 (2026): Juni 2026 (in progress)
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i3.6965

Abstract

Accurate waste classification plays a vital role in supporting effective waste management and promoting environmental sustainability, especially amid the continuing increase in global waste generation. This study investigates how the presence and removal of image backgrounds influence the performance of deep learning models in automated waste classification. Two Convolutional Neural Network architectures, namely MobileNetV2 and DenseNet169, were evaluated using a dataset comprising 5,054 images across six waste categories: cardboard, glass, metal, paper, plastic, and trash. Each architecture was trained and tested on two dataset variants: original images with backgrounds and images with the backgrounds removed. Model performance was assessed using accuracy, precision, recall, F1-score, and ROC AUC. The results indicate that DenseNet169 consistently outperformed MobileNetV2 across all evaluation metrics. The highest accuracy, reaching 88.33%, was achieved by DenseNet169 when trained on images retaining their original backgrounds. This suggests that background information may provide meaningful contextual features that enhance classification performance. Conversely, removing backgrounds can limit the visual information available to the model and potentially reduce predictive effectiveness. These findings emphasize the importance of carefully considering background characteristics during dataset preparation and model training. Moreover, the study demonstrates that selecting an appropriate model architecture in relation to dataset properties is essential for optimizing classification outcomes. Overall, this research offers practical insights for improving dataset design and model selection in future automated waste classification systems, while contributing to the advancement of scalable and intelligent deep learning-based waste management solutions.
Co-Authors A Damiyati Abidin Abidin Agus Setiawan Alvin Rahayu Amin Suyitno Andre Sahulata Andri Wijaya Andrie Suak Tiwa Anton Halim Anwan Chailes Aprilyanti, Rina Ardiane Rossi Kurniawan Maranto Arvin Lawistra Benny Daniawan Ceng Giap, Yo Culadi, Rafael Daniel Daniawan, Benny Dera Susilawati Deviastati Putri Sugiarta Karlim edy 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 Maranto, Ardiane Rossi Kurniawan Margaretha Natalya Margita, Santa Mariana Purnamasari Mesakh Septiadi Simijaya michael vernannes marpaung Nandivadhano, Revatta Manggala Nathaniel, Joese Nazzua Azzahra Niki Destiandi Oscar Hasan Putra Pannavira Philip Kristy Wijaya Raditya Rimbawan O Raditya Rimbawan Oprasto Rheza Vincentius Riki Riki Riki RIKI RIKI, RIKI Rino Rino Rino Rossi Kurniawan Maranto, Ardiane Rossi Samuel Rhesa Sevtian Ferdian Stanley Ananda 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