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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 E-Dimas: Jurnal Pengabdian kepada Masyarakat Format : Jurnal Imiah Teknik Informatika JOIV : International Journal on Informatics Visualization Tech-E Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal ULTIMA InfoSys 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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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): June 2026
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.
Sentiment Analysis of Indonesian Tourism Social Media Using Naïve Bayes for Decision Support Nathaniel Felix Fraderic; Aditiya Hermawan; Junaedi Junaedi
ULTIMA InfoSys Vol 17 No 1 (2026): Ultima InfoSys : Jurnal Ilmu Sistem Informasi
Publisher : Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/si.v17i1.4139

Abstract

The tourism sector remains one of the main contributors to Indonesia's economy, but its development is still challenged by inadequate communication and publication. Understanding visitors' opinions is essential for improving tourist destinations. This study aims to analyze public sentiment toward Indonesian tourist attractions by automatically processing visitor responses using sentiment analysis. The proposed approach applies text mining with the Naïve Bayes algorithm to classify sentiments efficiently. Data were collected through the X platform API using tourism-related keywords and hashtags, providing real-time public opinions on Indonesian destinations. A web-based application was developed using Python to visualize the sentiment analysis results as graphs showing the distribution of sentiment categories. The proposed model achieved an accuracy of 87%, demonstrating its effectiveness in classifying public responses. The findings provide useful insights for tourism stakeholders to evaluate visitor perceptions, identify areas for improvement, and support data-driven decision-making to enhance tourism services and visitor experiences. This study contributes to tourism research by applying the Naïve Bayes algorithm to Indonesian-language tweets related to tourist attractions, demonstrating the feasibility of text mining for automatically analyzing public opinion and supporting tourism development.
Inovasi Olahan Ikan Lele dan Pembuatan Mesin Spinner untuk Mendukung Pemberdayaan Masyarakat Kampung Pelita Habibie, Fadeli Muhammad; Indrawan, Indrawan; Qurrohman, Taufik; Berlian, Pio Putra; Mampow, Vanessa Keysa Immanuela; Shofa, Ghina Zahira; Damayanti, Siska; Susilo, Budi; Ramadhani, Tyas Ayu; Hermawan, Aditiya
E-Dimas: Jurnal Pengabdian kepada Masyarakat Vol 17, No 1 (2026): E-DIMAS
Publisher : Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/e-dimas.v17i1.26674

Abstract

Kampung Pelita Sumber Rejo yang terletak di tengah Kota Balikpapan dikenal dengan kegiatan budidaya kolam ikan lele yang menjadi komoditas utama masyarakat setempat. Selain dijual sebagai ikan segar, sebagian hasil panen juga diolah menjadi berbagai produk makanan seperti pempek, otak- otak, dan nugget lele. Namun, proses pengolahan tersebut menghasilkan limbah berupa kulit dan tulang ikan yang belum dimanfaatkan secara optimal, padahal limbah tersebut memiliki potensi untuk diolah menjadi produk pangan bernilai tambah. Untuk menjawab permasalahan ini, diperlukan pendampingan teknologi dan inovasi dalam pemanfaatan limbah hasil olahan ikan. Melalui program pengabdian masyarakat yang berkolaborasi dengan mitra UP2K Kampung Pelita, dilakukan upaya peningkatan produktivitas yang juga didukung dengan pembuatan mesin spinner sederhana sebagai alat bantu produksi. Kegiatan telah dilaksanakan dengan baik ini dengan memberdayakan UP2K dalam mengolah limbah ikan lele menjadi produk amplang dan kerupuk kulit, sekaligus melaksanakan rebranding usaha untuk memperkuat identitas pasar. Dengan demikian, kegiatan pengabdian masyarakat ini tidak hanya menghasilkan produk alternatif, tetapi juga memperkuat daya saing usaha, memperluas peluang pemasaran, serta berkontribusi terhadap peningkatan kesejahteraan masyarakat secara berkelanjutan.
Optimization of Multimodal Deep Learning for Depression Detection Aditiya Hermawan; Benny Daniawan; Edy Edy; Joese Nathaniel
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 19, No 4 (2025): October
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.111407

Abstract

Depression is a complex and often underdiagnosed mental health condition that manifests through subtle verbal, acoustic, and behavioral cues. Traditional unimodal detection systems struggle to capture the full spectrum of depressive symptoms, often leading to inaccurate or incomplete assessments. This study proposes a multimodal deep learning framework that integrates textual, audio, and visual modalities to improve the robustness and reliability of automatic depression detection, achieving an overall classification accuracy of 74%. The approach prioritizes privacy and interpretability by using facial keypoints and gaze direction rather than raw video frames, and applies attention mechanisms to align and fuse features across modalities. Each modality is processed through dedicated neural architectures tailored to its data type, and their outputs are combined within a fusion model that learns to capture cross-modal emotional patterns. Experimental results demonstrate that the proposed multimodal system significantly outperforms its unimodal counterparts in terms of classification performance. The visual modality was found to contribute most strongly to detection accuracy, as confirmed by ablation analysis. These findings highlight the value of multimodal integration in capturing complex psychological signals and support the development of intelligent, non-invasive screening tools for use in digital mental health applications.
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.
Pelatihan Membuat Presentasi Menarik Menggunakan Canva Untuk Anggota Penyuluh Agama Buddha Provinsi Banten Ardie Halim; Yo Ceng Giap; Raditya Rimbawan O; Wiyono; Andri Wijaya; Aditiya Hermawan; Jessen Laorenza Suwandi; Arvin Lawistra
Jurnal Igakerta Vol. 1 No. 4 (2024): Jurnal Igakerta
Publisher : IGAKERTA Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70234/h700cz90

Abstract

Pengabdian kepada masyarakat (PkM) yang dilakukan oleh tim dosen dan mahasiswa Fakultas Sains dan Teknologi Universitas Buddhi Dharma, Tangerang bertujuan untuk memberikan pelatihan dalam membuat  materi  pembelajaran  secara  interaktif  dan  inovatif  menggunakan  media  berbasis  digital canva  kepada  anggota  penyuluh  agama  Buddha  (PAH)  agar  dapat  membuat  materi  ceramah  yang sesuai  dengan  cara  yang  interaktif  dan  inovatif.  Metode  yang  digunakan  dalam  PkM  ini  adalah demontrasi  (peragaan),  praktikum  (membuat  materi  ceramah  secara  langsung  menggunakan komputer  di  laboratoriom  komputer  Universitas  Buddhi  Dharma)  dan  studi  kasus  (latihan  mandiri membuat materi presentasi yang dipandu oleh tutor). Hasil PkM ini adalah para anggota PAH dapat memperoleh  pemahaman  dan  pengetahuan  tentang  cara  membuat  slide  presentasi  dalam  beberapa bentuk seperti teks, gambar, audio, video dengan menggunakan canva, sehingga akan mempermudah dalam  penyampaian  materi  ceramah.  Hal  ini  terlihat  dari  hasil  kuesioner  dengan  persentase  74% peserta  sangat  setuju  bahwa  materi  yang  disampaikan  sangat  bermanfaat  dan  dapat  dipraktekkan untuk mendukung pembuatan materi presentasi
Trends and Keyword Networks in Machine Learning-Based Click Fraud Detection Research Kevin Kevin; Aditiya Hermawan
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.v17i2.4131

Abstract

The rapid advancement of the digital economy has significantly increased the use of online advertising while concurrently giving rise to critical challenges, particularly in the form of click fraud”a manipulative act that harms advertisers by generating fraudulent clicks on digital advertisements. As click fraud attack patterns grow increasingly complex, machine learning (ML)-based research has emerged as a principal approach for detecting and mitigating these threats. This study aims to map the research landscape of ML-based click fraud detection through a bibliometric analysis to identify publication trends, patterns of international and institutional collaboration, and key thematic domains within this field. Employing a bibliometric methodology, the study analyzed 61 publications retrieved from Dimensions.ai spanning the years 2015–2024. The data were collected, refined using OpenRefine, and visualized with VOSviewer to examine keyword co-occurrences and research trends. The findings reveal a marked increase in publication volume since 2019, with dominant contributions from India, China, Saudi Arabia, and the United States. Furthermore, four principal research clusters were identified: cybersecurity, the relationship between click fraud and the digital advertising industry, dataset processing and evaluation techniques, and the development of ML-based detection systems. Each cluster offers practical contributions in areas such as system protection strategies, ad budget optimization, improved detection accuracy, and the development of scalable, real-time detection solutions. Recent trends highlight growing scholarly interest in model performance evaluation and the challenges posed by class imbalance (class skewness). This study concludes that more effective data management and the development of adaptive ML models capable of addressing evolving attack patterns are pivotal for future research. By providing a clearer mapping of current trends, this study aims to support the scientific community in developing more accurate and efficient click fraud detection strategies, thereby strengthening the integrity of the global digital advertising ecosystem.
Penguatan Literasi Digital Pendidik Agama Buddha melalui Pelatihan Terintegrasi ChatGPT dan Canva: Evaluasi Pretest–Posttest pada Komunitas PERGABI Aditiya Hermawan; Lianny Wydiastuty; Hartana Wijaya; Santa Margita
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.
Ethical Dimensions of Artificial Intelligence in the Digital Entrepreneurship Ecosystem: A Systematic Literature Review Salman Alfarisi; Adith Aulia Rahman; Zaqi Kurniawan; Aditiya Hermawan; Yan Everhard Riwurohi
RUBINSTEIN Vol. 4 No. 2 (2026): RUBINSTEIN (juRnal mUltidisiplin BIsNis Sains TEknologI & humaNiora)
Publisher : LP3kM Buddhi Dharma University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31253/rubin.v4i2.4580

Abstract

The rapid adoption of artificial intelligence (AI) in digital entrepreneurship has created new opportunities for innovation, efficiency, and data-driven decision-making, while simultaneously raising ethical concerns related to fairness, transparency, privacy, accountability, and explainability. This study presents a systematic literature review to examine the ethical dimensions of AI within the digital entrepreneurship ecosystem. Guided by the PRISMA 2020 protocol and the PICO framework, searches were conducted across Web of Science, Scopus, and IEEE Xplore for studies published between 2020 and 2026. From 512 initially identified articles, 24 studies met the inclusion criteria and quality assessment requirements. The selected studies were analyzed using thematic coding, narrative synthesis, and quality-based evidence mapping to identify recurring ethical dimensions, operational mechanisms, and governance gaps. The findings reveal four dominant ethical problem clusters: algorithmic fairness in entrepreneurial decision-making, transparency deficits in black-box AI systems, data privacy and cybersecurity vulnerabilities, and weak accountability mechanisms in AI governance. The review further shows that responsible AI frameworks, explainability techniques, bias audits, data governance protocols, and risk-based regulatory approaches are central mechanisms for translating ethical principles into practice. The findings contribute to responsible AI scholarship, digital entrepreneurship governance, and policy-oriented debates by offering practical guidance for entrepreneurs, regulators, and researchers concerned with ethical AI adoption in resource-constrained business environments. This study provides an evidence-based roadmap for strengthening ethical, accountable, and socially responsible AI implementation in digital entrepreneurship ecosystems.
Aplikasi E-voting Berbasis Blockchain dengan Metode Smart Contract Junaedi Junaedi; Albert Fernando; Aditiya Hermawan
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 6 No. 2 (2024): September
Publisher : Universitas Wahid Hasyim

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

Abstract

Voting is an important process in many contexts, such as in organizational decisions and the selection of leaders. With the development of technology, the concept of electronic voting emerged, where humans can cast their votes electronically. Electronic voting has become an important alternative in organizational decision making and leader selection, but often faces challenges related to security and fraud. In this study, a blockchain-based electronic voting application has been developed using the Smart Contract method. Blockchain technology was chosen because of its decentralized and public nature, which can provide a high level of security and transparency to voting data. Evaluation of the application was carried out through a questionnaire filled out by 39 respondents. The results showed a high level of agreement, with 91.46% of respondents stating that they strongly agreed with the effectiveness of this application. This indicates that the implementation of a blockchain-based electronic voting system has the potential to increase trust and integrity in the election process. In addition, this application also offers easy access and data transparency, which is expected to increase public participation in the democratic process. By continuing to conduct further research and development, this blockchain-based electronic voting application has great potential to become the standard in future elections.
Co-Authors A Damiyati Abidin Abidin Adith Aulia Rahman Agus Setiawan Albert Fernando Alvin Rahayu Amin Suyitno Andre Sahulata Andri Wijaya Andrie Suak Tiwa Anton Halim Anwan Chailes Aprilyanti, Rina Ardiane Rossi Kurniawan Maranto Ardie Halim Arvin Lawistra Benny Daniawan Berlian, Pio Putra Budi Susilo Ceng Giap, Yo Culadi, Rafael Daniel Daniawan, Benny Dera Susilawati Deviastati Putri Sugiarta Karlim edy Edy Edy Edy Edy Edy Edy Edy Ellysha Dwiyanthi Kusuma Eva Eva Evan - Evien Fadeli Muhammad Habibie Fernando, Albert Gustayo, Teven Halim Wijaya, Ardie Hargiani, Fransisca Xaveria Hartana Wijaya Henry Henry Indrawan Intan Anjali Putri Jelvin Putra Halawa Jessen Laorenza Suwandi Joese Nathaniel 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 Lianny Wydiastuty Kusuma Lidya Lunardi Luis Alpianto Lunardi, Lidya Mampow, Vanessa Keysa Immanuela Maranto, Ardiane Rossi Kurniawan Margaretha Natalya Mariana Purnamasari Mesakh Septiadi Simijaya michael vernannes marpaung Nandivadhano, Revatta Manggala Nathaniel Felix Fraderic Nathaniel, Joese Nazzua Azzahra Niki Destiandi Oscar Hasan Putra Philip Kristy Wijaya Qurrohman, Taufik Raditya Rimbawan O Raditya Rimbawan Oprasto Ramadhani, Tyas Ayu Rheza Vincentius Riki Riki Riki RIKI RIKI, RIKI Rino Rossi Kurniawan Maranto, Ardiane Rossi Salman Alfarisi Samuel Rhesa Santa Margita Sevtian Ferdian Shofa, Ghina Zahira Siska Damayanti Stanley Ananda Sutopo, Prihantoro Syahdu Suwitno Tia Nurapriyanti Wicaksono, Baghas Budi Willy Wijaya, Willy Wiyono Wydiastuty Kusuma, Lianny Yan Everhard Riwurohi Yance Gusnadi Yanti, Lia Dama Yo Ceng Giap Yo Ceng Giap Yuliastati Putri Sugiarta Karlim Yunia Oktari Yusuf Kurnia Yusuf Kurnia, Yusuf