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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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Designing a Real-Time Fuel Consumption Monitoring Dashboard for Ships Andre Sahulata; Aditiya Hermawan
RUBINSTEIN Vol. 3 No. 2 (2025): 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.v3i2.3784

Abstract

Fuel usage on ships is a crucial aspect of maritime operations, affecting cost efficiency and sustainability. A calculation carried out by the operational team is required in order to obtain information about the ship's fuel usage. However, the method of calculating fuel usage that is still manual by sounding is still prone to calculation errors and inefficient. Therefore, a system is needed that can assist the operational team in calculating and obtaining fuel usage data quickly and efficiently. This research aims to design a dashboard monitoring system that can monitor ship fuel usage in real-time. Real-time monitoring is important for quick identification of inefficiencies, ensuring safety, and supporting timely decision making. This system can assist the operational team in obtaining data and information on fuel usage more quickly and accurately. The methodology used in this research includes analyzing system requirements, designing system architecture, and implementing dashboard monitoring software. The result of this research is a dashboard system that is able to provide real-time information about fuel usage, making it easier for the operational team to monitor and make decisions. With this system, the efficiency of fuel usage can be increased and the pros of fuel usage can be improved.
Implementasi Text-Mining untuk Analisis Sentimen pada Twitter dengan Algoritma Support Vector Machine Hermawan, Aditiya; Jowensen, Indrico; Junaedi, Junaedi; Edy
JST (Jurnal Sains dan Teknologi) Vol. 12 No. 1 (2023): April
Publisher : Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/jstundiksha.v12i1.52358

Abstract

Setiap tahun, jumlah orang yang menggunakan media sosial bertambah seiring dengan jumlah orang yang menggunakan internet. Peningkatan tersebut diiringi dengan meningkatnya informasi pada internet yang tentunya informasi tersebut mempunyai nilai jika dilakukan analisa. Untuk menganalisa data dalam jumlah besar dapat menggunakan teknik text mining. Text mining mampu memproses untuk memperoleh informasi berkualitas tinggi dari teks. Text mining juga dapat digunakan untuk menganalisa informasi seperti sentimen dari sebuah kalimat dengan sangat cepat untuk memudahkan dalam mendapatkan informasi yang berkualitas. Informasi diproses berasal dari media sosial berbasis text yaitu twitter yang mana pengambilan data dilakukan dengan bantuan Application Programming Interface dan menggunakan kata kunci berupa sebuah kata atau hashtag. Kalimat tersebut akan dilakukan proses text mining dengan menggunakan algoritma Support Vector machine untuk menghasilkan klasifikasi dari sentimen suatu kalimat ke dalam sentiment positif, netral atau negatif. Tingkat akurasi yang dihasilkan oleh proses ini adalah sebesar 73% berdasarkan data sentimen yang dimiliki. Tingkat akurasi dalam melakukan text mining sangat dipengarui pada proses Pre-Processing karena terdapat banyak kata perlu dilakukan pengelolahan lebih lanjut.
Optimization of Multimodal Deep Learning for Depression Detection Hermawan, Aditiya; Daniawan, Benny; Edy, Edy; Nathaniel, Joese
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.
Optimizing Artificial Neural Network for Customer Churn: Advanced Data Balancing and Feature Selection Hermawan, Aditiya; Wijaya, Willy; Daniawan, Benny
JOIV : International Journal on Informatics Visualization Vol 9, No 3 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.3.3064

Abstract

Customers are valuable assets in the dynamic business world. However, service dissatisfaction often leads them to switch to competitors, a phenomenon known as customer churn. In the telecommunications industry, churn poses a significant challenge as it directly impacts revenue and influences other customers within their social networks to do the same. Consequently, predicting churn has become essential, with numerous researchers employing various methods to classify potential churners. This study builds upon prior research that utilized Artificial Neural Networks (ANN) or Deep Learning to predict churn, achieving an accuracy of 88.12%. To improve model performance, this research implements an Artificial Neural Network (ANN) as the primary algorithm, along with Random Over-Sampling (ROS) and Synthetic Minority Oversampling Technique (SMOTE) for data balancing, and three feature selection methods: Minimum Redundancy Maximum Relevance (mRMR), Lasso Regression, and XGBoost. The results demonstrate a 0.38% increase in accuracy compared to previous studies. The finding suggests opportunities for further exploration. Future studies can consider alternative feature selection techniques, such as Wrapper Methods or Heuristic/Metaheuristic approaches, which may produce more optimal feature combinations. Other data balancing methods, such as Undersampling techniques (e.g., Random Undersampling, Tomek Links) or Hybrid Methods (e.g., SMOTE combined with Tomek Links), could be explored to address imbalanced datasets effectively. These approaches are expected to provide better combinations and to improve overall prediction performance, enabling researchers to develop more robust and accurate models for customer churn prediction in subsequent studies.
Optimasi Penyediaan Internet Murah Dengan Kecepatan Yang Baik Guna Media Pembelajaran Jarak Jauh Riki, Riki; Yanti, Lia Dama; Oktari, Yunia; Hermawan, Aditiya; Kurnia, Yusuf; Giap, Yo Ceng; Aprilyanti, Rina
Abdi Dharma Vol. 1 No. 2 (2021): Jurnal Abdi Dharma (Jurnal Pengabdian Masyarakat)
Publisher : LP3kM Universitas Buddhi Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (259.707 KB) | DOI: 10.31253/ad.v1i2.699

Abstract

Kesadaran pendidikan secara umum, kemampuan panduan teknologi dan kemampuan pembelajaran online masih rendah, sehingga menyulitkan orang tua dan siswa (terutama siswa sekolah dasar) guna menggunakan fasilitas teknologi dan memperoleh informasi dari pembelajaran. Berdasarkan paparan di atas, berikut beberapa permasalahan yang dapat diidentifikasi: 1) Tidak semua warga di lingkungan RT 06 RW 07 Griya Sangiang Mas memiliki fasilitas Internet yang cepat dan murah. Sebagai warga dan pendidik, kami berinisiatif menyediakan fasilitas internet murah dan cepat kemudian memberikan pelatihan. Secara umum kegiatan ini telah berlangsung selama 1 tahun, dan terdapat pengguna aktif pembelajaran sebanyak 15 siswa SD dan SMP setiap harinya. Secara umum bagi warga Perumahan Griya Sangiang Mas, Internet di RT ini bisa menjadi alternatif internet yang murah
Pelatihan Desain Slide dengan Canva Junaedi; Wydiastuty Kusuma, Lianny; Ceng Giap, Yo; Suwitno; Hermawan, Aditiya; Rino; Daniawan, Benny; Riki
Abdi Dharma Vol. 2 No. 2 (2022): 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.v2i2.1737

Abstract

Perkembangan di era sekarang banyak sekali perubahan dari segi teknologi. Perkembangan teknologi ini meliputi desain grafis, pemrograman, aplikasi dan sebagainya. Hal inilah yang membuat penulis sebagai pendidik untuk melakukan pengabdian kepada masyarakat dengan cara melakukan sebuah workshop dengan tema “Pelatihan Desain Slide dengan Canva” yang bertujuan untuk memperkenalkan membuat sebuah desain pada slide presentasi dengan baik, rapih dan interaktif sebagai media penyampaian pembelajarannya. Pelatihan Desain Slide dengan Canva ini juga didukung oleh Universitas Buddhi Dharma sebagai tempat dilaksanakannya pelatihan ini. Para peserta dari Pelatihan Desain Slide dengan Canva ini diikuti oleh para romo dan rahmani dari Magabudhi Kota Tangerang. Hasil dari pelatihan ini akan di implementasikan dalam pembuatan presentasi sebagai media pembelajaran.
Penyuluhan E-Commerce untuk Mendorong Ekonomi Digital Dalam Rangka Pengabdian Kepada Masyarakat pada Pemuda Tridharma Indonesia Cabang Wihara Dharma Pala Kurnia, Yusuf; Riki; Giap, Yo Ceng; Hermawan, Aditiya; Gustayo, Teven
Abdi Dharma Vol. 3 No. 1 (2023): 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.v3i1.2069

Abstract

Perkembangan teknologi informasi di era revolusi 4.0 memberikan dampak yang signifikan terhadap perekonomian Indonesia. Platform digital dalam mendukung kegiatan perekonomian dan pemenuhan kebutuhan masyarakat bertumbuh dengan cepat dengan dukungan internet. dengan diterapkannya teknologi baru akan berdampak pada produkvitas dan menurunkan biaya produksi yang akan meningkatkan daya beli sehingga akan tercipta lapangan kerja baru. Metode kegiatan Pengabdian Kepada Masyarakat ini dilakukan melalui 4 tahapan (metode) yaitu metode sosialisasi, metode demonstrasi, metode praktek/latihan, evaluasi kegiatan. Pelaksanaan PkM ini melibatkan mitra kerjasama, yaitu Wihara Dharma Pala yang beralamat di Jl. Ir. Soekarno Kp, Jl. Raya Rawa Kompeni No.66, RT.003/RW.008, Benda, Kec. Benda, Kota Tangerang, Banten 15125. Tim pelaksana kegiatan Pengabdian kepada Masyarakat (PkM) terdiri dari 4 orang dosen & 1 orang mahasiswa. Tim dosen pelaksana Pkm terdiri atas dosen-dosen yang memiliki keterampilan & kemampuan dibidang teknologi informasi yang sesuai dengan tema PkM yang dilaksanakan serta memiliki kualifikasi akademik. Dari hasil hasil evaluasi yang dilakukan oleh Tim Pelaksana, dapat disimpulkan hal bahwa mayoritas peserta yang mengikuti kegiatan ini, sudah mengetahui tentang E-Commerce dan peserta masih kurang pengalaman dalam menggunakan layanan E-commerce seperti Market Place.
Trends and Keyword Networks in Machine Learning-Based Click Fraud Detection Research Kevin, Kevin; 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.v17i2.4131

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.
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.
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