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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Lontar Komputer: Jurnal Ilmiah Teknologi Informasi IPTEK The Journal for Technology and Science Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) CommIT (Communication & Information Technology) Jurnal Ilmiah Kursor Prosiding SNATIKA Vol 01 (2011) Jurnal Sistem dan Informatika EKOMBIS REVIEW: Jurnal Ilmiah Ekonomi dan Bisnis MIX : Jurnal Ilmiah Manajemen Register: Jurnal Ilmiah Teknologi Sistem Informasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Indonesian Journal of Information System Jurnal Eksplora Informatika MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer WIDYABHAKTI Jurnal Ilmiah Populer Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) CSRID (Computer Science Research and Its Development Journal) CCIT (Creative Communication and Innovative Technology) Journal SENSITEK E-JURNAL JUSITI : Jurnal Sistem Informasi dan Teknologi Informasi Buletin Ilmiah Sarjana Teknik Elektro Journal of Robotics and Control (JRC) Journal of Applied Data Sciences International Journal of Robotics and Control Systems ADMA: Jurnal Pengabdian dan Pemberdayaan Masyarakat Bulletin of Social Informatics Theory and Application Jurnal Pengabdian Masyarakat (JURDIMAS)
Evi Triandini
Institut Teknologi Dan Bisnis STIKOM Bali
Aerospace Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Languange, Linguistic, Communication & Media Mechanical Engineering Nursing Social Sciences Other

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Application of Formal Concept Analysis and Clustering Algorithms to Analyze Customer Segments Budaya, I Gede Bintang Arya; Dharmendra, I Komang; Triandini, Evi
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 2 (2025): April 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i2.6184

Abstract

Business development cannot be separated from relationships with customers. Understanding customer characteristics is important both for maintaining sales and even for targeting new customers with appropriate strategies. The complexity of customer data makes manual analysis of the customer segments difficult, so applying machine learning to segment the customer can be the solution. This research implements K-Means and GMM algorithms for performing clustering based on the Transaction data transformed to the Recency, Frequency, and Monetary (RFM) data model, then implements Formal Concept Analysis (FCA) as an approach to analyzing the customer segment after the class labeling. Both K-Means and GMM algorithms recommended the optimal number of clusters as the customer segment is four. The FCA implementation in this study further analyzes customer segment characteristics by constructing a concept lattice that categorizes segments using combinations of High and Low values across the RFM attributes based on the median values, which are High Recency (HR), Low Recency (LR), High Frequency (HF), Low Frequency (LF), High Monetary (HM), and Low Monetary (LM). This characteristic can determine the customer category; for example, a customer that has HM and HR can be considered a loyal customer and can be the target for a specific marketing program. Overall, this study demonstrates that using the RFM data model, combined with clustering algorithms and FCA, is a potential approach for understanding MSME customer segment behavior. However, special consideration is necessary when determining the FCA concept lattice, as it forms the foundation of the core analytical insights.
Deep Learning Approach to Lung Cancer Detection Using the Hybrid VGG-GAN Architecture Pamungkas, Yuri; Kuswanto, Djoko; Syaifudin, Achmad; Triandini, Evi; Hapsari, Dian Puspita; Nakkliang, Kanittha; Uda, Muhammad Nur Afnan; Hashim, Uda
International Journal of Robotics and Control Systems Vol 5, No 3 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i3.1923

Abstract

Lung cancer ranks among the primary contributors to cancer-related deaths globally, highlighting the need for accurate and efficient detection methods to enable early diagnosis. However, deep learning models such as VGG16 and VGG19, commonly used for CT scan image classification, often face challenges related to class imbalance, resulting in classification bias and reduced sensitivity to minority classes. This study contributes by proposing an integration of the VGG architecture and Generative Adversarial Networks (GANs) to improve lung cancer classification performance through balanced and realistic synthetic data augmentation. The proposed approach was evaluated using two datasets: the IQ-OTH/NCCD Dataset, which classifies patients into Benign, Malignant, and Normal categories based on clinical condition, and the Lung Cancer CT Scan Dataset, annotated with histopathological labels: Adenocarcinoma, Squamous Cell Carcinoma, Large Cell Carcinoma, and Normal. The method involves initial training of the VGG model without augmentation, followed by GAN-based data generation to balance class distribution. The experimental results show that, prior to augmentation, the models achieved relatively high overall accuracy, but with poor performance on minority classes (marked by low precision and F1-scores and FPR exceeding 8% in certain cases). After augmentation with GAN, all performance metrics improved dramatically and consistently across all classes, achieving near-perfect precision, TPR, F1-score, and overall accuracy of 99.99%, and FPR sharply reduced to around 0.001%. In conclusion, the integration of GAN and VGG proved effective in overcoming data imbalance and enhancing model generalization, making it a promising solution for AI-based lung cancer diagnostic systems.
Performance Analysis of Prediction Methods on Tokyo Airbnb Data: A Comparative Study of Hyperparameter-Tuned XGBoost, ARIMA, and LSTM Nurfalah, Rizal Farhan Nabila; Hostiadi, Dandy Pramana; Triandini, Evi
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 11 No. 2 (2025): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v11i2.30631

Abstract

The rapid growth of the digital economy has increased the importance of accurately predicting Airbnb property occupancy rates, especially in dynamic and competitive markets such as Tokyo, Japan. Property owners face significant challenges in forecasting occupancy rates due to seasonal patterns, non-linear trends, and complex temporal dependencies within the data. Addressing these challenges, this study investigates the performance of ARIMA, XGBoost, and LSTM models in predicting Airbnb occupancy rates in Tokyo. The dataset is collected from Airbnb listings and includes relevant features such as location, price, customer reviews, and historical occupancy rates. The models were optimized using Grid Search for ARIMA and Random Search for XGBoost and LSTM to identify the best hyperparameter configurations. Evaluation metrics included Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Coefficient of Determination (R²), which are more appropriate for regression tasks. The results indicate that XGBoost achieves the highest R² (0.23), followed by LSTM (0.19) and ARIMA (0.03). However, the low R² values suggest that the models struggle to capture occupancy rate variations, indicating the potential influence of unmodeled external factors such as seasonality and policy changes. This study highlights the importance of hyperparameter tuning in improving prediction accuracy and contributes by providing an in-depth comparison of regression-based models for Airbnb occupancy forecasting.
Exploring the Determinants of User Acceptance for the Digital Diary Application in Type 1 Diabetes Management: A Structural Equation Modeling Approach Triandini, Evi; Permana, Putu Adi Guna; Hanief, Shofwan; Kuswanto, Djoko; Pamungkas, Yuri; Perwitasari, Rayi Kurnia; Hisbiyah, Yuni; Rochmah, Nur; Faizi, Muhammad
Journal of Applied Data Sciences Vol 6, No 3: September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i3.767

Abstract

Effective management of Type 1 Diabetes (T1D), especially in children, requires continuous monitoring and care. Digital health applications have become vital in supporting routine T1D management, including insulin delivery, glucose monitoring, nutrition, and physical activity tracking. This study investigates factors influencing user acceptance of a digital diary app designed for children with T1D and their families. Using an extended Technology Acceptance Model incorporating Trust, Perceived Risk, Perceived Enjoyment, and Social Influence, a survey was conducted with 114 participants, including parents, physicians, and dietitians. Data were analyzed using Partial Least Squares Structural Equation Modeling. Findings indicate that perceived usefulness, trust, and social influence significantly affect users' attitudes and intentions to use the app, through the accepted hypothesis that considered path coefficients and p-values. Conversely, hypothesis that shows relation between perceived ease of use, enjoyment, and risk toward intention were rejected, showing unsignificant relations toward user intention to use. Furthermore, this study recommends prioritizing robust security features, fostering user trust, and engaging social networks to enhance digital health adoption in pediatric care. Future research should further explore the roles of perceived risk and enjoyment in sustaining long-term engagement
Cranioplasty Training Innovation Using Design Thinking: AugmentedReality and Interchangeability-Based Mannequin Prototype Kuswanto, Djoko; Alifah Putri, Athirah Hersyadea; Zulaikha, Ellya; Apriawan, Tedy; Pamungkas, Yuri; Triandini, Evi; Jafari, Nadya Paramitha; Chusak, Thassaporn
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 24 No. 3 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v24i3.5055

Abstract

Cranioplasty, a surgical procedure to reconstruct the anatomical structure of the human skull, is commonlyperformed in Indonesia due to the malignancy of diseases, traffic accidents, and workplaceinjuries. If left untreated, this condition can lead to serious complications. Although cranioplasty isgenerally considered a relatively easy surgery, it has a fairly high postoperative complication rate ofaround 10.3%. The decreasing availability of cadavers for anatomical studies has significantly limitedtraining opportunities. Therefore, efficient and effective training tools are essential, especially whentraditional resources are insufficient to meet educational needs. Additionally, the training capabilitiesof commercially available mannequins or replicas used in medical institutions remain limited. Themain objective of this project was to develop a smart, modular cranioplasty training mannequin designedfor repeated use, incorporating Augmented Reality (AR) technology to visualize anatomicalstructures that cannot be physically replicated. Using a design thinking approach, data was collectedthrough interviews with neurosurgeons, neurosurgery residents, and cranioplasty specialists, as well asthrough a review of relevant literature. Usability testing of the developed prototype yielded promisingresults, with high ratings for ease of use (4.8), training effectiveness (4.5), anatomical realism (4.3),and material durability (4.5) on a 5-point Likert scale. These findings demonstrated strong user approvaland confirmed the model’s potential to support surgical skill development in a practical andreproducible manner. The resulting AR-integrated training mannequin offers an innovative, engaging,and durable solution to address current challenges in neurosurgical education, especially in resourceconstrainedsettings.
A Comprehensive Review of EEGLAB for EEG Signal Processing: Prospects and Limitations Pamungkas, Yuri; Rangkuti, Rahmah Yasinta; Triandini, Evi; Nakkliang, Kanittha; Yunanto, Wawan; Uda, Muhammad Nur Afnan; Hashim, Uda
Journal of Robotics and Control (JRC) Vol. 6 No. 4 (2025)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v6i4.27084

Abstract

EEGLAB is a MATLAB-based software that is widely used for EEG signal processing due to its complete features, analysis flexibility, and active open-source community. This review aims to evaluate the use of EEGLAB based on 55 research articles published between 2020 and 2024, and analyze its prospects and limitations in EEG processing. The articles were obtained from reputable databases, namely ScienceDirect, IEEE Xplore, SpringerLink, PubMed, Taylor & Francis, and Emerald Insight, and have gone through a strict study selection stage based on eligibility criteria, topic relevance, and methodological quality. The review results show that EEGLAB is widely used for EEG data preprocessing such as filtering, ICA, artifact removal, and advanced analysis such as ERP, ERSP, brain connectivity, and activity source estimation. EEGLAB has bright prospects in the development of neuroinformatics technology, machine learning integration, multimodal analysis, and large-scale EEG analysis which is increasingly needed. However, EEGLAB still has significant limitations, including a high reliance on manual inspection in preprocessing, low spatial resolution in source modeling, limited multimodal integration, low computational efficiency for large-scale EEG data, and a high learning curve for new users. To overcome these limitations, future research is recommended to focus on developing more accurate automation methods, increasing the spatial resolution of source analysis, more efficient multimodal integration, high computational support, and implementing open science with a standardized EEG data format. This review provides a novel contribution by systematically mapping EEGLAB’s usage trends and pinpointing critical technical and methodological gaps that must be addressed for broader neurotechnology adoption.
Transforming EEG into Scalable Neurotechnology: Advances, Frontiers, and Future Directions Pamungkas, Yuri; Triandini, Evi; Forca, Adrian Jaleco; Sangsawang, Thosporn; Karim, Abdul
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 3 (2025): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i3.13824

Abstract

Electroencephalography (EEG) is a key neurotechnology that enables non-invasive, high-temporal resolution monitoring of brain activity. This review examines recent advancements in EEG-based neuroscience from 2021 to 2025, with a focus on applications in neurodegenerative disease diagnosis, cognitive assessment, emotion recognition, and brain-computer interface (BCI) development. Twenty peer-reviewed studies were selected using predefined inclusion criteria, emphasizing the use of machine learning on EEG data. Each study was assessed based on EEG settings, feature extraction, classification models, and outcomes. Emerging trends show increased adoption of advanced computational techniques such as deep learning, capsule networks, and explainable AI for tasks like seizure prediction and psychiatric classification. Applications have expanded to real-world domains including neuromarketing, emotion-aware architecture, and driver alertness systems. However, methodological inconsistencies (ranging from varied preprocessing protocols to inconsistent performance metrics) pose significant challenges to reproducibility and real-world deployment. Technical limitations such as inter-subject variability, low spatial resolution, and artifact contamination were found to negatively impact model accuracy and generalizability. Moreover, most studies lacked transparency regarding bias mitigation, dataset diversity, and ethical safeguards such as data privacy and model interpretability. Future EEG research must integrate multimodal data (e.g., EEG-fNIRS), embrace real-time edge processing, adopt federated learning frameworks, and prioritize personalized, explainable models. Greater emphasis on reproducibility and ethical standards is essential for the clinical translation of EEG-based technologies. This review highlights EEG’s expanding role in neuroscience and emphasizes the need for rigorous, ethically grounded innovation.
Co-Authors Abdul Karim Achmad Syaifudin Agus Gian Angga Permana Alifah Putri, Athirah Hersyadea Arie Indrawan Arif Djunaidy Artana, I Gede Edy Aryanto, I Komang Agus Ady Ayu Chrisniyanti Bayu Iswara Budaya, I Gede Bintang Arya Cahya Ayuu Pertami Candra Ahmadi Chusak, Thassaporn Dandy Pramana Hostiadi Daniel Oranova Siahaan Dian Puspita Hapsari DwAyu Agung Indra Swari EDWAR EDWAR Fajar Astuti Hermawati Forca, Adrian Jaleco Franky Rawung Ganda Werla Putra Gde Sastrawangsa Gusti Ngurah Aditya Krisnawan Hashim, Uda Hendra Wijaya Hisbiyah, Yuni I Gede Putu Krisna Juliharta I Gede Suardika I Gusti Ayu Widari Upadani I Gusti Bagus Wiksuana I Ketut Dedy Suryawan I Ketut Putu Suniantara I Ketut Suniantara I Komang Dharmendra I Komang Rinartha Yasa Negara I Made Dwi Darma Artanaya I Made Suniastha Amerta I Nyoman Suraja Antarajaya Indrawan, Arie Indrianto Indrianto Iswara, Bayu Jafari, Nadya Paramitha Jayanatha, Sadu Kabnani, Ezra Tifanie Gabriela Kadek Surya Adi Saputra Karolita, Devi Krisnawan, Gusti Ngurah Aditya Kuswanto , Djoko Kuswanto, Djoko Made Pradnyana Ambara, Made Pradnyana Maneetham, Dechrit Marco Ariano Kristyanto Muhammad Faizi, Muhammad Nakkliang, Kanittha Ni Ketut Dewi Ari Jayanti Ni Luh Putri Srinadi Ni Luh Putu Indiani Ni Wayan Deriani, Ni Wayan Ni Wayan Ni Wayan Novia Ari Sandra Nur Rochmah, Nur Nurfalah, Rizal Farhan Nabila Nuryananda, Praja Firdaus Pamungkas, Yuri Perwitasari, Rayi Kurnia Puji Purwatiningsih, Aris Putra, Chrystia Aji Putra, I Gd Windu Sara Adi Putu Adi Guna Permana Putu Ayu Sita Laksmi Putu Suarma Widiada Rangkuti, Rahmah Yasinta Ratna Kartika W Ratna Kartika Wiyati Ravi Vendra Rishika Reza Fauzan Reza Fauzan Rijal, Muhammad Syamsu Riko Setya Wijaya Rusli, M Rusli, M Sadu Jayanatha Sangsawang, Thosporn Saputra, Kadek Surya Setiawan , I Wayan Agus Hery Setini, Made Shofwan Hanief Siti Rochimah Suardana, Gede Sugiarto Sugiarto S Sugiarto Sugiarto Sugiarto Sugiarto Suniantara , I Ketut Putu Suradarma, IB Suradarma, IB Tedy Apriawan Thwe, Yamin Uda, Muhammad Nur Afnan Wawan Yunanto Werla Putra, Ganda Widari Upadani, I Gusti Ayu Wijaya, I Gusti Ngurah Satria Wulandari, Riza Yohanes Priyo Atmojo Zulaikha, Ellya