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All Journal Jurnal Informatika Jurnal Sarjana Teknik Informatika Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Telematika : Jurnal Informatika dan Teknologi Informasi International Journal of Advances in Intelligent Informatics JOIV : International Journal on Informatics Visualization Knowledge Engineering and Data Science ILKOM Jurnal Ilmiah WIDYA LAKSANA Buletin Ilmiah Sarjana Teknik Elektro SABDAMAS Majalah Kesehatan Indonesia Science in Information Technology Letters International Journal of Robotics and Control Systems Applied Engineering and Technology Bulletin of Social Informatics Theory and Application Journal of Artificial Intelligence in Architecture Journal of Business and Information System
Andri Pranolo
Universitas Ahmad Dahlan
Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Materials Science & Nanotechnology Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Enhanced Multivariate Time Series Analysis Using LSTM: A Comparative Study of Min-Max and Z-Score Normalization Techniques Pranolo, Andri; Setyaputri, Faradini Usha; Paramarta, Andien Khansa’a Iffat; Triono, Alfiansyah Putra Pertama; Fadhilla, Akhmad Fanny; Akbari, Ade Kurnia Ganesh; Utama, Agung Bella Putra; Wibawa, Aji Prasetya; Uriu, Wako
ILKOM Jurnal Ilmiah Vol 16, No 2 (2024)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v16i2.2333.210-220

Abstract

The primary objective of this study is to analyze multivariate time series data by employing the Long Short-Term Memory (LSTM) model. Deep learning models often face issues when dealing with multivariate time series data, which is defined by several variables that have diverse value ranges. These challenges arise owing to the potential biases present in the data. In order to tackle this issue, it is crucial to employ normalization techniques such as min-max and z-score to guarantee that the qualities are standardized and can be compared effectively. This study assesses the effectiveness of the LSTM model by applying two normalizing techniques in five distinct attribute selection scenarios. The aim of this study is to ascertain the normalization strategy that produces the most precise outcomes when employed in the LSTM model for the analysis of multivariate time series. The evaluation measures employed in this study comprise Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), and R-Squared (R2). The results suggest that the min-max normalization method regularly yields superior outcomes in comparison to the z-score method. Min-max normalization specifically resulted in a decreased mean absolute percentage error (MAPE) and root mean square error (RMSE), as well as an increased R-squared (R2) value. These improvements indicate enhanced accuracy and performance of the model. This paper makes a significant contribution by doing a thorough comparison analysis of normalizing procedures. It offers vital insights for researchers and practitioners in choosing suitable preprocessing strategies to improve the performance of deep learning models. The study's findings underscore the importance of selecting the appropriate normalization strategy to enhance the precision and dependability of multivariate time series predictions using LSTM models. To summarize, the results indicate that min-max normalization is superior to z-score normalization for this particular use case. This provides a useful suggestion for further studies and practical applications in the field. This study emphasizes the significance of normalization in analyzing multivariate time series and contributes to the larger comprehension of data preprocessing in deep learning models
Comparative Electromagnetic Performance Analysis of Double Stator and Single Stator Superconducting Generators for Direct-Drive Wind Turbines Elhindi, Mohamed; Abdalla, Modawy Adam Ali; Omar, Abdalwahab; Pranolo, Andri; Mirghani, Abdelhameed; Omer, Abduelrahman Adam
International Journal of Robotics and Control Systems Vol 4, No 2 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i2.1385

Abstract

Superconducting synchronous generators, especially for 10-MW direct-drive wind power systems, are gaining prominence due to their lightweight, compact design, lowering energy generation costs compared to conventional generators. With the ability to generate high magnetic fields. various approaches are exist for designing such generators for example modular superconducting generators which allow for easier assembly, maintenance, and scalability by dividing the generator into smaller, interchangeable components and single stator which simplifying the generator's design and reducing manufacturing costs. This study introduces a novel concept of a double-stator superconducting generator alongside a conventional single-stator superconducting generator, aiming to investigate and contrast the electromagnetic performance of both machine types considering different number pole pairs. Booth of the machines has been designed and studied applying 2d finite element model (COMSOL Multiphysics). The compared machine parameters include: the flux linkage and electromagnetic torque. Our study and compression of the two machines reveal that the double stator superconducting generator is characterized by high electromagnetic torque compared to its single-stator counterpart. the analysis also reveals that increasing the pole pairs number leads to high electromagnetic torque and higher magnetic flux density.
PELATIHAN PENINGKATAN DAYA EKSPOR IMPOR BAGI MAHASISWA ASING DI TIONGKOK Hariyanti, Nunik; Salim, Agus; Pranolo, Andri; Fadillah, Dani; Khotimah, Husnul; Firdaus, Nalendra
JURNAL WIDYA LAKSANA Vol 13 No 1 (2024)
Publisher : Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/jwl.v13i1.69636

Abstract

Tiongkok menjadi salah satu negara tujuan pendidikan dunia karena memiliki jajaran perguruan tinggi top kelas dunia. Selain mahasiswa Indonesia, terdapat mahasiswa asing di Tiongkok yang berasal dari negara lain seperti Kamboja, Malaysia, Sudan, dan Indonesia. Tidak jarang kondisi keterbatasan ekonomi dihadapi oleh para mahasiswa. Sehingga muncul permasalahan dimana banyak dari para mahasiswa membuka peluang usaha namun tidak diimbangi dengan strategi dan komunikasi bisnis yang memadai. Berikutnya, bisnis yang sudah dibuat terkadang belum menjangkau pasar internasional yang lebih luas terutama di bidang ekspor impor. Tujuan dari pengabdian ini adalah untuk memberikan pelatihan pengembangan bisnis lanjutan berupa pelatihan ekspor impor bagi mahasiswa asing di Tiongkok. Metode pelaksanaan kegiatan ini dilakukan secara luring dan daring berupa presentasi, diskusi, studi kasus, evaluasi, pre-test dan post-test. Hasil dari kegiatan ini didapat pengetahuan peserta terkait dengan eskpor impor dan dokumen yang perlu dipersiapkan meningkat.
Privacy-Preserving U-Net Variants with pseudo-labeling for radiolucent lesion segmentation in dental CBCT Ismail, Amelia Ritahani; Azlan, Faris Farhan; Noormaizan, Khairul Akmal; Afiqa, Nurul; Nisa, Syed Qamrun; Ghazali, Ahmad Badaruddin; Pranolo, Andri; Saifullah, Shoffan
International Journal of Advances in Intelligent Informatics Vol 11, No 2 (2025): May 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v11i2.1529

Abstract

Accurate segmentation of radiolucent lesions in dental Cone-Beam Computed Tomography (CBCT) is vital for enhancing diagnostic reliability and reducing the burden on clinicians. This study proposes a privacy-preserving segmentation framework leveraging multiple U-Net variants—U-Net, DoubleU-Net, U2-Net, and Spatial Attention U-Net (SA-UNet)—to address challenges posed by limited labeled data and patient confidentiality concerns. To safeguard sensitive information, Differential Privacy Stochastic Gradient Descent (DP-SGD) is integrated using TensorFlow-Privacy, achieving a privacy budget of ε ≈ 1.5 with minimal performance degradation. Among the evaluated architectures, U2-Net demonstrates superior segmentation performance with a Dice coefficient of 0.833 and an Intersection over Union (IoU) of 0.881, showing less than 2% reduction under privacy constraints. To mitigate data annotation scarcity, a pseudo-labeling approach is implemented within an MLOps pipeline, enabling semi-supervised learning from unlabeled CBCT images. Over three iterative refinements, the pseudo-labeling strategy reduces validation loss by 14.4% and improves Dice score by 2.6%, demonstrating its effectiveness. Additionally, comparative evaluations reveal that SA-UNet offers competitive accuracy with faster inference time (22 ms per slice), making it suitable for low-resource deployments. The proposed approach presents a scalable and privacy-compliant framework for radiolucent lesion segmentation, supporting clinical decision-making in real-world dental imaging scenarios.
GAN-Enhanced multimodal fusion and ensemble learning for imbalanced chest X-Ray classification Snani, Aissa; Khadir, Mohammed Tarek; Pranolo, Andri; Abdalla, Modawy Adam Ali
International Journal of Advances in Intelligent Informatics Vol 11, No 3 (2025): August 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v11i3.2092

Abstract

Chest X-ray (CXR) classification tasks often suffer from severe class imbalance, resulting in biased predictions and suboptimal diagnostic performance. To address this challenge, we propose an integrated framework that combines high-fidelity data augmentation using Generative Adversarial Networks (GANs), ensemble learning via hard and soft voting, and multimodal feature fusion. The method begins by partitioning the majority class into multiple subsets, which are individually balanced through GAN-generated synthetic images. Deep learning models, specifically DenseNet201 and EfficientNetV2B3, are trained separately on each balanced subset. These models are then combined using ensemble voting to improve robustness. Additionally, features extracted from the most performant models are fused and used to train traditional classifiers such as Logistic Regression, Multilayer Perceptron, CatBoost, and XGBoost. Evaluations on a publicly available CXR dataset demonstrate consistent improvements across key metrics, including accuracy, precision, recall, F1-score, AUROC, AUPRC, MCC, and G-mean. This framework shows superior performance in multiclass scenarios.
Urban Traffic Volume Prediction using LSTM and Bi-LSTM: Performance Evaluation on the Metro Interstate Dataset Pranolo, Andri; Saifullah, Shoffan; Putra, Agung Bella Utama; Dreżewski, Rafał; Wibawa, Aji Prasetya
ILKOM Jurnal Ilmiah Vol 17, No 3 (2025)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v17i3.3001.227-240

Abstract

Urban traffic forecasting underpins the mitigation of congestion, enhancement of road safety, and reduction of emissions in intelligent transportation systems. We benchmark Long Short-Term Memory (LSTM) and Bidirectional LSTM (Bi-LSTM) models on the Metro Interstate Traffic Volume dataset under an identical preprocessing and training pipeline for a fair comparison. Using a 24-hour multivariate input window (temperature, rainfall, snowfall, cloud cover), LSTM delivers the best overall balance of accuracy and efficiency on the full test sequence (RMSE = 0.196, MAPE = 2.36%, R² = 0.480; 7,344 s training). Bi-LSTM achieves competitive short-window accuracy but underperforms on the full sequence (RMSE = 0.231, MAPE = 2.92%, R² = 0.280; 12,672 s training). We attribute the Bi-LSTM gap to prediction "flattening" over long horizons, i.e., over-smoothed peaks from bidirectional averaging, despite its slightly stronger short-segment fit. Compared with prior RNN/GRU/CNN baselines on the same data, LSTM improves variance explanation while remaining deployable for near-real-time use. We also examine seasonality (daily/weekly cycles), weather effects, and data imbalance (peak versus off-peak) as factors that shape model error. These results support LSTM as a practical default for city-scale forecasting and motivate future work with attention/Transformer encoders and richer exogenous signals (incidents, events). The findings inform policy by enabling proactive traffic management that can reduce delays, emissions, and crash risk through earlier, data-driven interventions.
Family History and Smoking as Key Predictors of Obesity among Young Adults in Latin America: A Secondary Data Analysis Utami, Nurul Putrie; Pranolo, Andri
Majalah Kesehatan Indonesia Vol. 6 No. 4 (2025)
Publisher : Utan Kayu Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47679/makein.2025276

Abstract

The prevalence of obesity continues to increase in various countries. Obesity is a multifactorial condition influenced by lifestyle, diet, and genetic factors. This study aims to identify risk factors associated with obesity using secondary data analysis conducted using the Obesity Dataset, which consists of 485 individuals aged 14–61 years from Mexico, Peru, and Colombia. The research data were tested using the chi-square test to assess the relationship between obesity risk factors, followed by a multivariate test using logistic regression. The results indicated that a significant relationship with obesity was only found in the factors of a family history of obesity, smoking status, and age group. Individuals with a family history of overweight were almost five times more likely to be obese (OR = 4.98; 95% CI: 2.25–11.04; p < 0.001). Smokers had nearly three times higher odds of obesity compared to non-smokers (OR = 2.91; 95% CI: 1.17–7.24; p = 0.022). In addition, older age was associated with an increased likelihood of obesity (OR = 4.84; 95% CI: 1.51–15.49; p = 0.008). These findings conclude that genetic factors and smoking habits have a stronger association with obesity than dietary factors and physical activity. This study suggests that public health interventions should encompass not only diet and physical activity but also incorporate smoking prevention and cessation programs as essential components of obesity control strategies. Futhermore, population with family history of overweight and older age need to optimize prevention and nutritional intervention efforts to prevent and overcome obesity.
Revisiting the J-Curve of Indonesia-China bilateral trade Salim, Agus; Pranolo, Andri; Hariyanti, Nunik; Fadillah, Dani; Khotimah, Husnul; Firdaus, Nalendra Putra
Journal of Business and Information Systems (e-ISSN: 2685-2543) Vol. 5 No. 2 (2023): Journal of Business and Information Systems
Publisher : Department of Accounting, Faculty of Business, Universitas PGRI Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36067/jbis.v5i2.215

Abstract

The examination of the J-curve effect has been grown primarily for the case of aggregate trade level in some countries. Most studies assume that the exchange rate affects the dynamics of aggregate trade balance symmetrically. To fill the gap of the empirical studies, this manuscript attempt to revisit the presence of J-curve and analyze the effect of Indonesian Rupiah vis-à-vis Chinese Yuan on bilateral trade balance between Indonesia and China, whether symmetric or asymmetric. We employ autoregressive distributed lag (ARDL) and its partial sum concept of nonlinear manner to estimate quarterly data from 2002:I to 2017:IV. The result shows evidence of the asymmetric effect of the exchange rate on the trade balance, and the nonlinear approach provides more support to discover the presence of the J-curve impact of bilateral trade between Indonesia and China.
A Comprehensive Visualization for Music Education and Artificial Intelligence Sularso, Sularso; Wadiyo, Wadiyo; Cahyono, Agus; Suharto, Suharto; Pranolo, Andri
JOIV : International Journal on Informatics Visualization Vol 9, No 2 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.2.2500

Abstract

Artificial intelligence (AI) has revolutionized traditional methods and improved decision-making and automation. AI has also been used to enhance teaching methods, student learning, and research in music education. This study will examine literature on music education and AI. This study aims to investigate significant themes, trends, and achievements in this burgeoning discipline. This study will examine scholarly articles, conference papers, and other relevant literature to explore AI's applications, issues, and future in music education. Machine learning, natural language processing, computer vision, and deep learning are utilized in music education. These techniques are used in music composition, performance evaluation, instructional support, and individualized learning. Adaptive training, real-time feedback, and intelligent music production demonstrate the transformative potential of AI. This study will illuminate the obstacles AI faces in music education. Ethical considerations, data privacy, algorithmic bias, and human competence must be thoroughly investigated. In addition, the analysis would identify knowledge deficits for future research and development. This research could assist educators, researchers, and policymakers utilize AI in music education by conducting a comprehensive literature review. This work can assist in the development of AI-based instruments, the improvement of pedagogy, and the promotion of music education.
Geographic-Origin Music Classification from Numerical Audio Features: Integrating Unsupervised Clustering with Supervised Models Pranolo, Andri; Sularso, Sularso; Anwar, Nuril; Putra, Agung Bella Utama; Wibawa, Aji Prasetya; Saifullah, Shoffan; Dreżewski, Rafał; Nuryana, Zalik; Andi, Tri
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 4 (2025): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i4.13400

Abstract

Classifying the geographic origin of music is a relevant task in music information retrieval, yet most studies have focused on genre or style recognition rather than regional origin. This study evaluates Support Vector Machine (SVM) and Convolutional Neural Network (CNN) models on the UCI Geographical Origin of Music dataset (1,059 tracks from 33 non-Western regions) using numerical audio features. To incorporate latent structure, we first applied K-means clustering with the optimal number of clusters (k=2) determined by the Elbow and Silhouette methods. The cluster assignments were used as auxiliary signals for training, while evaluation relied on the true region labels. Classification performance was assessed with Accuracy, Precision, Recall, and F1-score. Results show that SVM achieved 99.53% accuracy (95% CI: 97.38–99.92%), while CNN reached 98.58% accuracy (95% CI: 95.92–99.52%); Precision, Recall, and F1 mirrored these values. The differences confirm SVM’s superior performance on this dataset, though the near-perfect scores also suggest strong separability in the feature space and potential risks of overfitting. Learning-curve analysis indicated stable training, and cluster supervision provided small but consistent benefits. Overall, SVM remains a reliable baseline for tabular music features, while CNNs may require spectro-temporal representations to leverage their full potential. Future work should validate these findings across multiple datasets, apply cross-validation with statistical significance testing, and explore hybrid deep models for broader generalization.
Co-Authors ., Suparman AA Sudharmawan, AA Abdalla, Modawy Adam Ali Achmad Fanany Onnilita Gaffar Adhi Prahara Adhi Prahara Adhi Prahara, Adhi Adhi Susanto Adhi Susanto Afief Akmal Afiqa, Nurul Agung Bella Putra Utama Agus Cahyono Agus Dianto Agus Salim Ahmad Azhari Aji Prasetya Wibawa Akbari, Ade Kurnia Ganesh Albas, Juan Alin Khaliduzzaman Anasyua, Fairuz Khairunnisa Andiko Putro Suryotomo Anton Satria Prabuwono Anton Yudhana Ardi Pujiyanta Azhari, Ahmad Azlan, Faris Farhan Ba, Abdoul Fatakhou Bambang Widi Pratolo Camargo, Jair Dani Fadillah Daniel Happy Putra Drezewski, Rafał Dwiyanto, Felix Andika Elhindi, Mohamed Fachrul Kurniawan Fadhilla, Akhmad Fanny Felix Andika Dwiyanto Firdaus, Nalendra Firdaus, Nalendra Putra Ghazali, Ahmad Badaruddin Hanafi Hanafi Hariyanti, Nunik Heni Pujiastuti Heri Pramono Hoz, César De La Husnul Khotimah Ismail, Amelia Ritahani Khadir, Mohammed Tarek Leonel Hernandez Leonel Hernandez, Leonel Mao, Yingchi Mirghani, Abdelhameed Mokhtar, Nur Azizah Mohammad Muhammad, Abdullahi Uwaisu Nanang Fitriana Kurniawan Nathalie Japkowicz Nisa, Syed Qamrun Noormaizan, Khairul Akmal Nor Amalina Abdul Rahim Nuril Anwar Nuril Anwar, Nuril Nuryana, Zalik Omar, Abdalwahab Omer, Abduelrahman Adam Onie Yudho Sundoro Paramarta, Andien Khansa’a Iffat Prayitno Prayitno Putra, Agung Bella Utama Rafal Drezewski Rafał Dreżewski Roman Voliansky Saifullah, Shoffan Sarina Sulaiman Sarina Sulaiman Seno Aji Putra Setyaputri, Faradini Usha Snani, Aissa Sri Winiarti Suharto Suharto Sularso Sularso, Sularso Suparman Supriadi Supriadi Supriyanto Taqwa Hariguna Tedy Setyadi Tri Andi, Tri Triono, Alfiansyah Putra Pertama Uriu, Wako Utama, Agung Bella Putra Utami, Nurul Putrie Wadiyo Wadiyo Wilis Kaswijanti Yingchi Mao Yingchi Mao Yingchi Mao Yingchi Mao Zhou, Xiaofeng