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All Journal JTEV (Jurnal Teknik Elektro dan Vokasional Bulletin of Electrical Engineering and Informatics Jurnal Informatika Jurnal Sarjana Teknik Informatika Jurnal Teknologi Prosiding Semnastek International Journal of Advances in Intelligent Informatics Annual Research Seminar Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika JOURNAL OF APPLIED INFORMATICS AND COMPUTING Martabe : Jurnal Pengabdian Kepada Masyarakat JIKA (Jurnal Informatika) Jurnal Teknik Informatika (JUTIF) Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Journal of Computing Theories and Applications
Ardi Pujiyanta
Program Studi Teknik Informatika Fakultas Teknologi Industri Universitas Ahmad Dahlan Yogyakarta Jl. Prof. Dr. Soepomo, S.H., Warungboto, Janturan, Yogyakarta 55164 Telp : (0274) 563515 Ext. 3208
Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Social Sciences

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Stacking-Based Support Vector Machine and Multilayer Perceptron for Dysarthria Detection Using MFCC Features Pujiyanta, Ardi; Noviyanto, Fiftin; Ismail, Taufiq
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.4.5199

Abstract

The manual diagnosis of dysarthria is often time-consuming and requires the expertise of trained specialists, which can delay early intervention and treatment. This study aims to develop an automated detection system to improve diagnostic accuracy and efficiency. Mel-Frequency Cepstral Coefficients (MFCC) are used as the primary features, and three classification models are evaluated: Support Vector Machine (SVM), Multilayer Perceptron (MLP), and a stacking ensemble that combines both. The evaluation is conducted on a dataset of 240 audio samples. Experimental results show that the stacking ensemble achieves the highest performance, with an accuracy of 97.92%, surpassing SVM (95.83%) and MLP (93.75%). These findings highlight the significant potential of voice-based classification to accelerate dysarthria diagnosis, thus supporting clinical screening and speech therapy applications.
Sosialisasi Implementasi Teknik Elektro dari Rumah ke Revolusi Industri 4.0 di SMK Muhammadiyah 2 Playen Ferbriyanto, Suko; Darucandra, Nawa; Mulyadi, Mulyadi; Ali Akbar, Son; Pujiyanta, Ardi; Aridansyah, Aridansyah
Martabe : Jurnal Pengabdian Kepada Masyarakat Vol 8, No 8 (2025): MARTABE : JURNAL PENGABDIAN KEPADA MASYARAKAT
Publisher : Universitas Muhammadiyah Tapanuli Selatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31604/jpm.v8i8.%p

Abstract

Pengabdian masyarakat yang dilaksanakan di SMK Muhammadiyah 2 Playen bertujuan untuk meningkatkan literasi teknologi dan kesadaran siswa mengenai peran penting Teknik Elektro dalam keseharian hingga era Revolusi Industri 4.0. Kegiatan sosialisasi dilaksanakan dengan pendekatan edukatif dan praktis, yaitu melalui presentasi interaktif, diskusi. Materi yang disampaikan meliputi penerapan Internet of Things (IoT) di rumah, teknologi energi terbarukan, dan pengontrolan proses secara otomatis. Analisis data yang dihimpun dari pretest dan posttest menunjukkan terjadi peningkatan rata-rata yang signifikan, yaitu dari 75,31 menjadi 85,17, dengan p-value 0,001 dan ukuran efek (Cohen’s d) mencapai 1,20. Hal tersebut mengindikasikan bahwa kegiatan yang diselenggarakan mampu meningkatkan pengetahuan dan keterampilan siswa mengenai teknologi elektro yang tengah diterapkan di era Revolusi Industri 4.0. Keberhasilan kegiatan juga tampak dari antusiasme siswa saat sesi tanya jawab, yang lebih luas dan mendalam mengenai penerapan teknologi tersebut, sehingga siswa lebih siap dan mandiri untuk menghadapi tantangan teknologi di masa mendatang. Dengan demikian, kegiatan pengabdian masyarakat ini memberikan kontribusi penting, bukan hanya dari aspek edukasi, tetapi juga dari aspek sosial dan ekonomi, yaitu menumbuhkan keterampilan dan kemandirian teknologi di kalangan siswa SMK.
Fuzzy Logic and Neural Network-Based Self-Tuning PID for Vacuum Pressure Stabilization Sanjaya, Berza H.; Pujiyanta, Ardi; Puriyanto, Riky Dwi
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10945

Abstract

The conventional PID controller is widely used for vacuum pressure control; however, it has limitations when faced with nonlinear system characteristics and external disturbances, leading to a decline in performance. Several previous studies have proposed the integration of PID with intelligent methods, such as neural networks or fuzzy logic separately. Nevertheless, these singular approaches still encounter limitations in terms of adaptability and robustness. This study aims to develop a self-tuning PID method based on the combination of Neural Networks (NN) and Fuzzy Inference Systems (FIS) to enhance the stability and accuracy of vacuum pressure control. A nonlinear vacuum system plant model is constructed within the Simulink environment to generate a dataset used for training the NN with the Levenberg-Marquardt algorithm. The NN is employed to predict changes in PID parameters adaptively, while the FIS provides fine corrections to strengthen system stability. Simulation results demonstrate that the proposed approach effectively reduces overshoot from 36.47% to 31.51%, decreases steady-state error from 0.069 to 0.052, and lowers the RMSE value from 0.125 to 0.108 compared to conventional PID. Thus, the integration of NN and FIS within the self-tuning mechanism proves to be more effective in addressing nonlinear dynamics and external disturbances, resulting in a more stable and accurate system response.
Optimizing job scheduling on cloud resources using the first-come, first-served-SlotFree method Pujiyanta, Ardi; Noviyanto, Fiftin; Ismail, Taufiq
Bulletin of Electrical Engineering and Informatics Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i5.9681

Abstract

Cloud computing environments encounter significant challenges in job scheduling, particularly due to excessive waiting times and inefficient resource utilization associated with conventional algorithms such as first-come, first-served (FCFS) and backfilling. This study introduces FCFS-SlotFree, a novel scheduling algorithm that enhances resource allocation efficiency by dynamically sorting jobs based on their arrival times and workloads, and subsequently assigning them to a fixed set of virtual machines (VMs) without relying on rigid time-slot constraints. This flexible scheduling approach facilitates better adaptation to heterogeneous workloads. Extensive experiments conducted under realistic cloud scenarios demonstrate that FCFS-SlotFree significantly reduces average waiting time (AWT) by approximately 32.78% compared to FCFS and by 9.68% compared to backfilling, while concurrently improving resource utilization by 3.58% and 1.27%, respectively. The results substantiate the algorithm’s effectiveness in optimizing scheduling performance and resource efficiency within complex cloud environments.
Perbandingan Metode K-Means Clustering Dan Metode Ward Dalam Mengelompokkan Pelangan Mall Iklima, Tia; Pujiyanta, Ardi
JURNAL FASILKOM Vol. 13 No. 3 (2023): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jf.v13i3.6040

Abstract

Bagi setiap perusahaan pelanggan adalah aspek yang penting, apalagi dalam dunia bisnis. Karena pelanggan berperan penting dalam kemajuan perusahaan salah satunya Mall. Oleh karena itu perlu dilakukannya analisa lebih lanjut untuk menganalisis data pelanggan. Namun dalam menganalisa data pelanggan perlu menggunakan metode yang tepat dalam proses pengelompokan data pelanggan. Dalam penelitian ini dilakukan perbandingan dua metode yang berbeda, yaitu metode K-means dan metode Ward. Dataset yang digunakan dalam penelitian ini mengandung pola tingkah laku dari masing-masing pelanggan. Untuk menghitung jarak minimum dari setiap data dalam cluster digunakan Euclidean Distance. Data pelanggan yang digunakan yaitu data pelanggan Mall, yang diambil dari data public pada platform Kaggle.com, yang terdiri dari 5 variabel yaitu CustomerID, Gender, Age, Total Earning, dan Spending Score, dengan jumlah data dari masing-masing parameter ialah sebanyak 1000 data. Hasil penelitian menunjukkan bahwa metode K-means dan metode Ward dapat diterapkan pada dataset yang digunakan. Hasil pengelompokkan didapatkan 4 kelompok pelanggan yang berbeda. Nilai akurasi pada masing-masing metode dilakukan pengujian dengan menggunakan metode Silhouette Coefficient. Hasil pengelompokkan data pelanggan dengan metode K-means untuk nilai s(i) sebesar 0.67. Sedangkan nilai s(i) sebesar 0.81 untuk metode Ward. Berdasarkan hasil penelitian, untuk mengetahui kelompok pelanggan berdasarkan tingkat kemiripan setiap objek, penggunaan metode Ward lebih baik dibandingkan dengan metode KMeans dalam proses Clustering pada dataset pelanggan Mall.
Log-Transformed Regime-Based Prediction of Cloud Job Length Using Machine Learning Ardi Pujiyanta; Bambang Robiin; Faisal Fajri Rahani
Journal of Computing Theories and Applications Vol. 3 No. 4 (2026): JCTA 3(4) 2026
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.15866

Abstract

Cloud job-length prediction remains challenging when the target distribution is highly skewed and contains rare extreme values. This study proposes a log-transformed, regime-based machine learning framework for robust prediction of cloud job length, represented in million instructions (MI). The approach integrates sequential feature engineering, logarithmic target transformation, weighted learning, and regime-aware modeling to distinguish between normal and extreme job-length behavior. Using an ordered GoCJ-derived cloud job-length sequence of 1000 jobs, the dataset exhibits a heavy-tailed distribution, with a mean of 129,662 MI, a median of 93,000 MI, a 95th percentile of 525,000 MI, a 99th percentile of 900,000 MI, and a skewness of 3.695. The proposed model is evaluated against sequential baselines and stronger machine learning baselines, including Naive_Last, RollingMean_5, Global_Log_ExtraTrees, RandomForest, GradientBoosting, and MLP_Log. On the main test split, the proposed Regime_Log_ExtraTrees achieved the best RMSE of 206,255.66 and the least negative R² of −0.01062, while Global_Log_ExtraTrees remained competitive in terms of MAE, MedAE, and RMSLE. Additional walk-forward validation confirms that the regime-aware model consistently achieves the best mean RMSE and mean R² across temporal folds. Ablation results further show that regime-aware learning is the primary contributor to robustness, although accurate prediction of extreme jobs remains challenging. These findings indicate that log-transformed, regime-based learning provides a practical and more robust strategy for cloud job-length prediction under heavy-tailed workload conditions.
Synergistic preprocessing approaches for improved time series analysis Andri Pranolo; Ardi Pujiyanta; Supriyanto Supriyanto
International Journal of Advances in Intelligent Informatics Vol 12, No 1 (2026): February 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v12i1.2321

Abstract

This paper systematically evaluates the performance of an LSTM baseline model, along with four smoothing augmentation methods (Kalman, Laplace, Moving Average, Savitzky-Golay), under different normalization strategies (Min-Max and Z-Score) for multivariate time-series forecasting. Experiments were conducted on six publicly available datasets (electricity consumption, energy consumption, sensor data, household energy, Indian electricity, and Brazilian temperature), and model performance was comprehensively compared using three metrics: MAPE, RMSE, and R². Results indicate that Laplace smoothing achieved the best performance across five datasets, effectively reducing errors while maintaining high fit quality, demonstrating its advantage in handling highly volatile and noisy time-series data. However, in some instances, Laplace smoothing, along with MA and SG methods, may produce an “over-smoothing” effect, causing forecasts to lose sensitivity to spike fluctuations. The choice of normalization strategy is equally critical: Min-Max is more suitable for data with stable distributions, while Z-Score demonstrates greater advantages for data with large numerical ranges and significant volatility. Notably, in temperature datasets with small sample sizes and high volatility, complex smoothing methods actually degraded performance, making the baseline LSTM + Z-Score the optimal choice. However, the LSTM-Laplace model with Min-Max normalization achieves the best performance among the models. Overall, the study concludes that improving prediction performance relies not only on model architecture but also on optimizing data scale, distribution characteristics, and preprocessing strategies.
Co-Authors Agus Arif Setiyawan Ali Akbar, Son Alvi Mufidatun Andri Pranolo Ari Pujiantoro Ari Pujiantoro Aridansyah, Aridansyah Arnusani Rohmatika Bambang Robiin Cahyoko Hariya Pamilu Cendi Praseptyo Darucandra, Nawa Diana Riyanti Elfani Elfani Faisal Fajri Rahani Ferbriyanto, Suko Fernandya Riski Hartantri Fiftin Novianto Fiftin Noviyanto Handoko Dwi Cahyo Iklima, Tia Imam Riadi Irfan Maulana, Irfan Kairudin Kairudin Khawa Sukmawati Kirana Permata Putri Kurnia Adhiguna Lukito Edi Nugroho Moh Amin Aqib Mukhlis Mukhtar Mulyadi Mulyadi Pujiantoro, Ari Puriyanto, Riky Dwi Putra, Marta Dwi Darma Redha Rizky Firdaus Reny Retnowati Romy Dwiputra Roy Widiono Rusydi Umar Sanjaya, Berza H. Sany Yuliani Sigit Buddy Prakoso Siti Nurul Khofiyah Supriyanto Taufik Ismail Taufiq Ismail Widyawan