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All Journal (JELIKU) Jurnal Elektronik Ilmu Komputer Udayana Telematika : Jurnal Informatika dan Teknologi Informasi Jurnal Teknologi Informasi dan Ilmu Komputer Seminar Nasional Informatika (SEMNASIF) Register: Jurnal Ilmiah Teknologi Sistem Informasi Conference SENATIK STT Adisutjipto Yogyakarta Journal of Information Systems and Informatics International Journal of Economics, Business and Accounting Research (IJEBAR) JUSTIN (Jurnal Sistem dan Teknologi Informasi) Paradigma Masyarakat Berkarya: Jurnal Pengabdian dan Perubahan Sosial
Prapcoyo, Hari
Sistem Informasi, Fakultas Teknik Industri Universitas Pembangunan Nasional “Veteran” Yogyakarta
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mechanical Engineering Public Health

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Implementation of Mel Frequency Cepstral Coefficient and Dynamic Time Warping For Bird Sound Classification Prapcoyo, Hari; Adhita Putra, Bertha Pratama; Perwira, Rifki Indra
SENATIK STT Adisutjipto Vol 5 (2019): Peran Teknologi untuk Revitalisasi Bandara dan Transportasi Udara [ISBN 978-602-52742-
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/senatik.v5i0.326

Abstract

Lovebird (Agapornis) is a type of bird that has become the belle of new pet birds lately. The interest of the hobbyist in this one song is because Lovebird has a unique chirp. For beginner lovebird fans, the lack of knowledge and experience about lovebird birds results in various cases of fraud in choosing a quality lovebird. They were disappointed expensive lovebirds that had been purchased but did not match what was expected.Lovebird chirping voice recognition can be learned and recognized through the learning process of speaker recognition, which is part of voice recognition. Speaker recognition captures the frequency of the lovebird's voice, then compares it with the sound frequency of the existing training data. The sound frequency and the long duration of chirping of lovebird birds will be extracted through the Mel-Frequency Cepstral Coefficient (MFCC) method. Information in the form of Mel Frequency Cepstrum Coefficients from input data and training data is then compared to the Dynamic Time Warping method. The methodology used in this study uses the grapple method.The results of this study were obtained an accuracy value of sound validation by 80%. It is hoped that with the capabilities of this system, it can help bird chirping lovers know the sound quality of lovebird birds that are good, moderate, and less. Also, it can help the jury of birds chirping, so that it can be used as an accurate standard in classifying lovebird sounds.
Implementasi Perancangan dan Pemeliharaan Jaringan Internet Menuju Smart School pada MA Raden Fattah Ahmad Taufiq Akbar; Bagus Muhammad Akbar; Shoffan Saifullah; Andiko Putro Suryotomo; Rochmat Husaini; Hari Prapcoyo
Masyarakat Berkarya : Jurnal Pengabdian dan Perubahan Sosial Vol. 2 No. 1 (2025): Februari : Masyarakat Berkarya : Jurnal Pengabdian dan Perubahan Sosial
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/karya.v2i1.1079

Abstract

Internet Network is one of the fields in informatics and electronics engineering which is now growing rapidly due to the issue of the industrial revolution 4.0 which is increasingly closely related to Cloud computing technology and the Internet of Things. Without resources and knowledge about computer networks, the Internet of things and Cloud computing are quite impossible to design. Computer networks give birth to internet access which is very much needed by every agency and even the entire community in the world. Especially in educational institutions such as Madrasah Aliyah (MA) Raden Fatah, which is located in Kalasan, Yogyakarta when in the era of the Covid-19 pandemic, it faces the challenge of disruption from offline learning to online learning. To answer the demands of the times, MA Raden Fattah is very enthusiastic in developing its institution towards a quality smart school. The network infrastructure available at MA Raden Fattah has not been optimized, so through this service, network design and management are carried out so that the need for access points that help students and teachers can be met. This service has succeeded in increasing the number of access points, optimizing the management of internet network resources at MA Raden Fattah, and improving the quality of teaching and learning services at the institution
Klasifikasi Ekspresi Wajah Menggunakan Covolutional Neural Network Taufiq Akbar, Ahmad; Akbar, Ahmad Taufiq; Saifullah, Shoffan; Prapcoyo, Hari
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 11 No 6: Desember 2024
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2024118888

Abstract

Pengenalan ekspresi wajah adalah tantangan penting dalam pengolahan citra dan interaksi manusia-komputer karena kompleksitas dan variasi yang ada. Penelitian ini mengusulkan arsitektur sederhana Convolutional Neural Network (CNN) untuk meningkatkan efisiensi klasifikasi emosi pada dataset kecil. Dataset yang digunakan adalah Jaffe, yang terdiri dari 213 citra berukuran 256x256 piksel dalam tujuh kategori ekspresi. Citra-citra tersebut di-resize menjadi 128x128 piksel untuk mempercepat pemrosesan. Data diproses menggunakan arsitektur CNN yang terdiri dari 3 lapisan konvolusi, 2 lapisan subsampling, dan 2 lapisan dense. Kami mengevaluasi model dengan 5-fold dan 10-fold cross-validation untuk estimasi kinerja yang robust, serta teknik hold-out (70:30, 80:20, 85:15, dan 90:10) untuk perbandingan hasil yang jelas. Hasil menunjukkan akurasi tertinggi sebesar 90.6% dengan learning rate 0.001 pada pembagian 85% data latih dan 15% data uji, melebihi model yang lebih kompleks. Meskipun tidak menggunakan transfer learning atau augmentasi data, model ini tetap unggul dibandingkan pendekatan tradisional seperti Local Binary Pattern (LBP) dan Histogram Oriented Gradient (HOG). Dengan demikian, arsitektur CNN yang sederhana ini terbukti efektif untuk pengenalan ekspresi wajah pada dataset kecil.   Abstract Facial expression recognition is a significant challenge in image processing and human-computer interaction due to its inherent complexity and variability. This study proposes a simple Convolutional Neural Network (CNN) architecture to enhance the efficiency of emotion classification on small datasets. Jaffe's dataset consists of 213 images sized 256x256 pixels across seven expression categories. These images were resized to 128x128 pixels to accelerate processing. The data was processed using a CNN architecture comprising 3 convolutional layers, 2 subsampling layers, and 2 dense layers. We evaluated the model with 5-fold- and 10-fold cross-validation for robust performance estimation and hold-out techniques (70:30, 80:20, 85:15, and 90:10) for clear result comparison. The results indicated the highest accuracy of 90.6% with a learning rate of 0.001 using the 85% training and 15% testing data split, surpassing that of more complex models. Although the model does not employ transfer learning or data augmentation, it still outperforms traditional approaches such as Local Binary Pattern (LBP) and Histogram Oriented Gradient (HOG). Thus, this simple CNN architecture proves effective for facial expression recognition on small datasets.
EfficientNet B0 Feature Extraction with L2-SVM Classification for Robust Facial Expression Recognition Akbar, Ahmad Taufiq; Saifullah, Shoffan; Prapcoyo, Hari; Rustamadji, Heru; Cahyana, Nur Heri
Journal of Information System and Informatics Vol 7 No 2 (2025): June
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i2.1071

Abstract

Facial expression recognition (FER) remains a challenging task due to the subtle visual variations between emotional categories and the constraints of small, controlled datasets. Traditional deep learning approaches often require extensive training, large-scale datasets, and data augmentation to achieve robust generalization. To overcome these limitations, this paper proposes a hybrid FER framework that combines EfficientNet B0 as a deep feature extractor with an L2-regularized Support Vector Machine (L2-SVM) classifier. The model is designed to operate effectively on limited data without the need for end-to-end fine-tuning or augmentation, offering a lightweight and efficient solution for resource-constrained environments. Experimental results on the JAFFE and CK+ benchmark datasets demonstrate the proposed method’s strong performance, achieving up to 100% accuracy across various hold-out splits (90:10, 80:20, 70:30) and 99.8% accuracy under 5-fold cross-validation. Evaluation metrics including precision, recall, and F1-score consistently exceeded 95% across all emotion classes. Confusion matrix analysis revealed perfect classification of high-intensity emotions such as Happiness and Surprise, while minor misclassifications occurred in more ambiguous expressions like Fear and Sadness. These results validate the model’s generalization ability, efficiency, and suitability for real-time FER tasks. Future work will extend the framework to in-the-wild datasets and incorporate model explainability techniques to improve interpretability in practical deployment Keywords: Facial Expression Recognition, EfficientNet, SVM, Deep Features, Emotion Classification
Robust Classification of Beef and Pork Images Using EfficientNet B0 Feature Extraction and Ensemble Learning with Visual Interpretation Taufiq Akbar, Ahmad; Saifullah, Shoffan; Prapcoyo, Hari; Yuwono, Bambang; Rustamaji, Heru Cahya
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 11 No 1 (2025): January
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v11i1.4045

Abstract

Distinguishing between beef and pork based on image appearance is a critical task in food authentication, but it remains challenging due to visual similarities in color and texture, especially under varying lighting and capture conditions. To address these challenges, we propose a robust classification framework that utilizes EfficientNet B0 as a deep feature extractor, combined with an ensemble of Regularized Linear Discriminant Analysis (RLDA), Support Vector Machine (SVM), and Random Forest (RF) classifiers using soft voting to enhance generalization performance. To improve interpretability, we incorporate Gradient-weighted Class Activation Mapping (Grad-CAM) to visualize classification decisions and validate that the model focuses on relevant regions of the meat, such as red-channel intensity and muscle structure. The proposed method was evaluated on a public dataset containing 400 images evenly split between beef and pork. It achieved a hold-out accuracy of 99.0% and a ROC-AUC of 0.995, outperforming individual learners and demonstrating strong resilience to limited data and variation in imaging conditions. By integrating efficient transfer learning, ensemble decision-making, and visual interpretability, this framework provides a powerful and transparent solution for binary meat classification. Future work will focus on fine-tuning the CNN backbone, applying GAN-based augmentation, and extending the approach to multiclass meat authentication tasks.
Forecasting Performance of Double Exponential Smoothing Model and ETS Model for Predicting Crude Oil Prices Prapcoyo, Hari; As'ad, Mohamad; Sujito, Sujito; Setyowibowo, Sigit; Farida, Eni
Telematika Vol 20 No 2 (2023): Edisi Juni 2023
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v20i2.8104

Abstract

Purpose: This study aims to predict the price of monthly crude oil quickly and accurately by using an easy model and with easily available software.Design/methodology/approach: This study compares the DES-Holts and ETS models to predict price of monthly crude oil.Findings/result: The results of this study recommend the ETS(M,N,N) model to predict the price of monthly crude oil which produces an accuracy value of RMSE and MAPE of 4.385812 and 6.499007 %, respectively.Originality/value/state of the art: This study implements the DES_Holt's and ETS models to predict price of monthly crude oil with an RMSE and MAPE forecasting accuracy that has never been done in previous studies. 
Preprocessing Using SMOTE and K-Means for Classification by Logistic Regression on Pima Indian Diabetes Dataset Akbar, Ahmad Taufiq; Husaini, Rochmat; Prapcoyo, Hari
Telematika Vol 20 No 2 (2023): Edisi Juni 2023
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v20i2.9676

Abstract

Purpose: Our study aims to combine pre-processing methods to develop a training data model from the Indian diabetic Pima dataset so that it can improve the performance of machine learning in recognizing diabetesDesign/methodology/approach: This research was started through several stages such as collecting the Pima indian diabetes dataset, pre-processing including k-means clustering, oversampling using SMOTE, then undersampling the dataset whose cluster is a minority in each class. Furthermore, the dataset is classified using machine learning namely logistic regression through 10 cross validationFindings/result: The results of this classification performance show that the accuracy reaches 99.5% and is higher than the method in previous studies.Originality/value/state of the art:The method in this study uses SMOTE to handle data imbalances and k-means clustering to remove outliers by removing labels that do not match the majority cluster in each class so that clean data is produced and validation using logistic regression is more accurate than previous studies.Tujuan: Penelitian ini bertujuan untuk menerapkan metode pre-processing untuk membentuk model data latih dari dataset Pima Indian diabetes sehingga dapat meningkatkan performa mesin pembelajaran dalam mengenali diabetes.Perancangan/metode/pendekatan: Riset ini dimulai melalui beberapa tahap yakni pengumpulan dataset Pima Indian diabetes, pre-processing meliputi clustering, oversampling menggunakan SMOTE, kemudian undersampling pada dataset pada klaster  minoritas pada setiap kelas. Selanjutnya dataset diklasifikasikan menggunakan machine learning yakni metode regresi logistik melalui 10 cross validationHasil: Hasil dari performa klasifikasi ini menunjukkan akurasi mencapai 99,5% dan lebih tinggi daripada metode pada penelitian sebelumnya.Keaslian/ state of the art: Metode dalam penelitian ini menggunakan SMOTE untuk menangani ketidakseimbangan data dan k-means klastering untuk membuang outlier dengan cara menghapus label yang tidak sesuai dengan klaster mayoritas pada setiap kelas sehingga dihasilkan data yang bersih dan pada validasi menggunakan logistic regression lebih akurat daripada penelitian sebelumnya.
Comparative Analysis of Email Spam Detection Using SVM with TF-IDF and Word2Vec on Multilingual Datasets Katamsyi, Kaifa Ahlal; Akbar, Ahmad Taufiq; Nurkholis, Andi; Prapcoyo, Hari; Akbar, Bagus Muhammad; Saifullah, Shoffan
Paradigma - Jurnal Komputer dan Informatika Vol. 28 No. 1 (2026): March 2026 Period
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/p.v28i1.12339

Abstract

The rapid growth of email communication has increased the prevalence of spam emails, which can disrupt productivity and compromise information security. This study presents a comparative analysis of two text representation methods—TF-IDF and Word2Vec—for spam email classification using a Support Vector Machine (SVM) with a Radial Basis Function kernel. The experiments utilized Indonesian and English email datasets totaling 5,421 emails, split into 75% training and 25% testing sets. Two scenarios were evaluated: baseline with default parameters and after hyperparameter optimization using Grid Search combined with K-Fold Cross Validation. The results indicate that TF-IDF consistently outperformed Word2Vec across both languages, achieving the highest accuracy of 0.9562 on the English dataset after tuning. Word2Vec showed substantial improvement following parameter adjustment, reducing the performance gap with TF-IDF. The findings highlight the importance of hyperparameter optimization for enhancing the quality of feature representations and improving classification performance. This study also demonstrates that TF-IDF provides more stable results across different linguistic contexts, while Word2Vec benefits significantly from careful tuning. The results provide practical insights for implementing efficient spam email detection systems in multilingual environments. Future research could explore additional classifiers, deep learning approaches, and contextual embeddings to further improve classification accuracy and robustness.
Co-Authors Adhita Putra, Bertha Pratama Ahmad Taufiq Akbar Akbar, Ahmad Taufiq Akbar, Bagus Muhammad Andi Nurkholis Andiko Putro Suryotomo As'ad, Mohamad Bambang Yuwono Eni Farida Frans Richard Kodong Frans Richard Kodong Herry Sofyan Heru Cahya Rustamaji Husaini, Rochmat Katamsyi, Kaifa Ahlal Mohamad As'ad Nur Heri Cahyana Rifki Indra Perwira Rifki Indra Perwira Rochmat Husaini Rochmat Husaini Rustamadji, Heru Saifullah, Shoffan Sigit Setyowibowo Sujito Sujito Sujito Sujito Taufiq Akbar, Ahmad