Article Per Year (5 Year)
p-Index From 2020 - 2025
4.168
P-Index
This Author published in this journals
All Journal Jurnal Informatika dan Teknik Elektro Terapan Register: Jurnal Ilmiah Teknologi Sistem Informasi Sistemasi: Jurnal Sistem Informasi JOIV : International Journal on Informatics Visualization RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Indonesian Journal of Artificial Intelligence and Data Mining Building of Informatics, Technology and Science Dinasti International Journal of Education Management and Social Science Jurnal Teknologi Dan Sistem Informasi Bisnis Abdimas: Jurnal Pengabdian Masyarakat Universitas Merdeka Malang Journal of Computer System and Informatics (JoSYC) Jurnal Teknik Informatika (JUTIF) Instal : Jurnal Komputer International Journal Of Computer, Network Security and Information System (IJCONSIST) Kaunia: Integration and Interconnection of Islam and Science Journal Jurnal Sains dan Teknologi Jurnal Sistem Informasi dan Bisnis Cerdas Journal of Educational Management Research Ekasakti Pareso Jurnal Akuntansi SmartComp Intechno Journal : Information Technology Journal Edu Komputika Journal
Pratama, Irfan
Unknown Affiliation
Religion Aerospace Engineering Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences

Published : 30 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 30 Documents
Search

 1   2   3 
Hybrid Model for Speech Emotion Recognition using Mel-Frequency Cepstral Coefficients and Machine Learning Algorithms Nurdiawan, Odi; Ade Kurnia, Dian; Sudrajat, Dadang; Pratama, Irfan
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.5143

Abstract

Speech Emotion Recognition (SER) is a subfield of affective computing that focuses on identifying human emotions through voice signals. Accurate emotion classification is essential for developing intelligent systems capable of interacting naturally with users. However, challenges such as background noise, overlapping emotional features, and speaker variability often reduce model performance. This study aims to develop a lightweight hybrid SER model by combining Mel-Frequency Cepstral Coefficients (MFCC) as feature representations with three machine learning algorithms: Support Vector Machine (SVM), Decision Tree (DT), and K-Nearest Neighbors (KNN). The methodology involves audio data preprocessing, MFCC-based feature extraction, and classification using the selected algorithms. The RAVDESS dataset, consisting of 1,440 English-language audio samples across four emotions (happy, angry, sad, neutral), was used with an 80/20 train-test split to ensure class balance.. Experimental results show that the KNN model achieved the highest performance, with an accuracy of 78.26%, precision of 85.09%, recall of 78.26%, and F1-score of 77.06%. The Decision Tree model produced comparable results, while the SVM model performed poorly across all metrics. These findings demonstrate that the proposed hybrid approach is effective for recognizing emotions in speech and offers a computationally efficient alternative to deep learning models. The integration of MFCC features with multiple machine learning classifiers provides a robust framework for real-time emotion recognition applications, especially in environments with limited computing resources.
Measuring Resampling Methods on Imbalanced Educational Dataset’s Classification Performance Pratama, Irfan; Prasetyaningrum, Putri Taqwa; Chandra, Albert Yakobus; Suria, Ozzi
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 10 No 1 (2024): January
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v10i1.3397

Abstract

Imbalanced data refers to a condition that there is a different size of samples between one class with another class(es). It made the term “majority” class that represents the class with more instances number on the dataset and “minority” classes that represent the class with fewer instances number on the dataset. Under the target of educational data mining which demands accurate measurement of the student’s performance analysis, data mining requires an appropriate dataset to produce good accuracy. This study aims to measure the resampling method’s performance through the classification process on the student’s performance dataset, which is also a multi-class dataset. Thus, this study also measures how the method performs on a multi-class classification problem. Utilizing four public educational datasets, which consist of the result of an educational process, this study aims to get a better picture of which resampling methods are suitable for that kind of dataset. This research uses more than twenty resampling methods from the SMOTE variants library. as a comparison; this study implements nine classification methods to measure the performance of the resampled data with the non-resampled data. According to the results, SMOTE-ENN is generally the better resampling method since it produces a 0,97 F1 score under the Stacking classification method and the highest among others. However, the resampling method performs relatively low on the dataset with wider label variations. The future work of this study is to dig deeper into why the resampling method cannot handle the enormous class variation since the F1 score on the student dataset is lower than the other dataset.
Subduction and Local Fault Earthquake Analysis Using ST-DBSCAN Clustering Algorithm in The Special Region of Yogyakarta (DIY) Handayani, Wuri; Pratama, Irfan; Wibowo, Nugroho Budi
Kaunia: Integration and Interconnection Islam and Science Journal Vol. 21 No. 1 (2025)
Publisher : Fakultas Sains dan Teknologi UIN Sunan Kalijaga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/kaunia.5347

Abstract

This study aims to analyze the spatio-temporal patterns of subduction and local fault earthquakes in the Special Region of Yogyakarta using the ST-DBSCAN (Spatio-Temporal Density-Based Spatial Clustering of Applications with Noise) algorithm. A total of 5,403 earthquake events from 2019 to 2024 were clustered using spatial parameters (2–5 km) and a temporal window of 10 days. The results were evaluated using the Davies-Bouldin Index (DBI) and Silhouette Score. In the subduction zone, nine clusters were identified with a DBI of 3.23 and a Silhouette Score of 0.18, indicating moderate separation. Meanwhile, 25 clusters were detected in the local fault zone, particularly around the Opak and Oyo Faults, with a higher DBI of 3.82 and a negative Silhouette Score (-0.14), suggesting overlapping clusters and weak structure. The clustering outcomes correlate with geological features and offer insights for improving earthquake hazard assessment and early warning systems in Yogyakarta.
Implementasi Sistem Pendukung Keputusan Rekomendasi Pemasok Kayu Furniture Dengan Menggunakan Metode Smart (Studi Kasus : Mebel Cempaka Jaya) Paneo, Anto Farwanto; Pratama, Irfan
Jurnal Sains dan Teknologi (JSIT) Vol. 3 No. 3 (2023): September - Desember
Publisher : CV. Information Technology Training Center - Indonesia (ITTC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jsit.v3i2.973

Abstract

Pada era saat ini teknologi sudah semakin berkembang, hampir seluruh kegiatan manusia yang dikerjakan secara manual sudah semakin berkurang dan banyak di tangani dengan teknologi terkhususnya di bidang usaha mebel, pada saat ini berbisnis dapat dilakukan dengan memanfaatkan teknologi, namun pada era yang canggih seperti ini Mebel Cempaka Jaya masih menggunakan cara manual dalam menentukan pemasok kayu furniture terbaik, Permasalahan pada Mebel Cempaka jaya sekarang mereka mengalami kesulitan dalam menentukan supplier kayu yang baik untuk dijadikan bahan pembuatan furniture. Tujuan yang ingin diselesaikan dalam penelitian ini yaitu agar owner maupun pemilik mebel cempaka jaya dapat menemukan beberapa rekomendasi pemasok kayu furniture yang sesuai dengan mebel tersebut dan sesuai standarisasi yang diinginkan. Metode yang digunakan dalam penelitian ini yaitu metode SMART (Simple Multi Attribute Rating Technique) dimana pengambilan keputusan ini menangani permasalahan multi-kriteria berdasarkan pada nilai-nilai yang dimiliki oleh setiap alternatif pada masing-masing kriteria yang telah diberi bobot. Bobot setiap kriteria digunakan untuk membandingkan antara tingkat kepentingan antara kriteria satu dengan yang lain. Pada Pembahasan akan menggunakan 4 kriteria yang telah di tetapkan agar bisa dilakukannya perhitungan dalam pengambilan keputusan, kriteria yang di tentukan yaitu mulai dari Harga, Pengiriman, Kualitas, dan juga Customer Service maupun Pelayanan, Pada perhitungan manual data yang di ambil adalah 6 data sebagai sample dari 12 data pemasok sebagai alternatif. Pada penelitian yang sudah dilakukan, maka di daperoleh kesimpulan bahwasanya dari sebanyak 12 alternatif. Hasil yang menjadi rekomendasi untuk mebel cempaka jaya adalah Toko Palgam dengan nilai 0,86, Toko Flamingo dengan Nilai 0,83, dan Toko Dwi Restu dengan nilai 0,77.
PENJADWALAN MASA TANAM PADI DAN JAGUNG BERDASARKAN HASIL PREDIKSI CURAH HUJAN MENGGUNAKAN ARIMA DI WILAYAH SLEMAN Pratama, George Recksy Sandy; Pratama, Irfan
Jurnal Informatika dan Teknik Elektro Terapan Vol. 11 No. 3s1 (2023)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v11i3s1.3375

Abstract

Kabupaten Sleman didukung oleh irigasi teknis dan sebagian besar wilayahnya merupakan lahan pertanian. Iklim tropis dan kelembaban yang tinggi akan berdampak pada produksi pertanian di beberapa daerah. Untuk mendukung produksi pertanian di wilayah Sleman, hasil prediksi curah hujan digunakan untuk menentukan penjadwalan tanam yang tepat. Untuk memprediksi curah hujan dilakukan dengan metode ARIMA, karena data curah hujan berasal dari himpunan waktu yang tidak stasioner, ARIMA digunakan untuk menghimpun waktu yang tidak stasioner. Dengan model ARIMA dalam memprediksi curah hujan dan mendapatkan penjadwalan musim tanam pertanian, harus ditentukan nilai minimum AIC (Akaike Information Criterion) dan BIC (Bayesian Information Criterion) yang ditentukan dari beberapa model ARIMA yang digunakan. Kemudian menghitung nilai RMSE dan MAPE hasil perhitungan presisi. Petani dapat mempersiapkan perubahan curah hujan dalam produksi pertanian dengan prediksi curah hujan di masa mendatang. Selain itu, berdasarkan hasil prediksi curah hujan wilayah Sleman dapat membantu menentukan penjadwal tanam tanaman yang tepat.
Klasifikasi Kanker Payudara Berdasarkan Gambar Histopatologi Menggunakan Metode Convolutional Neural Network Dengan Arsitektur VGG-16 Nandasari, Dayang; Pratama, Irfan
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7377

Abstract

Breast cancer is one of the deadliest diseases with a high prevalence worldwide, especially in women. Breast cancer is the third leading cause of death in Indonesia. Based on Globocan Center data, there will be approximately 408,661 new cases and nearly 242,099 deaths in Indonesia by 2022. Early detection through histopathology images is very important to increase the patient's chances of recovery. However, the diagnosis process carried out manually by pathologists is quite time consuming and affects subjectivity. This study aims to develop a histopathology image-based breast cancer classification system using VGG-16. The dataset to be used consists of histopathology images that are grouped into 2 classes, namely benign and malignant. The data went through several preprocessing stages, including splitting and augmentation, to improve data quality. Test results show that this model achieves 91% accuracy, along with high precision, recall, and F1-scores on the test data. The performance of this model compares favorably with ensemble architectures such as, MobileNet, MobileNetV2. These findings indicate that the proposed approach can be an effective solution as a histopathology image-based breast cancer diagnosis tool.
Analysis of Forecasting Methods on Rice Price Data at Milling Level According to Quality Aulia, Indira Dhekawanti; Pratama, Irfan
Edu Komputika Journal Vol. 11 No. 1 (2024): Edu Komputika Journal
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/edukom.v11i1.4763

Abstract

Rice is a primary source of carbohydrates for many Indonesians, and its prices often surge due to uncontrolled demand. Therefore, the government is crucial in monitoring rice prices to maintain stability. Information technology, particularly data mining such as forecasting, is essential for providing accurate information on future rice prices. It will assist various stakeholders in making informed pricing policy decisions. This study employs Random Forest Regression and Gradient Boosting Regressor methods to predict rice prices using a dataset that includes monthly average rice prices at milling levels, categorized by quality (Premium and Medium), spanning from January 2013 to April 2024. The dataset consists of 136 rows, each representing a unique combination of year, month, and quality, and is stored in CSV format. Methodological steps include data collection, preprocessing, modeling, and model evaluation using monthly average rice prices at milling levels based on quality, including premium and medium grades. The results from Random Forest Regression indicate Root Mean Square Error (RMSE) values of 24.90 for premium rice and 25.47 for medium rice. The study reveals that Random Forest Regression outperforms Gradient Boosting Regressor in this context. Future research should explore additional prediction methods and consider other variables influencing rice prices to enhance model accuracy.
Feature Extraction Implementation in the Forecasting Method to Predict Indonesian Oil and Gas Exports and Imports Pradana, Michael Anggun Kado; Pratama, Irfan
Edu Komputika Journal Vol. 11 No. 1 (2024): Edu Komputika Journal
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/edukom.v11i1.7879

Abstract

Future export and import predictions can use data mining and forecasting applications of data mining. Then, normalisation is carried out using datasets taken at the centre of the statistical agency using a mix-max scaler. The normalisation results are then calculated using several forecasting methods, such as Exponential Smoothing, SARIMAX, XGBoost, and CatBoost. The accuracy of this method can be improved by using feature extraction decomposition. They are decomposing, such as trend, residue, and seasonal. The results of the decomposition then become new features that are entered into the prediction model. The prediction results are evaluated using the root mean square error (RMSE). The smaller the RMSE, the better the results. The prediction results without using the method obtained by the Exponential Smoothing method have the best level of accuracy with an average RMSE value of 0.111 and the SARIMAX method with an average RMSE value of 0.146. Meanwhile, the prediction results using the CatBoost and XGBoost feature extraction methods have the best level of accuracy with an RMSE value of 0.046. From the results of the comparison of predictions, the addition of decomposition features to most forecasting methods can significantly increase the accuracy of the calculation.
Improving Learning Performance Through Evaluation of the Free Nutritional Meal Program in Schools Arobi, M. Rizki; Munandar, Aris; Pratama, Irfan; Lestari, Fia; Wulandari, Eva; Pohan, Sulistyawati; Ayu Aprilia, Veni; Aprila , Zulma
Journal of Educational Management Research Vol. 4 No. 6 (2025)
Publisher : Al-Qalam Institue

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61987/jemr.v4i6.1466

Abstract

Education in Indonesia faces significant challenges related to student nutritional needs, which impact concentration and motivation to learn. The Free Nutritional Meal Program (MBG) at Ahmad Dahlan Junior High School in Jambi City aims to improve student nutritional status and support learning. This study aims to evaluate the impact of the MBG Program on student learning quality, focusing on concentration, motivation, and engagement. The method used was a qualitative case study approach, involving observation, interviews, and documentation. The results showed that the MBG Program had a positive impact on student concentration and attendance, but faced challenges related to menu incompatibility with student preferences and issues with hygiene and food quality. Menu adjustments and hygiene monitoring are crucial to improve the program's effectiveness. This study contributes to understanding how nutrition programs can support learning. It emphasizes the importance of menu adjustments and regular evaluation in improving the success of similar programs in the future.
Spice Image Classification Using ResNet50 and Augmentation Technique Bacun, Julio Francisco; Pratama, Irfan
Indonesian Journal of Artificial Intelligence and Data Mining Vol 8, No 3 (2025): November 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v8i3.37862

Abstract

This research aimed to develop an automatic classification system for Indonesian spices using a deep learning approach based on the ResNet50 architecture. The classification task involved 31 spice categories with 210 images per class. Two training strategies were implemented: training the model from scratch and using transfer learning with pre-trained weights from ImageNet. The model trained from scratch achieved a validation accuracy of 57%, while the transfer learning approach combined with fine-tuning of the last 33 layers resulted in a significantly higher validation accuracy of 96%. Image preprocessing, data augmentation, and class weighting were applied to improve the model’s generalization and handle data imbalance. The confusion matrix analysis showed that most predictions aligned with the true labels, especially in the transfer learning model. These findings demonstrate that transfer learning with ResNet50 can effectively classify spice images with high accuracy, even when visual similarity between certain classes exists. This research highlights the potential of deep convolutional neural networks to support automatic and reliable identification systems for biodiversity mapping and agricultural industries
Co-Authors Ade Kurnia, Dian Akbar, Muhammad Rizky Mawandhyka Albert Yakobus Chandra Alifudin, Arif Andre Bustari Aprila , Zulma Aris Munandar Arobi, M. Rizki Asanah, Nurul Aulia, Indira Dhekawanti Ayu Aprilia, Veni Ayuningsih, Ekatri Bacun, Julio Francisco Dadang Sudrajat Dwi Krisbiantoro, Dwi Edi Wibowo Eva wulandari, Eva Guno Wibowo, M Ioni Abdurrahman Hubu, Muhammad Rizki Iqbal, Sayid Muhammad Kurniawan, Akbar Sidiq Kurniawan, Muhammad Rifki Lestari, Fia Manurung, Syalom Kristian Nandasari, Dayang Nugroho Budi Wibowo Odi Nurdiawan Paneo, Anto Farwanto Pohan, Sulistyawati Pradana, Michael Anggun Kado Prasetyaningrum, Putri Taqwa Pratama, George Recksy Sandy Prayuda, Arya Priatama, Candra Rina Asmeri, Rina Sarmini Sarmini Suria, Ozzi Swapurba, Gangsar Taqwa Prasetyaningrun, Putri Uswatun Hasanah Waluyo, Retno Widianti, Anisa Wuri Handayani