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All Journal International Journal of Electrical and Computer Engineering Jurnal Sistem Komputer Bulletin of Electrical Engineering and Informatics Jurnal Informatika Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Bulletin of Electrical Engineering and Informatics Telematika : Jurnal Informatika dan Teknologi Informasi Sinergi Jurnal Teknologi Informasi dan Ilmu Komputer JUITA : Jurnal Informatika International Journal of Advances in Intelligent Informatics Seminar Nasional Informatika (SEMNASIF) Register: Jurnal Ilmiah Teknologi Sistem Informasi JURNAL NASIONAL TEKNIK ELEKTRO Bulletin of Electrical Engineering and Informatics Jurnal Teknologi dan Sistem Komputer Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JIKO (Jurnal Informatika dan Komputer) Jurnal Sisfokom (Sistem Informasi dan Komputer) ILKOM Jurnal Ilmiah Compiler MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) GERVASI: Jurnal Pengabdian kepada Masyarakat Systemic: Information System and Informatics Journal Journal of Information Systems and Informatics Buletin Ilmiah Sarjana Teknik Elektro International Journal of Engineering, Technology and Natural Sciences (IJETS) Indonesian Journal of Electrical Engineering and Computer Science International Journal of Advances in Data and Information Systems Journal of Innovation Information Technology and Application (JINITA) Science in Information Technology Letters Paradigma Masyarakat Berkarya: Jurnal Pengabdian dan Perubahan Sosial JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Saifullah, Shoffan
Universitas Pembangunan Nasional Veteran Yogyakarta
Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Public Health Social Sciences Other

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TOURISM’S TREND RANKING ON SOCIAL MEDIA DATA USING FUZZY-AHP VS. AHP Shoffan Saifullah
JURNAL INFORMATIKA DAN KOMPUTER Vol 6, No 2 (2022): ReBorn -- September 2022
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat - Universitas Teknologi Digital Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (952.264 KB) | DOI: 10.26798/jiko.v6i2.304

Abstract

This research discusses multi-criteria decision making (MCDM) using Fuzzy-AHP methods of tourism. The fuzzy-AHP method will rank tourism trends based on data from social media. Social media is one of the channels with the largest source of data input in determining tourism development. The development uses social media interactions based on the facilities visited, including reviews, stories, likes, forums, blogs, and feedback. This experimental analysis aims to prioritize facilities that are the trend of tourism. The priority ranking uses the fuzzy-AHP method for the process of determining weight criteria and the ranking process. The highest-ranking is on the Parks/Picnic Spots attraction and make it a priority to develop.
Nondestructive Chicken Egg Fertility Detection Using CNN-Transfer Learning Algorithms Shoffan Saifullah; Rafal Drezewski; Anton Yudhana; Andri Pranolo; Wilis Kaswijanti; Andiko Putro Suryotomo; Seno Aji Putra; Alin Khaliduzzaman; Anton Satria Prabuwono; Nathalie Japkowicz
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 9, No 3 (2023): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i3.26722

Abstract

This study explores the application of CNN-Transfer Learning for nondestructive chicken egg fertility detection. Four models, VGG16, ResNet50, InceptionNet, and MobileNet, were trained and evaluated on a dataset using augmented images. The training results demonstrated that all models achieved high accuracy, indicating their ability to accurately learn and classify chicken eggs’ fertility state. However, when evaluated on the testing set, variations in accuracy and performance were observed. VGG16 achieved a high accuracy of 0.9803 on the testing set but had challenges in accurately detecting fertile eggs, as indicated by a NaN sensitivity value. ResNet50 also achieved an accuracy of 0.98 but struggled to identify fertile and non-fertile eggs, as suggested by NaN values for sensitivity and specificity. However, InceptionNet demonstrated excellent performance, with an accuracy of 0.9804, a sensitivity of 1 for detecting fertile eggs, and a specificity of 0.9615 for identifying non-fertile eggs. MobileNet achieved an accuracy of 0.9804 on the testing set; however, it faced challenges in accurately classifying the fertility status of chicken eggs, as indicated by NaN values for both sensitivity and specificity. While the models showed promise during training, variations in accuracy and performance were observed during testing. InceptionNet exhibited the best overall performance, accurately classifying fertile and non-fertile eggs. Further optimization and fine-tuning of the models are necessary to address the limitations in accurately detecting fertile and non-fertile eggs. This study highlights the potential of CNN-Transfer Learning for nondestructive fertility detection and emphasizes the need for further research to enhance the models’ capabilities and ensure accurate classification.
Real-time mask-wearing detection in video streams using deep convolutional neural networks for face recognition Suhirman, Suhirman; Saifullah, Shoffan; Hidayat, Ahmad Tri; Kusuma, M. Apriandi; Drezewski, Rafał
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i1.pp1005-1014

Abstract

This research aims to develop a real-time mask-wearing detection system using deep convolutional neural networks (CNNs). This is crucial in the coronavirus disease 2019 (COVID-19) pandemic to alert individuals who are not wearing masks early on, thereby reducing the spread of the virus. Since COVID-19 primarily spreads through respiratory droplets and mask-wearing is recommended, our proposed study utilizes computer vision techniques, specifically image processing, to detect masked and unmasked faces. We employ a customized CNN architecture consisting of five convolutional layers, followed by max-pooling layers and fully connected (FC) layers. The final output layer utilizes softmax activation for classification. The model is updated with optimized layer configurations and parameter values. We are developing an application that uses a digital camera as an input device. The application utilizes a dataset comprising 11,792 image samples, which are used for training and testing purposes with the 80:20 ratio. Real-time testing is conducted using human subjects captured by the camera. The experimental results demonstrate that the CNN method achieves a classification accuracy of 99% on the training data and 98.83% during real-time video testing. These findings suggest that the real-time mask detection system using CNN performs effectively.
Redefining brain tumor segmentation: a cutting-edge convolutional neural networks-transfer learning approach Saifullah, Shoffan; Dreżewski, Rafał
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i3.pp2583-2591

Abstract

Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to precisely delineate tumor boundaries from magnetic resonance imaging (MRI) scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The model is rigorously trained and evaluated, exhibiting remarkable performance metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical image analysis and enhance healthcare outcomes. This research paves the way for future exploration and optimization of advanced CNN models in medical imaging, emphasizing addressing false positives and resource efficiency.
Prediction of palm oil production using hybrid decision tree based on fuzzy inference system Tsukamoto Tundo, Tundo; Saifullah, Shoffan; Yel, Mesra Betty; Irawansah, Opi; Mubarak, Zulfikar Yusya; Saidah, Andi
Bulletin of Electrical Engineering and Informatics Vol 13, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i6.7773

Abstract

This research addresses the challenge of optimizing rule creation for palm oil production at PT Tapiana Nadenggan. It deals with the complexity of diverse agricultural variables, environmental factors, and the dynamic nature of palm oil production. The existing problem lies in the limitations of conventional decision tree models—J48, reduced error pruning (REP), and random—in capturing the nuanced relationships within the intricate palm oil production system. The study introduces hybrid decision tree models—specifically J48-REP, REP-Random, and Random-J48—to address this challenge via combination scenarios. This approach aims to refine and update the rule creation process, enabling the recognition of nuanced performance processes within the selected decision tree combinations. To comprehensively tackle this challenge and problem, the study employs Tsukamoto’s fuzzy inference system (FIS) for a sophisticated performance comparison. Despite the complexity, intriguing results emerge after the forecasting process, with the standalone J48 decision tree achieving 85.70% accuracy and the combined J48-REP excelling at 93.87%. This highlights the potential of decision tree combinations in overcoming the complexities inherent in forecasting palm oil production, contributing valuable insights for informed decision-making in the industry.
Mapping crime determinants in Central Java: an in-depth exploration through local spatial association and regression analysis Humairoh, Nanda Lailatul; Purwaningsih, Tuti; Saifullah, Shoffan; Dwiyanto, Felix Andika; Rabbimov, Ilyos
Science in Information Technology Letters Vol 3, No 1 (2022): May 2022
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/sitech.v3i1.1212

Abstract

Economic development often brings prosperity to communities, but it can also be accompanied by growing disparities that, when unaddressed, lead to increased crime rates. Central Java, an Indonesian province, has been grappling with a persistent high crime rate, necessitating an in-depth examination of the factors underlying this phenomenon. In this study, we employ a rigorous research methodology, incorporating data sources from the Central Java Central Statistics Agency (BPS) and utilizing key independent variables, including population, unemployment, poverty, Age-Dependency Ratio (APS), and Relative Location Quotient (RLS). Through the application of advanced spatial analysis techniques such as the Local Indicator of Spatial Association (LISA) and the Spatial Autoregressive Model (SAR), this research offers a nuanced exploration of the spatial relationships and regression analysis of these variables. Notably, the study presents a tree map highlighting crime distribution in Central Java's districts and cities. The findings reveal that these five variables exhibit a 75.48% accuracy in predicting crime in Central Java. Through this comprehensive analysis, our research aims to provide valuable insights for policymakers, law enforcement, and the community at large, enabling informed strategies for crime reduction and the promotion of a safer, more prosperous Central Java
Improving sentiment analysis on PeduliLindungi comments: a comparative study with CNN-Word2Vec and integrated negation handling Jayadianti, Herlina; Arianti, Berliana Andra; Cahyana, Nur Heri; Saifullah, Shoffan; Dreżewski, Rafał
Science in Information Technology Letters Vol 4, No 2 (2023): November 2023
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/sitech.v4i2.1184

Abstract

This study investigates sentiment analysis in Google Play reviews of the PeduliLindungi application, focusing on the integration of negation handling into text preprocessing and comparing the effectiveness of two prominent methods: CNN-Word2Vec CBOW and CNN-Word2Vec SkipGram. Through a meticulous methodology, negation handling is incorporated into the preprocessing phase to enhance sentiment analysis. The results demonstrate a noteworthy improvement in accuracy for both methods with the inclusion of negation handling, with CNN-Word2Vec SkipGram emerging as the superior performer, achieving an impressive 76.2% accuracy rate. Leveraging a dataset comprising 13,567 comments, this research introduces a novel approach by emphasizing the significance of negation handling in sentiment analysis. The study not only contributes valuable insights into the optimization of sentiment analysis processes but also provides practical considerations for refining methodologies, particularly in the context of mobile application reviews.
Seasonal meat stock demand used comparison of performance smoothing-average forecasting Tundo, Tundo; Saifullah, Shoffan; Dharmawan, Tio; Junaidi, Junaidi; Devia, Elmi
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 1: January 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i1.pp425-433

Abstract

Seasonal patterns significantly influence the demand for beef stock, especially in rural areas that rely on natural feed. Accurate forecasting is essential for managing this demand due to beef's status as a government-regulated nutritional commodity. Food production, consumption, and income levels affect the demand for beef stocks. This research aims to identify the most precise forecasting method for predicting future beef stock needs. We evaluated multiple techniques, including single exponential smoothing (SES), double exponential smoothing (DES), single moving average (SMA), and double moving average (DMA), using the mean absolute percentage error (MAPE) metric, focusing specifically on beef supplies in Pemalang. The results indicated that the DMA method achieved the highest accuracy with a MAPE value of 5.993% at the 4th -order parameter. Additionally, increasing the data volume improved forecasting accuracy, demonstrating the effectiveness of the DMA method for beef stock prediction.
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.
Co-Authors Abdul Fadlil Adityo Nugroho, Adityo Afiqa, Nurul Agus Sasmito Aribowo Ahmad Taufiq Akbar Ahmad Tri Hidayat Aji Prasetya Wibawa Akbar, Ahmad Taufiq Akbar, Bagus Muhammad Alek Setiyo Nugroho Alfiani, Oktavia Dewi Alin Khaliduzzaman Alin Khaliduzzaman Alisya Amalia Putri Hasanah Andi Muhammad Dirham Dewantara Andi Nurkholis Andiko Putro Suryotomo Andri Pranolo Anton Satria Prabuwono Anton Satria Prabuwono Anton Yudhana Arianti, Berliana Andra Arief Hermawan Awang Hendrianto Pratomo Azlan, Faris Farhan Azrul Mahfurdz Bambang Yuwono Betty Yel, Mesra Budi Santosa Devia, Elmi Dharmawan, Tio Dreżewski, RafaÅ‚ Drezewski, Rafal Drezewski, Rafał Dwi Wahyuningrum Dwiyanto, Felix Andika Faqihuddin Al-anshori Ghazali, Ahmad Badaruddin Haekal, Haekal Herlina Jayadianti Heru Cahya Rustamaji Hidayat, Ahmad Tri Humairoh, Nanda Lailatul Ismail, Amelia Ritahani Isna Nur Aini Ivana Puspita Sari Japkowicz, Nathalie Judanti Cahyaning Junaidi Junaidi Kaswijanti, Wilis Katamsyi, Kaifa Ahlal Khaliduzzaman, Alin Kusuma, M. Apriandi Lean Karlo Tolentino Luh Putu Ratna Sundari Mubarak, Zulfikar Yusya Muhammad Nur Hendra Alvianto Nathalie Japkowicz Nisa, Syed Qamrun Noormaizan, Khairul Akmal Nur Heri Cahyana Nuril Anwar, Nuril Nuryana, Zalik Opi Irawansah, Opi Prapcoyo, Hari Putra, Agung Bella Utama Putra, Seno Aji Rabbimov Ilyos Rabbimov, Ilyos Rafal Drezewski Rafal Drezewski Rafal Drezewski Rochmat Husaini Rochmat Husaini Rustamadji, Heru Saidah, Andi Santosa, Budi Satya Ghifari Adipratama Seno Aji Putra Suhirman SUHIRMAN SUHIRMAN Sularso Sularso, Sularso Sunardi - Sunardi - Sunardi Sunardi Sunardi, Sunardi Taufiq Akbar, Ahmad Tri Andi, Tri Tundo, Tundo Tuti Purwaningsih, Tuti Wahyu Adjie Saputra Wilis Kaswidjanti Wilis Kaswidjanti Wilis Kaswijanti Yuhefizar Yuhefizar Yuli Fauziah Yuli Fauziyah