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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) TEKNIK Transmisi: Jurnal Ilmiah Teknik Elektro JURNAL SISTEM INFORMASI BISNIS Jurnal Penelitian dan Pengembangan Pendidikan TRANSIENT: Jurnal Ilmiah Teknik Elektro Journal of Information System Jurnal Teknologi dan Sistem Komputer Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Indonesian Journal of Electrical Engineering and Computer Science
Wahyul Amien Syafei
Departemen Teknik Elektro, Universitas Diponegoro Semarang Jl. Prof Sudharto, SH, Kampus UNDIP Tembalang, Semarang 50275, Indonesia
Computer Science & IT Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering

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Gaussian filter-based dark channel prior for image dehazing enhancement Nurhayati, Oky Dwi; Surarso, Bayu; Syafei, Wahyul Amien; Nugraheni, Dinar Mutiara Kusumo
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i5.pp5765-5778

Abstract

The presence of haze in an image is one of the challenges in computer vision tasks, such as remote sensing, object monitoring, and traffic monitoring applications. The hazy image is considered to contain noise and it can interfere with the image analysis process. Thus, image dehazing becomes a necessity as part of image enhancement. Dark channel prior (DCP) is one of the images dehazing methods that works based on a physical degradation model and utilizes low-intensity values from outdoor image characteristics. The DCP method generally consists of some steps, which are finding the dark channel and gradient image, estimating the sky region, atmospherical light, and transmission map, and reconstructing the dehazed image. This study introduces image dehazing by utilizing the Gaussian filter combined with the DCP method to increase the sharpness and accentuate the details of hazy images. Experimental results show that the proposed method could produce dehazed images with a visual quality is 18.94 dB on average or an increase of 11.91% compared to the original hazy image with a similarity index is 66.71% on average or an increase of 8.10%. Therefore, it is expected that this study can contribute to the image dehazing method enrichment based on DCP.
Metode Decision Tree untuk Meningkatkan Kualitas Rencana Pelaksanaan Pembelajaran dengan Algoritma C4.5 Solehuddin, Muhammad; Syafei, Wahyul Amien; Gernowo, Rahmat
Jurnal Penelitian dan Pengembangan Pendidikan Vol. 6 No. 3 (2022): Oktober
Publisher : LPPM Undiksha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/jppp.v6i3.52840

Abstract

Aktivitas pembelajaran di kelas dipengaruhi oleh berbagai aspek, salah satunya adalah aspek perencanaan pembelajaran yang dilaksanakan oleh guru. Guru sering menemui kesulitan dalam penyusunan RPP karena mayoritas pendidik belum memahami perumusan parameter dan tujuan belajar mengajar. Penelitian ini bertujuan untuk menganalisis metode decision tree untuk meningkatkan kualitas rencana pelaksanaan pembelajaran dengan algoritma C4.5. Metode pada studi ini memakai decision tree algortitma C4.5 dalam melaksanakan klasifikasi data kedalam kelas yang sudah tersedia. Metode ini digunakan karena keunggulannya dalam hal kecepatan dan klasifikasi sederhana sehingga mudah untuk diinterprestasikan oleh manusia. Jenis Pengumpulan data dengan wawancara, studi pustaka, dan studi lapangan. Hasil dari penelitian ini menunjukkan dari total data sebanyak 927 berbentuk csv dan data training yang yang dicoba diinputkan kedalam  sistem sebanyak 30 menghasilkan tingkat akurasi 90%. Studi ini harapannya bisa berguna bagi pendidik sebagai rujukan didalam penyusunan RPP yang baik dan benar untuk kedepannya. Implikasi penelitian ini diharapkan dapat meningkatkan kualitas rencana pelaksanaan pembelajaran.
Identification of Grouper Fish Types using Convolutional Neural Network Resnet-50 Algorithm Nuraini, Rini; Syafei, Wahyul Amien; Wibowo, Adi; Jaya, Indra
Jurnal Sistem Informasi Bisnis Vol 15, No 2 (2025): Volume 15 Number 2 Year 2025
Publisher : Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/vol15iss2pp173-178

Abstract

Grouper is a type of fish that is popular with the public. It is necessary to identify the type of grouper fish based on color patterns with increase the epoch value to get the best accuracy. The purpose of the research is to predict the type of grouper. This research use CNN Resnet-50 algorithm. 30 data used. The accuracy of prediction is 75 % to predict the image groupers. In the grouper prediction process, the more we increase the epoch value, we will get the best accuracy value. Epoch is a factor that affects the time of training an AI model and affects the accuracy value of the AI model.
Boosting real-time vehicle detection in urban traffic using a novel multi-augmentation Ashari, Imam Ahmad; Syafei, Wahyul Amien; Wibowo, Adi
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 1: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i1.pp656-668

Abstract

Real-time vehicle object detection in urban traffic is crucial for modern traffic management systems. This study focuses on improving the accuracy of vehicle identification and classification in heavy traffic during peak hours, with particular emphasis on challenges such as small object sizes and interference from light reflections. The use of multi-label images enables the simultaneous detection of various vehicle types within a single frame, providing more detailed information about traffic conditions. You only look once (YOLO) was chosen for its capability to perform real-time object detection with high accuracy. Multi-augmentation techniques were applied to enrich the training data, making the model more robust to varying lighting conditions, viewpoints, object occlusions, and issues related to small objects. YOLOv8n and YOLOv9t were selected for their speed and efficiency. Models without augmentation, 10 single-augmentation techniques, and 5 multi-augmentation techniques were tested. The results show that YOLOv8n with multiaugmentation (scaling, zoom in, brightness adjustment, color jitter, and noise injection) achieved the highest mAP50-95 score of 0.536, surpassing YOLOv8n with single-augmentation Blur, which had an mAP50-95 of 0.465, as well as YOLOv8n without augmentation, which scored 0.390. Multiaugmentation proved to significantly enhance YOLO’s performance.
Air Pollution Control Analysis at the Tofu Industry Center in Sugihmanik Village, Grobogan Regency Huboyo, Haryono Setiyo; Ramadan, Bimastyaji Surya; Undari, Melinda Tri; Fauziyah, Fitria Umi; Syafei, Wahyul Amien; Jassey, Babucarr
TEKNIK Vol 46, No 2 (2025) April 2025
Publisher : Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/teknik.v46i2.70130

Abstract

Air pollution in Grobogan Regency, especially particulate parameters, annually shows an average value of 69% of ambient air quality standards with an average concentration of PM2.5 reaching 38 μg/m³, primarily due to industrial activities, transportation, and the burning of fossil fuels. In the Sugihmanik Village Tofu Industrial Centre, Grobogan Regency, there are 30 home-based tofu SMEs that use rice husks as fuel for boiler furnaces, which produce pollutants such as SO₂, NO₂, CO₂, CO, PM₂.₅, PM₁₀, and TSP. The largest tofu factory in Sugihmanik Village uses 400 kg of rice husks daily. The chimney design, which does not comply with the technical standards of Kepdal No. 205 of 1996, further increases the risk of air pollution. Therefore, the design of an air pollution control device and a chimney redesign are required to mitigate these negative effects. After calculating the emission concentrations and comparing them with PermenLH No. 7 of 2007, only total particle parameter close to the quality standard of 350 mg/m3 with a particulate loading emitted from the furnace of 232 mg/s. By using a cyclone as an emission control device, there is a particulate removal efficiency of 53.05%. With the implementation of air pollution control devices, the ambient air concentration of particulates, previously a peak concentration of around 300 µg/m3, can be reduced to around 68.8 µg/m3.
Traffic flow prediction using long short-term memory-Komodo Mlipir algorithm: metaheuristic optimization to multi-target vehicle detection Ashari, Imam Ahmad; Syafei, Wahyul Amien; Wibowo, Adi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i4.pp3343-3353

Abstract

Multi-target vehicle detection in urban traffic faces challenges such as poor lighting, small object sizes, and diverse vehicle types, impacting traffic flow prediction accuracy. This study introduces an optimized long short-term memory (LSTM) model using the Komodo Mlipir algorithm (KMA) to enhance prediction accuracy. Traffic video data are processed with YOLO for vehicle classification and object counting. The LSTM model, trained to capture traffic patterns, employs parameters optimized by KMA, including learning rate, neuron count, and epochs. KMA integrates mutation and crossover strategies to enable adaptive selection in global and local searches. The model's performance was evaluated on an urban traffic dataset with uniform configurations for population size and key LSTM parameters, ensuring consistent evaluation. Results showed LSTM-KMA achieved a root mean square error (RMSE) of 14.5319, outperforming LSTM (16.6827), LSTM-improved dung beetle optimization (IDBO) (15.0946), and LSTM-particle swarm optimization (PSO) (15.0368). Its mean absolute error (MAE), at 8.7041, also surpassed LSTM (9.9903), LSTM-IDBO (9.0328), and LSTM-PSO (9.0015). LSTM-KMA effectively tackles multi-target detection challenges, improving prediction accuracy and transportation system efficiency. This reliable solution supports real-time urban traffic management, addressing the demands of dynamic urban environments.
Co-Authors Achmad Hidayatno Adi Wibowo Adi Wibowo Adian Fatchur Rochim Afiq, Raihan Aghus Sofwan Ahmad Siddik Prayogi, Ahmad Siddik Ajub Ajulian Zahra Macrina Amelia Syarfina Qisthi Anky Setyadewa Ardea Satya Hasnanta Ardian Wijaya Aulia Iqbal M Baharuddin, Yusuf Bayu Surarso Bayu Surarso Budi Warsito Darjat Darjat Dinar Mutiara Kusumo Nugraheni Dionis Balan Pratama Durio Etgar Endriawan Endriawan Farich Novriana Sandy Farid Arifiyanto Fatkhurrahman, Muhammad Arief Fauziyah, Fitria Umi Fuad Rajab Nugroho Galoh Randicha Haryono Setiyo Huboyo Hayu Pratista Herlinah Baharuddin, Herlinah Hiroshi Ochi Imam Ahmad Ashari, Imam Ahmad Imam Santoso Indra Jaya Indra Jaya Jack Zijlmans Jassey, Babucarr Joanna Francisca Socaningrum Kodrat Imam Satoto Kusnadi Kusnadi Laksono Trisnantoro Listyono, A. F. M. Solehuddin Maman Somantri Muhammad Alfian, Muhammad Muhammad Satriya Utama Nugraheni, Dinar Oky Dwi Nurhayati Prayogi, A. S. R. Edith Indera Bagaskara R. Rizal Isnanto Rahmat Gernowo Ramadan, Bimastyaji Surya Raymond Octhario Retno Kusumaningrum Rini Indah Sulistiyawati Rini Nuraini Rini Nuraini, Rini Santoso, Imam Sri Mulyono Subchan Ajie Ari Bowo Sukiswo Sukiswo Sulistiyo, Budi Tri Murwanto Undari, Melinda Tri Valentina Samaya Sari Dewi Yama Aryadanangjaya Yudhitya Sorrenti Yuli Chritiyono Yunda Kumala Nasution Zhaorui Yuan Zuhrotul Maulida