Article Per Year (5 Year)
p-Index From 2020 - 2025
2.064
P-Index
This Author published in this journals
All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics International Journal of Advances in Intelligent Informatics Jurnal Inspiration Panrita Abdi - Jurnal Pengabdian pada Masyarakat JOIV : International Journal on Informatics Visualization Journal TEPAT: Teknologi Terapan untuk Pengabdian Masyarakat EPI International Journal of Engineering Jurnal Penelitian Enjiniring Fakultas Teknik Unhas Jurnal Teknologi Elekterika Indonesian Journal of Electrical Engineering and Computer Science Community Development Journal: Jurnal Pengabdian Masyarakat JURNAL SIPISSANGNGI: Jurnal Pengabdian Kepada Masyarakat Jurnal INFOTEL
Indrabayu Indrabayu
Departemen Teknik Informatika, Fakultas Teknik, Universitas Hasanuddin
Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Public Health Other

Published : 26 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 26 Documents
Search

 1   2   3 
PENERAPAN STRATEGI PENGEMBANGAN USAHA BERBASIS BUSINESS MODEL CANVAS DENGAN TREN DIGITAL PADA UKM RISOLLE INDONESIA KOTA PARE-PARE Indrabayu, Indrabayu; Munir, Abdul Razak; basri, Basri; Ibrahim, Rafiqa Nurcahyani; Anisah, Siti Nur
JURNAL SIPISSANGNGI: Jurnal Pengabdian Kepada Masyarakat Vol 4, No 3 (2024): Sipissangngi Volume 4, Nomor 3, September 2024
Publisher : Lembaga Penelitan dan Pengabdian Masyarakat, Universitas Al Asyariah Mandar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35329/jurnal.v4i3.5803

Abstract

Kegiatan Pengabdian ini bertujuan untuk meningkatkan kapasitas pelaku UMKM di Kota Parepare, khususnya Gerai Risolle Indonesia, melalui sosialisasi dan pelatihan berbasis Business Model Canvas (BMC) dan teknologi kecerdasan buatan (AI). Metode yang digunakan meliputi sosialisasi strategi bisnis, pelatihan penerapan teknologi, dan pendampingan intensif. Fokus utama adalah revitalisasi perencanaan bisnis, redesain kemasan, dan optimalisasi manajemen persediaan. Hasil kegiatan menunjukkan peningkatan signifikan pada pemahaman peserta terkait perencanaan bisnis berbasis BMC dan penggunaan AI, serta kemampuan dalam manajemen persediaan. Redesain kemasan produk berhasil meningkatkan daya tarik dan potensi pemasaran Gerai Risolle Indonesia. Hal ini diharapkan mampu meningkatkan daya saing usaha di pasar yang lebih luas. 
Application of Artificial Neural Network and Gray Level Co-occurrence Matrix to detect blood glucose levels through the skin of the hands. Umar, Usman; Syarif, Syafruddin; Nurtanio, Ingrid; Indrabayu, Indrabayu
Jurnal Teknologi Elekterika Vol. 19 No. 2 (2022): Nopember
Publisher : Jurusan Teknik Elektro Politeknik Negeri Ujung Pandang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31963/elekterika.v6i2.3756

Abstract

Increased glucose in the blood can cause a buildup so that it cannot be absorbed by all of the body's cells, this problem can cause various disorders in the body's organs. To avoid problems, it is necessary to check the blood glucose level regularly. Monitoring blood sugar levels is currently still using invasive techniques that are painful, non-invasive monitoring is needed. This study develops a non-invasive method to predict blood glucose through image processing. For investigation, several invasive images and glucose levels were taken. Types of samples based on age classification, 20-60 years. For accuracy and simple analysis, 37 images of participants as volunteers, samples were evaluated and investigated under the gray level co-occurrence matrix (GLCM). In this study, an artificial neural network (ANN) was used for all training and hand texture testing to detect glucose levels. The performance of this model is evaluated using Root Mean Square Error (RMSE) and correlation coefficient (r). Clarke Error Grid Analysis (EGA) variance was used in this investigation to determine the accuracy of the method. The results showed that the RMSE was close to the standard value, the regression coefficient was 0.95, and the Clarke EGA analysis: 81.08 % was in the A zone. So that the blood glucose prediction model using the GLCM-ANN method is feasible to apply.
Fermented and Unfermented Cocoa Beans for Quality Identification Using Image Features Basri, Basri; Indrabayu, Indrabayu; Achmad, Andani; Areni, Intan Sari
JOIV : International Journal on Informatics Visualization Vol 8, No 3 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.3.2578

Abstract

Fermented cocoa bean products are one of the high-quality requirements of the cocoa processing industry. On an automated industrial scale, early identification of cocoa bean quality is essential in the processing industry. This study aims to identify the condition of quality cocoa beans based on fermentation and non-fermentation characteristics. This study applies analysis based on static images taken using a camera with a distance variation of 5 cm, 10 cm, and 15 cm in both classes, with 500 image data each. The Feature extraction Approach uses the Oriented Gradient (HOG) method with a Support Vector Machine (SVM) classification technique. Image analysis of both object classes was also performed with a color change to show the dominance of the color pattern on the skin of the cocoa beans to be analyzed. The results showed that fermented cocoa beans show a color pattern and texture that tends to be darker and coarser than non-fermented cocoa beans. Computational results with performance analysis using Receiver Operating Characterisic (ROC) on both classes showed the results that the distance of 5 cm and 15 cm has 100% accuracy, but based on the best performance, comprehensively seen in terms of Precision, Recall, and F1-Score shows the best value is at a distance of 15 cm. The results of this research based on the literature review conducted have better achievements, thus enabling further research on the development of conveyor models with real-time video data for automation systems.
A Thorough Review of Vehicle Detection and Distance Estimation Using Deep Learning in Autonomous Cars Rahmat, Muhammad Abdillah; Indrabayu, Indrabayu; Achmad, Andani; Salam, Andi Ejah Umraeni
JOIV : International Journal on Informatics Visualization Vol 8, No 4 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.4.2665

Abstract

Autonomous vehicle technologies are rapidly advancing, and one key factor contributing to this progress is the enhanced precision in vehicle detection and distance calculation. Deep Learning Networks (DLNs) have emerged as powerful tools to address this challenge, offering remarkable capabilities in accurately detecting and estimating vehicle positions. This study comprehensively reviews DLN applications for vehicle detection and distance estimation. It examines prominent DLN models such as YOLO, R-CNN, and SSD, evaluating their performance on widely used datasets such as KITTI, PASCAL VOC, and COCO. Analysis results indicate that YOLOv5, developed by Farid et al. achieves the highest accuracy level with a mAP (mean Average Precision) of 99.92%. Yang et al. showcased that YOLOv5 performs exceptionally in detection and distance estimation tasks, with a mAP of 96.4% and a low mean relative error (MRE) of 10.81% for distance estimation. These achievements highlight the potential of DLNs to enhance the accuracy and reliability of vehicle detection systems in autonomous vehicles. The study also emphasizes the importance of backbone architectures like DarkNet 53 and ResNet in determining model efficiency. The choice of the appropriate model depends on the specific task requirements, with some models prioritizing real-time detection and others prioritizing accuracy. In conclusion, developing DLN-based methods is crucial in advancing autonomous vehicle technology. Research and development remain crucial in ensuring road safety and efficiency as autonomous vehicles become more common in transportation systems.
Cocoa bean quality identification using a computer vision-based color and texture feature extraction Basri, Basri; Indrabayu, Indrabayu; Achmad, Andani; Areni, Intan Sari
International Journal of Advances in Intelligent Informatics Vol 11, No 1 (2025): February 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v11i1.1609

Abstract

The current pressing issue in the downstream processing of cocoa beans in cocoa production is a strict quality control system. However, visually inspecting raw cocoa beans reveals the need for advanced technological solutions, especially in Industry 4.0. This paper introduces an innovative image-processing approach to extracting color and texture features to identify cocoa bean quality. Image acquisition involved capturing video with a data acquisition box device connected to a conveyor, resulting in image samples of Good-quality and Poor-quality of non-cutting cocoa beans dataset. Our methodology includes multifaceted advanced pre-processing, sharpening techniques, and comparative analysis of feature extraction methodologies using Hue-Saturation-Value (HSV) and Gray Level Cooccurrence Matrix (GLCM) with correlated features. This study used 15 features with the highest correlation. Machine Learning models using Support Vector Machine (SVM) with some parameter variation value alongside an RBF kernel. Some parameters were measured to compare each approach, and the results show that pre-processing without sharpening achieves better accuracy, notably with the HSV and GLCM combination reaching 0.99 accuracy. Adequate technical lighting during data acquisition is crucial for accuracy. This study sheds light on the efficacy of image processing in cocoa bean quality identification, addressing a critical gap in industrial-scale implementation of technological solutions and advancing quality control measures in the cocoa industry.
Comparative Performance Analysis of YOLO and Faster R-CNN in Detecting Species and Estimating the Weight of Grouper and Snapper Fish Using Digital Images Sidehabi, Sitti Wetenriajeng; Indrabayu, Indrabayu; Warni, Elly; Bake, Sabda Ansari
JOIV : International Journal on Informatics Visualization Vol 9, No 4 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.4.3121

Abstract

Grouper and snapper fish are widely consumed species with high economic value in the global market. To determine their economic value, identifying the species and estimating the weight are essential in the pricing and quality determination of the traded fish. The commonly used manual methods are often time-consuming and labor-intensive. Based on this, a more effective computer-based method is needed for these repetitive tasks. This research aims to analyze the performance of two commonly used deep learning models, YOLO and Faster R-CNN, in detecting species and estimating the weight of specific grouper and snapper fish. The data used consisted of 2991 samples divided into 18 classes. This data was then augmented using rotate and flip features to create 6843 image samples. A threshold of 0.8 was used in the detection process, meaning objects detected with confidence below 0.8 would be ignored. Once trained, the performance of both models was tested using precision, recall, and accuracy parameters to assess how accurately the models predicted fish species from the input data and Mean Absolute Percentage Error (MAPE) to evaluate the estimation results of the models. There were differences in the quantitative evaluation results between the YOLO and Faster R-CNN models. The YOLO model achieved precision, recall, and accuracy rates of 0.98, 0.98, and 0.96, respectively, while the Faster R-CNN model had precision, recall, and accuracy rates of 0.97, 0.98, and 0.95, respectively. Additionally, the MAPE for weight estimation was 2.42% for image data and 3.66% for video data for the YOLO model. In contrast, for the Faster R-CNN model, the results were 14.62% for image data and 13.59% for video data. Thus, it can be concluded that the YOLO model provides better quantitative evaluation results compared to the Faster R-CNN model.
Co-Authors A. Ais Prayogi Alimuddin A.Ais Prayogi Abdul Razak Munir Adnan Adnan Ady Wahyudi Paundu Ahmad Rifaldi Amil A. Ilham Amil Ahmad Ilham Andani Achmad Andani Achmad Andini D. Achmad Andri Anto Aman Andri Anto Aman, Andri Anto Anugrayani Bustamin Anugrayani Bustamin Anugrayani Bustamin Aqfir Areni, Intan Sari Arifin, Nurhikma Aulia Darnilasari Bake, Sabda Ansari Basri Basri Bustamin, Anugrayani Christoforus Yohanes Dewi, Andi Ratna Sari Edi Yanto Elly Warni Elly Warni Elyas Palantei Fakhriyah, Sitti Fakhriyah Ibrahim, Rafiqa Nurcahyani Ida Ayu Putu Sri Widnyani Ifwan, Ichsanul Al Ingrid Nurtanio Intan Sari Areni Intan Sari Areni Intan Sari Areni Intan Sari Areni Iqra Aswad Marindah, Tyanita Puti Muh Anshar Muh Niswar Muh Niswar, Muh Muh. Alief Fahdal Imran Oemar Muhammad Abdillah Rahmat, Muhammad Abdillah Muhammad Irsan Sabir Muhammad Ismail Muhammad Niswar Mukarramah Yusuf Nurul Fathanah Mustamin Rahayu, Mesra Rahmat Hardian Putra Rhiza S. Sadjad Rieka Zalzabillah Putri Rizka Irianty Rohmah Nur Hidayah Rushadah, Nur Indriani Salam, Andi Ejah Umraeni Sespajayadi, Ary Sidehabi, Sitti Wetenriajeng Siska Anraeni Syafruddin Syarif Syafruddin Syarif Syahputra, Rendy Aziz Syukriyanto Latif Usman Umar Warni, Elly Yohannes, Christoforus Zaenab Muslimin Zaenab Muslimin Zahir Zainuddin Zulfajri Basri Hasanuddin Zulkifli Tahir