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All Journal JOIV : International Journal on Informatics Visualization Jurnal Sinergitas PkM & CSR Journal of Applied Business and Technology
Farkhan, Mochammad
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Environmental Science Public Health Social Sciences

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PELATIHAN KELOMPOK USAHA PASTEL MINI DESA PANDAU JAYA MELALUI DIVERSIFIKASI DAN FORTIFIKASI UDANG Simanjuntak, Jan Stepfely; Sukmiwati, Mery; Leksono, Tjipto; Herdini, Herdini; Syawal, Henni; Azizah, Afra; Farrand Noor, Andi Almer; Yovanie, Faira; Nabila, Febi; Fazira, Meisya Amanda; Farkhan, Mochammad; Nabellia, Nabellia; Salsabila Hendri, Putri Noviayu; Anugrah, Rio Persal
Jurnal Sinergitas PKM & CSR Vol. 6 No. 2 (2022): OCTOBER
Publisher : Universitas Pelita Harapan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19166/jspc.v6i2.5874

Abstract

Diversification and fortification are efforts that can be made in increasing the value of food quality in terms of increasing product varieties and adding product nutritional value through the addition of food ingredients, one example of this product is fish or shrimp. The purpose of this service is to provide training on CPPB-IRT, technology transfer for mini pastel product processing, labeling, mini pastel training SOPs, product packaging and label design, and website creation, Facebook, Instagram (information and promotions) as well as making fish/shrimp flavours on mini pastels. The method of implementation that would be carried out in this activity was in the form of education, with the method of lectures, discussions, demonstrations and evaluation of product processing/manufacturing techniques for mini pastel businesses. Targets and outcomes to be achieved from community service activities, namely mini pastel products sold to the market must meet product specifications in accordance with SNI 7388: 2009. Packaging and labeling were in accordance with food packaging standardization PP No. 69 of 1999, standard operating procedures for guarantees quality of mini pastels. Based on the results obtained, the responses from the owners and employees of the mini pastel business group were quite enthusiastic in participating in the counseling activities.abtract in bahasaDiversifikasi dan fortifikasi merupakan upaya yang dapat dilakukan dalam meningkatkan nilai mutu pangan dalam hal peningkatan varietas produk dan penambahan nilai gizi produk melalui penambahan bahan pangan, salah satu contoh produk ini adalah ikan atau udang. Tujuan pengabdian ini adalah memberikan pelatihan CPPB-IRT, alih teknologi pengolahan produk mini pastel, pelabelan, SOP pelatihan mini pastel, desain kemasan dan label produk, dan pembuatan website, facebook, instagram (informasi dan promosi) serta membuat rasa ikan/udang di mini pastel. Metode pelaksanaan yang akan dilakukan dalam kegiatan ini berupa edukasi, dengan metode ceramah, diskusi, demonstrasi dan evaluasi teknik pengolahan/pembuatan produk untuk usaha mini pastel. Sasaran dan hasil yang ingin dicapai dari kegiatan pengabdian kepada masyarakat yaitu produk mini pastel yang dijual ke pasaran harus memenuhi spesifikasi produk sesuai dengan SNI 7388 : 2009. Pengemasan dan pelabelan sudah sesuai dengan standarisasi kemasan pangan PP No. 69 Tahun 1999, standar operasional prosedur untuk menjamin kualitas mini pastel. Berdasarkan hasil yang diperoleh, respon dari pemilik dan karyawan cukup antusias mengikuti kegiatan penyuluhan.
Automated Waste Classification Using YOLOv11 A Deep Learning Approach for Sustainable Recycling Nasien, Dewi; Adiya, M. Hasmil; Farkhan, Mochammad; Rahmadhani, Ummi Sri; Samah, Azurah A.
Journal of Applied Business and Technology Vol. 6 No. 1 (2025): Journal of Applied Business and Technology
Publisher : Institut Bisnis dan Teknologi Pelita Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35145/jabt.v6i1.205

Abstract

The rapid increase in waste generation due to urbanization and population growth has necessitated more efficient waste management solutions. Traditional waste sorting methods rely on manual labor, which is time-consuming, error-prone, and inefficient at large scales. This paper proposes an automated waste classification system using YOLOv11, the latest iteration of the YOLO family, which is known for its high speed and accuracy in object detection. By leveraging a custom dataset containing 10,464 labeled waste images from various categories—such as biodegradable, plastic, metal, paper, and glass—this study trains and evaluates a deep learning model capable of real-time waste identification and categorization. Experimental results demonstrate that YOLOv11 achieves high detection accuracy, with an overall classification accuracy of 94% and a mean average precision (mAP) exceeding previous methods. The model effectively differentiates between various waste types, though some misclassifications occur, particularly between visually similar materials like transparent plastic and glass. Performance metrics, including precision and recall, indicate the robustness of the proposed system in real-world applications. This research highlights the potential of YOLOv11 for integration into smart waste management systems, such as automated sorting machines and AI-powered recycling bins, to enhance efficiency and reduce environmental impact. Future work will focus on optimizing model performance by incorporating additional training data, applying advanced image augmentation techniques, and exploring hybrid approaches such as texture analysis and spectral imaging to improve classification accuracy. The implementation of this technology is expected to streamline waste recycling processes, minimize contamination in recyclable materials, and contribute to sustainable waste management practices.
Convolutional Neural Network Model for Sex Determination Using Femur Bones Nasien, Dewi; Adiya, M. Hasmil; Afrianty, Iis; Farkhan, Mochammad; Butar-Butar, Rio Juan Hendri
JOIV : International Journal on Informatics Visualization Vol 7, No 4 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.7.4.1711

Abstract

Forensic anthropology is the critical discipline that applies physical anthropology in forensic education. One valuable application is the identification of the biological profile. However, in the aftermath of significant disasters, the identification of human skeletons becomes challenging due to their incompleteness and difficulty determining sex. Researchers have explored alternative indicators to address this issue, including using the femur bone as a reliable sex identifier. The development of artificial intelligence has created a new field called deep learning that has excelled in various applications, including sex determination using the femur bone. In this study, we employ the Convolutional Neural Network (CNN) method to identify the sex of human skeleton shards. A CNN model was trained on 91 CT-scan results of femur bones collected from Universiti Teknologi Malaysia, comprising 50 female and 41 male patients. The data pre-processing involves cropping, and the dataset is divided into training and validation subsets with varying percentages (60:4, 70:30, and 80:20). The constructed CNN architecture exhibits exceptional accuracy, achieving 100% accuracy in both training and validation data. Moreover, the precision, recall, and F1 score attained a perfect score of 1, validating the model's precise predictions. The results of this research demonstrate excellent accuracy, confirming the reliability of the developed model for sex determination. These findings demonstrate that using deep learning for sex determination is a novel and promising approach. The high accuracy of the CNN model provides a valuable tool for sex determination in challenging scenarios. This could have important implications for forensic investigations and help identify victims of disasters and other crimes.
Co-Authors Adiya, M. Hasmil Anugrah, Rio Persal Azizah, Afra Butar-Butar, Rio Juan Hendri Dewi Nasien Farrand Noor, Andi Almer Fazira, Meisya Amanda Henni Syawal Herdini , Herdini Iis Afrianty Mery Sukmiwati Nabellia, Nabellia Nabila, Febi Rahmadhani, Ummi Sri Salsabila Hendri, Putri Noviayu Samah, Azurah A. Simanjuntak, Jan Stepfely Tjipto Leksono Yovanie, Faira