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All Journal Jurnal Teknoin Jurnal Teknik Kimia USU Jurnal Simetris Telematika : Jurnal Informatika dan Teknologi Informasi Dharmakarya : Jurnal Aplikasi Ipteks Untuk Masyarakat Jurnal Informatika dan Teknik Elektro Terapan Journal of Electrical Technology UMY Jurnal Gizi Klinik Indonesia Communications in Science and Technology Journal of Appropriate Technology for Community Services Prosiding Seminar Nasional Multidisiplin Ilmu Jurnal Pengabdian Dharma Bakti Respati Charity : Jurnal Pengabdian Masyarakat Journal mechanical and manufacture technology (JMMT) Jurnal Teknik Kimia USU ETAM: Jurnal Pengabdian kepada Masyarakat
Listyalina, Latifah
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Journal : Telematika : Jurnal Informatika dan Teknologi Informasi

 1 
The Application of Artificial Intelligence in Waste Classification as an Effort In Plastic Waste Management Listyalina, Latifah; Utami, Ratri Retno; Arifin, Uma Fadzilia; Putri, Naimah
Telematika Vol 21, No 1 (2024): Edisi Februari 2024
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v21i1.11977

Abstract

Purpose: Sorting waste before it is deposited in the Final Disposal Site (TPA) is crucial to reduce the increasing amount of waste accumulation each year. This issue can be addressed by implementing machines capable of automatically sorting waste.Design/methodology/approach: This research is quantitative and utilizes secondary data, namely image data of various types of waste. The images will be classified into organic and inorganic waste using a deep learning model. The measurement conducted involves assessing the accuracy of the designed deep learning model in classifying waste images into appropriate categories.Fondings/results: Based on the available dataset, waste identification will be performed, including food waste, paper, wood, leaves, electronic waste, metal, plastic, and bottles. The overall accuracy of the model is 94.42%, indicating that the model correctly classifies 94.42% of waste samples.Originality/value/state of the art: This research can classify 8 types of waste classes successfully using deep learning.
Identifying Types of Waste as Efforts in Plastic Waste Management Based on Deep Learning Buyung, Irawadi; Munir, Agus Qomaruddin; Wijaya, Nurhadi; Listyalina, Latifah
Telematika Vol 20, No 3 (2023): Edisi Oktober 2023
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v20i3.10804

Abstract

Purpose: This research aims at designing a computer algorithm for automatic waste sorting.Design/methodology/apprach: This research is quantitative and uses secondary data, specifically images of various types of waste. The images will be classified into organic and inorganic waste types with the assistance of a deep learning model. In this research, we propose the EfficientNet method for Waste Type Identification as an Effort in Plastic Waste Management. Experiments were conducted on a secondary dataset from Kaggle.com, which involved classifying various types of waste into 'Plastic' and 'Non-Plastic' categories, showing the effectiveness of the proposed method.Findings/result: The measurement is performed to compute the accuracy of the designed deep learning model in classifying waste images into the appropriate waste types. Based on the research results, our system achieved the highest accuracy of 97% during testing.Originality/value/state of the art: The designed method can perform fast and automatic waste sorting, which is useful in reducing the increasing amount of waste accumulating each year. 
The Application of Artificial Intelligence in Waste Classification as an Effort In Plastic Waste Management Listyalina, Latifah; Utami, Ratri Retno; Arifin, Uma Fadzilia; Putri, Naimah
Telematika Vol 21 No 1 (2024): Telematika : Jurnal Informatika dan Teknologi Informasi
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v21i1.11977

Abstract

Purpose: Sorting waste before it is deposited in the Final Disposal Site (TPA) is crucial to reduce the increasing amount of waste accumulation each year. This issue can be addressed by implementing machines capable of automatically sorting waste.Design/methodology/approach: This research is quantitative and utilizes secondary data, namely image data of various types of waste. The images will be classified into organic and inorganic waste using a deep learning model. The measurement conducted involves assessing the accuracy of the designed deep learning model in classifying waste images into appropriate categories.Fondings/results: Based on the available dataset, waste identification will be performed, including food waste, paper, wood, leaves, electronic waste, metal, plastic, and bottles. The overall accuracy of the model is 94.42%, indicating that the model correctly classifies 94.42% of waste samples.Originality/value/state of the art: This research can classify 8 types of waste classes successfully using deep learning.
Rubber Leaf Image Classification Using Artificial Intelligence Methods as an Effort to Improve Plantation Production Results Buyung, Irawadi; Utari, Evrita Lusiana; Mustiadi, Ikhwan; Winardi, Sugeng; Ariyanto, Ipan; Listyalina, Latifah
Telematika Vol 21 No 3 (2024): Edisi Oktober 2024
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v21i2.13587

Abstract

Purpose: Rubber is one of the plantation commodities that contributes positively to the trade surplus in the agricultural sector. Seeing the positive trend in global rubber consumption and production, demand is expected to continue increasing in the future. To enhance rubber productivity, rubber processing technology can be used to make it more efficient, thus increasing the amount of latex extracted from the sap and reducing waste materialDesign/methodology/approach: One technology that can be developed to increase the productivity efficiency of rubber plants is by using Artificial Intelligence. This technology is expected to be implemented in the rubber plantation sector, specifically in the automatic recognition of rubber leaves.Findings/result: The measurement and performance analysis of the rubber leaf image classification algorithm based on Artificial Intelligence has also been evaluated, showing near-perfect accuracy on training data (99.86%) and very good performance on validation data (97.43%), with a very low validation loss (0.0873), indicating that the model has learned well by the last epochOriginality/value/state of the art: The population in this study consists of image data from various tree leaves, including 10 types of rubber leaves and non-rubber leaves 
Identifying Types of Waste as Efforts in Plastic Waste Management Based on Deep Learning Buyung, Irawadi; Munir, Agus Qomaruddin; Wijaya, Nurhadi; Listyalina, Latifah
Telematika Vol 20 No 3 (2023): Edisi Oktober 2023
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v20i3.10804

Abstract

Purpose: This research aims at designing a computer algorithm for automatic waste sorting.Design/methodology/apprach: This research is quantitative and uses secondary data, specifically images of various types of waste. The images will be classified into organic and inorganic waste types with the assistance of a deep learning model. In this research, we propose the EfficientNet method for Waste Type Identification as an Effort in Plastic Waste Management. Experiments were conducted on a secondary dataset from Kaggle.com, which involved classifying various types of waste into 'Plastic' and 'Non-Plastic' categories, showing the effectiveness of the proposed method.Findings/result: The measurement is performed to compute the accuracy of the designed deep learning model in classifying waste images into the appropriate waste types. Based on the research results, our system achieved the highest accuracy of 97% during testing.Originality/value/state of the art: The designed method can perform fast and automatic waste sorting, which is useful in reducing the increasing amount of waste accumulating each year. 
Co-Authors Adetya, Nais Pinta Agus Qomaruddin Munir Andri Saputra Andri Saputra Ariyanto, Ipan Bagus Seno Wulung, Raden Desty Ervira Puspaningtyas Dharmawan, Dhimas Arief Dwi Wibowo, Midarto Eko Susilo Ellianto, Mario Sariski Dwi Evrita Lusiana Utari Evrita Lusiana Utari Evrita Lusiana Utari, Evrita Lusiana Hanung Adi Nugroho Ikhwan Mustiadi Irawadi Buyung, Irawadi Mario Sarisky Dwi Ellianto Mario Sarisky Dwi Ellianto Muh. Wahyu Syabani Muhammad Ikhwan Novi Irawati Oktoeberza, Widhia KZ Pambudi, Wisnu Pani Satwikanitya Putrama, Fala Putri, Naimah Ratnaningsih, Wahyu Ratri Retno Utami RR. Ella Evrita Hestiandari Sabdullah, Mursid Sarisky Dwi Ellianto, Mario Satwikanitya, Pani Sugeng Winardi Suharyanto Sunu Wibirama Suwarno, Yuli Syahbani, Muhammad Wahyu Uma Fadzilia Arifin Uma Fadzilia Arifin, Uma Fadzilia Wahyu Ratnaningsih Wijaya, Nurhadi Wisnu Pambudi Yudianingsih Yudianingsih Yudianingsih, Yudianingsih