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Listyalina, Latifah
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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 
PENENTUAN PENYAKIT PARU DENGAN MENGGUNAKAN JARINGAN SARAF TIRUAN Listyalina, Latifah; Utari, Evrita Lusiana; Puspaningtyas, Desty Ervira
Simetris: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer Vol 11, No 1 (2020): JURNAL SIMETRIS VOLUME 11 NO 1 TAHUN 2020
Publisher : Fakultas Teknik Universitas Muria Kudus

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24176/simet.v11i1.3667

Abstract

Organisasi Kesehatan Dunia (WHO) dan Organisasi Penanggulangan Kanker Dunia (UICC) memprediksikan terjadinya peningkatan kejadian kanker sebesar 300% pada tahun 2030. Salah satu jenis kanker ialah kanker paru. Deteksi kanker paru dapat dilakukan menggunakan rontgen. Diagnosis kanker paru dapat dilakukan melalui pengamatan hasil dari citra foto rontgen secara teliti. Otomasi dari hasil citra X-Ray dapat digunakan oleh praktisi kesehatan dalam pendeteksian kanker paru. Perancangan perangkat lunak jaringan saraf tiruan dari citra foto rontgen dilakukan melalui beberapa langkah yaitu pengolahan citra, seperti median filter, ekualisasi histogram adaptif, dan transformasi kosinus diskrit sebagai ekstraksi fitur citra yang selanjutnya dijadikan masukan jaringan saraf tiruan serta dengan didahului oleh proses pra pengolahan citra. Tingkat akurasi pendeteksian kanker paru melalui citra foto rontgen paru sebesar 72,97%.  
Co-Authors Adetya, Nais Pinta Agus Qomaruddin Munir Andri Saputra Ariyanto, Ipan Bagus Seno Wulung, Raden Desty Ervira Puspaningtyas Dharmawan, Dhimas Arief Dwi Wibowo, Midarto Eko Susilo Evrita Lusiana Utari Evrita Lusiana Utari Evrita Lusiana Utari, Evrita Lusiana Hanung Adi Nugroho Ikhwan Mustiadi Irawadi Buyung, Irawadi Mario Sarisky Dwi Ellianto Muh. Wahyu Syabani Novi Irawati Oktoeberza, Widhia KZ Pambudi, Wisnu Pani Satwikanitya Putri, Naimah Ratnaningsih, Wahyu Ratri Retno Utami RR. Ella Evrita Hestiandari Sabdullah, Mursid Sarisky Dwi Ellianto, Mario Satwikanitya, Pani Sugeng Winardi Sunu Wibirama Suwarno, Yuli Uma Fadzilia Arifin Uma Fadzilia Arifin, Uma Fadzilia Wahyu Ratnaningsih Wijaya, Nurhadi Wisnu Pambudi Yudianingsih Yudianingsih Yudianingsih, Yudianingsih