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Journal : Jurnal Informatika dan Teknik Elektro Terapan

PERANCANGAN SMART TRASH BIN MENGGUNAKAN LOGIKA FUZZY BERBASIS ARDUINO DI SDN 5 MAWASANGKA, BUTON TENGAH Nurjannah, Nurjannah; Muchtar, Mutmainnah; Sarimuddin, Sarimuddin; Sya'ban, Kharis; Karim, Rahmat; Al Jum'ah, Muhammad Na'im
Jurnal Informatika dan Teknik Elektro Terapan Vol 12, No 3 (2024)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v12i3.4358

Abstract

Smart Trash Bin is a technological innovation that integrates sensors and automation systems to enhance waste management efficiency. This study aims to design and implement a Smart Trash Bin using fuzzy logic based on Arduino at SDN 5 Mawasangka, Buton Tengah. In this research, the system utilizes ultrasonic sensors to detect the trash level inside the bin, servo motors to control the automatic lid of the trash bin, and DFPlayer Mini along with a speaker to provide audio notifications to users. Fuzzy logic method is employed to regulate the system's decisions in managing the trash bin operations based on environmental conditions. The study involves the stages of design, fabrication, and system testing in the elementary school environment. The test results indicate that the designed Smart Trash Bin can effectively manage waste with adequate accuracy. It is expected that the implementation of this Smart Trash Bin can help raise awareness of environmental cleanliness within the school and surrounding community
KLASIFIKASI TINGKAT KEMATANGAN CABAI MERAH KERITING MENGGUNAKAN SVM MULTICLASS BERDASARKAN EKSTRAKSI FITUR WARNA Irma, Irma; Muchtar, Mutmainnah; Adawiyah, Rabiah; Sarimuddin, Sarimuddin
Jurnal Informatika dan Teknik Elektro Terapan Vol 12, No 3 (2024)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v12i3.4430

Abstract

The utilization of digital image processing holds significant potential for classifying the ripeness of curly red peppers (Capsicum annuum L.). This study aims to develop an automatic classification method using multiclass Support Vector Machine (SVM) with a linear kernel. Images of peppers, captured using a smartphone camera, were categorized into three classes: ripe, unripe, and semi-ripe. Features such as mean, variance, and range from the RGB color space were extracted for training and testing data. Testing was conducted by dividing the data into training and test sets and employing 10-fold cross-validation. Results demonstrated a classification accuracy of 98.33%. The combination of mean, variance, and range features significantly improved accuracy compared to single features. This research demonstrates the effectiveness of the developed method and its applicability in automated classification systems to support the agricultural sector.