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Journal : Building of Informatics, Technology and Science

Penerapan Algoritma K-Means Clustering untuk Daerah Penyebaran Sampah Kelurahan Yantria Gusta Nugraha; Maimunah Maimunah; Pristi Sukmasetya
Building of Informatics, Technology and Science (BITS) Vol 5 No 2 (2023): September 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i2.4158

Abstract

Waste in Indonesia, especially in Magelang City, has become a serious problem due to rapid population growth. Waste management issues, including landfills and collection, need effective handling. Data mining methods, such as K-Means clustering, can help identify areas with the highest levels of waste generation. This approach provides insights for the development of a more focused and efficient waste management strategy, a significant contribution to the improvement of Magelang City. By identifying the areas with the highest waste generation, waste management measures can be directed more efficiently and effectively. This includes increasing the transparency, capacity, and role of waste banks, as well as other efforts to reduce the negative impact of waste on the environment and human health. After clustering, the waste in Magelang City was grouped into 3 clusters according to the supplier area and the volume of waste. Then after the evaluation stage with the silhouette score displays a value of 0.79 which is a good value because it is close to the value of 1.0. With this method, it is expected that the city government in handling waste in Magelang city can be done optimally, efficiently, and on target
Pengaruh Data Preprocessing terhadap Imbalanced Dataset pada Klasifikasi Citra Sampah menggunakan Algoritma Convolutional Neural Network Resa Arif Yudianto, Muhammad; Sukmasetya, Pristi; Abul Hasani, Rofi; Sasongko, Dimas
Building of Informatics, Technology and Science (BITS) Vol 4 No 3 (2022): December 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i3.2575

Abstract

Garbage is one of Indonesia's most significant problems with an increase in waste each year reaching 187.2 million tonnes/year. Various efforts to reduce the amount of waste such as Garbage Banks have been encouraged. However, this program has not run well, because some people have difficulty distinguishing the type of waste. One solution to overcome this problem is that need a system that can classify the type of waste. The deep learning approach with the CNN algorithm is currently widely used to solve classification problems. This method requires a large number of datasets to increase the level of accuracy. Getting a garbage dataset is a particular problem in the training process because the dataset is unbalanced. The dataset used amounted to 2527 data consisting of 6 classes. Several treatments such as undersampling and image augmentation are applied to overcome imbalanced datasets. Other treatments such as the type of input image channel and the use of filters are combined into 24 experimental scenarios to achieve the highest accuracy. The results of the experiment get the best scenario, namely, the dataset is undersampling and then augmented with 5 geometric transformation parameters with the input image being RGB and applying a sharpening filter to get an accuracy value of 0.9919 with 20 epochs.
Co-Authors Abul Hasani, Rofi Aditya Prasetyawan Afidah, Inayatun Najihatul Afif Prasetyo Agung Vinia Rahma Agus Setiawan Ahmad Husen Ardiyansah Ajarwiro, Cweto Bolodiko Aji Purwoko Aji, Ridho Catur Novi Alan Kusuma Aliudin, Habib Said Almas Nurfarid Budi Prasetyo Almira, Venia Alvine Candra Amelia Anggraini Annisa Annisa Shabilla Arazka Firdaus Anavyanto Ardhin Primadewi Arham Rahim Arrojak, Muhamad Yusril Arthalia Wulandari, Ika Arya Prayoga Asmanto, Budi Athoetan, Salma Atmaja, Audi Ilham Auzi Asfarian Avian Ali Mas'ud Basunondro, Wibiartono Bayu Agustian Budi Setyo Wulan Cahya Sonny Surachman Catur Rahmawati Catur Rahmawati Catur Wulandari Dana I. Sensuse Danu Rendra Krisna Meganatara Devi Oktaviani Dimas Sasongko Dwihantoro, Prihatin Dwika Oktavian Eko Muh Widodo Elin Cahyaningsih Emilya Ully Artha Endah Ratna Arumi Endah Ratna Arumi, Endah Ratna Erzi Hidayat Fadhlillah Jatmiko Utomo Famila Zidda Febriyanto, Yusril Firdaus Anavyanto, Arazka Haryanto, Taufiq Hasani, Rofi Abul Hasbi Hutauruk1, Dzakiyyah Hasna Nur Arifaini Heni Apriyani Herlin Lutfiannisa, Alifia Hery Setiawan Hidayat, Chandra Nur Hidayat, Erzi Ika Arthalia Ika Arthalia Wulandari Imam Saputra Iqbal Ridwan Darmawan Jihan Nuariputri Laela Dian Angraeni Lusi Nurlatifah Maimunah Maimunah Maimunah Maimunah, Maimunah Maulida, R.Bima Gofiruli Muhammad Hafizh Hamdanuddinsyah Muhammad Resa Arif Yudianto Muhammad Riyan Andriyanto Muhammad Riyan Andriyanto Mujito Mukhtar Hanafi Muliasari Muliasari Nafiah, Anisatun Nawangsari, Rosiska Syekhrum Noris Mohd Norowi Nurrohman, Muhamad Nuryanto Nuryanto Pangestiaji, Yongki Pradana Putra Utomo Purwono Hendradi Purwono Hendradi Putri Winly Apriliani Rahendra Firman Sunartama Ramadhan, Dean Apriana Rayinda Faizah Resa Arif Yudianto Resa Arif Yudianto Resa Arif Yudianto, Muhammad Resa Arif Yudiyanto Reza Pradana Adiharsa Rochiyanto, Andi Rofi Abul Hasani Sadewi, Fungki Ayu Safira Ayu Muthi'ah Salam, Muhammad Ifsyaus Salsadila Arsuliyanti Sandi Satria Alamsyah Sari, Nur Ita Sari, Wina Permana Satrio Satrio Sensuse, Dana I. Setiawan, Agus Setiya Nugroho Slamet Hidayat Sophia Ikhsanti Sugiarto, Bagus Susanti, Dwi Tito Rizki Purnomo Tri Wahyuni Uky Yudatama Uky Yudatama, Uky Utuh Setyaning Janji Wachiddin M Huda Wahyu Santoso Wina Permana Sari Wisnu Nugroho Wulandari, Ika Arthalia Yantria Gusta Nugraha Yudianto, Resa Arif Yusril Febriyanto Yusril Febriyanto Zahwa Dwi Larasati