Analisis food waste dengan pendekatan algoritma k-means clustering (Studi kasus di kantin UPH) = Analisis food waste dengan pendekatan algoritma K- means clustering (studi kasus di kantin uph)

Viyanto, Billy (2020) Analisis food waste dengan pendekatan algoritma k-means clustering (Studi kasus di kantin UPH) = Analisis food waste dengan pendekatan algoritma K- means clustering (studi kasus di kantin uph). Bachelor thesis, Universitas Pelita Harapan.

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Abstract

Food waste is all food that are not consumed or thrown away even though it is fit for human comsumption. Food waste is one of the most recent global issue that is getting more attention nowadays. There are more countries all over the world that are trying to solve their food waste issue. Food waste can affect environment by increasing the consumption of energy and materials while increasing the number of waste so that it might cause social problems as well. Even though food waste has became a global issue, it still doesn't get much attention in everyday life. To better understand the issue of food waste, there is a need of a closer example in people's life. If people know what is food waste and how much it affect their environment, it is hoped that there will be a conscience to reduce food waste. This study was designed to find out what type of food waste and how much food waste are being produced. The place for this study is University of Pelita Harapan cafetaria. Data collection was done by weighing the post-customer food waste that are being produced there. Food waste there are divided into 4 category which are : rice, vegetables, meats, and other product. Another data that are collected are done using questionnare, which is being used to gather reason on why people produce food waste. All the data from collection are then processed with a statistic software to find its descriptive statistic and to do k-means clustering.From k-means clustering with 4 cluster, we got 4 different clusters that have their own pattern of producing food waste. The result of the study can be used to plan how to handle food waste at cafetaria and how to reduce food waste by learning the pattern of food waste and its reason. / Food waste adalah makanan apapun yang tidak dimakan ataupun dibuang meskipun masih layak untuk dikonsumsi oleh manusia. Food waste adalah salah satu isu global yang tergolong baru dan akhir-akhir ini mendapat lebih banyak perhatian. Semakin banyak negara di dunia yang mencoba menyelesaikan masalah food waste di negaranya masing-masing. Food waste memengaruhi lingkungan dengan meningkatkan konsumsi energi dan penggunaan sumber daya serta kuantitas limbah hingga dapat menyebabkan masalah sosial..Walaupun isu food waste semakin sering dibahas dalam tingkat global, namun isu ini masih kurang diperhatikan dalam kehidupan sehari-hari. Untuk dapat mengerti masalah dan dampak food waste, perlu adanya contoh yang lebih nyata(konkret) dan dapat dimengerti banyak orang. Jika orang tahu dan mengerti akan apa itu food waste dan tahu seberapa banyak food waste di lingkungan mereka, diharapkan akan ada kesadaran untuk mengurangi food waste. Penelitian dirancang untuk mencari jenis food waste apa yang sering dihasilkan dan seberapa banyak food waste yang dihasilkan. Tempat di lingkungan sekitar yang dipilih untuk pengambilan data adalah kantin UPH. Pengumpulan data dilakukan dengan menimbang food waste yang ada di kantin UPH. Food waste yang ada dibagi menjadi 4 kategori yaitu : nasi, sayur, daging, dan lain-lain. Data lain yang dikumpulkan adalah kuesioner yang digunakan untuk pembanding hasil penelitian dan juga untuk mengetahui alasan orang menyisakan makanan. Data yang didapatkan kemudian diolah dengan software statistik untuk mendapatkan statistik deskriptif dan k-means clustering. Dari hasil k-means clustering dengan pembagian 4 cluster, didapatkan cluster-cluster data yang memiliki pola masing-masing. Hasil penelitian dapat digunakan untuk merencanakan cara menangani dan mengurangi food waste di kantin UPH dengan melihat pola food waste dan alasan orang menyisakan makanan.

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