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All Journal JUTI: Jurnal Ilmiah Teknologi Informasi JOURNAL OF INFORMATION SYSTEM MANAGEMENT (JOISM)
Maria Mediatrix Sebatubun
STMIK AKAKOM Yogyakarta
Computer Science & IT Dentistry Education Library & Information Science

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KLASIFIKASI VARIETAS KOPI BERDASARKAN GREEN BEAN COFFEE MENGGUNAKAN METODE MACHINE LEARNING M. Agung Nugroho; Maria Mediatrix Sebatubun
Journal of Information System Management (JOISM) Vol. 1 No. 2 (2020): Januari
Publisher : Universitas Amikom Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (232.571 KB) | DOI: 10.24076/joism.2020v1i2.24

Abstract

Kopi memiliki jenis yang terdiri dari kopi robusta dan kopi arabika. Kopi arabika memiliki beragam varietas berdasarkan daerah asal atau dikenal dengan nama single origin. Petani dan coffee expert membedakan varietas kopi arabika melalui bentuk dan karakter dari green bean atau kopi mentah. Perbedaan ciri ini meliputi perbedaan warna, bentuk, atau tekstur. Setiap varietas kopi memiliki harga yang berbeda-beda tergantung dari jenis varietasnya. Walaupun demikian, tidak semua petani, dan pemilik coffee shop mampu mengenali varietas kopi dengan hanya melihat green bean. Sehingga, bisa terjadi kesalahan dalam mengenali varietas kopi jika pemilik coffee shop tidak mengetahui pengetahuan tentang kopi. Hal ini bisa disolusikan dengan pemodelan yang dapat mengidentifikasi varietas kopi arabika agar dapat digunakan sebagai second opinion untuk mengindentifikasi varietas kopi arabika. Salah satu metode yang digunakan adalah metode pencitraan. Penelitian ini dimulai pada tahap pra-proses yaitu cropping citra, tahapan selanjutnya adalah melakukan proses klasifikasi menggunakan metode Machine Learning. Metode ini dapat melakukan ekstraksi fitur secara otomatis sehingga tahap pengenalan varietas kopi dapat dilakukan dengan lebih cepat dan diharapkan mampu memberikan tingkat akurasi yang tinggi.
IMPLEMENTATION OF MULTILAYER PERCEPTRON FOR STUDENT FAILURE PREDICTION Cosmas Haryawan; Maria Mediatrix Sebatubun
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 18, No. 2, July 2020
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v18i2.a990

Abstract

University is one of the educational institutions and can be established by the government or the individual. At this time, Indonesia has hundreds of universities spread throughout the region. As an educational institution, university of course must be able to educate its students and issue quality graduates with the academically and non-academically qualified. In its implementation, there are many problems that should be resolved as well as possible, such as when there are students who intentionally stop or disappear before completing their education or are even unable to complete their education and issued by institution (dropout).Based on these problems, this research makes a model for predicting students who have the potential to fail or dropout during their studies using one of the data mining methods namely Multilayer Perceptron by referring to personal and academic data. The results obtained from this research are 86.9% an accuracy rate with the 54.7% sensitivity, and 95.4% specificity. This research is expected to be used to determine the need strategies to minimize the number of students who stop or dropout.
Co-Authors Cosmas Haryawan Nugroho, Muhammad Agung