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All Journal JUTI: Jurnal Ilmiah Teknologi Informasi Jurnal Teknologi Informasi dan Ilmu Komputer Informasi interaktif : jurnal informatika dan teknologi informasi
Sebatubun, Maria Mediatrix
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Computer Science & IT Dentistry Education Engineering Library & Information Science

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Implementasi Algoritma Convolutional Neural Network untuk Klasifikasi Jenis Keris Sebatubun, Maria Mediatrix; Haryawan, Cosmas
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 11 No 3: Juni 2024
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.937260

Abstract

UNESCO telah menetapkan keris Indonesia sebagai Masterpiece of The Oral and Intangible Heritage of Humanity. Keris memiliki bilah yang terdiri dari pamor, dhapur, dan Tangguh yang merupakan istilah yang digunakan untuk menyebut nama bentuk dari bilah keris. Dhapur keris ada yang berbentuk lurus dan lengkok (Luk dalam bahasa jawa). Yang berbentuk luk, jumlahnya bermacam-macam, mulai dari luk 3 (tiga) sampai luk 29 (dua puluh Sembilan). Jenis keris berdasarkan dapur yang diakui secara baku sekitar 150 jenis. Namun, bentuk dhapur keris tidak mudah dikenali secara langsung. Selain karena jenisnya yang banyak, bentuk dhapur ini terkadang memiliki karakteristik yang mirip meskipun jenisnya berbeda. Hal ini menyebabkan tidak semua orang dapat mengenali keris dengan mudah. Penelitian ini akan mengimplementasikan metode deep learning dengan algoritma Convolutional Neural Network (CNN) yang dapat melakukan tugas klasifikasi secara langsung pada citra, untuk membangun sebuah model untuk klasifikasi jenis keris berdasarkan dhapur. Data yang digunakan adalah citra keris jawa yang diambil secara manual dan maupun dari buku. Data citra terdiri dari 67 citra keris yang terdiri dari dua kelas yaitu 19 keris Parung Sari dan 48 keris Tilam Upih. Akurasi proses training sebesar 75% dan nilai validasi sebesar 66,67%.
IMPLEMENTASI METODE ELBOW DAN K-MEANS CLUSTERING UNTUK MENGETAHUI KAPASITAS SOLAR PANEL Sebatubun, Maria Mediatrix; Prayitna, Adiyuda
Informasi Interaktif : Jurnal Informatika dan Teknologi Informasi Vol 8 No 2 (2023): JII Volume 8, Number 2, Mei 2023
Publisher : Program Studi Informatika Fakultas Teknik Universitas Janabadra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37159/jii.v8i2.23

Abstract

According to electricity growth data, electricity consumption in Indonesia continues to increase. In 2015 electricity consumption was 910 kilowatt hours (kWh)/capita, then increased to 1,084 kWh/capita in 2019. Currently, Indonesia still relies on thermal power plants whose main energy sources come from fuel oil, gas and coal. With increasing electricity consumption, it is necessary to increase generating capacity. Indonesia has the potential to develop new and renewable energy, because Indonesia is located in the tropics where the sun shines almost all year round. This study aims to determine how much power capacity is obtained by solar panels, so it can meet the demand when it is cloudy and raining for a relatively long time. The data used is data collected for 2 days with an interval of collection every 15 seconds. The research was carried out by preprocessing, then looking for k cluster values using the Elbow method, after that the clustering stage used the k-means method. The results showed that the largest capacity obtained by solar panels was around 60 Watts, while the smallest capacity was around 17 Watts.
IMPLEMENTATION OF MULTILAYER PERCEPTRON FOR STUDENT FAILURE PREDICTION Haryawan, Cosmas; Sebatubun, Maria Mediatrix
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.
Ekstraksi Fitur Circularity untuk Pengenalan Varietas Kopi Arabika Sebatubun, Maria Mediatrix; Nugroho, Muhammad Agung
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 4 No 4: Desember 2017
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (863.229 KB) | DOI: 10.25126/jtiik.201744505

Abstract

AbstrakKopi merupakan salah satu minuman yang sangat populer di dunia dan digemari oleh banyak orang termasuk di Indonesia. Kopi terdiri dari berbagai varietas, salah satunya adalah varietas arabika. Varietas kopi dapat memiliki kenampakan yang berbeda – beda misalnya seperti perbedaan warna, bentuk, ataupun tekstur. Oleh karena itu, terkadang petani ataupun pemilik coffee shop dapat melakukan kesalahan dalam mengenali varietas kopi arabika yang dijual ataupun yang dibeli. Hal ini juga akan mempengaruhi penentuan harga kopi tersebut, karena masing-masing varietas kopi arabika memiliki harga yang berbeda-beda. Untuk itu, diperlukan sistem yang juga mampu mengenali varietas kopi arabika secara akurat sehingga dapat digunakan sebagai second opinion bagi para petani ataupun pemilik coffee shop dalam mengenali varietas kopi arabika. Salah satu cara yang dapat dilakukan adalah dengan metode pencitraan. Tahap awal yang dilakukan adalah praproses yaitu cropping citra yang dilakukan secara manual, kemudian segmentasi menggunakan metode Otsu. Tahap selanjutnya adalah ekstraksi fitur bentuk menggunakan circularity dan klasifikasi menggunakan MultiLayer Perceptron. Hasil klasifikasi menunjukkan tingkat akurasi yang diperoleh sebesar 80%, sensitivitas 83,33% dan spesifisitas 76,7%.Kata kunci: ekstraksi fitur, kopi, klasifikasi, segmentasiAbstractCoffee is one of the most popular beverages in the world  and is favored by many people including Indonesians. Coffee consists of many variety, one of them is arabica. Coffee variety can have different features such as differences in color, shape, or texture. Therefore, sometimes farmers of coffee shop owners can make mistakes in recognizing the variety of arabica coffee that are sold or purchased. This will also affect the determination of coffee’s price, because each variety of arabica coffee have different prices. Hence, a capable system which can recognize arabica coffee accurately is required to be used as a second opinion for farmers or coffee shop owners in recognizing these variety. One of the methods that can be done is imaging. The initial stage is pre-processing by cropping the images manually, followed by segmentation using Otsu method. The next stage is shape based feature extraction using circularity and the last is classification using MultiLayer Perceptron. Classification results show 80% level of accuracy, 83.33% sensitivity, and 76.7% specificity.Keywords: feature extraction, coffee, classification, segmentation
Co-Authors Haryawan, Cosmas Nugroho, Muhammad Agung Prayitna, Adiyuda