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All Journal Creative Information Technology Journal SISFOTENIKA JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Jurnal Eksplora Informatika Jurnal Teknimedia: Teknologi Informasi dan Multimedia Jurnal Ilmiah IT CIDA : Diseminasi Teknologi Informasi
Emha Taufiq Luthfi
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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Algoritma K-Means pada Pengelompokan Pembelajaran Tatap Muka Terbatas Sesudah Vaksinasi COVID-19 Lili Kartikawati; Kusrini Kusrini; Emha Taufiq Luthfi
Jurnal Eksplora Informatika Vol 11 No 1 (2021): Jurnal Eksplora Informatika
Publisher : Bagian Perpustakaan dan Publikasi Ilmiah - Institut Teknologi dan Bisnis STIKOM Bali

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30864/eksplora.v11i1.560

Abstract

Pandemi COVID-19 menimbulkan potensi dampak sosial yang kurang baik, peranan sekolah dalam proses belajar mengajar tidak terlihat. Akses dan kualitas pembelajaran jarak jauh berbanding lurus dengan capaian belajar peserta didik. Capaian hasil belajar peserta didik menunjukkan tingkat kualitas pembelajaran yang telah dilakukan. Peserta didik yang terkendala dalam mencapai hasil belajar maka dibutuhkan langkah perbaikan proses belajar di antaranya melalui pembelajaran tatap muka terbatas. Pembelajaran tatap muka terbatas wajib menaati prosedur kesehatan, di antaranya pembatasan jumlah maksimal peserta didik dalam satu Rombel. Pengelompokan peserta didik dipilih sebagai solusi pembatasan jumlah peserta didik dalam Rombel. Metode eksperimen penelitian ini diterapkan pada hasil capaian belajar selama tengah semester genap 2020/2021 peserta didik X Usaha Perjalanan Wisata 1 di SMK Negeri 4 Yogyakarta. Pengelompokan dan pembuktian kualitas pengelompokan algoritma K-Means menggunakan metode silhouette coefficient serta rumus euclidean distance untuk mengukur jarak di antara dokumennya. Hal yang membedakan dengan penelitian clustering sebelumnya di antaranya dilakukan pengukuran kualitas peng-cluster-an dan melakukan penggalian lebih dalam pada pola-pola baru hasil peng-cluster-an yang terbentuk sehingga didapatkan pengetahuan baru. Kualitas clustering menghasilkan nilai Si=0,8313 yang berarti memiliki karakteristik struktur kuat dan menampilkan pengetahuan baru berupa kelompok rumpun mata pelajaran yang menginformasikan tingkatan daya serap peserta didik terhadap mata pelajaran.
AUGMENTASI DATA MENGGUNAKAN DCGAN PADA GAMBAR TANAH PATMAWATI, PATMAWATI; Andi Sunyoto; Emha Taufiq Luthfi
TEKNIMEDIA: Teknologi Informasi dan Multimedia Vol. 4 No. 1 (2023): Juni 2023
Publisher : Badan Penelitian dan Pengabdian Masyarakat (BP2M) STMIK Syaikh Zainuddin NW Anjani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46764/teknimedia.v4i1.100

Abstract

Several studies related to soil type classification have been conducted. However, each of these studies uses different datasets. Only a small number of researchers share soil image datasets publicly. In addition, published datasets have an imbalance in the amount of data in each class which will result in poor model performance or over fit, especially deep learning. With data augmentation, new data variations can be formed that can handle the limited number of datasets. One of the modern augmentation models is DCGAN which is an extension of GAN. DCGAN is considered a good model in improving the stability of GAN training and the quality of image results. The resulting synthesized image is the result of mapping randomized latent vectors in an n-dimensional latent space. A meaningful image transformation is generated from the latent vector through arithmetic operations in the latent space dimension. The size of the latent space dimension is very important in enabling accurate reconstruction of the training data. To test the effect of latent space dimension on images, a quantitative evaluation using Fre'chet Inception Distance (FID) is used. The following results were obtained, for the best image quality in the alluvial soil category using latent space dimension 10 with FID score = 322.0. For clay soil category, the best image quality is generated using latent space dimension 100 with score FID = 332.84 and 512 with score FID = 322.08. In the black soil category, the best latent space dimension is 128 with FID score = 360.80. And for red soil, the best image quality is generated with the use of 512 latent spaces that have a FID score = 256.67.
Co-Authors Abdul Rokhim Andi Sunyoto Arief Setyanto Astika Wulansari Bambang Soedijono Wiraatmadja Christin Nandari Dengen Elik Hari Muktafin Krisnawati Krisnawati Kusrini Kusrini Lili Kartikawati M Suyanto Patmawati Patmawati, Patmawati Pendi Ventri Hendika Ripto Sudiyarno Siti Alvi Sholikhatin Siti Nurhayati Supriatin Supriatin