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Analisis dan Perancangan Kamus Interaktif Bahasa Isyarat Indonesia dengan Speech Recognition Ahmad Zuli Amrullah; Khurniawan Eko Saputro
Jurnal Bumigora Information Technology (BITe) Vol 1 No 2 (2019)
Publisher : Prodi Ilmu Komputer Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (283.641 KB) | DOI: 10.30812/bite.v1i2.604

Abstract

ABSTRAK Intisari – Menurut data Survei Sosial Ekonomi Nasional (Susenas) pada tahun 2012 terdapat sekitar 9,9 juta anak Indonesia menyandang disabilitas. Sekitar 7.87% dari total jumlah penyandang disabilitas tersebut mengalami tunarungu atau keterbatasan mendengar. Penyandang tunarungu melakukan komunikasi dengan menggunakan Bahasa isyarat. Karena tidak semua orang mengerti dengan bahasa isyarat maka dibutuhkan alat bantu atau aplikasi untuk berkomunikasi dengan penyandang tunarungu. Keterbatasan dalam berkomunikasi antara orang biasa dengan penyandang tunarungu. Oleh karena ity, untuk membantu mahasiswa dan dosen berkomunikasi dengan mahasiswa yang tunarung maka dibutuhkan aplikasi kamus Bahasa isyarat dengan Speech Recognition. Pengembangan aplikasi ini menggunakan metode pengembangan aplikasi waterfall. Dimana setiap alur berjalan secara selaras dan memudahkan untuk mencari kesalahan system. Pengujian dilakukan dengan verifikasi kebutuhan untuk memastikan produk perangkat lunak yang dihasilkan sesuai dengan spesifikasi yang ditentukan. Kata Kunci: Bahasa isyarat; kamus; speech recognition; ABSTRACT Digest - According to data from the National Socio-Economic Survey (Susenas) in 2012 there were around 9.9 million Indonesian children with disabilities. Around 7.87% of the total number of persons with disabilities experience hearing impairment or hearing impairment. People with hearing impairment communicate using sign language. Because not everyone understands sign language, tools or applications are needed to communicate with deaf people. Limitations in communicating between ordinary people and hearing impaired people. Therefore, to help students and lecturers communicate with students who are fussy, it requires a sign language dictionary application with Speech Recognition. This application development uses the waterfall application development method. Where each flow runs in harmony and makes it easy to find system errors. The test is carried out by verifying the need to ensure that the software product is produced according to the specified specifications. Keywords: Signal language; dictionary; speech recognition;
K-means-SMOTE untuk menangani ketidakseimbangan kelas dalam klasifikasi penyakit diabetes dengan C4.5, SVM, dan naive Bayes Hairani Hairani; Khurniawan Eko Saputro; Sofiansyah Fadli
Jurnal Teknologi dan Sistem Komputer Volume 8, Issue 2, Year 2020 (April 2020)
Publisher : Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (73.3 KB) | DOI: 10.14710/jtsiskom.8.2.2020.89-93

Abstract

The occurrence of imbalanced class in a dataset causes the classification results to tend to the class with the largest amount of data (majority class). A sampling method is needed to balance the minority class (positive class) so that the class distribution becomes balanced and leading to better classification results. This study was conducted to overcome imbalanced class problems on the Indian Pima diabetes illness dataset using k-means-SMOTE. The dataset has 268 instances of the positive class (minority class) and 500 instances of the negative class (majority class). The classification was done by comparing C4.5, SVM, and naïve Bayes while implementing k-means-SMOTE in data sampling. Using k-means-SMOTE, the SVM classification method has the highest accuracy and sensitivity of 82 % and 77 % respectively, while the naive Bayes method produces the highest specificity of 89 %.