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All Journal STORAGE: Jurnal Ilmiah Teknik dan Ilmu Komputer Clean Energy and Smart Technology
Erna Dwi Astuti
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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STUDI KOMPARASI ALGORITMA NAÏVE BAYES DAN K-NN UNTUK KLASIFIKASI PENERIMAAN BEASISWA DI MI AL – ISLAMIYAH KARANGSAWAH Muslim Hidayat; Afif Nazmi Fuadi; Dimas Prasetyo Utomo; Erna Dwi Astuti; Dian Asmarajati
STORAGE: Jurnal Ilmiah Teknik dan Ilmu Komputer Vol. 2 No. 4 (2023): November
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/storage.v2i4.2865

Abstract

Pemberian beasiswa dilakukan agar para siswa dapat tetap melanjutkan sekolah, dalam menyeleksi siswa parameter yang digunakan terdiri dari jarak, tanggungan, pekerjaan orag tua, pendapatan orang tua, kelengkapan keluarga dan kelayakan. Dikarenakan belum ada metode untuk menentukan penerima beasiswa maka sering salah sasaran dalam memberikan beasiswa. Oleh karena itu diperlukan klasifikasi penerima beasiswa yang tepat dan akurat. Salah satunya data mining dengan metode deskriptif analitis. Bisa dikatakan penelitian deskriptif analitis mengambil masalah atau memperhatikan masalah- masalah yang ada saat penelitian kemudian diolah untuk mendapatkan sebuah kesimpulan.Berdasarkan hasil analisis dan pembahasan studi komparasi algoritma naïve bayes dan K-NN untuk klasifikasi penerimaan beasiswa di MI AL-Islamiyah, dari 186 data siswa yang terdiri dari 150 data training dan 36 data testing diperoleh Hasil klasifikasi dengan Naïve Bayes dan K-Nearest Neighbor diperoleh masing-masing 91,67% dan 75,00%. Berdasarkan nilai akurasi yan diperoleh dari dua algoritma tersebut, maka akurasinya termasuk excellent classification., dan algoritma Naïve Bayes lebih baik dalam klasifikasi penerimaan beasiswa dibandingkan algoritma K-Nearest Neighbor.
IMAGE CLASSIFICATION RECOGNITION OF GAMELAN MUSICAL INSTRUMENT TYPES USING CNN METHOD ANDROID BASED Muhamad Mufid Bachri; Erna Dwi Astuti; Hidayatus Sibyan
Clean Energy and Smart Technology Vol. 2 No. 2 (2024): April
Publisher : Nacreva Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58641/cest.v2i2.81

Abstract

In the ever-evolving digital age, the use of digital images has become a significant and widespread phenomenon in various fields. Digital image processing and understanding has become an important requirement in various applications, including pattern recognition and computer vision. On the other hand, the sustainability and understanding of cultural treasures, such as Gamelan, is becoming increasingly crucial. UNESCO has recognized Gamelan as Indonesia's 12th World Intangible Cultural Heritage, reminding us of the responsibility to maintain and preserve this cultural heritage. In the digital era, where interest in traditional musical instruments is declining, Convolutional Neural Network (CNN) is implemented as a solution to classify Gamelan musical instrument types based on visual patterns in images. CNN, implemented in an Android system, showed good results with accuracy reaching 98% in the model test stage and 79% in the Android application test. The classification model using TensorFlow Lite, specifically MobilNetV2, was able to recognize Gamelan musical instrument types in the training dataset. However, it should be noted that this model is limited to that dataset. This research contributes to the merging of technology and cultural heritage, enabling the use of technology to enhance cultural understanding and sustainability.
Co-Authors Afif Nazmi Fuadi dian asmarajati Dimas Prasetyo Utomo Hidayatus Sibyan Muhamad Mufid Bachri Muslim Hidayat