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All Journal Jurnal Teknik Elektro dan Komputer TRIAC 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 Energy 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.
Transfer Learning Analysis VGG16 For the Detection of Tuberculosis Erna Dwi Astuti; Muslim Hidayat
Jurnal Teknik Elektro dan Komputer TRIAC Vol 12, No 1 (2025): Mei 2025
Publisher : Jurusan Teknik Elektro Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/triac.v12i1.28672

Abstract

- Indonesia is still one of the countries with the highest growth of TB disease in the world. TB is an infectious disease that can cause severe lung damage, even death. TB is a critical case to be detected early so that patients immediately get the proper treatment. The challenge is the difficulty in diagnosing symptoms that are not specific and similar to other diseases. Therefore, further research is needed to find a faster, more accurate, affordable TB detection method. VGG16 is one of the Convolutional Neural Network (CNN) architectures that has the characteristic of recognizing delicate patterns of chest X-ray images of TB patients. Transfer learning on VGG16 can increase the accuracy of detecting TB disease even though it uses a small amount of training data. The trial results show that the VGG16 transfer learning technique can produce better performance with an accuracy of 94%. The accuracy value can be used to benchmark that the VGG16 transfer learning technique is proven effective in detecting TB disease
Co-Authors Afif Nazmi Fuadi dian asmarajati Dimas Prasetyo Utomo Hidayatus Sibyan Muhamad Mufid Bachri Muslim Hidayat