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All Journal JOIV : International Journal on Informatics Visualization Media Jurnal Informatika
Sidik Ramdani, Cecep Muhamad
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Computer Science & IT

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Klasifikasi Penyakit Pulpitis Pada Citra Radiografi Periapikal Menggunakan Metode Convolutional Neural Network (CNN) Lavenia, Febby; Sidik Ramdani, Cecep Muhamad; Hoeronis, Irani
Media Jurnal Informatika Vol 16, No 1 (2024): Media Jurnal Informatika
Publisher : Teknik Informatika Universitas Suryakancana Cianjur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35194/mji.v16i1.4098

Abstract

Gigi adalah bagian penting dari tubuh manusia. Kurangnya perawatan gigi dapat menyebabkan berbagai penyakit gigi, salah satunya pulpitis. Pulpitis adalah peradangan pada pulpa gigi (bagian terdalam gigi yang berisi saraf dan pembuluh darah) dan jaringan di sekitar akar gigi. Penyakit ini juga bisa disebabkan oleh sakit gigi atau gigi tanggal, terutama pada orang muda. Untuk mendiagnosis pulpitis, dokter gigi menggunakan teknik radiografi periapikal. Teknik ini memberikan gambar jelas dari seluruh lapisan gigi, memungkinkan diagnosis kondisi gigi dan jaringan sekitarnya. Namun, hasil radiografi ini hanya dapat diinterpretasikan oleh dokter spesialis radiologi gigi, yang jumlahnya terbatas. Oleh karena itu, untuk memudahkan mendeteksi penyakit pulpitis, digunakan klasifikasi dengan teknik pengolahan citra (image processing) untuk membantu dokter dalam mengklasifikasikan penyakit pulpitis berdasarkan citra radiografi. Penelitian ini menggunakan metode Convolutional Neural Network (CNN) untuk mengklasifikasikan penyakit pulpitis berdasarkan citra radiografi. CNN adalah variasi dari Multi Layer Perceptron (MLP) yang memiliki sedikit parameter bebas karena tidak memerlukan pra-pemrosesan, segmentasi, atau ekstraksi fitur. Penelitian ini menggunakan 1000 data citra yang dibagi menjadi dua kelas: pulpitis dan normal. Hasil pengujian menunjukkan bahwa hyperparameter seperti nilai epoch dan optimizer sangat mempengaruhi akurasi. Akurasi tertinggi yang dicapai adalah 98,75% dengan menggunakan optimizer RMSPROP dan nilai epoch 50. Penelitian ini menunjukkan bahwa penggunaan Convolutional Neural Network (CNN) dapat membantu dokter gigi dalam mendiagnosis penyakit pulpitis. Sistem ini dapat digunakan untuk mempermudah dan membantu dokter dalam menentukan diagnosis pulpitis berdasarkan citra radiografi.
Implementation of Ensemble Machine Learning Classifier and Synthetic Minority Oversampling Technique for Sentiment Analysis of Sustainable Development Goals in Indonesia Gufroni, Acep Irham; Hoeronis, Irani; Fajar, Nur; Rachman, Andi Nur; Sidik Ramdani, Cecep Muhamad; Sulastri, Heni
JOIV : International Journal on Informatics Visualization Vol 8, No 2 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.2.1949

Abstract

As part of the Sustainable Development Goals (SDGs), governments worldwide have committed to improving people's lives to improve the quality of life for all, including the 17 such goals that were agreed upon in 2015 to benefit the human race as a whole. It would be interesting to see how society responds to the SDGs after approximately half of them have been achieved. This public response was analyzed in terms of sentiment. Within the total number of internet users in Indonesia, there are 18.45 million Twitter users. The platform enables anyone to write about anything they are experiencing in their lives, such as what is happening in their environment, what is happening in their education system, what is happening in the food industry, how people feel, and many more. The platform enables anyone to write about anything they are experiencing in their lives, such as what is happening in their environment, what is happening in their education system, what is happening in the food industry, how people feel, and many more. To model the data collected, the researchers used Ensemble Machine Learning Classifiers (EMLC) to model the data by using a machine learning classifier that uses machine learning techniques. The best model in this study is EMLC-Stacking with a data splitting of 80:20 and using SMOTE, which obtains an accuracy of 91%. This accuracy results from a 5% increase compared to when not using SMOTE. From 15,698 tweets, this research found that 47% were positive sentiments, 28% were negative sentiments, and 25% were neutral sentiments. The results that we measured offer hope that there will be a positive trend in the journey of the SDGs until 2030 if these findings are true.
Co-Authors Acep Irham Gufroni Irani Hoeronis Lavenia, Febby Nur Fajar Rachman, Andi Nur Sulastri, Heni