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All Journal REKAYASA Techno Nusa Mandiri : Journal of Computing and Information Technology JISICOM (Journal of Information System, Infomatics and Computing)
Yan Rianto
Universitas Nusa Mandiri
Agriculture, Biological Sciences & Forestry Computer Science & IT Electrical & Electronics Engineering Engineering Physics

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Optimization Of Recurrent Neural Network In Indonesia Stock Exchange Price Prediction Modeling Dwi Andriyanto; Yan Rianto
Journal of Information System, Informatics and Computing Vol 6 No 1 (2022): JISICOM: June 2022
Publisher : Sekolah Tinggi Manajemen Informatika dan Komputer Jayakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52362/jisicom.v6i1.744

Abstract

One of the online trades is stock trading on the stock exchange. To increase the number of investors, the government invites the public to invest in the capital market by buying shares regularly and periodically in the form of shares. With the increase in potential investors in the stock market, deep knowledge about stock trading is needed so that the returns are as desired. This research can help to make it easier for the public to predict the desired stock price through machine learning technology even though they do not have more in-depth technical knowledge about stocks. Three types of algorithms RNN, LSTM, GRU are used to find the best method by optimizing parameters so as to get an r-square error of 0.96 through the number of epochs of 100 and a learning rate of 0.01.
COMPARATIVE CLASSIFICATION OF LUNG X-RAY IMAGES WITH CONVOLUTIONAL NEURAL NETWORK, VGG16, DENSENET121 Muhammad Ilham Prasetya; Yuris Alkhalifi; Rifki Sadikin; Yan Rianto
Techno Nusa Mandiri: Journal of Computing and Information Technology Vol 19 No 1 (2022): TECHNO Period of March 2022
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/techno.v19i1.3010

Abstract

Lungs are one of the organs of the human body, and lung tissue will ultimately affect human abilities. The respiratory system exchanges oxygen and carbon dioxide in the blood. Problems that often occur are polluted air quality, many bacteria that attack the lungs, and lung disease can cause shortness of breath, mobility difficulties, and hypoxia, so that if not detected immediately it can cause death. In this regard, the aim of this study is to compare the classification of normal lungs with those of those suffering from Cardiomegaly. The preparation of this dataset is a form of contribution in improving the quality of the disease classification system on X-ray images. CNN, VGG 16 and DenseNet methods were chosen as classification methods to ensure performance and which method is the best for classifying Lung Diseases. It can be concluded that by using the DenseNet121 model, X-Ray images in this research dataset get an accuracy of 67.06%, for the VGG16 model it gets an accuracy of 68.94% and for the CNN model it gets the highest accuracy of 80.54%.
Identification of Acne Vulgaris Type in Facial Acne Images Using GLCM Feature Extraction and Extreme Learning Machine Algorithm Riyan Latifahul Hasanah; Yan Rianto; Dwiza Riana
Rekayasa Vol 15, No 2: Agustus 2022
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/rekayasa.v15i2.14580

Abstract

Acne vulgaris or acne is a common inflammatory pilosebaceous condition that affects up to 90% of teenagers, begins during adolescent years, and often persists into adulthood. Acne vulgaris, especially on the face, has a major impact on the emotional, social and psychological health of patients. In treating acne, it is necessary to identify the exact type of acne. The manual method is considered less effective, so it is proposed an automatic method using a computer, which uses image processing techniques. This research was conducted to identify the types of acne on facial acne images. The methods used are K-Means Clustering for segmentation, Gray Level Co-occurrence Matrix (GLCM) for feature extraction, and Extreme Learning Machine (ELM) for classification. The dataset is 100 images and consists of 3 classes, namely Nodules, Papules and Pustules. Testing is done in two stages, namely testing 2 classes (Nodules and Papules), followed by testing 3 classes (Nodules, Papules and Pustules). Testing of 2 classes produces the highest accuracy of 95,24% and testing of 3 classes produces the highest accuracy of 80%.
DIGITAL IMAGE IDENTIFICATION OF PLANKTON USING REGIONPROPS AND BAGGING DECISION TREE ALGORITHM Hikmatulloh Hikmatulloh; Yan Rianto; Anton Anton
Techno Nusa Mandiri Vol 20 No 1 (2023): TECHNO Period of March 2023
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/techno.v20i1.4028

Abstract

Peranan plankton sangat penting bagi kehidupan organisme disekitarnya, sehingga penelitian prihal plankton sangatlah dibutuhkan karena kaitannya dengan kelangsungan kehidupan mahluk hidup lainnya. Kendala yang sering didapatkan dalam hal penelitian plankton khususnya dalam hal pengidentifikasian plankton yaitu tidak efisiennya dalam aspek waktu dan organisme ini memiliki ukuran rata-rata yang sangat kecil. Dalam hal ini diperlukan alternatif yang lebih baik dalam pengidentifikasian jenis plankton ini dengan cara pemrosesan gambar pada citra plankton secara digital atau biasa disebut dengan istilah “Digital Image Processing”. Penelitian ini bertujuan untuk melakukan pengolahan citra digital plankton sebanyak 144 citra yang yang dibagi menjadi 75% sebagai data pelatihan dan 25% sebagai data pengujian, dan citra tersebut didapatkan dari riset pada yayasan Kanopi Indonesia. Dalam prosesnya citra ini dianalisa bentuk menggunakan fungsi Regionprops sehingga didapatkan fitur pembeda dari masing-masing jenis plankton. Setelah citra terekstraksi fitur nya selanjutnya dilakukan pengolahan data dengan mengklasifikasikan setiap jenis plankton tersebut. Untuk menghasilkan sebuah klasifikasi data yang lebih baik, dalam penelitian ini menggunakan algoritma Bagging Decision Tree dalam pengolahan data nya dan menghasilkan akurasi sebesar 92.59%. Algoritma Bagging Decision Tree ini cukup baik dan mudah untuk di implemntasikan kedalam sebuah program identifikasi jenis plankton, terbukti dengan pengujian pada data citra pengujian menghasilkan 33 citra teridentifikasi dengan benar dari total pengujian sebanyak 36 citra.
Co-Authors Anton Anton Dwi Andriyanto Dwiza Riana Hikmatulloh Hikmatulloh Muhammad Ilham Prasetya Rifki Sadikin Riyan Latifahul Hasanah Yuris Alkhalifi