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All Journal Justek : Jurnal Sains Dan Teknologi Sang Pencerah: Jurnal Ilmiah Universitas Muhammadiyah Buton Infotekmesin Insearch: Information System Research Journal
Dodi Arif
Universitas Gunadarma
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Mathematics Mechanical Engineering Social Sciences Other

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Mengatasi Ketimpangan Data Deep Neural Network dengan Pelipatan Fitur Data Klasifikasi Spektroskopi Darah Widi Hastomo; Adhitio Satyo Bayangkari Karno; Sutarno Sutarno; Dodi Arif; Eka Sally Moreta; Sudjiran Sudjiran
Sang Pencerah: Jurnal Ilmiah Universitas Muhammadiyah Buton Vol 8 No 2 (2022): Sang Pencerah: Jurnal Ilmiah Universitas Muhammadiyah Buton
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat Universitas Muhammadiyah Buton

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1673.005 KB) | DOI: 10.35326/pencerah.v8i2.2251

Abstract

Permasalahan utama dalam penelitian ini adalah ketimpangan data masukan menghasilkan dampak negatif yang signifikan terhadap hasil prediksi dari model Deep Neural Network (DNN). Kemampuan klasifikasi DNN sangat akurat hanya untuk dataset yang berimbang, namun DNN pada awalnya tidak di rancang untuk menangani ketimpangan data. Ketimpangan data merupakan hal yang sering dijumpai dalam dunia nyata, menjadikan ini sebagai tantangan besar dalam prediksi klasifikasi menggunakan model DNN. Penelitian ini berfokus untuk memprediksi tingkat kandungan kolesterol tinggi, kolesterol rendah dan hemoglobin, menggunakan data kasus di kompetisi Zindi Blood Spectroscopy Classification Challenge. Dengan melakukan analisa data, cleansing outlier, fine tunning, model neural network, jaringan pengelompokan data target dengan kategori sejenis, urutan pemrosesan, pemilihan nilai pelipatan (7 pelipatan) yang tepat terhadap data input train dan data test serta epoch 60, dapat meningkatkan hasil nilai score prediksi yang cukup tinggi sebesar 0.94594.
Exloratory Data Analysis Untuk Data Belanja Pelanggan dan Pendapatan Bisnis Widi Hastomo; Adhitio Satyo Bayangkari Karno; Sudjiran; Dodi Arif; Eka Sally Moreta
Infotekmesin Vol 13 No 2 (2022): Infotekmesin: Juli, 2022
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v13i2.1547

Abstract

A more quantifiable perspective is assuming the role of mechanistic management in an effort to enhance business based on its capacity to transform data into knowledge and insight. The industry has not completely supported its business strategy also with driven data. Using a transaction dataset taken from one of the Kaggle.com challenges, this experiment attempts to determine consumer spending patterns and Retail Fashion business revenues (H&M Personalized Fashion Recommendations). The results of the experiment are the number of transactions based on customer age, the most sales product and one-time purchased item, and the type of product that generates the highest and smallest income. The approach employed is EDA using the Python language. In order for businesses to generate analytical findings that provide future perspectives and to help identify the gap by delivering analytical results in the form of suggestions that can be perpetuated, the findings of this experiment are intended to support the capabilities of simulation. The challenge in this experiment is the abundance of datasets, which necessitates a suitable operating environment.
Identification of 29 Types of Plant Diseases using Deep Learning EfficientNetB3 Adhitio Satyo Bayangkari Karno; Widi Hastomo; Indra Sari Kusuma Wardhana; Sutarno Sutarno; Dodi Arif
Insearch: Information System Research Journal Vol 2, No 02 (2022): Insearch (Information System Research) Journal
Publisher : Fakultas Sains dan Teknologi UIN Imam Bonjol Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15548/isrj.v2i02.4389

Abstract

To supply the world's food needs in the midst of the existing food crisis, farmers urgently need to expand crop production. By establishing it simple to recognize the kind of plant disease so that earlier control efforts could be conducted, farmers' harvest failures driven on by disease attacks must be prevented. In this study, one of the Convolutional Neural Network (CNN) architectures known EfficeintNetB3 is applied to generate a classification model for 29 different types of plant diseases. A model is created after 3,170 image data are used for validation and 57,067 image data were utilized for training. 3,171 image data tests were conducted as part of the model testing phase, and the total test results were produced an extraordinarily high accuracy score of 0.99 percentage and an F1-score
MobilenetV2 Architecture To Detect Covid-19 X-Ray Imagery Widi Hastomo; Adhitio Satyo Bayangkari Karno; Ellya Sestri; Eva Karla; Stevianus Stevianus; Dodi Arif
Justek : Jurnal Sains dan Teknologi Vol 5, No 2 (2022): November
Publisher : Unversitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/justek.v5i2.11820

Abstract

Abstract:  The COVID-19 pandemic has hit all over the world, in the last two years and has changed the pace, structure and nature of social life. This study aims to detect COVID-19 using a chest x-ray image dataset sourced from kaggle.com, which is divided into 4 categories. The proposed method is CNN with MobileNetV2 architecture, by dividing 80% train data and 20% test data into 224x224 and batch size 32. The optimizer uses SGD, lr 0.005, momentum 0.9 and epoch 20. The results of the study with the achievement of precision values for the covid category 0.99, lung opacity 0.98, normal 0.96 and viral pneumonia category reached 0.99. Further studies can use the development of the CNN model and can try with other optimizers.Abstrak: Pandemi covid-19 telah melanda diseluruh dunia, dalam dua tahun terakhir dan mengubah langkah, struktur dan sifat kehidupan bermasyarakat. Penelitian ini  bertujuan untuk mendeteksi covid-19 menggunakan dataset citra chest x-ray yang bersumber dari kaggle.com, yang dibagi menjadi 4 kategori. Metode yang diusulkan yaitu CNN dengan arsitektur MobileNetV2, dengan membagi data train 80% dan data test 20% ukuran citra menjadi 224x224 dan batch size 32. Optimizer menggunakan SGD, lr 0.005, momentum 0.9 serta epoch 20. Hasil penelitian ini dengan capaian nilai presisi untuk kategori covid 0.99, lung opacity 0.98, normal 0.96 dan kategori viral pneumonia mencapai 0.99. Studi selanjutnya dapat menggunakan pengembangan dari model CNN serta dapat mencoba dengan optimizer yang lain.
Co-Authors Adhitio Satyo Bayangkari Karno Eka Sally Moreta Elliya Sestri Eva Karla Indra Sari Kusuma Wardhana Stevianus Stevianus Sudjiran Sutarno Sutarno Sutarno Sutarno Widi Hastomo