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All Journal JRST (Jurnal Riset Sains dan Teknologi) Jurnal Rekayasa elektrika
Andri Ariyanto
Universitas Jenderal Achmad Yani
Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering

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Identifikasi Citra Kualitas Minyak Kelapa Sawit Berbasis Android Menggunakan Algoritma Convolutional Neural Network Deny Haryadi; Sasmi Hidayatul Yulianing Tyas; Adi Kuncoro; Fiqry Firdhan Pratama Putra; Andri Ariyanto
Jurnal Rekayasa Elektrika Vol 18, No 4 (2022)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (882.601 KB) | DOI: 10.17529/jre.v18i4.28617

Abstract

The Central Statistics Agency reports that the average development of palm cooking oil consumption at the household level in Indonesia during the 2015-2020 period has increased by 2.32% per year. The use of cooking oil repeatedly is commonplace among the people of Indonesia and quite a lot. Even though the use of cooking oil can endanger health because the frying process at high temperatures can damage the chemical structure of the oil. Therefore, in this study, image processing was carried out to identify the quality of palm oil using the Convolutional Neural Network (CNN) algorithm. This research was conducted through several stages, namely dataset collection, dataset preprocessing, CNN algorithm implementation, testing, and development of information systems. The dataset consists of image data of palm cooking oil that has not been used, palm cooking oil used for frying twice, and palm cooking oil used for frying more than twice. The total amount of data is 3000 image data. Distribution of training data and test data using the Pareto division of 80:20. Based on the test, the best accuracy is 97.08%. This research produces an android-based information system that can identify the quality of cooking oil based on the classification.
Klasifikasi Metode Persalinan pada Ibu Hamil Menggunakan Algoritma Random Forest Berbasis Mobile Dewi Marini Umi Atmaja; Arif Rahman Hakim; Amat Basri; Andri Ariyanto
JRST (Jurnal Riset Sains dan Teknologi) Volume 7 No. 2 September 2023: JRST
Publisher : Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/jrst.v7i2.16705

Abstract

Tren angka kematian ibu pada saat melahirkan masih tinggi di Indonesia, yakni sekitar 300 per 100.000 kelahiran. Pemerintah Indonesia berencana untuk menurunkan angka tersebut menjadi 183 per 100.000 kelahiran pada tahun 2024 mendatang. Salah satu faktor penyebab kematian ibu hamil di Indonesia disebabkan oleh hipertensi dan terjadinya pendarahan pada saat melahirkan dan dibutuhkannya metode penanganan dalam persalinan. Adapun metode persalinan ibu hamil secara garis besar terbagi menjadi dua metode yaitu normal dan Caesar. Caesar adalah alternatif terakhir dalam persalinan, dikarenakan faktor risiko yang cukup tinggi, meskipun demikian, jumlah ibu yang menggunakan metode Caesar pada saat persalinan mengalami peningkatan yang cukup signifikan, khususnya di Indonesia. Metode persalinan pada ibu hamil dapat diklasifikasikan sesuai dengan kondisi ibu untuk menghindari risiko kematian ibu akibat pemilihan metode persalinan yang tidak tepat. Permasalahan tersebut dapat diselesaikan dengan memanfaatkan teknologi Machine Learning menggunakan algoritma random forest, dengan tujuan untuk membangun sebuah sistem yang dapat mengklasifikasi metode persalinan yang tepat berdasarkan kumpulan data persalinan ibu hamil yang telah disediakan. Dengan adanya sistem ini diharapkan dapat membantu para ibu hamil dalam melakukan screening awal untuk menentukan tindakan yang harus dilakukan agar proses persalinan berjalan dengan lancar dan meminimalisir risiko kematian ibu.
Identifikasi Citra Kualitas Minyak Kelapa Sawit Berbasis Android Menggunakan Algoritma Convolutional Neural Network Deny Haryadi; Sasmi Hidayatul Yulianing Tyas; Adi Kuncoro; Fiqry Firdhan Pratama Putra; Andri Ariyanto
Jurnal Rekayasa Elektrika Vol 18, No 4 (2022)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17529/jre.v18i4.28617

Abstract

The Central Statistics Agency reports that the average development of palm cooking oil consumption at the household level in Indonesia during the 2015-2020 period has increased by 2.32% per year. The use of cooking oil repeatedly is commonplace among the people of Indonesia and quite a lot. Even though the use of cooking oil can endanger health because the frying process at high temperatures can damage the chemical structure of the oil. Therefore, in this study, image processing was carried out to identify the quality of palm oil using the Convolutional Neural Network (CNN) algorithm. This research was conducted through several stages, namely dataset collection, dataset preprocessing, CNN algorithm implementation, testing, and development of information systems. The dataset consists of image data of palm cooking oil that has not been used, palm cooking oil used for frying twice, and palm cooking oil used for frying more than twice. The total amount of data is 3000 image data. Distribution of training data and test data using the Pareto division of 80:20. Based on the test, the best accuracy is 97.08%. This research produces an android-based information system that can identify the quality of cooking oil based on the classification.
Co-Authors Adi Kuncoro Amat Basri Arif Rahman Hakim Deny Haryadi Dewi Marini Umi Atmaja Fiqry Firdhan Pratama Putra Sasmi Hidayatul Yulianing Tyas