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All Journal Syntax Jurnal Informatika Infotech Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL LENTERA : Kajian Keagamaan, Keilmuan dan Teknologi Bina Insani ICT Journal JIEET (Journal of Information Engineering and Educational Technology) Jurnal Pengabdian Pada Masyarakat JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Informatika Universitas Pamulang INTECOMS: Journal of Information Technology and Computer Science Explore IT : Jurnal Keilmuan dan Aplikasi Teknik Informatika J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Informasi dan Komputer Indonesian Journal of Applied Informatics Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Teknologi Terpadu EDUMATIC: Jurnal Pendidikan Informatika Cyber Security dan Forensik Digital (CSFD) JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Pemberdayaan Komunitas MH Thamrin J-SAKTI (Jurnal Sains Komputer dan Informatika) Innovative: Journal Of Social Science Research Nuansa Informatika JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Aries Suharso
Universitas Singaperbangsa Karawang
Religion Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Environmental Science Health Professions Law, Crime, Criminology & Criminal Justice Mathematics Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Journal : JOURNAL OF APPLIED INFORMATICS AND COMPUTING

 1 
Comparison of Deep Learning Architectures in Identifying Types of Medicinal Plant Leaf Images Salsabila, Sarah; Suharso, Aries; Purwantoro, Purwantoro
Journal of Applied Informatics and Computing Vol. 8 No. 1 (2024): July 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i1.6289

Abstract

This study focuses on the identification of 3500 images of medicinal plant leaves using Deep Learning CNN Transfer Learning models such as MobileNet, VGG16, DenseNet121, ResNet50V2, and NASNetMobile. The dataset used is the "Indonesian Herb Leaf Dataset 3500," consisting of 10 classes of medicinal plants. This research has the potential to efficiently and accurately recognize medicinal plants using machine learning workflow methods. The objective of this study is to compare the performance of these five methods in conducting plant identification. The testing phase involves various data handling schemes, dividing the data into two scenarios: 80:10:10 and 70:20:10. Performance comparison is also done between augmented and non-augmented data. The research findings indicate that MobileNet exhibits the best performance with an accuracy, precision, recall, and f1-Score of 98.86%. Accurate leaf identification supports further research on the properties and benefits of medicinal plants and can be applied in the development of decision support systems for plant recognition.
Implementation of Information Gain for Sentiment Analysis of PSE Policy using Naïve Bayes Algorithm Pramudja, Stevanus Ertito; Umaidah, Yuyun; Suharso, Aries
Journal of Applied Informatics and Computing Vol. 7 No. 2 (2023): December 2023
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v7i2.6359

Abstract

The Ministry of Communication and Information Technology of Indonesia (Kominfo) has established the Penyelenggara Sistem Elektronik (PSE) policy as a mandatory registration requirement for both domestic and foreign Electronic Systems (ES). As a result, Kominfo will impose sanctions on all ES by temporarily suspending their access if they fail to register by July 29, 2022, at 23:59 WIB. This policy has sparked both support and opposition among the Indonesian public, and it has become a topic of discussion, including among Twitter users. Therefore, sentiment analysis is employed as a solution to identify public concerns or issues regarding the policy based on negative and positive tweets. The objective of this research is to evaluate the results of feature selection using Information Gain and the Naïve Bayes Classifier algorithm in analyzing Twitter users' sentiment towards the policies of the Information and PSE of the Ministry of Communication and Information Technology. A total of 1153 lines of tweets were collected from the Twitter platform using the keyword "PSE Kominfo," which were then analyzed using the Naïve Bayes Classifier algorithm and Information Gain feature selection with three scenarios: 90:10, 80:20, and 70:30. Based on the evaluation using the confusion matrix, overall, Scenario 1 with a 90:10 ratio and Information Gain feature selection performed the best, achieving an accuracy of 79.7%, recall of 85%, and an F-1 score of 88%. However, the best precision was observed in Scenario 2 with an 80:20 ratio, reaching 92% due to the higher proportion of positive predictions made by the model compared to other scenarios.
Co-Authors Ade Andri Hendriadi Adhi Rizal Alex Mulyana Andreanov Ridhovan Angga Adhi Pramana Anisah Anwar Fuadi Arip Solehudin Assegaf, Farhan Ayu Shafira Tubagus Aziz Kustiyo Chaerur Rozikin Dewi Ratih Nurcahyani Dian Widyarini, Ratna Diandara Tresya Haniva Didi Juardi Dimas Fathu Ramdhani E Haodudin Nurkifli Eno Hakimah Kusuma Dewi Enri, Ultach garno, Garno Hasanah, Mau'idzoh Ihya Ullummuddien, Damareindra Iqbal Maulana Iqbal Maulana Irawan, Agung Susilo Yuda Irfan Maulana Zidmy Jadid Alif Ramadhan Jaman, Jajam Haerul Jejen Zaelani Juardi, Didi Kurnia Abdullah, Kunaifi Kusnadi, Kusnadi Maelani, Yeyen Mahardhika Pratama, Gilang Miftahuljannah, Virna Muhamad Alghivary, Mirza Muhamad Muhaimin MUHAMMAD HARIS Muhammad Raffi Muhammad Yusuf Mukhlis Mulyana, Alex Nurul Fitriyani Az-zahra, Anggi Nurul Iman, Ihsan Pramudja, Stevanus Ertito Prayogo, Janny Eka Purwantoro Purwantoro Purwantoro Putra, Ikhwan Wiratama R, Aria Renalda Rafa Abdul Fattah Riyanto Riyanto Riyanto Riyanto Rizal Saepul Mahdi Rizal, Adhi Rizki Windiawan Rozikin, Chaerur Ruziq Maulana Salsabila, Sarah Sandra Nathalie Silalahi, Jovanka Sofi Defiyanti Stefanus Deo Agape Tamba, Reynold Julian Umaidah, Yuyun Wildan Bahari, Bayu