Article Per Year (5 Year)
p-Index From 2021 - 2026
3.958
P-Index
This Author published in this journals
All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) SITEKIN: Jurnal Sains, Teknologi dan Industri CESS (Journal of Computer Engineering, System and Science) JURNAL MEDIA INFORMATIKA BUDIDARMA Indonesian Journal of Artificial Intelligence and Data Mining Seminar Nasional Teknologi Informasi Komunikasi dan Industri Jurnal Informatika Universitas Pamulang Malikussaleh Journal of Mathematics Learning (MJML) Jurnal Nasional Komputasi dan Teknologi Informasi JURIKOM (Jurnal Riset Komputer) TEKTRIKA - Jurnal Penelitian dan Pengembangan Telekomunikasi, Kendali, Komputer, Elektrik, dan Elektronika Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Jurnal Tekinkom (Teknik Informasi dan Komputer) Indonesian Journal of Electrical Engineering and Computer Science JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) Jurnal Sistem Komputer dan Informatika (JSON) Jurnal Computer Science and Information Technology (CoSciTech) KLIK: Kajian Ilmiah Informatika dan Komputer Jurnal Sains dan Informatika : Research of Science and Informatic
Jasril Jasril
Universitas Islam Negeri Sultan Syarif Kasim Riau
Aerospace Engineering Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mathematics Neuroscience Social Sciences Transportation Other

Published : 43 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 43 Documents
Search

 1   2   3   4   5 
Klasifikasi Citra Daging Sapi dan Daging Babi Menggunakan CNN Arsitektur EfficientNet-B6 dan Augmentasi Data M. Fadil Martias; Jasril Jasril; Suwanto Sanjaya; Lestari Handayani; Febi Yanto
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 4 No. 4 (2023): Juni 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i4.6195

Abstract

In daily life, beef often serves as a staple food for humans. However, the high and expensive price of beef has prompted traders to adulterate it with pork for the sake of profit. Such adulteration has serious implications in the Islamic religion, where not all types of meat are considered halal (permissible for consumption), such as pork. As a result, consumers often remain unaware that the beef they purchase has been adulterated with pork. At a glance, both types of meat exhibit similar appearance and texture, making them difficult to differentiate. This research aims to classify beef and pork using a deep learning model with the Convolutional Neural Network (CNN) method, combined with data augmentation. The model used is EfficientNet-B6 with variations in the testing scenario. The variations include the ratio of training and testing data, learning rates, and optimizer for EfficientNet-B6. Data augmentation is performed using techniques such as random rotation, shifting, image scaling, vertical and horizontal flipping, and nearest pixel filling. Evaluation results using the confusion matrix show that the model with data augmentation achieves the highest accuracy for the classes of beef, pork, and adulterated samples at 92.00%, while the model without augmentation achieves an accuracy of 91.67%. However, from this experiment, the best scenario to avoid misclassifying pork and adulterated samples as beef can be obtained. This scenario involves a model with data augmentation, a 90:10 data split, SGD optimizer, and a learning rate of 0.01, which achieves the highest precision for the beef class at 96.05%. The research findings demonstrate that the use of data augmentation on images can improve the model's performance, and the model with data augmentation, a 90:10 data split, SGD optimizer, and a learning rate of 0.01 exhibits the best performance in classifying beef images.
Penerapan Algoritma C4.5 Mengklarifikasi Penerimaan Bantuan Sosial Menggunakan Feature Selection M Wandi Dwi Wirawan; Siska Kurnia Gusti; Jasril Jasril; Pizaini Pizaini
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 5 No. 1 (2023): September 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v5i1.6653

Abstract

The Indonesian government's efforts to overcome poverty in Indonesia are through the Smart Indonesia Card (KIP) program which is carried out by the government in the form of providing assistance to underprivileged families. The main aim of distributing KIP assistance is to help send underprivileged children to continue their education, the difficulties found in receiving KIP are due to the large number of residents registering, as well as the data having several conditions, the limited time available in providing KIP by sub-district parties, the completion base is relatively low, therefore the provision of assistance must be right on target. Therefore, the aim of this research is to look for the most influential attributes in receiving KIP assistance in order to improve the results of the data verification process. After carrying out Feature Selection using Information Gain, the most influential attributes can be obtained. The influences are Number of Art, Number of Rooms, Cooking Room, Refrigerator, Motorbike. Therefore, we need to know some of the attributes that most influence the selection of KIP assistance so that we can get accuracy values from decision tree modeling using the C4.5 algorithm or decision tree. Test This experiment can produce a decision tree in which the Number of Art attribute is the most influential attribute with the success rate of KIP acceptance. This evaluation uses a confusion matrix to obtain an accuracy value of 98.21%, precision of 98.21%, recall of 99.48%.
Penerapan Seleksi Fitur Untuk Klasifikasi Penerima Bantuan Sosial Pangkalan Sesai Menggunakan Metode K-Nearest Neighbor Muhammad Fauzan; Siska Kurnia Gusti; Jasril Jasril; Pizaini Pizaini
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 5 No. 1 (2023): September 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v5i1.6654

Abstract

The inability to fulfill basic human needs is how poverty is defined. To address this issue, the indonesian goverment implements various social assistance programs, one of which is Kartu Indonesia Pintar (KIP), aimed at providing free education to children aged 7-18 who are economically disadvantaged. However, in the distribution of aid in the Pangkalan sesai sub-district, distributing officers often face challenges due to the high number of eligible recipients applying, complex data requierements, and limited time for the officers. Distributing this social assistance accurately is crusial. Therefore, this research aims to determine the accuracy value for the data of potential recipients of the Kartu Indonesia Pintar (KIP to enhance the data verification process’s outcomes. To tackle this issue, the research employs the K-Nearest Neighbor (K-NN) algoritm and also employs feature selection using Information Gain to reduce less influential attributes. The data used consists of 1998 records of KIP beneficiaries from the 2023 in excel format, with 33 attributes. After performing data cleaning an Information Gain-based feature selection, the dataset is reduced to 1675 records, with 5 selected attributes. The best classification result in this study is achieved with ratios of 7:3 and 8:2, and a value of k = 5, yielding the highest accuracy of 98,21%. The lowest accuracy is obtained using a ratio of 9:1 with the same k value when not using Information Gain, resulting in an accuracy of 89,82%.
Co-Authors Afiana Nabilla Zulfa Ahmad Paisal Ahyani Junia Karlina Alwis Nazir Amalia Hanifah Artya Dardiri Dardiri Deny Ardianto Dodi Efendi Eka Pandu Cynthia Elin Haerani Elin Haerani Elin Haerani Elin Haerani Elin Hearani Elvia Budianita Fadhilah Syafria Febi Yanto Fikri Utri Amri Fitri Insani Fitri Insani Gusrifaris Yuda Alhafis Gusti, Siska Kurnia Hafez Almirza Iis Afrianty Ikhwanul Akhmad DLY Imam Hanafi Ismu Kusumanto Isnan Mellian Ramadhan Iwan Iskandar Lestari Handayani Lestari Handayani Lola Oktavia M Wandi Dwi Wirawan M. Fadil Martias MAS`UD ZEIN Maulana Junihardi Meiky Surya Cahyana Mori Hovipah Mori Hovipah Muhammad Affandes Muhammad Affandes Muhammad Fauzan Muhammad Hasbi Assidiqqi Muhammad Irsyad Musa Thahir Mustakim Mustakim Mustakim Mustakim Nadila Handayani Putri Novia Kumala Sari Nurul Gayatri Indah Reza Pizaini Pizaini Rahayu Anggraini Rahma Shinta Reski Mai Candra Rizuan Rizuan Said Nanda Saputra Saputra, Elvino Dwi Sarah Lasniari Sarah Lasniari Sola Huddin Sonya Meitarice Surya Agustian Suwanto Sanjaya Suwanto Suwanto Tiara Ramayanti Yusra, Yusra Zulkifli M. Nuh