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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

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Implementasi Learning Vektor Quantization (LVQ) dalam Mengidentifikasi Citra Daging Babi dan Daging Sapi Jasril Jasril; Meiky Surya Cahyana; Lestari Handayani; Elvia Budianita
Seminar Nasional Teknologi Informasi Komunikasi dan Industri 2015: SNTIKI 7
Publisher : UIN Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (586.668 KB)

Abstract

Widespread circulation of adulterated meat and based on the word of Allah which confirms the prohibition of pork to eat, it needs to be made of a system that can distinguish between beef and pork to avoid cheating merchants and keep halal meat we eat. This study makes a system for identifying the image of beef and pork and meat adulterated with the color feature extraction HSV (Hue, Saturation, Value) and texture feature extraction GLCM (Grey Level Co-occurent Matrix) using classification LVQ (Learning Vector Quantization). A result of image identification adulterated meat pig is considered as a pork class. Image data on the image of the study consisted of 107 primary and 13 secondary image. Identification testing conducted on the distribution of training data and test data are different. Accuracy of the highest success with an average of 94.81% on the distribution of the 80 training data and test data 20 and the accuracy of the lowest success with an average of 82.22% on the distribution of training data and test data 50 50 with Learning Rate of 0.01, 0.05, 0.09. More increase the distribution of training data and more decrease division of the test data, so more increase the accuracy of success in identifying the image.Keywords: beef, GLCM, HSV, Learning Rate, LVQ, pork
Pengaruh Hyperparameter Convolutional Neural Network Arsitektur ResNet-50 Pada Klasifikasi Citra Daging Sapi dan Daging Babi Sarah Lasniari; Jasril Jasril; Suwanto Sanjaya; Febi Yanto; Muhammad Affandes
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 5, No 3 (2022): Juni 2022
Publisher : Program Studi Teknik Informatika, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v5i3.4424

Abstract

Abstrak - Kasus kecurangan pedagang mencampur daging sapi dengan daging babi masih terjadi hingga saat ini. Membedakan daging sapi dan babi dapat dilakukan dengan mengamati secara langsung satu persatu, tetapi hal ini dapat dilakukan oleh para ahli, Tetapi secara kasat mata masih sulit membedakannya. Perilaku pedagang seperti ini sangat merugikan konsumen khususnya pemeluk agama Islam karena berkaitan dengan makanan yang halal atau haram. Pada penelitain ini menggunakan metode Deep Learning untuk klasifikasi citra dengan Convolutional Neural Network (CNN) arsitektur ResNet-50. Jumlah data sebanyak 457 citra yang terbagi menjadi 3 kelas, yaitu daging babi, daging oplosan dan daging sapi. Setiap kelas memiliki ukuran gambar yang sama yaitu 300 x 300 pixel. Pembagian data menggunakan split data dengan perbandingan 70% data uji : 30% data uji, 80% data latih : 20% data uji, dan 90% data latih : 10% data uji. Hasil dari pengujian model dengan Confusion Matrix menunjukkan performa klasifikasi tertinggi dengan 100% accuracy, 100% precision, dan 100% recall, pada data citra asli dengan penggunaan batch size 32, 0.001 learning rate, epoch 75 dan split data 90% : 10%.Kata kunci: Convolutional Neural Network, Daging Babi dan Sapi, Deep Learning, Klasifikasi Citra, ResNet  Abstract - Traders mixing beef and pork are still committing fraud today. Although professionals can discern between beef and pork by watching them one by one, it is still impossible to do so with the naked eye. This kind of behavior is very detrimental to consumers, especially Muslims because it is related to halal or haram food. This research uses Deep Learning method to classify images with Convolutional Neural Network (CNN) ResNet-50 architecture. The number of data is 457 images which are divided into 3 classes, namely pork, mixed meat and beef. Each class has the same image size, which is 300 x 300 pixels. data distribution using split data with a comparison of 70% training data: 30% test data, 80% training data: 20% test data, and 90% training data: 10% test data. The results of model testing using the Confusion Matrix show the highest classification performance with 100% accuracy, 100% precision, and 100% recall, on the original image data using batch size 32, 0.001 learning rate, epoch 75 and split data 90%: 10%..Kata kunci: Convolutional Neural Networ, Deep Learning, Image Classification, Pork and Beef, ResNet
Penerapan Algoritma Convolutional Neural Network Arsitektur ResNet-50 untuk Klasifikasi Citra Daging Sapi dan Babi Dodi Efendi; Jasril Jasril; Suwanto Sanjaya; Fadhilah Syafria; Elvia Budianita
JURIKOM (Jurnal Riset Komputer) Vol 9, No 3 (2022): Juni 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v9i3.4176

Abstract

Meat is one of the food ingredients needed by humans. The price of pork is cheaper than beef, which has led to the practice of mixing beef with pork for the purpose of making big profits. In plain view, the difference between beef and pork is not striking, so it is difficult for ordinary people to distinguish between them. In terms of color, pork is paler than beef. In terms of texture, beef is stiffer and tougher than pork. In terms of fiber, beef is clearer than pork, so we need a system that can identify the two types of meat. This study uses the Convolutional Neural Network (CNN) algorithm with the ResNet-50 architecture with 3 types of optimizers such as Stochastic Gradient Descent (SGD), Adam, and RMSprop. The dataset used for training first goes through 2 stages of preprocessing, namely cropping and resizing. The results of the study show that the SGD optimizer can outperform the Adam and RMSprop optimizers with 97.83% accuracy, 97% precision, 97% recall, and 97% f1 score with batch size 32, learning rate 0.01, and epoch 50.
Klasifikasi Citra Daging Sapi dan Daging Babi Menggunakan Ekstraksi Ciri dan Convolutional Neural Network Gusrifaris Yuda Alhafis; Jasril Jasril; Suwanto Sanjaya; Fadhilah Syafria; Elvia Budianita
JURIKOM (Jurnal Riset Komputer) Vol 9, No 3 (2022): Juni 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v9i3.4175

Abstract

Cases of mixing beef and pork are still happening today. The increasing demand for beef causes many traders to mix meat to gain more profit. Distinguishing beef and pork can be done by sight and smell, but still has weaknesses. This study uses Deep Learning method for image classification with Convolutional Neural Network architecture EfficientNet-B0. The amount of data is 3,000 images which are divided into 3 classes, beef, pork, and mixed meat. This study uses original image data and image data of Contrast Limited Adaptive Histogram Equalization. The data is divided by the ratio of training data and test data of 80:20. The results of testing the model with the confusion matrix show the highest classification performance with 95.17% accuracy, 92.72% precision, 95.5% recall, and 94.09% f1 score, in the original image data with the use of neurons in the first dense amounting to 256, 32 batch size, 0.01 learning rate, and Adam's optimizer
Implementasi Convolutional Neural Network Untuk Klasifikasi Daging Menggunakan Fitur Ekstraksi Tekstur dan Arsitektur AlexNet Amalia Hanifah Artya; Jasril Jasril; Suwanto Sanjaya; Fadhilah Syafria; Elvia Budianita
JURIKOM (Jurnal Riset Komputer) Vol 9, No 3 (2022): Juni 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v9i3.4177

Abstract

The demand for meat began to increase rapidly, causing drastic price changes and causing the existence of scammers to inflate the price of meat to get big profits by mixing beef and pork. Few consumers are aware of the mixing of meat, to distinguish between beef and pork can be seen in terms of color and texture, but this theory still has weaknesses. This research uses the Deep Learning method, namely Convolutional Neural Network with Local Binary Pattern texture extraction feature and AlexNet architecture for meat classification. The research conducted stated that the accuracy of the meat image classification can be measured using various parameters and optimizers. The highest accuracy results obtained from this study were 68.6% accuracy, 62% precision, 57.6% recall, and 59% f1-score using the Stochastic Gradient Descent (SGD) optimizer, 0.01 learning rate, 32 batch size, and 0.9 momentum. Compared to the original dataset, the accuracy of the LBP dataset type is still below the original dataset with the results obtained from the accuracy of the original dataset are 84.1% accuracy, 78.6% precision, 79% recall, and 79% f1-score using the RMSprop optimizer, 0 .0001 learning rate, 32 batch sizes, and momentum So it can be concluded that the AlexNet architecture by setting the existing parameter values can increase the accuracy value.
Klasifikasi Citra Daging Babi dan Daging Sapi Menggunakan Deep Learning Arsitektur ResNet-50 dengan Augmentasi Citra Sarah Lasniari; Jasril Jasril; Suwanto Sanjaya; Febi Yanto; Muhammad Affandes
Jurnal Sistem Komputer dan Informatika (JSON) Vol 3, No 4 (2022): Juni 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v3i4.4167

Abstract

Beef is an example of an animal protein-rich food. The consumption of meat in Indonesia is increasing year after year, in tandem with the country's growing population. Many traders purposefully combine beef and pork in order to maximize profits. With the naked eye, it's difficult to tell the difference between pork and beef. In Muslim-majority countries, the assurance of halal meat is crucial. This study uses Deep Learning with the Convolutional Neural Network (CNN) method and ResNet-50 with data augmentation to classify images of beef and pork. The original meat picture databases contain 457 images, however following the data augmentation process, there are 2742 images in total, divided into three classes. The distribution of training and test data is 90 percent:10 percent in the comparison test scenario between the two original data schemes and supplemented data. With an average of 87.64 % accuracy, 87.59 % recall, and 90.90 % precision, the Confusion Matrix is the best classification performance model. There was no evidence of overfitting based on observations from the visualization of the training and testing process.
Eigenvalue of Analytic Hierarchy Process as The Determinant for Class Target on Classification Algorithm Mustakim Mustakim; Novia Kumala Sari; Jasril Jasril; Ismu Kusumanto; Nurul Gayatri Indah Reza
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 3: December 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v12.i3.pp1257-1264

Abstract

Data mining has two main concepts of data distribution, namely supervised learning and unsupervised learning. The most easily recognizable concepts from data distribution is related to the dataset, with and without target class. Analytic Hierarchy Process (AHP) technique that carries the concept of pairwise comparison able to answer the problem related to the dataset, which is to change unsupervised to be supervised by determining eigenvalue value of each attribute and sub attribute in AHP method. The case study conducted in this issue is related to determining the target classes used to predict the success of a student learning in UIN Suska Riau. The three main attributes are Procrastination, Total Credits (SKS) and Number of Repeated Courses, each having eigenvalues of 0.319; 0.189 and 0.171 which become the feedback in the determination of the Target Timely Graduation (TG) or Possibility of Timely Graduation (PTG). The biggest consistency ratio generated in the AHP case is 9.4% in the GPA attribute. This research recommends that further research should use datasets that have been arranged based on experimental combinations of the three main attributes above, then applied to the classification or prediction algorithm. So that it would obtain a decision of accuracy from data used against the real result on the field.
Pengelompokkan Penyakit Pasien Menggunakan Algoritma K-Means Rahayu Anggraini; Elin Haerani; Jasril Jasril; Iis Afrianty
JURIKOM (Jurnal Riset Komputer) Vol 9, No 6 (2022): Desember 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v9i6.5145

Abstract

Health is one of the most important factors besides education and income. Everyone has the same human rights to get good health services. A government agency that functions to serve all people who need medical services in Indonesia, namely the puskesmas. Ujung Batu Health Center which is located in Ujung Batu sub-district, Rokan Hulu Regency as one of the government agencies. The Ujung Batu health center stores patient medical record data, only sorting out the disease. Therefore, the medical record data needs to be processed using clustering or grouping using the K-Means method. This algorithm partitions the data into clusters so that data with the same characteristics are grouped into the same cluster and data with different characteristics are grouped. into another cluster. The data used consisted of 3875 records and 5 attributes, namely Gender, Participant Type, Diagnosis, Return Status, Address. From the test using the K-means algorithm, the clustering results show that cluster 1 has 710 data while cluster 2 has 3165 data. The results of the study show that the use of 2 clusters is the best cluster with a Silhouette Coefficient value showing results with a SC value of 0.646.
IMPLEMENTASI ALGORITMA FOLD-GROWTH UNTUK MENEMUKAN POLA KELULUSAN MAHASISWA Muhammad Hasbi Assidiqqi; Alwis Nazir; Iwan Iskandar; Jasril Jasril; Fitri Insani
TEKTRIKA Vol 7 No 2 (2022): TEKTRIKA Vol.7 No.2 2022
Publisher : Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/tektrika.v7i2.5530

Abstract

Timely graduation of students is important for a study program and higher education institution. According to BAN-PT Regulation Number 23 of 2022 concerning Instruments for Monitoring and Evaluation of Higher Education Accreditation Ratings, the ideal percentage of student graduation on time is ? 37.5% for academic tertiary institutions and ? 47.5% for vocational tertiary institutions. The timely graduation of Informatics Engineering Study Program students at UIN Sultan Syarif Kasim Riau every year is still below the standards set by BAN-PT. In 2021 the number of students who graduate on time is only 12%. By using student data in the form of acceptance and graduation data in the form of length of study and GPA, excavation of graduation patterns is carried out using the FOLD-Growth algorithm. The FOLD-Growth algorithm is a combined algorithm of FOLDARM and FP-Growth. With 274 student data, a minimum support value of 20%, and a minimum confidence of 50%, it produces two patterns with the strongest lift ratio value of 3.61, while with a minimum support of 10% and a minimum confidence of 50%, it produces three patterns. with the strongest lift ratio of 7.03. The results of this study can be used as a guideline for increasing student graduation rates on time, by providing a larger intake quota for new students on a pathway that results in on-time graduation, so that it will increase student graduation on time. Key Words: Student graduation, Association Rule, lift ratio, FOLD-Growth, FP-Growth
Klasifikasi Clickbait Menggunakan Transformers Mori Hovipah Mori Hovipah; Elin Hearani; Jasril Jasril; Fadhilah Syafria
Computer Science and Information Technology Vol 4 No 1 (2023): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v4i1.4713

Abstract

Clickbait is a news title created by the author with the aim of attracting the getting to get readers so they Never miss a headline. Clickbait headlines are typically quirky, confusing, and use exaggerated sentences to entice readers to click on links. However, clickbait headlines that look very attractive often do not match the information in the headlines and the content of the news, which can lead to the spread of fake news and hoaxes. Then classification of clickbait news titles is carried out, for this research, clickbait classification was carried out for news titles will be carried out using the Transformers method. The number of news titles used in this study amounted to 6632 news titles. The process of classification of news titles in this study includes: collecting data, labeling data, preprocessing, EDA, and classification using transformers. The best accuracy value obtained in this study was 63% for precision of 0.63 and recall of 1 using a data division of 10%: 90%.
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 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