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All Journal Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) Teknika Jupiter Techno.Com: Jurnal Teknologi Informasi SITEKIN: Jurnal Sains, Teknologi dan Industri Jurnal CoreIT Seminar Nasional Teknologi Informasi Komunikasi dan Industri Jurnal Nasional Komputasi dan Teknologi Informasi IJISTECH (International Journal Of Information System & Technology) JURIKOM (Jurnal Riset Komputer) Informatika : Jurnal Informatika, Manajemen dan Komputer JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) EDUMATIC: Jurnal Pendidikan Informatika Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Zonasi: Jurnal Sistem Informasi Jurnal Informatika Ekonomi Bisnis JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) Jurnal Sistem Komputer dan Informatika (JSON) JUKI : Jurnal Komputer dan Informatika Jurnal Ilmu Komputer IJISTECH Jurnal Computer Science and Information Technology (CoSciTech) Jurnal Pendidikan dan Teknologi Indonesia Jurnal Informatika Terpadu Bulletin of Computer Science Research KLIK: Kajian Ilmiah Informatika dan Komputer Bulletin of Information Technology (BIT) Jurnal Informatika Ekonomi Bisnis SATIN - Sains dan Teknologi Informasi
Muhammad Affandes
Universitas Islam Negeri Sultan Syarif Kasim Riau
Aerospace Engineering Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Neuroscience Social Sciences Transportation Other

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Journal : Jurnal Sistem Komputer dan Informatika (JSON)

 1 
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.
Klasifikasi Sentimen Transformasi dan Reformasi Sepak Bola Indonesia Pada Twitter Menggunakan Algoritma Bernoulli Naïve Bayes Destri Putri Yani; Siska Kurnia Gusti; Febi Yanto; Muhammad Affandes
Jurnal Sistem Komputer dan Informatika (JSON) Vol 4, No 3 (2023): Maret 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i3.5829

Abstract

Federation Internationale de Football Association (FIFA) carried out Transformations and Reformations to Indonesian Football with one of them Indonesia was chosen as the Host of the U-20 World Cup in 2023. The transformations and reformations carried out cause people to often provide opinions through social media Twitter. Opinions given by the public can be positive or negative. The research uses Text Mining to classify sentiment in 2 categories with the Bernoulli Naïve Bayes algorithm. This research aims to classify positive and negative sentiments and determine the level of accuracy value of the sentiment classification results of Indonesian Football Transformation and Reformation. The research stages carried out are data collection, text preprocessing, data labeling, TF-IDF weighting, Bernoulli Naïve Bayes classification, and evaluation. Based on the research results from 4907 data there is duplicate data and only uses 2125 data which is divided into 90% training data and 10% testing data, so as to get accuracy with a high category value of 88%. The classification results show that many tweets are positive sentiments.
Klasifikasi Sentimen Tragedi Kanjuruhan Pada Twitter Menggunakan Algoritma Naïve Bayes Iqbal Salim Thalib; Siska Kurnia Gusti; Febi Yanto; Muhammad Affandes
Jurnal Sistem Komputer dan Informatika (JSON) Vol 4, No 3 (2023): Maret 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i3.5852

Abstract

The Kanjuruhan Malang incident occurred on October 1 and resulted in 132 deaths, 96 serious injuries and 484 minor injuries. The cause of the riot occurred due to provocation between Arema Malang supporters and Persebaya Surabaya supporters who mentioned harsh words and other provocative actions that caused anger on both sides. Sentiment analysis of the Kanjuruhan tragedy using the Naive Bayes method was conducted through tweets taken through Twitter to understand the public's perception of the incident. The Naïve Bayes algorithm is performed for the sentiment classification of tweet data which is applied by processing the tweet text and classifying it into positive, negative, and neutral. In this study using data as much as 4843 data and carried out with tweet data that has been crawled resulting in 2,042 data. This research aims to classify sentiment and determine the level of accuracy in the Multinomial Naïve Bayes algorithm in the Kanjuruhan tragedy using a dataset in the form of tweets from twitter social media. The processed tweet data is divided into two types, namely 90% training data and 10% test data.  The results of this classification get a Naïve Bayes accuracy of 75% with a precission of 73%, recall of 75%, and f1-score value of 74%. The results of the tweet data used in this study can be concluded that the Naïve Bayes algorithm has a fairly good accuracy value.
Co-Authors Abdul Aziz Ahmad Efendi Al Fajri Albis Ya Albi Aldri Permana, Lutfi Alwis Nazir Alwis Nazir Alwis Nazir Amany Akhyar Amany Akhyar Amany Akhyar Andrian Wahyu Ardelia, Adinda Abidah Asmarita Asmarita Badri Yusuf Baehaqi Chandra, Reski Mai citra ainul mardhia putri Destri Putri Yani Dhymas Julyan Riyanto Eka Pandu Cynthia Elin Haerani Elin Haerani Elin Haerani Elvia Budianita Elvina Afriani Fadhilah Syafria Fatmawati Fatmawati Febi Yanto Fikry, Muhammad Fitri Insani Fitri Insani Fitri Wulandari Fitri Wulandari Gunawan Setia Wiguna Gusti, Siska Kurnia Harahap, Nazaruddin Safaat hariansyah, Aldi imelda amuis Iqbal Salim Thalib Iwan Iskandar Iwan Iskandar Iwan Iskandar Jasril Jasril Jasril Jasril Kana Saputra S Khairul Amri Khonofi, Khoidir Kurniansyah, Juliandi Lola Oktavia Mar'arif, Muhammad Mulky Marlina, Resti Muhammad Fikry Muhammad Irsyad Muhammad Irsyad Muhammad Ridha Muhammad Rizky Ramadhan Nazruddin Safaat Nazruddin Safaat H Nazruddin Safaat H Nazruddin Safaat H Novi Yanti Novialdi T Novri Rahman Novri Yanto Novriyanto Novriyanto Novriyanto Nurhapiza, Nurhapiza Oktavia, Lola Pizaini Pizaini Prameswari, Putri Rahmad Kurniawan Raja Sultan Firsky Ramu Will Sandra Reski Mai Candra Reski Mai Candra Reski Mai Candra Reski Mai Candra Reski Mai Candra Reski Mai Candra Rindi Yani Rometdo Muzawi, Rometdo Roni Setyawan Saputra, Rozi Sarah Lasniari Sarah Lasniari Surya Andika Suwanto Sanjaya Syarifuddin Syarifuddin Tania, Windy Teddie Darmizal Teddie Darmizal Thomas Alva Edison Tri Prastio Nugroho Wedi Kurniawan Wirasatria Putra Yazid, Fathuddin Yelfi Vitriani Yelfi Vitriani Zuriati Ardila Safitri