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All Journal Teknika Syntax Jurnal Informatika Jurnal Ilmiah Rekayasa dan Manajemen Sistem Informasi Jurnal Informatika Jurnal CoreIT JURNAL MEDIA INFORMATIKA BUDIDARMA JIEET (Journal of Information Engineering and Educational Technology) Indonesian Journal of Artificial Intelligence and Data Mining Seminar Nasional Teknologi Informasi Komunikasi dan Industri INOVTEK Polbeng - Seri Informatika Sebatik Jurnal Nasional Komputasi dan Teknologi Informasi Krea-TIF: Jurnal Teknik Informatika JURIKOM (Jurnal Riset Komputer) JOISIE (Journal Of Information Systems And Informatics Engineering) Building of Informatics, Technology and Science Jurnal Teknologi Informasi dan Multimedia Jurnal Informatika dan Rekayasa Elektronik Jurnal Teknologi Dan Sistem Informasi Bisnis Zonasi: Jurnal Sistem Informasi 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 Inovasi Teknik Informatika Jurnal Ilmu Komputer Jurnal Teknik Informatika (JUTIF) Jurnal Computer Science and Information Technology (CoSciTech) Bulletin of Computer Science Research KLIK: Kajian Ilmiah Informatika dan Komputer Bulletin of Information Technology (BIT) Malcom: Indonesian Journal of Machine Learning and Computer Science Jurnal Sains dan Informatika : Research of Science and Informatic SATIN - Sains dan Teknologi Informasi Journal Of Artificial Intelligence And Software Engineering Jurnal Indonesia : Manajemen Informatika dan Komunikasi
Febi Yanto
Teknik Informatika, Universitas Islam Negeri Sultan Syarif Kasim Riau, Indonesia
Aerospace Engineering Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Neuroscience Transportation Other

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Kombinasi algoritma kriptografi vigenere cipher dengan metode zig-zag dalam pengamanan pesan teks Faris Apriliano Eka Fardianto Faris; Febi Yanto Febi; Iwan Iskandar Iwan; Pizaini Pizaini
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.v4i1.4787

Abstract

The rapid development of technology makes it easier to get information quickly and easily. Some information is public and some are confidential. Confidential information certainly requires security in maintaining confidentiality by parties not interested in the information. There are many forms of personal information, one of which is information in the form of text. Providing security to text messages can be done with cryptographic techniques. Cryptographic techniques work by encrypting the original message (plaintext) into text that is difficult to understand (ciphertext), which usually contains no meaning. This research uses classical cryptography where the encryption technique uses the decryption key used the same as the encryption key at the time of encoding. This research uses vigenere cipher and zigzag cipher cryptographic algorithms. Vigenere cipher algorithm is a cryptographic technique that uses a word or sentence with the length of the key adjusting to the plaintext. In comparison, zigzag ciphers use transposition techniques from columns and rows. The use of two cryptographic algorithms at once is intended to provide super security, encryption with layered keys and cryptanalysts will be difficult in cracking the information that has been encoded. Testing is carried out by performing mathematical calculations from vigenere and zigzag algorithms first which are used as a basis for implementation into text encoding simulation systems. On the system that has been created gives the same result as mathematical calculations. In ciphertext cracking tests using the Boxentrix system cannot return plaintext in the absence of a predefined key. While in performance testing the time depends on the number of characters used, the more the number of characters, the encryption and decryption time also increases.
Klasifikasi Sentiment Review Aplikasi MyPertamina Menggunakan Word Embedding FastText dan SVM (Support Vector Machine) Mustasaruddin Mustasaruddin; Elvia Budianita; M Fikry; Febi Yanto
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.v4i3.5695

Abstract

The MyPertamina application is a requirement for buying subsidized fuel oil (BBM), namely pertalite and diesel, the goal is that subsidized (BBM) purchases are right on target. The MyPertamina application has received many ratings and comments from the public, both positive and negative, with these comments and ratings expected to help the government as a benchmark in implementing a program. Therefore, this research aims to assess the MyPertamina application by grouping sentiment classes 90:10, 80:20 and 70:30. In this study, the method used is Fasttext and Support Vector Machine (SVM) to review the MyPertamina application. This research uses 8000 data, the data is grouped into three portions of data, with portions of 90:10, 80:20 and 70:30. The best SVM model was obtained with a data portion of 90:10 with a total of 7200 training data and 800 testing data, obtained 80% accuracy, 50% recall and 84% precision without undersampling. Meanwhile, if the amount of data is balanced (undersampling) with the number of positive data 1325, neutral 1325 and negative 1325, that is, with the benchmark of the lowest data value from the sentiment class, an accuracy of 67% is obtained, recall is 69% and precision is 57%. The highest number of sentiment classes from the 90:10 portion of the data is negative, namely 4300, neutral 1575 and positive 1325, because many users found reviews of the MyPertamina application, namely "after updating the MyPertamina application the bugs are getting worse".
Klasifikasi Sentiment Ulasan Aplikasi Sausage Man Menggunakan VADER Lexicon dan Naïve Bayes Classifier M Ikhsan Maulana; Elvia Budianita; Muhammad Fikry; Febi Yanto
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.v4i3.5854

Abstract

Battle Royale games are games that mix adventure and survival elements with last man standing game modes. One of the most popular battle royale games is the Sausage Man game. The number of complaints such as bugs, cheaters, and FPS which continues to decrease makes the game annoying. The solution is that developers must improve and improve game security so that users feel comfortable playing the game. There are many opinions or reviews from users regarding problems in the game, sentiment analysis will be carried out on the Sausage Man application review data on the Google play store as a process to produce categorization of opinions through reviews. The purpose of the researcher is to carry out a sentiment analysis to see positive, neutral or negative opinions from Sausage Man game users. The stages carried out in this study were data collection using web scraping, data labeling, text preprocessing, document weighting, classification, and evaluation. The results of data labeling using the VADER Lexicon obtained 1089 reviews (36.3%) for positive sentiment, 912 reviews for neutral sentiment (30.4%), and 999 reviews for negative sentiment (33.3%). Classification using the Naïve Bayes Classifier. Evaluation using the Confusion Matrix by dividing 90% training data and 10% test data produces an accuracy of 75%, 79% precision, and 75% recall. For the division of 80% training data 20% of the test data produces an accuracy of 73%, 76% precision and 73% recall. Positive sentences are found more often, but the accuracy is still below 80%.
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.
Optimasi Convolutional Neural Network NASNetLarge Menggunakan Augmentasi Data untuk Klasifikasi Citra Penyakit Daun Padi Afiana Nabilla Zulfa; Jasril Jasril; Muhammad Irsyad; Febi Yanto; Suwanto Sanjaya
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 2 (2023): April 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i2.6056

Abstract

Diseases that attack rice are one of the elements that can reduce rice production. Rice diseases include Blast, Brown Spot, Leaf Smut, and so on. Distinguishing rice disease from sight has a weakness because rice disease has similar symptoms and characteristics. Farmers lack knowledge in identifying rice disease types so that technology is needed that can help distinguish rice diseases. The method used for rice image classification in this study is the Convolutional Neural Network NASNetLarge architecture. There are two classification processes, namely the classification process using data augmentation and without data augmentation. The data consists of 4 classes, namely Healthy, Leaf Smut, Blast, and Brown Spot with a total of 440 original images and 1320 augmented images. This study uses data augmentation, namely Horizontal Flips, Vertical Flips, and Contrast. The results for the classification process without data augmentation obtained the highest accuracy, namely 94.31%, 100% precision, 100% recall, and 100% f1-score at a ratio of 80:20, learning rate 0.1, dense 256, batch size 32, and optimizer Adam. While the accuracy obtained in the classification process using data augmentation is 98.73%, 96.11% precision, 100% recall, and 98.01% f1-score at a ratio of 70:30, learning rate 0.1, dense 16, batch size 128, and the Adagrad optimizer. The accuracy results show that the data augmentation and hyperparameters used can increase the accuracy in classifying rice leaf disease images.
Klasifikasi Citra Stroke Menggunakan Augmentasi dan Convolutional Neural Network EfficientNet-B0 Nadila Handayani Putri; Jasril Jasril; Muhammad Irsyad; Surya Agustian; Febi Yanto
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 2 (2023): April 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i2.5981

Abstract

A stroke is a sudden onset of brain dysfunction, lasting for 24 hours or longer, resulting from clinically focal and global brain dysfunction. As many as 15 million people die from stroke each year. The stroke patients need an immediate treatment to minimize the risk of brain damage. One of the proponents for the stroke diagnosis is through a computed tomography (CT) image. In recent years, the image processing techniques capable to detect stroke patterns in a brain image, it can be useful for doctors and radiologists in doing diagnosis and treatment. This study aims to compare the level of accuracy using augmentation and without augmentation and hyperparameters using the Convolutional Neural Network in the EfficientNet-B0 architecture to classify ischemic, hemorrhagic, and normal brain stroke images. The data augmentation is produced by rotating, horizontal flipping, and contrast tuning of the original data. Testing data is provided as much as 20% of the portion of the original and augmented data, and the other 80% is used for the training process to find the optimal model. The model search is based on the composition of the training and validation data with a ratio of 70:30, 80:20 and 90:10. The experimental results show that the best performance is obtained for the combined original and augmented images, with accuracies of 97%, 93%, and 94%, respectively, for the three types of data-test: original, augmented, and combined. The merging of original and augmentated images for training data has shown that the model is robust enough in producing high accuracy results.
Penerapan Metode K-Means Clustering untuk Pemetaan Pengelompokan Lahan Produksi Tandan Buah Segar Abdussalam Al Masykur; Siska Kurnia Gusti; Suwanto Sanjaya; Febi Yanto; Fadhilah Syafria
Jurnal Informatika Vol 10, No 1 (2023): April 2023
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/inf.v10i1.15621

Abstract

Di Perkebunan Sei Lukut, Desa Maredan Barat, Kecamatan Tualang, Kabupaten Siak, Provinsi Riau, PT. Surya Intisari Raya, sebuah perusahaan swasta, mengelola perkebunan kelapa sawit. Memiliki 4 bagian lahan kelapa sawit yang terdiri dari 216 blok dengan total sekitar 4.000 Ha. Blok kelapa sawit biasanya mencakup 20 hektar dan berisi 28.000 pohon kelapa sawit, dengan kapasitas produksi bulanan sebesar 57 ton. Pemetaan klaster produksi tandan buah segar berupaya membantu pelaku usaha memutuskan kebijakan apa yang akan diterapkan untuk meningkatkan akurasi dan produktivitas produksi minyak sawit. Metode K-Means merupakan komponen dari metode clustering, yang merupakan subset dari kelompok Unsupervised Learning dan digunakan untuk mempartisi data ke dalam berbagai kategori. Untuk mengelompokkan blok lahan berdasarkan delapan data variabel luas pokok, panjang panen, daun lepas, curah hujan, pupuk, tujuan, dan persentase keberhasilan, penelitian ini akan menerapkan Indeks Davies Bouldin dengan alat RapidMiner. Kesimpulan akhir dari penelitian ini adalah sebuah aplikasi yang dapat memetakan pengelompokan areal produksi tandan buah segar dengan menerapkan metode K-Means Clustering, dengan nilai Davies Bouldin Index terkecil sebesar 0,921 pada jumlah cluster 3 yang termasuk Cluster C1 (Produktivitas Sedang). Terdiri dari 96 blok tanah, Cluster C2 (Produktivitas Rendah) terdiri dari 41 blok tanah, dan Cluster C3 (Produktivitas Tinggi) terdiri dari 79 blok tanah.In Sei Lukut Estate, West Maredan Village, Tualang District, Siak District, Riau Province, PT. Surya Intisari Raya, a private business, administers oil palm plantations. It has 4 sections of oil palm land made up of 216 blocks totaling about 4,000 Ha. Blocks of oil palm typically cover 20 hectares and contain 28,000 palm trees, with a monthly output capacity of 57 tons. The mapping of the production clusters for fresh fruit bunches seeks to help the business decide what policies to implement to increase the accuracy and productivity of palm oil production. The K-Means method is a component of the clustering method, which is a subset of the Unsupervised Learning group and is used to partition data into various categories. In order to group land blocks based on the eight variable data areas of total principal, harvest length, loose leaf, rainfall, fertilizer, goal, and percentage of success, this study will apply the Davies Bouldin Index with RapidMiner tools. The final conclusion of this research is an application that can map the grouping of fresh fruit bunch production areas by applying the K-Means Clustering method, with the smallest Davies Bouldin Index value of 0.921 in the number of clusters 3 including Cluster C1 (Medium Productivity) consisting of 96 blocks land, Cluster C2 (Low Productivity) consists of 41 land blocks, and Cluster C3 (High Productivity) consists of 79 land blocks.
Klasifikasi Citra Penyakit Daun Tanaman Padi Menggunakan CNN dengan Arsitektur VGG-19 Rahma Shinta; Jasril; Muhammad Irsyad; Febi Yanto; Suwanto Sanjaya
SAINS DAN INFORMATIKA : RESEARCH OF SCIENCE AND INFORMATIC Vol. 9 No. 1 (2023): Jurnal Sains dan Informatika : Research of Science and Informatic
Publisher : Lembaga Layanan Pendidikan Tinggi (LLDIKTI) Wilayah X

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Penurunan produksi padi disebabkan oleh serangan hama dan penyakit yang biasa terdapat pada bagian daun. Penelitian terkait klasifikasi jenis penyakit daun padi telah banyak dilakukan. Penelitian ini menerapkan metode Convolutional Neural Network (CNN) dengan arsitektur VGG-19 untuk klasifikasi citra penyakit daun tanaman padi. Tujuan penelitian ini adalah untuk membandingkan hasil akurasi pengujian dari model yang menggunakan augmentasi dan tanpa augmentasi data. Data pada penelitian ini terbagi atas 4 kelas, yaitu blast, brown spot, leaf smut, dan healthy dengan jumlah data asli sebanyak 440 dan data augmentasi sebanyak 1320 citra. Hasil pengujian menunjukkan bahwa akurasi tertinggi menggunakan augmentasi data yang diperoleh sebesar 94.31%, sedangkan akurasi tertinggi tanpa augmentasi data yang diperoleh sebesar 93.18%. Hasil penelitian menunjukkan bahwa augmentasi dapat meningkatkan hasil akurasi. Penggunaan optimizer Nadam menghasilkan nilai akurasi yang lebih tinggi dibandingkan Adamax. Hyper Parameter yang digunakan juga berpengaruh terhadap hasil akurasi pengujian.
Image Classification of Beef and Pork Using Convolutional Neural Network Architecture EfficienNet-B1 Isnan Mellian Ramadhan; Jasril - Jasril; Suwanto Sanjaya; Febi Yanto; Fadhilah Syafria
Indonesian Journal of Artificial Intelligence and Data Mining Vol 6, No 1 (2023): Maret 2023
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v6i1.21843

Abstract

The increasing demand for beef has made many meat traders mix beef with pork to get more profit. Mixing beef and pork is harmful, especially for Muslims. In this study, the EfficientNet-B1 Convolutional Neural Network (CNN) approach was used to classify beef and pork. Experiments were conducted to compare accuracy using original data (without data augmentation) and with data augmentation. The data augmentation techniques used are rotation and horizontal flip. The total dataset after the data augmentation process is 3000 images. Many different settings were tested, including learning rates (0.00001, 0.0001, 0.001, 0.01, 0.1), batch size (32, 64), and optimizer (Adam, Adamax). After testing the Confusion Matrix, the highest accuracy results were obtained using data augmentation with a batch size of 32 of 98%. Meanwhile, those without data augmentation were 96%
Co-Authors Abdul Haris Abdussalam Al Masykur Adha, Martin Afiana Nabilla Zulfa Afriyanti, Liza Afroni, Hallend Agustina, Auliyah Alfitra Salam Alwis Nazir Andri Andri Aprilia, Risma Arif Mudi Priyatno Ariq At-Thariq Putra Baehaqi citra ainul mardhia putri Dafwen Toresa Dea Ropija Sari Destri Putri Yani Dewi, Nurika Dicky Abimanyu Dimas Ferarizki Dwitama, Raja Zaidaan Putera Dzaky Abdillah Salafy Edriyansyah Eka Pandu Cynthia Eka Pandu Cynthia Eka Pandu Cynthia, Eka Pandu Elin Haerani Elvia Budianita Fadhilah Syafria Fajar Febriyadi Fajri Fahreza Azeta Faris Apriliano Eka Fardianto Faris Fauzan Ray T Fauziyyah, Laila Nurul Fitra Kurnia Fitri Insani Fitri Insani Gusman, Deddy Gusti, Gogor Putra Hafi Puja Gusti, Siska Kurnia Hallend Afroni Hanif, Wan Muhammad Harni, Yulia Hatta, M Ilham Hidayat, Rizki Ichsan Permana Putra Idhafi, Zaky Iis Afrianty Iis Afrianty Ikhsan Hidayat Ikhwanul Akhmad DLY Illahi, Ridho Iqbal Salim Thalib Irma Welly, Irma Irsyad , Muhammad Isnan Mellian Ramadhan Iwan Iskandar Iwan Jannata, Nanda Jasril Jasril Jasril Jasril Jasril Jasril Jeki Dwi Arisandi Kurniansyah, Juliandi Lestari Handayani Lestari Handayani Lisnawita Lisnawita M Fikry M Ikhsan Maulana M. Afdal M. Fadil Martias Masaugi, Fathan Fanrita Mazdavilaya, T Kaisyarendika Morina Lisa Pura Muhammad Affandes Muhammad Fahri Muhammad Fikry Muhammad Fikry Muhammad Fikry Muhammad Haiqal Dani Muhammad Irsyad Muhammad Irsyad Muhammad Irsyad Mustasaruddin Mustasaruddin Nabyl Alfahrez Ramadhan Amril Nadila Handayani Putri Nazruddin Safaat H Nazruddin Safaat H Negara, Benny Sukma Niken Aisyah Maharani Herwanza Nining Erlina Novriyanto Novriyanto Nurika Dewi Okta Silvia M Permata, Rizkiya Indah Pizaini Pizaini Prananda, Alga Pratama, Dandi Irwayunda Putra, Wahyu Eka Putri Ayuni, Desy Putri Zahwa Rahma Shinta Rahmad Abdillah Rahman, Muhammad Taufikur Rahmat Al Hafiz Raja Joko Musridho Reski Mai Candra Reski Mai Candra Reski Mai Candra Rometdo Muzawi, Rometdo Roni Setyawan RR. Ella Evrita Hestiandari Sandy Ilham Hakim Syasri Sarah Lasniari Sarah Lasniari Shahira, Fayza Siti Ramadhani Sofiyah, Wan Sugandi, Hatami Karsa Surya Agustian Suwanto Sanjaya Syafria, Fadhillah Ulfah Adzkia Wang, Shir Li Wijaya, Andy Huang Wirdiani, Putri Syakira Yenggi Putra Dinata Yuli Novita Sari, Yuli Novita Yusra Yusra Yusra Yusra Yusra Yusra Yusra Yusra Yusra, Yusra