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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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PENERAPAN METODE K-MEANS UNTUK PENGELOMPOKAN DATA KAPAL BARANG (STUDI KASUS: KSOP PEKANBARU) Ariq At-Thariq Putra; Alwis Nazir; Febi Yanto; Suwanto Sanjaya; Fadhilah Syafria
SYNTAX Jurnal Informatika Vol 12 No 01 (2023): Mei 2023
Publisher : Universitas Singaperbangsa Karawang

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

Abstract

Transportation by sea is crucial for national development as it contributes to the growth of the economy and other ship transport sectors. With the increasing demand for cargo ships in the maritime transportation industry, data clustering is needed to review the growth of cargo ships in Riau. K-Means is a commonly used technique for clustering data that helps to classify data effectively. This algorithm is not influenced by data series and starts with the random determination of cluster centers during calculation. This cargo ship research aims to classify cargo ship data at the Pekanbaru KSOP, which allows the Pekanbaru KSOP to easily monitor the development of cargo ships.
Klasifikasi Sentimen Masyarakat di Twitter Terhadap Ancaman Resesi Ekonomi 2023 dengan Metode Naïve Bayes Classifier Dea Ropija Sari; Yusra Yusra; Muhammad Fikry; Febi Yanto; Fitri Insani
Jurnal Sistem Komputer dan Informatika (JSON) Vol 4, No 4 (2023): Juni 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i4.6276

Abstract

Economic recession is a condition in which the economic turnover of a country changes to slow or bad that can last for years as a result of the growth of the Gross Domestic Product (GDP) a country decreases over two decades significantly. Early warnings of the emergence of a global recession become a concern for all countries in the world, even global recessions also have a major impact on Indonesia. Such as declining public spending due to decreasing incomes, increasing unemployment, increasing poverty, and many of whom have to accept PHK or salary cuts. Economic strengthening will be important in minimizing these threats, this research needs to be done to see the response of the public to the threat of economic recession. Twitter provides a container to users to comment on the problem of the economy recession 2023 which can be used as sentiment classification information to know positive and negative comments. This research uses the naive bayes classifier algorithm. In this study there are seven main processes, namely data collection, manual labelling, processing, feature weighing (tf-idf), tresholding, naive bayes method classification, testing. From the 1408 comments data on Twitter about the threat of a 2023 economic recession. Based on the results of the classification, using 2 testing models namely data balance and non-balance data obtained the best balance data test results with the highest accuracy result with the process of classification using algortima naïve bayes classifier resulted in accurateness of 78% obtainable by using a comparison of 90% training data and 10% test data.
Perbandingan Algoritma C4.5 Dan Naïve Bayes Untuk Mengukur Tingkat Kepuasan Mahasiswa Dalam Penggunaan Edlink Dafwen Toresa; Ikhsan Hidayat; Edriyansyah Edriyansyah; Rometdo Muzawi; Taslim Taslim; Lisnawita Lisnawita; Febi Yanto
Jurnal Teknologi Dan Sistem Informasi Bisnis Vol 5 No 3 (2023): July 2023
Publisher : Prodi Sistem Informasi Universitas Dharma Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jteksis.v5i3.855

Abstract

The Faculty of Computer Science, Lancang Kuning University, as a private university in the city of Pekanbaru, uses the Sevima Edlink platform as a media for academic information systems and online learning. According to some students, there are still some obstacles encountered in understanding, using and functioning this edlink application. The purpose of this study was to measure the level of satisfaction of students of the Faculty of Computer Science in using Edlink using the C4.5 and Naïve Bayes algorithms. To measure the level of accuracy of the C4.5 and Naïve Bayes algorithms in order to measure the level of student satisfaction, the indicators used are the Servqual testing model, namely Tangible, Reability, Responsiveness, Assurance, and Empathy. Based on the level of accuracy of the two methods. In the dataset used there were 91 student respondents who had filled out the questionnaire. From the questionnaire data, it was then processed using both methods and 9 comparisons of the different Training Data and Testing Data were carried out. In general, students are satisfied and understand the use of the edlink application. This satisfaction was tested using the C4.5 Decision Tree Algorithm and the Naïve Bayes Classifier. Based on the comparison that has been carried out using the C4.5 Decision Tree Algorithm, it produces an average accuracy value of 77.78%, which is slightly more accurate than the Naïve Bayes Classifier which produces an average accuracy value of 71.11%.
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 : STMIK 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.
Sistem Manajamen Risiko Keamanan Aset Teknologi Informasi Menggunakan ISO 31000:2018 Candra, Reski Mai; Sari, Yuli Novita; Iskandar, Iwan; Yanto, Febi
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 5, No 1 (2019): Juni 2019
Publisher : Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (742.899 KB) | DOI: 10.24014/coreit.v5i1.8200

Abstract

DISKOMINFOPS Kabupaten Indragiri Hilir merupakan instansi yang menjadikan TI sebagai penggerak dalam keberlangsungan kinerja pemerintah. Disamping kesuksesan dalam merancang manajemen teknologi informasi, dibutuhkan juga manajemen risiko aset teknologi informasi pada DISKOMINFOPS Kabupaten Indragiri Hilir karena dinas tersebut belum menerapkan suatu kerangka kerja berbasis keamanan informasi dalam mengelola risiko aset teknologi informasi. Salah satu penyebabnya ialah kurangnya pemahaman pejabat teknologi informasi tentang manajemen keamanan terhadap aset teknologi informasi, sehingga memunculkan berbagai permasalahan, seperti perangkat keras di dinas tersebut masih banyak dalam keadaan tidak terawat dan rusak begitu saja tanpa adanya penanganan khusus. Dalam mewujudkan instansi pemerintah yang berbasis IT yang memiliki manajemen risiko yang baik, perlu menerapkan standar keamanan informasi yaitu ISO 31000:2018. Framework ini dapat memberikan prinsip dan pedoman yang generik pada manajemen risiko dengan konseptual. Penelitian ini bertujuan untuk membuat sistem manajemen keamanan risiko teknologi informasi yang ada di DISKOMINFOPS Kabupaten Indragiri Hilir menggunakan ISO 31000:2018.
Analisa dan Perbaikan Algoritma Line Maze Solving Untuk Jalur Loop, Lancip, dan Lengkung pada Robot Line Follower (LFR) Yanto, Febi; Welly, Irma
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 1, No 2 (2015): Desember 2015
Publisher : Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (654.097 KB) | DOI: 10.24014/coreit.v1i2.1231

Abstract

Pengujian robot line follower pada penelitian ini menggunakan arduino uno. Pada penelitian sebelumnya algoritma line maze solving yang digunakan sebagai metode pada robot line follower masih menyisakan kendala, di mana robot belum bisa melewati maze dengan jalur loop, jalur lengkung lebih dari 225 derajat, dan jalur lancip kurang dari sudut 45 derajat. Oleh karena itu dilakukan perbaikan, dan didapatkan algoritma yang baru. Algoritma ini kemudian diuji kembali pada robot dan berhasil menyelesaikan maze dengan jalur loop, jalur lengkung lebih dari 225 derajat , dan jalur lancip kurang dari 45 derajat.
Klasifikasi Citra Daging Sapi dan Babi Menggunakan CNN Alexnet dan Augmentasi Data Ikhwanul Akhmad DLY; Jasril Jasril; Suwanto Sanjaya; Lestari Handayani; Febi Yanto
Journal of Information System Research (JOSH) Vol 4 No 4 (2023): Juli 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v4i4.3702

Abstract

Konsumsi daging di Indonesia didominasi oleh sapi, kerbau, dan ayam. Namun, beberapa pedagang nakal mencampur daging sapi dengan daging babi sehingga sulit dibedakan oleh masyarakat awam. Beberapa penelitian telah menggunakan metode Convolutional Neural Network (CNN) untuk mengklasifikasikan citra, namun kekurangan data menjadi tantangan. Oleh karena itu, penelitian ini menerapkan teknik augmentasi data pada model CNN Alexnet untuk mengklasifikasikan daging sapi, babi, dan daging oplosan. Penelitian ini menggunakan dua rasio pembagian data yang berbeda, yaitu 90:10 dan 80:20, dengan total 600 data non-augmentasi dan 3000 data augmentasi yang dibagi menjadi tiga kelas. Beberapa hyperparameter diuji untuk mengoptimalkan kinerja model seperti optimizer Adaptive Moment Estimation (Adam), Stochastic Gradient Descent (SGD) dan Propagasi Root Mean Square (RMSprop) serta learning rate 0.1, 0.01, 0.001 dan 0.0001. Hasil menunjukkan bahwa penggunaan data citra augmentasi dengan optimizer Adam dan learning rate 0,001 memberikan accuracy tertinggi sebesar 85,00%. Sementara itu, penggunaan data citra non-augmentasi dengan skenario optimizer RMSprop dan learning rate 0, 0001 menghasilkan performa yang sedikit lebih rendah, yaitu mendapatkan accuracy 80.00%. Keduanya menggunakan perbandingan data 80:20. Teknik augmentasi data berhasil meningkatkan kinerja model deep learning dengan menciptakan data baru dari data yang ada.
Klasifikasi Sentimen Masyarakat di Twitter Terhadap Ancaman Resesi Ekonomi 2023 dengan Metode K-Nearest Neighbor Dimas Ferarizki; Yusra; Muhammad Fikry; Febi Yanto; Fitri Insani
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 2 (2023): Oktober 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i2.1306

Abstract

A recession is a decline in overall economic activity, this is considered a phase of significant and sustainable economic decline in various sectors and economic indicators. The threat of a recession in 2023 has become a topic of discussion in many countries, including Indonesia. This happens because Indonesia is threatened as a country affected by a recession due to weakening economic activity in the real sector. This sentiment classification research aims to analyze public opinion and opinion regarding the issue of recession news in 2023 which is conveyed via the social media platform Twitter. This research aims to understand whether these opinions fall into the category of positive sentiment or negative sentiment. Apart from that, this research also aims to measure the level of accuracy in classifying these sentiments into appropriate classes. This research has several main processes starting from data collection then manual data labeling, text processing, feature weighting (TF-IDF), Thresholding feature selection and K-Nearest Neighbor method classification. Based on the classification results using a testing model from a total of 1000 comment data divided between 596 positive class data and 404 negative class Twitter data regarding the threat of recession in 2023, the highest accuracy results were obtained at 85% at a value of k = 3 using the 90:10 comparison model training and testing data
Perbandingan Klasifikasi Citra CT-Scan Kanker Paru-Paru Menggunakan Contrast Stretching Pada CNN dengan EfficientNet-B0 Alfitra Salam; Febi Yanto; Surya Agustian; Siti Ramadhani
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 3 (2023): Desember 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i3.1448

Abstract

Data from the World Health Organization (WHO) indicates that in 2020, approximately 10 million people died from cancer. Smoking has been identified as a primary factor causing lung cancer, as cigarettes contain over 60 toxic substances that can trigger the development of the disease. The rate of lung cancer has rapidly increased due to excessive cigarette consumption. Detecting nodules in the lungs typically takes about 10-30 minutes. In this study, a Convolutional Neural Network (CNN) algorithm with EfficientNet-B0 architecture is employed to classify lung cancer. The preprocessing process involves contrast stretching, and various hyperparameter optimization techniques such as Adam, Adagrad, and SGD are used to enhance the CNN's performance. Average pooling with output dense layers of 64, 32, 16, 1 is utilized. Performance analysis is conducted using a confusion matrix. The highest classification results are achieved using the ADAM optimizer with a learning rate of 0.01, where accuracy reaches 72.48%, precision is 71.52%, recall is 64.2%, and the F1 score is 64.76%. Meanwhile, results obtained from the original dataset show differences. The highest classification result is obtained using the ADAM optimizer with a learning rate of 0.01, achieving an accuracy of 64.22%, precision of 52.69%, recall of 50.52%, and an F1 score of 43.51%. These results indicate that the use of contrast stretching in lung cancer classification preprocessing is highly effective in improving accuracy
Perbandingan Klasifikasi Citra CT-Scan Kanker Paru-Paru Menggunakan Image Enhancement CLAHE Pada EfficientNet-B0 Dzaky Abdillah Salafy; Febi Yanto; Surya Agustian; Fitri Insani
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 3 (2023): Desember 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i3.1514

Abstract

In recent years, there has been a significant increase in the global cancer related mortality rate. Among various cancer types, lung cancer has emerged as one of the highest incidence cases. Lung cancer predominantly affects males and is attributed to several factors, including exposure to cigarette smoke, long-term air pollution, and exposure to carcinogenic compounds such as radon, asbestos, arsenic, coal tar, and diesel fuel emissions. The growth of cancerous cells in the lungs can be detected using various imaging techniques, with CT-Scan being one of them. This research focuses on the classification of normal lung organs and those affected by cancerous cells. The classification process employs two types of data: original data and data processed with Contrast Limited Adaptive Histogram Equalization (CLAHE). The data is initially divided with 90:10 ratios before being trained using a Convolutional Neural Network (CNN). The CNN architecture used is EfficientNet-B0, with the assistance of different optimizers and learning rates. After testing, the model's performance is evaluated using a confusion matrix to compare the results between the use of original data and CLAHE-processed data. The use of CLAHE processed data yields higher evaluation metrics compared to the original data, achieving a precision of 87.9%, recall of 85.6%, F1-score of 85.11%, and accuracy of 85.29% in the 90:10 data split, with the Adam optimizer and a learning rate of 10-1. The research results reveal that the utilization of image enhancement, specifically Contrast Limited Adaptive Histogram Equalization (CLAHE), with an appropriate combination of clip limit and tile grid, can impact the model's performance in classifying image data.
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