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All Journal ComEngApp : Computer Engineering and Applications Journal Syntax Jurnal Informatika Jurnal Ilmu Komputer dan Agri-Informatika SITEKIN: Jurnal Sains, Teknologi dan Industri 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 JURNAL INSTEK (Informatika Sains dan Teknologi) Jurnal Informatika Universitas Pamulang Sebatik Jurnal Teknoinfo ICETIA Jurnal Nasional Komputasi dan Teknologi Informasi IJISTECH (International Journal Of Information System & Technology) JURIKOM (Jurnal Riset Komputer) Informatika : Jurnal Informatika, Manajemen dan Komputer Building of Informatics, Technology and Science Zonasi: Jurnal Sistem Informasi Jurnal Informatika Ekonomi Bisnis Jurnal Tekinkom (Teknik Informasi dan Komputer) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) Jurnal Sistem Komputer dan Informatika (JSON) JUKI : Jurnal Komputer dan Informatika IJISTECH Information System Journal (INFOS) Bulletin of Computer Science Research KLIK: Kajian Ilmiah Informatika dan Komputer JUSTIN (Jurnal Sistem dan Teknologi Informasi) Bulletin of Information Technology (BIT) Knowbase : International Journal of Knowledge in Database Malcom: Indonesian Journal of Machine Learning and Computer Science Jurnal Sains dan Informatika : Research of Science and Informatic Jurnal Informatika Ekonomi Bisnis
Suwanto Sanjaya
Universitas Islam Negeri Sultan Syarif Kasim Riau
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Neuroscience Social Sciences Transportation Other

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Journal : JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH)

 1 
Clustering Vaksinasi Penyakit Mulut dan Kuku Menggunakan Algoritma K-Means Adrian Maulana; Alwis Nazir; Reski Mai Candra; Suwanto Sanjaya; Fadhilah Syafria
Journal of Information System Research (JOSH) Vol 4 No 3 (2023): April 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v4i3.3363

Abstract

Foot and Mouth Disease (FMD) is a viral infectious disease that is acute and highly contagious in artiofactyl or even-toed hoofed animals. This disease is caused by tyoe A virus forum picornaviridae, genus Apthovirus namely Aphtaee epizootecae. This disease has a development period of 1-14 days since the infected animal. The defense of this virus is quite strong and survives in glands, milk bones and milk products. The morbidity rate is up to 100% and mortality is high in infected young animals. Areas with the highest transmission of foot and mouth disease are areas with high livestock density, so stricter biosecurity and animal traffic control must be implemented to prevent the disease. The problem in the Departement of Livestock and Animal Health of Riau Province is the difficulty in categorizing food and mouth disease vaccination data of which regions have done the first and second vaccine specifically for cattle in Riau Province. Therefore, this study will categorizing the first and sceond vaccine eith high immunity using K-Means algorithm. Parameter used are vaccination status, breed, gender and age. By applying the K-Means algorithm, two clusters are formed, namely the cluster with high immunity of 21232 cows, and the cluster with low immunity 48704 cows. Testing with DBI with K=2 produces a value of 0.416.
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 Kelayakan Air Minum dengan Backpropagation Neural Network Berbasis Penanganan Missing Value dan Normalisasi Kurniawan, Saifur Yusuf; Sanjaya, Suwanto; Vitriani, Yelfi; Afrianty, Iis
Journal of Information System Research (JOSH) Vol 6 No 1 (2024): Oktober 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i1.5871

Abstract

The issue of drinking water quality and its suitability for human consumption represents a significant concern in contemporary society, particularly in the context of maintaining public health. The existing research on the classification of drinking water eligibility has yet to yield conclusive results. The objective of this research is to utilize the backpropagation neural network method to categorize drinking water feasibility data, thereby ensuring that the water consumed meets established safety standards. The data utilized in this study were obtained from an open repository and encompass a total of 3,276 data points. The data set comprises nine water quality parameter attributes, namely pH, hardness, solids, chloramines, sulfate, conductivity, organic carbon, trihalomethanes, and turbidity. The data underwent a series of pre-processing steps, including the removal of missing values, the replacement of missing values with the average value of the attribute, and normalization using the MinMax Scaler and Z-score methods. The artificial neural network architecture comprises three principal components: input, hidden, and output neurons. The optimal architecture scenario is [9; 17; 15; 10; 1], comprising nine input neurons, 17 neurons in the initial hidden layer, 15 neurons in the second hidden layer, 10 neurons in the third hidden layer, and a single output neuron. The evaluation results demonstrate that this model effectively classifies drinking water eligibility data with an accuracy rate of 0.6579. However, the results indicate that the accuracy achieved requires further improvement for more reliable applications. These findings illustrate the promising potential of the BPNN method in classifying drinking water quality data.
Co-Authors Abdussalam Al Masykur Adrian Maulana Afiana Nabilla Zulfa Ahmad Fauzan Ahmad Paisal Ahmad, Rizmah Zakiah Nur Al Fiqri, M. Faiz Alwis Nazir Alwis Nazir Alwis Nazir Alwiz Nazir Amalia Hanifah Artya Annisa Putri Aqilah, M Alfandri Arif Mudi Priyatno Ariq At-Thariq Putra Aulia Ramadhani Baehaqi Cut Lira Kabaatun Nisa Darmila Deny Ardianto Dodi Efendi efni humairah Eka Pandu Cynthia Elin Haerani Elvia Budianita Erni Rouza, Erni Ersad Alfarsy Absar, Ersad Alfarsy Fadhilah Syafria Fadhilla Syafria Fakhrezi, Muhammad Dzaki Febi Yanto Felian Nabila Fitri Insani Fitri Insani Fitri Insani (Scopus ID: 57190404820) Fitri, Dina Deswara Gusrifaris Yuda Alhafis Gusti, Siska Kurnia Hafez Almirza Hardiansyah, Muhammad Vio Harni, Yulia Hartini Hartini Iis Afrianty Iis Afrianty Ikhwanul Akhmad DLY Insani (Scopus ID: 57190404820), Fitri Irman Hermadi Isnan Mellian Ramadhan Israldi, Tino Iwan Iskandar Iwan Iskandar Jasril Jasril Jasril Jasril Jasril Jasril Karina Julita Kurnia Rahman, Fikri Kurniawan, Saifur Yusuf Lestari Handayani Lestari Handayani Lestari Handayani Lia Anggraini Lola Oktavia M. Fadil Martias Masaugi, Fathan Fanrita Maulana Junihardi Mazdavilaya, T Kaisyarendika Megawati Megawati Morina Lisa Pura Muhammad Affandes Muhammad Fikry Muhammad Irfan Syah Muhammad Irsyad Muhammad Irsyad Nabyl Alfahrez Ramadhan Amril Nazir, Alwis Nazruddin Safaat Nazruddin Safaat H Negara, Benny Sukma Novi Yanti Novriyanto Novriyanto Novriyanto Pangestu, Yoga Pizaini Pizaini Puspa Melani Almahmuda Putri Ayuni, Desy Radili, Adi Rahma Shinta Rahmad Abdillah Rahmad Abdillah Ramadhan, Muhammad Ilham Ramu Will Sandra Reski Mai Candra Reski Mai Candra Reski Mei Candra Riska Yuliana Saputra, Nugroho Wahyu Sarah Lasniari Sarah Lasniari Shahira, Fayza Sugandi, Hatami Karsa SURYA ADITYA GD Surya Agustian Syaputra, Muhammad Dwiky Ulfah Adzkia Vitriani, Yelfi Yani, Susmi Syahfrida Yelfi Vitriani Yeni Fariati Yusra Yusra, Yusra Yusril Hidayat