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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) TELKOMNIKA (Telecommunication Computing Electronics and Control) SITEKIN: Jurnal Sains, Teknologi dan Industri JUSIFO : Jurnal Sistem Informasi Jurnal Ilmiah Rekayasa dan Manajemen Sistem Informasi Sistemasi: Jurnal Sistem Informasi JURNAL MEDIA INFORMATIKA BUDIDARMA Seminar Nasional Teknologi Informasi Komunikasi dan Industri INOVTEK Polbeng - Seri Informatika JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Inotera JURIKOM (Jurnal Riset Komputer) Jurnal Sistem Cerdas JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Building of Informatics, Technology and Science Jurnal Sistem informasi dan informatika (SIMIKA) Zonasi: Jurnal Sistem Informasi Indonesian Journal of Electrical Engineering and Computer Science JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal Sistem Komputer dan Informatika (JSON) Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Jurnal Teknik Informatika (JUTIF) Jurnal Pendidikan dan Teknologi Indonesia Indonesian Journal of Informatic Research and Software Engineering Malcom: Indonesian Journal of Machine Learning and Computer Science Journal of Informatics, Electrical and Electronics Engineering The Indonesian Journal of Computer Science INOVTEK Polbeng - Seri Informatika
Febi Nur Salisah
Program Studi Sistem Informasi, Fakultas Sains Dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau
Aerospace Engineering Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture 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 Materials Science & Nanotechnology Mathematics Mechanical Engineering Other

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Pengaruh Normalisasi Data Terhadap Performa Hasil Klasifikasi Algoritma Backpropagation: The Effect of Data Normalization on the Performance of the Classification Results of the Backpropagation Algorithm Permana, Inggih; Salisah, Febi Nur Salisah
Indonesian Journal of Informatic Research and Software Engineering (IJIRSE) Vol. 2 No. 1 (2022): Indonesian Journal of Informatic Research and Software Engineering
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (682.262 KB) | DOI: 10.57152/ijirse.v2i1.311

Abstract

Keberhasilan Algoritma Backpropagation (BP) tergantung pada kualitas data. Sehingga, normalisasi data merupakan proses yang penting. Akan tetapi, beberapa penelitian juga ada yang tidak menggunakan normalisasi data. Oleh sebab itu, penelitian ini mengukur pengaruh normalisasi data terhadap performa hasil klasifikasi Algoritma Backpropagation. Agar diketahui apakah normalisasi benar-benar bisa meningkatkan performa hasil klasifikasi pada Algoritma BP. Penelitian ini menggunakan tiga metode normalisasi data, yaitu: MinMax Normalization; MaxAbs Normalization; dan Z-Score Normalization. Berdasarkan hasil percobaan didapat bahwa jika data yang digunakan terdapat perbedaan rentang nilai antar atribut yang tidak berbeda jauh, maka BP tanpa normalisasi data bisa menjadi pilihan terbaik. Akan tetapi jika pada data terdapat atribut yang memiliki perbedaan rentang nilai yang jauh dari atribut lainnya, maka menggunakan normalisasi data bisa menjadi pilihan terbaik. Berdasarkan hasil percobaan juga didapat bahwa Z-Score Normalization merupakan metode normalisasi terbaik.
Penggunaan Metode FMEA dalam Penilaian Manajemen Risiko Keamanan Sistem Informasi Rumah Sakit Saputri, Setia Ningsih; Salisah, Febi Nur; Maita, Idria; Rozanda, Nesdi Evrilyan
Jurnal Inovtek Polbeng Seri Informatika Vol 9, No 1 (2024)
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/isi.v9i1.3951

Abstract

Untuk mengurangi risiko yang terkait dengan suatu organisasi, identifikasi dan pengendalian risiko disebut manajemen risiko. Tujuan manajemen risiko adalah untuk melindungi dan memperkecil setiap kegagalan keamanan sistem informasi perusahaan dari tingkat risiko yang paling tinggi sehingga tidak dapat mencapai tujuan perusahaan. Metode FMEA (Failure Mode and Effect Analysis) adalah salah satu metode yang digunakan dalam penilaian dan pengelolaan risiko. Penelitian ini menganalisa risiko pada sistem informasi suatu industri yang bergerak dibidang kesehatan, yaitu RSU Indah Bagan Batu. RSU Indah Bagan Batu menerapkan sistem informasi yang Bernama SIMRS Khanza. Menentukan tingkat risiko yang ada pada sistem informasi RSU Indah Bagan Batu adalah tujuan dalam penelitian ini, karena SIMRS Khanza harus melindungi data pasien, dokter, obat-obatan, dan staf lainnya dari potensi ancaman. Berdasarkan penilaian risiko menggunakan metode FMEA yang dihitung melalui perkalian severity, occurrence, dan detection maka mendapatkan hasil RPN dengan 34 risiko yang terdapat pada sistem informasi yang digunakan. Dari 34 RPN yang dihasilkan terdapat 5 kategori level RPN yaitu 3 kategori very high dengan rentang 500-504, 5 kategori high dengan rentang 120-180, 6 kategori moderate dengan rentang 84-108, 17 kategori low dengan rentang 20-72 dan 4 kategori very low dengan rentang 8-15.
Perbandingan Metode TAM Dan UTAUT Dalam Penerimaan Dan Kepuasan Sistem Informasi Administrasi Akademik Nurrahma, Intan; Salisah, Febi Nur; Ahsyar, Tengku Khairil; Rahmawita, Medyantiwi
Jurnal Inovtek Polbeng Seri Informatika Vol 8, No 2 (2023)
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/isi.v8i2.3768

Abstract

Universitas XYZ merupakan lembaga yang bergerak dalam bidang pendidikan. Saat ini banyak universitas yang menggunakan sistem informasi akademik untuk memudahkan pekerjaan agar menjadi lebih efektif dan efisien. Hasil survei menunjukkan bahwa adanya beberapa kendala pada sistem informasi administrasi akademik yaitu terjadinya error ataupun server down kondisi error ini seperti tidak bisa login ataupun tidak bisa mengakses fitur yang tersedia kendala berikutnya yaitu ketika mahasiswa ingin mengganti password mereka tidak bisa langsung mengubah di sistem tersebut tetapi harus ke sistem yang lain untuk mengubah password. Penelitian ini menggunanakan metode TAM Dan UTAUT. Tujuan penelitian ini yaitu untuk mengetahui metode mana yang lebih baik dalam penerimaan dan kepuasan penerapan sistem informasi. Hasil dari penelitian menunjukkan Metode UTAUT lebih mampu menjelaskan dengan baik dari pada metode TAM dalam membandingkan penerapan sistem informasi administrasi akademik dimana dengan menggunakan metode TAMdiperoleh nilai R-square pada TAM variabel IT Acceptance adalah 78,2% yang mana nilai ini termasuk kategori model yang kuat dan variabel User Satisfaction nilai R-square nya yaitu 65,3% dimana nilai tersebut masih termasuk dalam kategori kuat. sedangkan pada metode UTAUT R-square untuk variabel IT Acceptance yaitu 83,6% yang mana nilai ini termasuk kategori model yang kuat pada variabel User Satisfaction nilai R-square nya yaitu 74,6% dimana nilai tersebut termasuk kategori kuat.
Peramalan Jumlah Kedatangan Wisatawan Menggunakan Support Vector Regression Berbasis Sliding Window Fitriah, Ma’idatul; Permana, Inggih; Salisah, Febi Nur; Munzir, Medyantiwi Rahmawita; Megawati, Megawati
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 3 (2024): Juli 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i3.7408

Abstract

As a developing city, Pekanbaru has the potential for attractive tourist attractions for tourists. The arrival of tourists has had a big positive impact on the economy of Pekanbaru City. The number of tourist arrivals can experience ups and downs every month, for this reason it is necessary to forecast the number of tourists in the future. This research aims to apply the Orange Data Mining application in predicting the number of tourist arrivals by comparing the kernels in the Support Vector Regression (SVR) method and applying Sliding Window size 3 to window size 13 to transform into time series data. As well as sharing data using the K-Fold Validation method with a value of K-10. Then the performance of the kernels used can be seen using the Test and Score widget which presents the results of Root Mean Absolute Error (MAE), Mean Square Error (MSE), Root Mean Square Error (RMSE), dan R-squared(R2). The results for forecasting the number of tourist arrivals to Pekanbaru City using the SVR method show that the RBF Kernel is the optimal choice compared to the Polinomial and Linear Kernels. The results of the Test and Score widget show that the RBF Kernel with window size 10 has lower MAE, MSE and RMSE values, namely 0.118, 0.022 and 0.147. Apart from that, the comparison of R2 in window size 10 for Kernel RBF shows better performance with a value of 0.519.
Prediksi Jumlah Bayi Penerima Imunisasi DPT 1 dan DPT 2 Menggunakan Support Vector Regression Idriani R, Nova; Permana, Inggih; Salisah, Febi Nur; Megawati, Megawati; Rahmawita M, Medyantiwi
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 3 (2024): Juli 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i3.7694

Abstract

Vaccination against diphtheria, pertussis (whooping cough), and tetanus is known as DPT immunization, which protects a person from three serious diseases. This vaccine is given in the form of an injection where there are 5 antigens in one injection of the vaccine. DPT immunization is a complete routine immunization that will be continued in grades 1 to 6 elementary school. DPT immunization is feared by mothers because of the side effects that occur in babies after the vaccine injection, namely that the baby will have a fever and be fussy. This has resulted in delays in collecting data on babies who have received this immunization, which has an impact on estimates of babies who will receive DPT immunization in the following month. Of course, this will disrupt the stock of vaccines provided, causing the potential for them to be out of stock. To overcome this problem, it is necessary to collect data on babies who have received DPT in the previous month. This data will be used to predict babies who will receive DPT immunization in the following month using the Support Vector Regression (SVR) method. So that the community health center can provide information regarding the prediction of the number of babies who will receive DPT immunization. This method uses three kernels and a Sliding Window to divide the data into smaller segments, moving alternately across the time series data, making it suitable for predicting babies who will receive DPT immunization in the next time interval. From the three kernels used on the two data that have been separated into DPT 1 and DPT 2, windowing size 3 linear kernels were obtained which were selected as an accurate evaluation of model work on DPT 1 with MAPE values of 3.35, RMSE 0.193, and R2 0.1. And windowing size 3 RBF kernels are more optimal in DPT 2 with MAPE values of 7.86, RMSE 0.163, and R2 0.288.
Analysis User Satisfaction of XYZ Application with End User Computing Satisfaction Method and Delone and Mclean Giansyah, Qhoiril Aldi; Maita, Idria; Megawati, Megawati; Salisah, Febi Nur
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 18, No 4 (2024): October
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.101004

Abstract

Rapidly developing technology has made internet service providers such as XYZ provide an application which promises convenience for its consumers to make transactions to purchase credit, internet quota, and other services. this application is one of the fairly well-known providers and is currently competing with other providers. this provider targets young people because in terms of its products which are quite competitive with a wide reach and prices that are quite cheap for young people. Of course, customers will feel the convenience of transacting using the official service application. The convenience offered can make business processes more efficient and can be used to improve the quality of their services.This study uses the End User Computing Satisfaction model method and the Delone and Mclean model with 9 research variables (content, accuracy, format, ease of use, timeliness, system quality, information quality, and service quality, and security), The purpose of this study is to assess the effectiveness of the application..Out of the nine variables included in the research results, only two Service Quality and Accuracy have a statistically significant impact. The model provided in this study has a Rsquared score of 0.792, indicating a strong level of customer satisfaction.
Perbandingan Performa Algoritma NBC, C4.5, dan KNN dalam Analisis Sentimen Masyarakat terhadap Krisis Petani Muda pada Media Sosial Facebook Nurkholis, Nurkholis; Permana, Inggih; Salisah, Febi Nur; Mustakim, Mustakim; Afdal, M
Building of Informatics, Technology and Science (BITS) Vol 6 No 3 (2024): December 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i3.6082

Abstract

In Indonesia, young farmers face various challenges and crises that hinder the growth and sustainability of the agricultural sector. They face obstacles such as lack of access to capital, limited technology, climate change, and low selling prices for their crops. In addition, they also often face problems in obtaining accurate and relevant information in an effort to facilitate better decision-making in agricultural businesses, so that the interest of young people today to become farmers is decreasing. The study aims to Compare the Performance of NBC, C4.5, and KNN Algorithms in the Analysis of Public Sentiment towards the Young Farmer Crisis on Facebook Social Media. The application of the K-Fold Cross Validation method is (K = 10). Sentiment analysis is carried out with 3 labels (positive, negative, and neutral). The data used in making the classification model (data from preprocessing the stemming column) using (Google Colab) amounted to 4,878 data with Positive sentiment of 43.13% (2,104), Neutral 39.59% (1,931), Negative 17.28% (843) from the initial data without nested comments, which is 4,981 and the total number of Facebook data is 2,900 likes, 6,700 comments, and 3.3 million viewers. The accuracy of the NBC algorithm is 57.32%, the C4.5 algorithm is 98.42%, and the KNN algorithm (K = 19) is 97.33%. It can be concluded that the results of the comparison of the performance of the three algorithms using (Rapidminer10.3), the C4.5 algorithm gets a higher accuracy of 98.42% and is superior because it produces a decision tree.
IMPLEMENTASI SISTEM PREDIKSI KETERSEDIAAN PRODUK DENGAN METODE SIMPLE MOVING AVERAGE PADA UD. MITRA ARTHA JAYA Salisah, Febi Nur; Gustinov, Mhd Dion; Mawaddah, Zuriatul
Seminar Nasional Teknologi Informasi Komunikasi dan Industri 2024: SNTIKI 16
Publisher : UIN Sultan Syarif Kasim Riau

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

Abstract

UD. Mitra Artha Jaya menghadapi masalah dalam pengelolaan persediaan barang, yang menyebabkan beberapa produk sering kedaluwarsa atau kehabisan stok. Penelitian ini bertujuan untuk mengimplementasikan sistem peramalan menggunakan metode Simple Moving Average (SMA) untuk memperbaiki perencanaan kebutuhan pelanggan dan mengoptimalkan persediaan produk. Metode SMA dipilih karena kemampuannya dalam menggunakan data permintaan aktual untuk menghasilkan ramalan yang akurat. Penelitian ini menggunakan metode Waterfall untuk pengembangan sistem, yang mencakup identifikasi kebutuhan aplikasi, pengelolaan data master, dan perhitungan error menggunakan MAPE, MAD, dan MSE. Hasil penelitian ini didapatkan Teh Gelas dengan MAD dan MSE terendah, MAPE sebesar16,09% hal ini menunjukan bahwa produk Teh gelas Akurat dan Produk dengan peramalan paling tidak akurat terdapat pada produk Aqua dengan MAD dan MSE tertinggi, MAPE sebesar 20,92% dan juga produk Teh Pucuk dengan MAPE tertinggi sebesar 27,45%. Pengujian sistem yang dilakukan mendapatkan hasil yang bagus sehingga sitem dapat membanti dalam proses peramalan
Analisa Manejemen Risiko Sistem Informasi Penjualan Menggunakan Metode Failure Mode Effects and Analysis (FMEA) Syahri, Alfi; -, Megawati; Afdal, M; Nur Salisah, Febi
JATISI Vol 11 No 4 (2024): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v11i4.8438

Abstract

Teknologi informasi telah banyak diterapkan di berbagai perusahaan, salah satunya PT XYZ yang telah menerapkan sistem informasi penjualan atau BEI sebagai otomatisasi proses bisnis perusahaan. Namun pada praktiknya manajemen risiko sistem informasi BEI belum diterapkan dengan baik, masih terdapat kendala dan insiden pengelolaan. Penelitian ini bertujuan untuk menganalisis manajemen risiko sistem informasi ISX dengan menggunakan framework FMEA (Failure Mode Effects and Analysis). FMEA dapat mengidentifikasi risiko berdasarkan tingkat keparahan suatu kejadian, seberapa sering kejadian tersebut terjadi, dan seberapa tinggi tingkat deteksinya. Dari variabel-variabel tersebut akan dihasilkan Risk Priority Number (RPN) yang dapat menentukan prioritas risiko atau permasalahan yang paling kritis. Hasil penelitian ini menunjukkan bahwa sistem ISX mempunyai 2 risiko dengan tingkat sangat tinggi, 1 risiko dengan tingkat tinggi, 1 risiko dengan tingkat menengah, 21 risiko dengan tingkat rendah, dan 5 risiko dengan tingkat sangat rendah. Risiko yang berada pada tingkat yang sangat tinggi dan tinggi merupakan risiko yang diprioritaskan dan perlu mendapatkan tindakan dan pengendalian risiko.
Evaluasi User Experience Pada Game Playerunknown’s Battlegrounds (Pubg) Mobile Menggunakan Metode Heuristic Evaluation Danil Risaldi; Megawati; Idria Maita; Febi Nur Salisah
The Indonesian Journal of Computer Science Vol. 13 No. 6 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i6.4468

Abstract

Pubg Mobile game is a game developer from "Tencent Games", Pubg Mobile was released on February 9, 2018. The genre of this game is Battle royale, where players can play with 100 people at once online. There are several negative reviews from Pubg Mobile game users, namely, character bugs often occur during the game, often force close on 3 GB RAM smartphones and loading into the game is very long so that users are very uncomfortable when using it. This study uses the Heuristic Evaluation method as a measurement of user experience. This study aims to evaluate the user experience in the Pubg Mobile game application using the Heuristic Evaluation method. The results of this study indicate that the Visibility Of System Status variable gets a total value of p1 75.6%, total p2 72.9%, total p3 75.6% with the status "Good", Match Between System and Real World gets a total value of p1 64.8%, total p2 75.6%, total p3 72.9% with the status "Good". User Control And Freedom gets a total value of p1 72.9%, total p2 75.6%, with the status of "Good", Consistency And Standards gets a total value of p1 51.3%, total p2 70.2% with the status of "Not Good", Error Prevention gets a total value of p1 70.2%, total p2 70.2%, with the status of "Good", Recognition Rather Than Recall gets a total value of p1 72.9%, total p2 72.9%, with the status of "Good", Flexibility And Efficiency Of Use gets a total value of p1 56.7%, with the status of "Quite Good", Aesthetic And Minimalist Design gets a total value of p1 78.3%, with the status of "Good", Help User Recognize, Diagnose And Recover From Errors gets a total value of p1 78.3%, with the status of "Good", and the last variable Help And Documentation gets a total p1 78.4%, with the status of "Good". It is necessary to make improvements or enhancements to the Consistency And Standards variables to get better values ​​than before.
Co-Authors A Anggraini Afdal Muhammad Efendi Ahsyar, Tengku Khairil Anggi Widya Atma Nugraha Anggia Anfina Anisa Nirmala, Fitri Anwar, Tengku Khairil Arabiatul Adawiyah Arif Marsal Arif Marsal Arif Marsal Arrazak, Fadlan Bayu Putra Danil Risaldi Darmawan, Reza Dewi Astuti Efendi, Harisman Eki Saputra Eki Saputra Eki Saputra Elin Haerani Endah Purnamasari Esis Srikanti Fachrurozi Fadhilah Syafria Fadil Rahmat Andini Febrian, Dany Fernanda, Ustara Dwi Fiki Fitri Wulandari Fitriah, Ma’idatul Fitriah, Ma’idatul Fitriani Muttakin Fitriani Muttakin Giansyah, Qhoiril Aldi Gustinov, Mhd Dion Hasbi Sidiq Arfajsyah Hendri, Desvita Husaini, Fahri Idria Maita Idria Maita Idria Maita Idriani R, Nova Imam Muttaqin Indah Lestari, Indah Indri Dian Pertiwi Inggih Permana Intan, Sofia Fulvi Jayadi, Puguh Jazman , Muhammad Jazman, Muhammad Kusuma, Gathot Hanyokro Leony Lidya M Afdal M. Afdal M. Afdal M. Afdal M.Afdal Maulana, Rizki Azli Mawaddah, Zuriatul Mega wati, Mega Megawati Megawati - Megawati Megawati Mona Fronita, Mona Mubarak MR, Najmuddin Muhammad Afdal Muhammad Iqbal Indrawan Muhammad Jazman Muhammad Luthfi Muhammad Luthfi Hamzah Muhammad Munawir Arpan Munzir, Medyantiwi Rahmawita Mustakim Mustakim Muttakin, Fitriani Nabila Putri Nailul Amani Nardialis Nardialis Nasution, Nur Shabrina Naufal Fikri, R. Adlian Nesdi Evrilyan Rozanda Nesdi Evrilyan Rozanda Norhavina Norhavina Nuraisyah Nuraisyah Nurkholis Nurkholis Nurrahma, Intan Puput Iswandi Putri, Amanda Iksanul Rahmawita M, Medyantiwi Rahmawita, Medyantiwi Rangga Arief Putra Ria Agustina Rice Novita Rizka Fitri Yansi Rizki Pratama Putra Agri Rozanda, Nesdi Evrilyan Sanusi Saputri, Setia Ningsih Sari, Gusmelia Puspita Sarjon Defit Setiawati, Elsa Shir Li Wang Shulhan Abdul Gofar Siti Zainah Sulthan Habib Suryani Suryani Susilawati, Susilawati Syahri, Alfi Syaifullah Syaifullah Syaifullah Syaifullah Syaifullah Syaifullah Syarif, Yulia Tengku Khairil Ahsyar Tengku Khairil Ahsyar Tengku Khairil Ahsyar Tengku Khairil Ahsyar Tshamaroh, Muthia Uci Indah Sari Winda Wahyuti Wira Mulia, M. Roid Zarnelly Zarry, Cindy Kirana