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All Journal Sistemasi: Jurnal Sistem Informasi CogITo Smart Journal IT JOURNAL RESEARCH AND DEVELOPMENT Bulletin of Computer Science Research Journal of Informatics Management and Information Technology Mejuajua The Indonesian Journal of Computer Science INOVTEK Polbeng - Seri Informatika
Yudhi Arta, Yudhi
Universitas Islam Riau
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Public Health Other

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Analisis Perbandingan Kinerja Routing OSPF Dan EIGRP Novendra, Yoldi; Arta, Yudhi; Siswanto, Apri
IT JOURNAL RESEARCH AND DEVELOPMENT Vol 2 No 2 (2018)
Publisher : UIR PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25299/itjrd.2018.vol2(2).1373

Abstract

Dunia teknologi saat ini mengalami perkembangan yang cepat, terlebih pada teknologi internet. Teknlogi internet merupakan sebuah teknologi yang saat ini banyak digunakan oleh manusia untuk berkomunikasi dan mengirim berbagai data dalam jarak yang saling berjauhan dengan cepat. Dalam proses pengiriman data dan komunikasi pada teknologi internet tidak terlepas dari jalur yang digunakan, semakin pendek jalur yang digunakan maka akan semakin cepat data yang dikirim, masalah yang menjadi tolak ukur dalam pembuatan skripsi ini adalah membandingkan 2 (dua) buah routing protokol routing OSPF dan EIGRP memiliki fungsi yang sama yakni melakukan proses routing dimana akan diukur dengan parameter QoS seperti troughput, delay dan paket loss dengan beban pengiriman berupa audio dan video yang diukur dengan aplikasi wireshark. Hasil menunjukkan bahwa routing protocol OSPF memiliki nilai troughput, delay dan paket loss lebih kecil dibandingkan routing EIGRP.
Penerapan Metode Round Robin Pada Jaringan Multihoming Di Computer Cluster Arta, Yudhi
IT JOURNAL RESEARCH AND DEVELOPMENT Vol 1 No 2 (2017)
Publisher : UIR PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25299/itjrd.2017.vol1(2).677

Abstract

Pentingnya sebuah informasi saat ini dituntut sebuah sistem sanggup memberikan pelayanan yang terbaik, khususnya pelayanan informasi dengan media website. Meningkatnya jumlah akses terhadap website mengakibatkan beban kerja webserver menjadi meningkat dan tidak bisa diatasi dengan single server. Permasalahan tersebut dapat diatasi dengan menerapkan metode cluster load balance. Pada dasarnya cluster load balance bekerja dengan membagikan beban kerja webserver secara terdistribusi ke beberapa node server agar website menjadi seimbang. Algoritma penjadwalan weighted round robin dapat menyeimbangkan beban dengan menentukan jumlah bobot ke masing-masing node server. Dengan adanya load balance dengan menggunakan algoritma penjadwalan diharapkan mampu mencegah terjadinya request yang overload, meratakan beban kerja webserver, dan mempercepat respon time serta throughput terhadap kinerja webserver.
Simulasi Implementasi Intrusion Prevention System (IPS) Pada Router Mikrotik Arta, Yudhi; Syukur, Abdul; Kharisma, Roni
IT JOURNAL RESEARCH AND DEVELOPMENT Vol 3 No 1 (2018)
Publisher : UIR PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25299/itjrd.2018.vol3(1).1346

Abstract

Keamanan jaringan komputer merupakan bagian dari sebuah sistem yang sangat penting untuk menjaga validitas dan integritas data serta menjamin ketersediaan layanan bagi penggunanya. Sistem deteksi penyusup jaringan yang ada saat ini umumnya mampu mendeteksi berbagai serangan tetapi tidak mampu mengambil tindakan lebih lanjut. Namun disatu sisi manusia sudah sangat tergantung dengan sistem informasi. Hal itu yang menyebabkan statistik insiden keamanan jaringan terus meningkat tajam dari tahun ke tahun. Ini disebabkan karena kepedulian masyarakat yang sangat kurang terhadap sistem keamanan jaringan. Maka dari itu dibutuhkan sebuah sistem yang dapat membantu network administrator untuk digunakan sebagai monitor trafik jaringan dengan Intrusion Prevention System (IPS) yang  merupakan kombinasi antara fasilitas blocking capabilities dari Firewall.
Implementasi Intrusion Detection System Pada Rule Based System Menggunakan Sniffer Mode Pada Jaringan Lokal Arta, Yudhi
IT JOURNAL RESEARCH AND DEVELOPMENT Vol 2 No 1 (2017)
Publisher : UIR PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25299/itjrd.2017.vol2(1).979

Abstract

Intrusion Detection System (IDS) membantu pengguna dalam memonitor dan menganalisa gangguan pada keamanan jaringan. Tujuan penelitian ini adalah merancang IDS menggunakan Snort dengan tampilan antarmuka berbasiskan web dan implementasi sistem untuk memantau aktifitas para pengguna Hotspot Universitas Islam Riau. Penelitian ini berisi analisa gangguan pada jaringan nirkabel UIR, usulan solusi keamanan pada jaringan, proses dan cara kerja sistem IDS yang dibuat dengan basis web, serta evaluasi penerapan sistem IDS pada jaringan. Keamanan sebuah jaringan komputer diperlukan untuk menjaga validitas dan integritas data serta menjamin ketersedian layanan bagi penggunanya. Sistem harus dilindungi dari segala macam serangan dan usaha-usaha penyusupan yang dapat merusak sistem yang ada.
Penerapan Metode Recurrent Neural Network dengan Pendekatan Long Short-Term Memory (LSTM) Untuk Prediksi Harga Saham Hanafiah, Anggi; Arta, Yudhi; Nasution, Hafiza Oktasia; Lestari, Yuyun Dwi
Bulletin of Computer Science Research Vol. 4 No. 1 (2023): December 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v4i1.321

Abstract

Investing in shares is now increasingly popular and growing rapidly in Indonesia. By investing in shares, investors will get quite fast and large profits in a fairly short time. Investors need to analyze previous stock movements as a form of investment strategy to get maximum investment results. Many techniques have been applied to predict stock prices, one of which uses techniques in Deep Learning such as Recurrent Neural Network. In this research, research was conducted on stock price predictions using the Recurrent Neural Network method with the Long-Short Term Memory (LSTM) approach on BBNI stock data. The research only uses close data or data about daily closing stock prices. In designing LSTM there are several things that are configured such as dropout size, density, activation function, and number of neurons used, and in the training process one of the optimizers provided by the Keras framework is used, namely the ADAM (Adaptive Moment Estimation) optimizer. The test scenario is carried out using a number of epochs of 10 and 20 with a batch size of 32. The test results will produce MAE and MAPE values, where the lower the MAE and MAPE values, the better the model's performance in making accurate predictions. The test results using epoch 10 got an MAE value of 0.0286 and a MAPE value of 0.0488, while the test results using epoch 20 got an MAE value of 0.0150 and a MAPE value of 0.0257.
Analisis Performa Algoritma Machine Learning Untuk Identifikasi Depresi Pada Mahasiswa Fadhilla, Mutia; Wandri, Rizky; Hanafiah, Anggi; Rachmat Setiawan, Panji; Arta, Yudhi; Daulay, Suandi
Journal of Informatics Management and Information Technology Vol. 5 No. 1 (2025): January 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/jimat.v5i1.473

Abstract

Mental health, especially depression, is a major issue among college students due to academic, social, and social media pressures. Depression detection faces challenges such as stigma, low literacy, and ineffective conventional methods. Machine learning technology offers solutions with algorithms such as Naive Bayes, SVM, and Random Forest to improve detection accuracy, support early intervention, and improve the student mental health system. Mental health, especially depression, is a major issue among college students due to academic, social, and social media pressures. Depression detection faces challenges such as stigma, low literacy, and ineffective conventional methods. Machine learning technology offers solutions with algorithms such as Naive Bayes, K-Nearest Neighbor, Decision Tree, Logistic Regression, Random Forest, and Support Vector Machine to improve detection accuracy, support early intervention, and improve the student mental health system. Based on the results of the performance analysis of the machine learning algorithm, the most effective model in predicting depression status in students is Logistic Regression which has an accuracy rate of 95.62%. As a strategic step, machine learning technology can be integrated for early diagnosis of depression in students. This system is expected to be more effective and efficient, improve diagnostic accuracy, and open up opportunities for new approaches to responsive, data-driven mental health.
Prediction of Student Scholarship Recipients Using the K-Means Algorithm and C4.5 Wandri, Rizky; Arta, Yudhi; Hanafiah, Anggi; Oktaviaani, Rizka
The Indonesian Journal of Computer Science Vol. 12 No. 1 (2023): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v12i1.3145

Abstract

The government has a program called "KIP Lectures" to assist students in financing education. Where PTS makes the selection manually, with the Data Mining technique, a process will be carried out to speed up the manual process. This research will apply the clustering method with the K-Means algorithm and the classification method with the C4.5 algorithm, as well as a test using the RapidMiner application, which utilizes applicant data in 2022 with a total of 1298 participants. The test results found that 327 participants were in the highest cluster, "cluster_0", then the results of the c4.5 algorithm test obtained a decision tree if the participant has a KIP Card and the value obtained from the "Total Income" Criterion is more than 70 points, then the participant concerned is entitled to scholarship where 317 participants meet the criteria, and the university only has to choose participants from the results obtained in accordance with a predetermined quota.
Machine Learning-Based Counseling to Predict Psychological Readiness for Aspiring Entrepreneurs Syafitri, Nesi; Farradinna, Syarifah; Arta, Yudhi; Herawati, Icha; Jayanti, Wella
CogITo Smart Journal Vol. 10 No. 2 (2024): Cogito Smart Journal
Publisher : Fakultas Ilmu Komputer, Universitas Klabat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31154/cogito.v10i2.553.510-521

Abstract

Machine learning has become an exciting topic in psychology-related research, one of which is counseling psychological readiness for entrepreneurship. An intelligent application developed using a machine learning model to assist the counseling process in measuring a person's psychological readiness for entrepreneurship. This application was generated using the Entrepreneurship Psychological Readiness (EPR) instrument. In this study, to get the most suitable machine learning model, a comparison of 2 (two) machine learning models, namely, Naïve Bayesian (NB) and k-Nearest Neighbor (k-NN), involving 1095 training data. There are 4 (four) prediction classes recommended from the results of counseling: categories not ready for entrepreneurship, given training, guided, and prepared for entrepreneurship. The EPR instrument consists of 33 question items to measure 8 (eight) parameters used as inputs for the prediction process. The data has been randomized, and the experiment has been repeated 5 (five) times to check the consistency of performance of all techniques. 80% of the data was used as training data, and the other 20% was used as testing data. The results of the five (5) trials show that the Naïve Bayesian model provides the most consistent results in predicting a person's psychological readiness for entrepreneurship, with 89.58% accuracy, in testing. Therefore, the Naïve Bayesian model is recommended to be used in psychological counseling to predict a person's readiness for entrepreneurship
Pengembangan Aplikasi Sekolah di Madrasah Tsanawiyah (MTs) YKWI Pekanbaru Berbasis Web Dalam Peningkatan Efisiensi Administrasi Fadhilla, Mutia; Nur, Nuriman M; Syefriani; Arta, Yudhi; Wandri, Rizky; Hanafiah, Anggi
Mejuajua: Jurnal Pengabdian pada Masyarakat Vol. 5 No. 1 (2025): Agustus 2025
Publisher : Yayasan Penelitian dan Inovasi Sumatera (YPIS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52622/mejuajuajabdimas.v5i1.234

Abstract

Kemajuan teknologi informasi saat ini telah membawa perubahan signifikan dalam bidang pendidikan, sehingga mendorong sekolah untuk beralih dari sistem berbasis kertas ke solusi digital yang lebih efisien. MTs YKWI Pekanbaru, meskipun memiliki potensi strategis, masih menghadapi tantangan dalam pengelolaan sumber daya dan administrasi yang dilakukan secara manual. Dalam mengatasi masalah ini, dilakukan kegiatan pengabdian kepada masyarakat (PkM) yang bertujuan untuk mengembangkan aplikasi sekolah berbasis web dengan menggunakan metode waterfall. Pembangunan aplikasi sekolah ini bertujuan untuk  meningkatkan efisiensi dan efektivitas dalam pengelolaan data, termasuk manajemen penilaian, siswa, dan guru. Pembangunan aplikasi sekolah ini bertujuan untuk  meningkatkan efisiensi dan efektivitas dalam pengelolaan data, termasuk manajemen penilaian, siswa, dan guru. Aplikasi yang dibangun dapat meningkatkan efisiensi pada proses administrasi dengan kenaikan hingga tiga kali lebih cepat. Adapun berdasarkan feedback yang diperoleh menggunakan kuesioner, tingkat kepuasan terhadap kegiatan pengabdian sebesar 87,5% dan tingkat kepuasan aplikasi dalam meningkatkan efesiensi pada proses administrasi sebesar 78,8%.
Designing a Learning Game for Elementary School Students in Learning Mathematics using a Mobile Platform Wandri, Rizky; Setiawan, Panji Rachmat; Arta, Yudhi; Hanafiah, Anggi
Sistemasi: Jurnal Sistem Informasi Vol 13, No 3 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i3.4058

Abstract

Education is an important aspect in forming the potential of the young generation where the use of mobile platforms has become an integral part of everyday life. Over time, gadgets have become an access to learning besides books. Therefore, the increasingly complex development of the technological world encourages individuals to be able to apply technology in all their activities. Mathematics learning at elementary school level is often faced with challenges to make the learning process more fun and interactive. A common problem among students in Indonesia is that mathematics is considered a difficult subject. Monotonous learning reduces students' interest in learning. Therefore, designing learning games for elementary school students, especially in mathematics learning, can be an innovative solution to improve the quality and attractiveness of learning with interactive learning media. This research aims to create a game that allows students to learn more effectively which focuses on game development using the Game Development Life Cycle (GDLC) development model. This research produces a translation game from mathematics book learning material with the theme "Counting Numbers Up to 10,000" using the Unity 3D game engine which functions well and the application of the Game Development Life Cycle (GDLC) method also functions well.
Co-Authors Abdul Syukur Daulay, Suandi Fadhilla, Mutia Farradinna, Syarifah Hanafiah, Anggi Herawati, Icha Jayanti, Wella Kharisma, Roni Lestari, Yuyun Dwi Maripati, Maripati mohamad samuri, suzani Nasution, Hafiza Oktasia Novendra, Yoldi Nur, Nuriman M Oktaria, Wina Oktaviaani, Rizka Pandu Cynthia, Eka Rachmat Setiawan, Panji Rizky Wandri Setiawan, Panji Rachmat Syafitri, Nesi Syefriani