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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Techno.Com: Jurnal Teknologi Informasi Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Jurnal Teknologi Informasi dan Ilmu Komputer Jurnas Nasional Teknologi dan Sistem Informasi Proceeding of the Electrical Engineering Computer Science and Informatics JURNAL MEDIA INFORMATIKA BUDIDARMA IKRA-ITH Informatika : Jurnal Komputer dan Informatika PROCESSOR Jurnal Ilmiah Sistem Informasi, Teknologi Informasi dan Sistem Komputer Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Jurnal Ipteks Terapan The IJICS (International Journal of Informatics and Computer Science) JURIKOM (Jurnal Riset Komputer) Indonesian Journal of Electrical Engineering and Computer Science JIKI (Jurnal Ilmu Komputer dan Informatika) Jurnal Informatika Dan Tekonologi Komputer (JITEK) pendidikan, science, teknologi, dan ekonomi Jurnal Informatika dan Rekayasa Komputer Jurnal Manajemen Teknologi dan Sistem Informasi Jurnal Manajemen Sistem Informasi Jurnal Pengabdian Masyarakat Unama

Kurniabudi, Kurniabudi

Universitas Dinamika Bangsa

Author-ID : 657482

Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Public Health Other

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TEACHERS ACCEPTANCE AND INTENTION TO USE ICT IN LEARNING Kurniabudi, Kurniabudi
Jurnal Ipteks Terapan Vol 12, No 3 (2018): JIT
Publisher : LLDIKTI Wilayah X

Although ICT could bring many benefit in learning ,however implementation of ICT in the learning process is not easy. In fact, adoption of ICT in learning, is not only occurs in higher education but also in schools. The aim of This study is to identify the impact of Subjective Norm, Image and Computer Self-Efficacy against the behavior of teachers in using ICT in learning.Technology Acceptance Models (TAM) 2 used in the analysis of the behavior of the reception. Data was collected from high school teachers in the city of Jambi. Data were analyzed using SEM method with applications SmartPLS.This study reveals that the consistency of Perceived Usefulness and Perceived Ease of Use has a direct influence on Intention to Use . Subjective Norm and Image provides indirect influence on Intention to Use Computer Self - Efficacy whereas no effect on of Perceived Usefulness , Perceived Ease of Use and Intention to Use

CLUSTERING DATA UNTUK REKOMENDASI PENENTUAN JURUSAN PERGURUAN TINGGI MENGGUNAKAN METODE K-MEANS Jusia, Pareza Alam; Irfan, Fadhel Muhammad; Kurniabudi, Kurniabudi
IKRA-ITH INFORMATIKA : Jurnal Komputer dan Informatika Vol 3 No 3 (2019): IKRAITH-INFORMATIKA Vol 3 No 3 Bulan November 2019
Publisher : Fakultas Teknik Universitas Persada Indonesia YAI

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1630.117 KB)

Siswa-siswi SMA Negeri 2 Kota Jambi cenderung memilih jurusan berdasarkan karenaminat, dan keinginan orang tua. Beberapa di antaranya sudah memperhitungkan potensiyang ada pada diri mereka, maka komitmen untuk belajar dibidang itu tidak akan berjalanlancar, padahal jurusan yang dia pilih itu tidak sesuai kemampuannya. Oleh karena itu,penulis melakukan analisis data mining menggunakan data nilai siswa kelas XII darisemester satu sampai empat dan kuisoner yang penulis bagikan. Dalam melakukan analisispenulis menggunakan alat bantu tools WEKA dan RapidMiner. Metode yang digunakanadalah metode k-means clustering dengan 24 atribut dan 5 cluster. Jumlah cluster padaperhitungan manual adalah, C1 terdapat 62 data, C2 terdapat 28 data, C3 terdapat30 data,C4 terdapat 30 data, C5 terdapat 60 data. Jumlah cluster pada perhitungan RapidMineradalah, C1 terdapat 35 data, C2 terdapat 55 data, C3 terdapat 58 data, C4 terdapat 35 data,C5 terdapat 27 data. Jumlah cluster pada perhitungan WEKA adalah, C1 terdapat 30 data,C2 terdapat 49 data, C3 terdapat 41 data, C4 terdapat 32 data, C5 terdapat 58 data.

Komparasi Performa Tree-Based Classifier Untuk Deteksi Anomali Pada Data Berdimensi Tinggi dan Tidak Seimbang Kurniabudi, Kurniabudi; Harris, Abdul; Veronica, Veronica
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 1 (2022): Januari 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i1.3473

Anomaly detection is one solution to overcome the issue of data network traffic security, but is faced with the challenge of high data dimensionality and imbalanced data. High-dimensional and imbalanced data can affect the performance of the detection system. Therefore we need a feature selection technique that can reduce the dimensionality of the data by eliminating irrelevant features. In addition, the selected features need to be validated with the right classification algorithm to produce high anomaly detection performance. The purpose of this study is to produce a combination of feature selection techniques and appropriate classification algorithms to produce a system that is able to detect attacks on high-dimensional and imbalanced data. Chi-square feature selection technique was used to eliminate irrelevant features. To determine the ideal classification algorithm, in this study, a comparison of the performance of the tree-based classifer algorithm was carried out. This study also examines the performance of classification techniques in detecting traffic on high-dimensional and unbalanced data. Several Tree-based classification algorithms such as REPTree, J48, Random Tree and Random Forest were tested and compared. Testing with the best performance as a recommendation for the ideal combination of feature selection techniques and classification algorithms. This research produces an anomaly detection system that has high performance. For experimental data, the CICIDS-2017 dataset is used, which has high data dimensionality and contains unbalanced data. The test results show that Random Tree has an accuracy of 99.983% and Random Forest 99.984%.

Social Media Success for Knowledge Sharing: Instrument Content Validation Setiawan Assegaff; Kurniabudi Kurniabudi; Hendri Hendri
International Journal of Electrical and Computer Engineering (IJECE) Vol 6, No 5: October 2016
Publisher : Institute of Advanced Engineering and Science

Knowledge sharing is important activity for create a new knowledge. Information technology today brings big oppurtubity for people in conduct knowledge sharing. This media provides effective and competitive technology tool for knowledge sharing. The aimed of this study is to report the on process research that investigates the success of social media for sharing knowledge among scholars in Indonesia. This study focus to discuss the instrument development stages from the research especially discuss how content validity conduct for in instrument validation progress. Method for content validation progress was adopting from Beck and Gale approach in nursing area. This study resulted in a validated instrument from content validation approach.

Intrusion detection with deep learning on internet of things heterogeneous network Sharipuddin Sharipuddin; Benni Purnama; Kurniabudi Kurniabudi; Eko Arip Winanto; Deris Stiawan; Darmawijoyo Hanapi; Mohd. Yazid Idris; Rahmat Budiarto
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 10, No 3: September 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v10.i3.pp735-742

The difficulty of the intrusion detection system in heterogeneous networks is significantly affected by devices, protocols, and services, thus the network becomes complex and difficult to identify. Deep learning is one algorithm that can classify data with high accuracy. In this research, we proposed deep learning to intrusion detection system identification methods in heterogeneous networks to increase detection accuracy. In this paper, we provide an overview of the proposed algorithm, with an initial experiment of denial of services (DoS) attacks and results. The results of the evaluation showed that deep learning can improve detection accuracy in the heterogeneous internet of things (IoT).

Seleksi Fitur Dengan Information Gain Untuk Meningkatkan Deteksi Serangan DDoS menggunakan Random Forest Kurniabudi Kurniabudi; Abdul Harris; Abdul Rahim
Techno.Com Vol 19, No 1 (2020): Februari 2020
Publisher : LPPM Universitas Dian Nuswantoro

Tantangan deteksi serangan saat ini adalah jumlah trafik yang besar dan beragam serta hadir jenis serangan baru. Sehingga diperlukan teknik baru untuk meningkatkan performa deteksi. Dengan pesatnya perkembangan teknologi layanan komunikasi, menghasilkan trafik dengan informasi yang beragam. Pada dasarnya tidak semua informasi pada trafik jaringan digunakan untuk mendeteksi serangan seperti DDoS. Penelitian ini bertujuan meningkatkan performa Random Forest dalam mendeteksi serangan DDoS dengan seleksi fitur menggunakan teknik Information Gain. Berdasarkan hasil eksperimen diperoleh bahwa teknik yang diusulkan mampu meningkatkan akurasi deteksi DDoS hingga 99.99% dengan tingkat alarm palsu 0.001

Network anomaly detection research: a survey Kurniabudi Kurniabudi; Benni Purnama; Sharipuddin Sharipuddin; Darmawijoyo Darmawijoyo; Deris Stiawan; Samsuryadi Samsuryadi; Ahmad Heryanto; Rahmat Budiarto
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 7, No 1: March 2019
Publisher : IAES Indonesian Section

Data analysis to identifying attacks/anomalies is a crucial task in anomaly detection and network anomaly detection itself is an important issue in network security. Researchers have developed methods and algorithms for the improvement of the anomaly detection system. At the same time, survey papers on anomaly detection researches are available. Nevertheless, this paper attempts to analyze futher and to provide alternative taxonomy on anomaly detection researches focusing on methods, types of anomalies, data repositories, outlier identity and the most used data type. In addition, this paper summarizes information on application network categories of the existing studies.

Important Features of CICIDS-2017 Dataset For Anomaly Detection in High Dimension and Imbalanced Class Dataset Kurniabudi Kurniabudi; Deris Stiawan; Darmawijoyo Darmawijoyo; Mohd Yazid Bin Idris; Bedine Kerim; Rahmat Budiarto
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 9, No 2: June 2021
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v9i2.3028

The growth in internet traffic volume presents a new issue in anomaly detection, one of which is the high data dimension. The feature selection technique has been proven to be able to solve the problem of high data dimension by producing relevant features. On the other hand, high-class imbalance is a problem in feature selection. In this study, two feature selection approaches are proposed that are able to produce the most ideal features in the high-class imbalanced dataset. CICIDS-2017 is a reliable dataset that has a problem in high-class imbalance, therefore it is used in this study. Furthermore, this study performs experiments in Information Gain feature selection technique on the imbalance class datasaet. For validation, the Random Forest classification algorithm is used, because of its ability to handle multi-class data. The experimental results show that the proposed approaches have a very surprising performance, and surpass the state-of-the-art methods.

Enhanced Deep Learning Intrusion Detection in IoT Heterogeneous Network with Feature Extraction Sharipuddin Sharipuddin; Eko Arip Winanto; Benni Purnama; Kurniabudi Kurniabudi; Deris Stiawan; Darmawijoyo Hanapi; Mohd Yazid bin Idris; Bedine Kerim; Rahmat Budiarto
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 9, No 3: September 2021
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/.v9i3.3134

Heterogeneous network is one of the challenges that must be overcome in Internet of Thing Intrusion Detection System (IoT IDS). The difficulty of the IDS significantly is caused by various devices, protocols, and services, that make the network becomes complex and difficult to monitor. Deep learning is one algorithm for classifying data with high accuracy. This research work incorporated Deep Learning into IDS for IoT heterogeneous networks. There are two concerns on IDS with deep learning in heterogeneous IoT networks, i.e.: limited resources and excessive training time. Thus, this paper uses Principle Component Analysis (PCA) as features extraction method to deal with data dimensions so that resource usage and training time will be significantly reduced. The results of the evaluation show that PCA was successful reducing resource usage with less training time of the proposed IDS with deep learning in heterogeneous networks environment. Experiment results show the proposed IDS achieve overall accuracy above 99%.

Persepsi Kesesuaian dan Kepuasan Penggunaan Media Sosial pada Perkuliahan: Pengujian Model Kurniabudi Kurniabudi; Setiawan Assegaf
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 5 No 6: Desember 2018
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Penerimaan teknologi merupakan faktor penting, untuk keberlanjutan penggunaan sebuah teknologi. Model-model pengukuran telah banyak dikembangkan, namun belum mempertimbangkan kesesuaian dan kepuasan dalam penggunaan teknologi berkelanjutan. Pada penelitian yang sebelumnya penulis telah mengembangkan model kepuasan dan kesesuaian (Task-fit and Satisfaction Model) untuk mengidentifikasi persepsi dosen terhadap kesesuaian dan kepuasan penggunaan facebook sebagai sarana komunikasi dan informasi pada perkuliahan, namun belum diuji. Artikel ini menyajikan proses pengujian terhadap model tersebut. Responden penelitian ini adalah dosen di indonesia khususnya yang menggunakan facebook. Data penelitian dikumpulkan dengan menggunakan metode survey online. Metode Structural Equation Modeling (SEM) dan Partial Least Square (PLS) digunakan untuk analisis data. Hasil pengujian hipotesis memperlihatkan perceived task-fit, utilization dan satisfaction secara signifikan mempengaruhi continuance intention. Pengujian juga memperlihatkan bahwa Perceived task fit , confirmation, dan Service quality secara signifikan mempengaruhi satisfaction. Terdapat korelasi positif perceived task-fit terhadap utilization, dan service quality terhadap confirmation. Sedangkan pengujian coefficient of determination (R2), memperlihatkan continuance intention memperoleh nilai R2= 0.723, hal ini menunjukkan bahwa perentasi besarnya kemampuan model dalam memprediksi persepsi kesesuaian dan kepuasan dosen terhadap penggunaan facebook dalam perkuliahan sebesar 72.3%. AbstractAcceptance of technology is an important factor, for the continued use of a technology. Measurement models have been developed, but not many consider perceived of fitness and satisfaction in receiving technology. In the previous research the authors has developed a Task-fit and Satisfaction Model to identify lecturers' perceptions of the suitability and satisfaction of facebook usage as a means of communication and information on lectures, the model have not test yet. This paper aim to present the testing process for this model. Responden this research is a lecturer in Indonesia especially who use facebook. Data collected by online survey method. SEM with PLS approach used to data analysis. The results of hypothesis testing show that perceived task-fit, utilization and satisfaction significantly influence continuance intention. The results also show that Perceived task fit, confirmation, and Service quality significantly affect satisfaction. There is a positive correlation of perceived task-fit to utilization, and service quality to confirmation. While the coefficient of determination test, shows continuance intention obtained the value of R2 = 0.723, This shows that the magnitude of the model's ability to predict perceptions of fitness and lecturer satisfaction towards the use of Facebook in lectures is 72.3%.

Co-Authors Abdul Harris Abdul Harris Abdul Harris Abdul Harris Abdul Harris Abdul Rahim Abdul Rahim Ahmad Heryanto Albertus Edward Mintaria Albertus Edward Mintaria Ammar panji Pratama Bedine Kerim Bedine Kerim Candra Adi Rahmat Chindra Saputra Darmawijoyo, Darmawijoyo Dede Andri Wahyudin Deris Stiawan Dodi Sandra Dodi Sandra Dr. Hendri, S.Kom., S.H., M.S.I., M.H Eko Arip Winanto Eko Arip Winanto Elvi Yanti Elvi Yanti Elvira Rosanda Erick Fernando Erick Fernando Erick Fernando B311087192 Fachruddin Febriyan Nurmansyah Harid, Harid Harris, Abdul Hendri Hendri Hendri Hendri Hendy Saryanto Herry Mulyono Ibnu Sani Wijaya Idris, Mohd. Yazid Idris, Mohd. Yazid Imam Rofi’i Irawan, Beni Irfan, Fadhel Muhammad Kurniabudi Lola Yorita Astri, Lola Yorita Minal Juadli Mintaria, Albertus Edward Mohd Yazid bin Idris Mohd Yazid Bin Idris Mohd. Yazid Idris Mohd. Yazid Idris Muhammad Rafly Ramadhan Muhammad Riza Pahlevi Mulyono, Herry Nabila Kamila Hasna Pandapotan Siagian Pareza Alam Jusia, Pareza Alam Purnama, Benni Putri Nawang Wulan Rahman saibi Rahmat Budiarto Rahmat Budiarto Realensi Realensi Rilis Pebriyanti Siringo Ringo Ryan Sihopong Parlindungan Siregar Samsuryadi Samsuryadi Setiawan Assegaf Sharipuddin, Sharipuddin Sharipuddin, Sharipuddin Shelby Amalia Sandi Siagian, Pandapotan Suwaldo Aris Ferry Hutabarat Syamsul Arifin Syifa Munawarah Syifa Munawarah Syifqi, Achmad Triokta Putra Ulil Amri, Nugraha Valensia, Vally Veronica Veronica VERONICA VERONICA WILLY RIYADI Winarno Wirmaini, Wirmaini Yudi Novianto Yudi Novianto Yundari, Yundari Zulwaqar Zain Mohtar

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