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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Techno.Com: Jurnal Teknologi Informasi TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Pengabdian UntukMu NegeRI RABIT: Jurnal Teknologi dan Sistem Informasi Univrab CIRCUIT: Jurnal Ilmiah Pendidikan Teknik Elektro Seminar Nasional Teknologi Informasi Komunikasi dan Industri JITK (Jurnal Ilmu Pengetahuan dan Komputer) MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer ALGORITMA : JURNAL ILMU KOMPUTER DAN INFORMATIKA IJISTECH (International Journal Of Information System & Technology) EDUMATIC: Jurnal Pendidikan Informatika Jurnal Pengabdian Kepada Masyarakat MEMBANGUN NEGERI Journal of Electronics, Electromedical Engineering, and Medical Informatics Indonesian Journal of Electrical Engineering and Computer Science Computer Science and Information Technologies Journal of Education Informatic Technology and Science Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) IJISTECH Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Jurnal Computer Science and Information Technology (CoSciTech) Jurnal Pendidikan dan Teknologi Indonesia SATIN - Sains dan Teknologi Informasi Jurnal Pendidikan Dirgantara
Edi Ismanto
Universitas Muhammadiyah Riau
Religion Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mathematics Social Sciences Other

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Implementation of Backpropagation Artificial Neural Networks to Predict Palm Oil Price Fresh Fruit Bunches Edi Ismanto; Noverta Effendi; Eka Pandu Cynthia
IJISTECH (International Journal of Information System and Technology) Vol 2, No 1 (2018): November
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (863.123 KB) | DOI: 10.30645/ijistech.v2i1.17

Abstract

Riau Province is one of the regions known for its plantation products, especially in the oil palm sector, so that Riau Province and regional districts focus on oil palm plants as the main commodity of plantations in Riau. Based on data from the Central Bureau of Statistics (BPS) of Riau Province, the annual production of oil palm plantations, especially smallholder plantations in Riau province has always increased. So is the demand for world CPO. But sometimes the selling price of oil palm fresh fruit bunches (FFB) for smallholder plantations always changes due to many influential factors. With the Artificial Neural Network approach, the Backpropagation algorithm we conduct training and testing of the time series variables that affect the data, namely data on the area of oil palm plantations in Riau Province; Total palm oil production in Riau Province; Palm Oil Productivity in Riau Province; Palm Oil Exports in Riau Province and Average World CPO Prices. Then price predictions will be made in the future. Based on the results of the training and testing, the best Artificial Neural Network (ANN) architecture model was obtained with 9 input layers, 5 hidden layers and 1 output layer. The output of RMSE 0000699 error value and accuracy percentage is 99.97% so that it can make price predictions according to the given target value.
Pengembangan Media Pembelajaran e-Modul untuk Pembelajaran Berbasis Project Based Learning (PjBL) Edi Ismanto; Vitriani; Khairul Anshari
Jurnal Pengabdian UntukMu NegeRI Vol 6 No 2 (2022): Pengabdian Untuk Mu negeRI
Publisher : LPPM UMRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jpumri.v6i2.3628

Abstract

Pandemi covid 19 membuat banyak perubahan di berbagai bidang kehidupan manusia, termasuk dalam dunia pendidikan. Pemerintah sudah mengizinkan kegiatan belajar tatap muka untuk sekolah-sekolah yang berada di zona hijau dan kuning, dengan serangkaian langkah-langkah persiapan dan protokol kesehatan yang ketat. SD Muhammadiyah 1 Pekanbaru yang coba mengimplementasikan kebijakan pembelajaran model hybrid learning yang dikombinasi dengan model pembelajaran project based learning (PjBL). Keterbatasan guru dalam mengembangkan media pembelajaran untuk mendukung pembelajaran project based learning (PjBL) menimbulkan sebuah permasalahan.Untuk menyelesaikan permasalahan tersebut maka dilakukan pelatihan pengembangan media pembelajaran berbentuk e-modul. Metode kegiatan dilakukan dengan cara observasi, pelatihan pengembangan media pembelajaran dengan pendekatan focus group discussion (FGD), dan melakukan evaluasi dalam bentuk pretest dan postest pada peserta pelatihan. Dari hasil evaluasi dan monitoring terlihat peningkatan kopetensi pedagogic dan professional peserta sebesar 77.15%.
Pendekatan Machine Learning Dengan Menggunakan Algoritma Xgboost (Extreme Gradient Boosting) Untuk Peningkatan Kinerja Klasifikasi Serangan Syn Rahmad Gunawan Gunawan; Erik Suanda Handika; Edi Ismanto
Jurnal CoSciTech (Computer Science and Information Technology) Vol 3 No 3 (2022): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v3i3.4356

Abstract

Denial of Service (DoS) adalah salah satu serangan cyber populer yang ditargetkan pada situs web organisasi terkenal dan berpotensi memiliki biaya ekonomi dan waktu yang tinggi. Dalam makalah ini, beberapa metode pembelajaran mesin termasuk model ensemble dan pengklasifikasi deep learning berbasis autoencoder dibandingkan dan disetel menggunakan optimasi Bayesian. Kerangka autoencoder memungkinkan untuk mengekstrak fitur baru dengan memetakan input asli ke ruang baru. Metode tersebut dilatih dan diuji baik untuk klasifikasi biner dan multi-kelas pada kumpulan data Digiturk dan Labris, yang baru-baru ini diperkenalkan untuk mendeteksi berbagai jenis serangan DdoS. Semakin penting koneksi data melalui Internet membuat kebutuhan akan keamanan jaringan data semakin meningkat. Salah satu tools yang penting adalah Intrusion detection systems (IDS). Sistem Deteksi Intrusi (IDS) adalah proses pemantauan lalu lintas jaringan dalam sistem untuk mendeteksi pola dan aktivitas yang mencurigakan yang memungkinkan ada serangan dalam sistem itu. beberapa jenis serangan, yaitu Botnet, UDP, SYN, broadcast, sleep deprivation, dan serangan bertubi-tubi. klasifikasi pertama, hasilnya menunjukkan bahwa baik Precision (PR) dan Recall (RE) adalah 89% untuk Algoritma Random Forest. Akurasi rata-rata (AC) dari model yang kami usulkan adalah 89% yang luar biasa dan cukup baik. Pada klasifikasi kedua, hasilnya menunjukkan bahwa baik Precision (PR) dan Recall (RE)sekitar 90% untuk algoritma XGBoost. Akurasi rata-rata (AC) dari model yang kami sarankan adalah 90% pada dataset CICDDoS2019.
LSTM Network Hyperparameter Optimization for Stock Price Prediction Using the Optuna Framework Edi Ismanto; Vitriani Vitriani
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 9, No 1 (2023): March
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i1.24944

Abstract

In recent years, the application of deep learning-based financial modeling tools has grown in popularity. Research on stock forecasting is crucial to understanding how a nation's economy is doing. The study of intrinsic value and stock market forecasting has significant theoretical implications and a broad range of potential applications. One of the trickiest challenges in projects involving deep learning and machine learning is hyperparameter search. In this paper, we evaluate and analyze the optimal hyperparameter search in the long short-term memory (LSTM) model developed to forecast stock prices using the Optuna framework. We examined a number of hyperparameters with several LSTM architectures, including optimizers (SGD, Adagrad, RMSprop, Nadam, Adamax, dan Adam), LSTM hidden units, dropout rates, epochs, batch size, and learning rate. The results of the experiment indicated that of the four LSTM models tested—model 1 single LSTM, model 2 single LSTM, model 1 LSTM stacked, and model 2 LSTM stacked—model 1 single LSTM was the most effective. Single LSTM version 1 offers the lowest losses when compared to other models and had the lowest root mean square error (RMSE) score of 7.21. When compared to manual hyperparameter tuning, automatic hyperparameter tuning has lower losses and is better.
Sistem Pendukung Keputusan Penerimaan Karyawan Dengan Metode Simple Additive Weighting (SAW) Edi Ismanto; Noverta Effendi
SATIN - Sains dan Teknologi Informasi Vol 3 No 1 (2017): SATIN - Sains dan Teknologi Informasi
Publisher : STMIK Amik Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (761.608 KB) | DOI: 10.33372/stn.v3i1.208

Abstract

Universitas Muhammadiyah Riau is really need the support of technology information in order to facilitate its activities. At  the time, there are usually find a case of an error of the recruitment  process in an institution. It is also possible at Universitas Muhammadiyah Riau. It is actually depend on parties the agency that will make or break its own admission employes. Actually, the process of selecting the employee in accordance with the intelectual capability in quantity and the ability to work in accordance with its quality controlled. There are several criteria assessment in process of making decision recruitment at Universitas Muhammadiyah Riau. There assessment are based on the criteria of education, work experience, performance, test, interview, age, status, and address. The objectives to be achieved is to create a system that can help decision makers to determine the process recruitment optimally by using method of SAW (Simple Additive Weighting). The result of this research is building decision support system for acceptance new employes, and finally it can be uses as supporting for process accepting new employes.
A comparative study of machine learning algorithms for virtual learning environment performance prediction Edi Ismanto; Hadhrami Ab. Ghani; Nurul Izrin Binti Md Saleh
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 4: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i4.pp1677-1686

Abstract

Virtual learning environment is becoming an increasingly popular studyoption for students from diverse cultural and socioeconomic backgroundsaround the world. Although this learning environment is quite adaptable,improving student performance is difficult due to the online-only learningmethod. Therefore, it is essential to investigate students' participation andperformance in virtual learning in order to improve their performance. Usinga publicly available Open University learning analytics dataset, this studyexamines a variety of machine learning-based prediction algorithms todetermine the best method for predicting students' academic success, henceproviding additional alternatives for enhancing their academic achievement.Support vector machine, random forest, Nave Bayes, logical regression, anddecision trees are employed for the purpose of prediction using machinelearning methods. It is noticed that the random forest and logistic regressionapproach predict student performance with the highest average accuracyvalues compared to the alternatives. In a number of instances, the supportvector machine has been seen to outperform the other methods.
An LSTM-based prediction model for gradient-descending optimization in virtual learning environments Edi Ismanto; Noverta Effendi
Computer Science and Information Technologies Vol 4, No 3: November 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v4i3.p199-207

Abstract

A virtual learning environment (VLE) is an online learning platform that allows many students, even millions, to study according to their interests without being limited by space and time. Online learning environments have many benefits, but they also have some drawbacks, such as high dropout rates, low engagement, and students' self-regulated behavior. Evaluating and analyzing the students' data generated from online learning platforms can help instructors to understand and monitor students learning progress. In this study, we suggest a predictive model for assessing student success in online learning. We investigate the effect of hyperparameters on the prediction of student learning outcomes in VLEs by the long short-term memory (LSTM) model. A hyperparameter is a parameter that has an impact on prediction results. Two optimization algorithms, adaptive moment estimation (Adam) and Nesterov-accelerated adaptive moment estimation (Nadam), were used to modify the LSTM model's hyperparameters. Based on the findings of research done on the optimization of the LSTM model using the Adam and Nadam algorithm. The average accuracy of the LSTM model using Nadam optimization is 89%, with a maximum accuracy of 93%. The LSTM model with Nadam optimisation performs better than the model with Adam optimisation when predicting students in online learning.
Pengembangan Pengembangan Learning Management System (LMS) dengan Pendekatan Self Directed Learning (SDL) untuk Sekolah Menengah Kejuruan (SMK) di Kota Pekanbaru Edi Ismanto; Pratama Benny Herlandy; Renita Rahmadani
JURNAL FASILKOM Vol 14 No 1 (2024): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jf.v14i1.6882

Abstract

Perkembangan Learning Management System (LMS) telah mengalami evolusi yang signifikan dalam bidang pendidikan. Seiring dengan pergeseran menuju pembelajaran online dan kemajuan teknologi, LMS telah berkembang menjadi platform yang mengintegrasikan teknologi canggih seperti cloud computing, big data analytics, dan kecerdasan buatan. Hal ini memungkinkan LMS modern untuk menyediakan pengalaman pembelajaran yang lebih personal, adaptif, dan responsif terhadap kebutuhan individu. Pengembangan pembelajaran online sangat penting untuk SMK Muhammadiyah 3 Pekanbaru yang saat ini masih mengandalkan media tradisional. Selain itu aksesibilitas terhadap materi pembelajaran yang juga masih terbatas. Kurangnya dukungan sistem pembelajaran online telah mengakibatkan rendahnya minat dan hasil belajar siswa. Penelitian ini menggunakan pengembangan model Waterfall dengan pendekatan Self Directed Learning (SDL) untuk mengembangkan model LMS yang menarik dan efektif. Pengembangan LMS ini bertujuan menciptakan model pembelajaran online yang efektif yang fokus pada siswa untuk meningkatkan minat belajar dan memperbaiki hasil belajar. Teknik pengujian Usability digunakan untuk menguji antarmuka pengguna, navigasi, dan kemudahan penggunaan sistem secara keseluruhan. Berdasarkan hasil validasi Black box didapat nilai sebesar 100%, serta pengukuran aspek Usability sebesar 85%, dengan demikian LMS ini sangat lanyak untuk digunakan.
A Comparison of Enhanced Ensemble Learning Techniques for Internet of Things Network Attack Detection Edi Ismanto; Januar Al Amien; Vitriani Vitriani
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol 23 No 3 (2024)
Publisher : LPPM Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v23i3.3885

Abstract

Over the past few decades, the Internet of Things (IoT) has become increasingly significant due to its capacity to enable low-cost device and sensor communication. Implementation has opened up many new opportunities in terms of efficiency, productivity, convenience, and security. However, it has also brought about new privacy and data security challenges, interoperability, and network reliability. The research issue is that IoT devices are frequently open to attacks. Certain machine learning (ML) algorithms still struggle to handle imbalanced data and have weak generalization skills when compared to ensemble learning. The research aims to develop security for IoT networks based on enhanced ensemble learning by using Grid Search and Random Search techniques. The method used is the ensemble learning approach, which consists of Random Forest (RF), Adaptive Boosting (AdaBoost), Gradient Boosting Machine (GBM), and Extreme Gradient Boosting (XGBoost). This study uses the UNSW-NB15 IoT dataset. The study's findings demonstrate that XGBoost performs better than other methods at identifying IoT network attacks. By employing Grid Search and Random Search optimization, XGBoost achieves an accuracy rate of 98.56% in binary model measurements and 97.47% on multi-class data. The findings underscore the efficacy of XGBoost in bolstering security within IoT networks.
Development of Microsoft Office Virtual Reality (VR) Application with Four-D (4D) Approach Ismanto, Edi; Al Rian, Rahmad; Septian Alza
Computer Science and Information Technology Vol 5 No 1 (2024): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v5i1.6816

Abstract

Virtual Reality (VR) technology has made significant advancements in recent decades. With its evolving potential, VR can transform how we learn, work, play, and interact with the world around us. The development of VR applications for Microsoft Office training holds significant relevance, especially for students of Madrasah Tsanawiyah (MTs) Darul Hikmah Pekanbaru. Microsoft Office training is essential for MTs Darul Hikmah Pekanbaru students as it serves as a practical necessity in preparing them for academic and professional endeavors. One relevant challenge is how to integrate VR technology with appropriate learning methods, such as the Four D (4D) method, to make the training experience more effective and efficient. Therefore, this research aims to identify and address these issues and explore the potential of VR applications with the 4D method to enhance users' practical and intuitive Microsoft Office skills. The development of VR applications for Microsoft Office training using the Four D (4D) method, comprising the Define, Design, Develop, and Disseminate stages, has yielded highly favorable results based on comparisons of measurements from subject matter experts, media experts, and participants. From the measurement and validation results, this VR application has received high feasibility ratings. Subject matter experts rated it 100%, media experts rated it 91.83%, and participants rated it 96% from the VR application trial.
Co-Authors Abdul Fadlil Adam Ramadhan Agus Satria Ajeng Safitri Al Rian, Rahmad Ambiyar, Ambiyar Amelia Agustina Amran, Hasanatul Fu'adah Anton Yudhana Bella, Bella Fitria Sari Deprizon, Deprizon Diah Eka Ratna Diva Arifal Adha Dwi Sanggar Wati, Anisa Effendi, Noverta Eka Pandu Cynthia Eka Pandu Cynthia Eka Pandu Cynthia Erik Suanda Handika Gunawan, Rahmad Habil Maulana Hadhrami Ab Ghani Hadhrami Ab. Ghani Hammam Zaki Harun Mukhtar Herdani, Inka friska Herlandy, Pratama Benny Herman Januar Al Amien Januar Al Amien Januar Al Amien Khairul Anshari Kitagawa, Kodai Lisman, Muhammad Melly Novalia Melly Novalia Melly Novalia, Melly Mohamad, Mohd Saberi Nuraeni, Eneng Nurul Izrin Binti Md Saleh Nurul Izrin Md Saleh Nurul Safira, Natasya Oriana, Larisa Patlan Putra Humala Harahap Pratama Benny Herlandi Pratama Benny Herlandy Putri Ramahdani, Anggi Rahmad Al Rian Rahmad Al Rian Rahmad Alrian Rahmad Gunawan Gunawan Remli, Muhammad Akmal Renita Rahmadani Resmi Darni Ridhollah, Farhan Septian Alza Siti Niah Soni Sri Fitria Retnawaty Sunanto Sunanto Suryadila, Lusi Tri Wahono Vitriani Vitriani Vitriani Vitrian Wan Salihin Wong, Khairul Nizar Syazwan Wandi Syahfutra Yeeri Badrun