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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Advances in Applied Sciences Techno.Com: Jurnal Teknologi Informasi TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Journal of Natural Sciences and Mathematics Research Jurnal Pengabdian UntukMu NegeRI 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 JURNAL PENDIDIKAN TAMBUSAI 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 Didaktik : Jurnal Ilmiah PGSD STKIP Subang 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 Journal of Software Engineering and Information System (SEIS) SATIN - Sains dan Teknologi Informasi Jurnal Ilmu Komputer, Teknologi Dan Informasi ANTHOR: Education and Learning Journal Jurnal Pendidikan Dirgantara Jurnal Ilmu Komputer dan Teknik Informatika International Journal of Applied Science and Technology Application
Edi Ismanto
Universitas Muhammadiyah Riau
Religion Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Physics Social Sciences Other

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Recent systematic review on student performance prediction using backpropagation algorithms Edi Ismanto; Hadhrami Ab Ghani; Nurul Izrin Md Saleh; Januar Al Amien; Rahmad Gunawan
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 20, No 3: June 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v20i3.21963

Abstract

A comprehensive systematic study was carried out in order to identify various deep learning methods developed and used for predicting student academic performance. Predicting academic performance allows for the implementation of various preventive and supportive measures earlier in order to improve academic performance and reduce failure and dropout rates. Although machine learning schemes were once popular, deep learning algorithms are now being investigated to solve difficult predictions of student performance in larger datasets with more data attributes. Deep neural network prediction methods with clear modelling and parameter measurements formulated on publicly available and recognised datasets are the focus of the research. Widely used for academic performance prediction, backpropagation algorithms have been trained and tested with various datasets, especially those related to learning management systems (LMS) and massive open online courses (MOOC). The most widely used prediction method appears to be the standard artificial neural network approach. The long-short-term memory (LSTM) approach has been reported to achieve an accuracy of around 87 percent for temporal student performance data. The number of papers that study and improve this method shows that there is a clear rise in deep learning-based academic performance prediction over the last few years
Intrusion detection system for imbalance ratio class using weighted XGBoost classifier Januar Al Amien; Hadhrami Ab Ghani; Nurul Izrin Md Saleh; Edi Ismanto; Rahmad Gunawan
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 21, No 5: October 2023
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v21i5.24735

Abstract

The rapid development of the internet of things (IoT) has taken an important role in daily activities. As it develops, IoT is very vulnerable to attacks and creates IoT for users. Intrusion detection system (IDS) can work efficiently and look for activity in the network. Many data sets have already been collected, however, when dealing with problems involving big data and hight data imbalances. This article proposes, using the dataset used by BotIoT to evaluate the system framework to be created, the XGBoost model to improve the detection performance of all types of attacks, to control unbalanced data using the imbalance ratio of each class weight (CW). The experimental results show that the proposed approach greatly increases the detection rate for infrequent disturbances.
Deep learning approach to DDoS attack with imbalanced data at the application layer Rahmad Gunawan; Hadhrami Ab Ghani; Nurulaqilla Khamis; Januar Al Amien; Edi Ismanto
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 21, No 5: October 2023
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v21i5.24857

Abstract

A distributed denial of service (DDoS) attack is where one or more computers attack or target a server computer, by flooding internet traffic to the server. As a result, the server cannot be accessed by legitimate users. A result of this attack causes enormous losses for a company because it can reduce the level of user trust, and reduce the company’s reputation to lose customers due to downtime. One of the services at the application layer that can be accessed by users is a web-based lightweight directory access protocol (LDAP) service that can provide safe and easy services to access directory applications. We used a deep learning approach to detect DDoS attacks on the CICDDoS 2019 dataset on a complex computer network at the application layer to get fast and accurate results for dealing with unbalanced data. Based on the results obtained, it is observed that DDoS attack detection using a deep learning approach on imbalanced data performs better when implemented using synthetic minority oversampling technique (SMOTE) method for binary classes. On the other hand, the proposed deep learning approach performs better for detecting DDoS attacks in multiclass when implemented using the adaptive synthetic (ADASYN) method.
ANALISIS KESIAPAN SISWA DALAM MENGHADAPI PEMBELAJARAN KODING DAN KECERDASAN ARTIFISIAL SISWA SMA DAN SMK KECAMATAN PANGKALAN KURAS KABUPATEN PELALAWAN Rosma Kumala Dewi; Rahmad Al Rian; Edi Ismanto
Didaktik : Jurnal Ilmiah PGSD STKIP Subang Vol. 12 No. 02 (2026): Volume 12 No. 2, Juni 2026 Public
Publisher : STKIP Subang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36989/didaktik.v12i02.14695

Abstract

The development of the Industrial Era 4.0 and Society 5.0 has brought significant changes to the field of education, especially in the use of technology in the learning process. This condition requires students to be prepared to face technology-based learning such as coding and artificial intelligence. This study aims to determine the level of readiness of senior high school and vocational high school students in facing coding and artificial intelligence learning in Pangkalan Kuras District, Pelalawan Regency. The research employed a quantitative descriptive method with an evaluative approach. The research subjects consisted of tenth-grade students from SMAN 1 Pangkalan Kuras, SMAN 2 Pangkalan Kuras, SMKN 1 Pangkalan Kuras, and SMK Amanatulhuda Surya Indah. Data collection was conducted using questionnaires with the Proportional Random Sampling technique and a Likert scale, then analyzed using descriptive statistics in the form of percentages. Proportional Random Sampling is a sampling technique carried out randomly according to the proportion of students in each school, and the sample size was determined using the Slovin formula. The results showed several indicators of student readiness in facing coding and artificial intelligence learning among senior high school and vocational high school students in Pangkalan Kuras District, Pelalawan Regency. Overall, students’ readiness was categorized as very ready, with a percentage level of 83.6%. Meanwhile, the dominant aspect of students’ readiness in facing coding and artificial intelligence learning was the skills aspect.
Pendampingan Transformasi Digital Pemasaran Produk UMKM Olahan Ikan Patin melalui Media Sosial dan Marketplace Vitriani, Vitriani; Ismanto, Edi; Willyansah, Willyansah; Novalia, Melly
Jurnal Pengabdian UntukMu NegeRI Vol. 10 No. 2 (2026): Pengabdian Untuk Mu negeRI
Publisher : LPPM UMRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jpumri.v10i2.12120

Abstract

Usaha Mikro, Kecil, dan Menengah (UMKM) memiliki peran penting dalam mendukung perekonomian daerah, namun masih menghadapi kendala dalam pemasaran akibat belum optimalnya pemanfaatan teknologi digital. UMKM Blado Snack Indonesia di Kabupaten Kampar yang memproduksi stik patin dan peyek patin masih memiliki jangkauan pemasaran yang terbatas serta belum memanfaatkan media sosial dan marketplace secara optimal. Kegiatan pengabdian kepada masyarakat ini bertujuan meningkatkan kapasitas mitra dalam menerapkan pemasaran digital melalui media sosial dan marketplace untuk memperluas jangkauan pasar serta meningkatkan daya saing usaha. Metode yang digunakan adalah pendampingan partisipatif dengan pendekatan learning by doing melalui pelatihan, praktik langsung, pendampingan pengelolaan media sosial dan marketplace, serta monitoring dan evaluasi. Hasil kegiatan menunjukkan bahwa mitra mampu mengelola akun media sosial bisnis secara lebih optimal, menyusun konten promosi digital yang lebih menarik, serta mengoperasikan marketplace sebagai kanal penjualan produk. Selain itu, kegiatan ini menghasilkan strategi pemasaran digital yang lebih terstruktur sehingga meningkatkan visibilitas produk dan memperluas peluang pemasaran. Dengan demikian, pendampingan transformasi digital berhasil meningkatkan kapasitas mitra dalam mengimplementasikan pemasaran digital secara mandiri sebagai upaya mendukung keberlanjutan dan daya saing UMKM di era ekonomi digital.
Pengembangan Modul Pembelajaran Informatika Berbasis STEAM untuk Meningkatkan Nilai Kreativitas pada Materi Algoritma dan Pemrograman Kelas IX Serly Ferdina Mei Winda; Pratama Benny Herlandy; Edi Ismanto
ANTHOR: Education and Learning Journal Vol 5 No 4 (2026): Anthor 2026
Publisher : Institut Teknologi Pendidikan Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/anthor.v5i4.1104

Abstract

Pembelajaran algoritma dan pemrograman memerlukan bahan ajar yang menghubungkan konsep, praktik, dan kreativitas. Penelitian ini bertujuan mengembangkan modul pembelajaran Informatika berbasis Science, Technology, Engineering, Art, and Mathematics (STEAM) menggunakan Kodular, menilai validitas, praktikalitas, dan efektivitasnya, serta mengukur kreativitas peserta didik. Penelitian menggunakan metode Research and Development dengan model ADDIE pada 41 peserta didik kelas IX SMP Negeri 13 Pekanbaru. Data dikumpulkan melalui observasi, wawancara, dokumentasi, validasi ahli, angket praktikalitas, pretest-posttest, dan angket kreativitas. Analisis meliputi persentase, Shapiro-Wilk, Paired Sample t-test, dan N-Gain. Validitas media mencapai 90,00% dan validitas materi 96,43%, keduanya sangat valid. Praktikalitas guru sebesar 95,00% dan peserta didik 87,02%, keduanya sangat praktis. Rata-rata pretest 69,17 meningkat menjadi 89,02 pada posttest. Data berdistribusi normal dengan signifikansi 0,331 dan 0,099. Uji t menghasilkan p < 0,001, sedangkan N-Gain 0,65 berkategori sedang. Kreativitas peserta didik mencapai 89,07% dengan kategori sangat kreatif. Modul dinyatakan layak, praktis, dan efektif untuk mendukung pembelajaran berbasis proyek menggunakan MIT App Inventor.
Design and Evaluation of Smart Medical Mechanical Systems for Real-Time Rehabilitation Monitoring Kodai Kitagawa; Edi Ismanto
International Journal of Applied Science and Technology Application Vol. 1 No. 2 (2026): INPRESS Online: September 2026
Publisher : Raskha Media Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/ijapset.v1i2.8

Abstract

This research aims to develop and evaluate Smart Medical Mechanical Systems based on the integration of mechanical engineering, medical sensor engineering, embedded systems, and the Internet of Medical Things (IoMT) to support real-time rehabilitation monitoring. The research uses a Research and Development (R&D) approach with stages of needs analysis, mechanical design, medical sensor integration, embedded system development, laboratory testing, and initial clinical validation. The research subjects involved 42 participants consisting of post-stroke rehabilitation patients, mechanical engineers, biomedical engineers, and rehabilitation doctors. The research instruments include Electromyography (EMG) sensors, Inertial Measurement Units (IMU), load cells, motion capture, usability testing, and a cloud-based rehabilitation monitoring system. The research results show that the system successfully performed real-time monitoring of patients' biomechanical and physiological parameters with a sensor accuracy rate of 94.2%, a 28% increase in movement efficiency, and a 31% increase in user comfort. The system also supports more objective rehabilitation evaluations thru a cloud-based monitoring dashboard. In addition, the ergonomic mechanical design and multimodal sensing integration have proven to enhance the quality of human-rehabilitation device interaction. This research concludes that the integration of smart medical engineering and IoMT can enhance the effectiveness of modern rehabilitation and support the development of data-driven rehabilitation within the smart healthcare ecosystem. This research also contributes to the development of smarter rehabilitation systems that are more adaptive, personalized, and integrated for both clinical rehabilitation and telemedicine.
Co-Authors Abdul Fadlil Adam Ramadhan Afandi Alsyar Agus Satria Ahmad Gunawan Dalimunthe Ajeng Safitri Al Rian, Rahmad Ambiyar, Ambiyar Amelia Agustina Amran, Hasanatul Fu'adah Anton Yudhana Asha Yuda, Agim Sahrija Azaki Khoirudin Azzahra Chairunnisa Bella, Bella Fitria Sari Celvin Arafat Chintya, Indri Davie Rizky Akbar Delopinli, Crystian 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 Fadli Rahmad Hidayatullah Fadlil, Fadlil Fatihul Ihsan, Tengku Fawwaz Fauza Addinunnisa Fikri Abdul Jafar Gunawan, Rahmad Habil Maulana Hadhrami Ab Ghani Hadhrami Ab Ghani Hadhrami Ab. Ghani Hammam Zaki Harun Mukhtar Hendra, Zana Vania Herdani, Inka friska Herlandy, Pratama Benny Herman Ilham Ramadhan Januar Al Amien Januar Al Amien Januar Al Amien Khairul Anshari Kitagawa, Kodai Kodai Kitagawa Lisman, Muhammad Maulana, M.Rizky Melly Novalia Melly Novalia, Melly Mohamad, Mohd Saberi Muhammad Cavin Ramadhan Muhammad Desfriyan Arif Rosady Muhammad Iqbal Muhammad Ridwansyah Nabil Ibrahim Faisal Nuraeni, Eneng Nurul Izrin Binti Md Saleh Nurul Izrin Md Saleh Nurul Izrin Md Saleh Nurul Safira, Natasya Nurulaqilla Khamis Oriana, Larisa Patlan Putra Humala Harahap Pramudya, Muhammad Rayenra Azthi Pratama Benny Herlandi Pratama Benny Herlandy Putri Ramahdani, Anggi Rahmad Al Rian Rahmad Al Rian Rahmad Al Rian Rahmad Alrian Rahmadani, Delia Syaf Rahmatullah, Yuvi Ramadani, Tasya Ramadhani, Monica Alya Remli, Muhammad Akmal Renita Rahmadani Resmi Darni Ridhollah, Farhan Riski Amin Putra Rohima Zalti, Ulfani Rose Darmakusuma, Dinda Rosma Kumala Dewi Safitri, Ajeng Septian Alza Septiawan, Raffi Serly Ferdina Mei Winda Siti Niah Soni Sri Fitria Retnawaty Sunanto Sunanto Suryadila, Lusi Tri Wahono Vitriani Vitriani Vitriani Vitrian Vitriani, Vitriani Wan Salihin Wong, Khairul Nizar Syazwan Wandi Syahfutra willyansah, willyansah Winson Ardhika Ramadhani Yeeri Badrun