Article Per Year (5 Year)
p-Index From 2021 - 2026
5.64
P-Index
This Author published in this journals
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) 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 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 Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Physics Social Sciences Other

Published : 59 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 59 Documents
Search

 2   3   4   5   6 
Analisis Kinerja Algoritma K-Nearest Neighbors (KNN) dan Random Forest untuk Klasifikasi Kondisi Cuaca Asha Yuda, Agim Sahrija; Muhammad Desfriyan Arif Rosady; Nabil Ibrahim Faisal; Edi Ismanto
Computer Science and Information Technology Vol 6 No 2 (2025): 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.v6i2.9827

Abstract

The development of information technology has encouraged the use of machine learning algorithms in various fields, including in the analysis and prediction of weather conditions. This study aims to analyze and compare the performance of two machine learning algorithms, namely K-Nearest Neighbors (KNN) and Random Forest, in the classification of weather conditions based on historical meteorological data. The dataset used includes features such as rainfall, maximum temperature, minimum temperature, and wind speed, with target categories in the form of weather types such as rain, sunny, fog, drizzle, and snow. The process includes data pre-processing, feature scaling, training and test data sharing, and model training using the scikit-learn library. Performance evaluations are conducted using accuracy, precision, recall, and F1-score metrics. The results showed that the Random Forest model had higher accuracy (82%) than KNN (78%), with more stable performance in the majority class. However, both models experienced significant performance declines in minority classes due to data imbalances. The study recommends further optimizations such as class balancing and model parameter selection to improve the overall accuracy of weather classification.
Integrasi Psikologi dan Teknologi Informasi dalam Pengembangan Baby Daycare Ramah Anak di Pekanbaru Ismanto, Edi; Vitriani, Vitriani; Safitri, Ajeng
Jurnal Pengabdian UntukMu NegeRI Vol. 9 No. 3 (2025): Pengabdian Untuk Mu negeRI
Publisher : LPPM UMRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jpumri.v9i3.10409

Abstract

Kegiatan pengabdian kepada masyarakat ini bertujuan untuk meningkatkan kualitas pengasuhan dan pengelolaan lembaga Baby Daycare Siti Walidah Pekanbaru melalui integrasi pendekatan psikologi dan teknologi informasi. Permasalahan utama yang dihadapi mitra meliputi rendahnya pemahaman pengasuh terhadap konsep pengasuhan ramah anak serta pencegahan perilaku bullying, disertai dengan sistem manajemen yang masih dilakukan secara manual sehingga kurang efisien dan tidak terdokumentasi dengan baik. Untuk menjawab permasalahan tersebut, tim dosen dari Universitas Muhammadiyah Riau melaksanakan kegiatan pelatihan dan pendampingan dengan fokus pada dua aspek utama, yaitu peningkatan kapasitas psikologis pengasuh dan penerapan sistem digital sederhana untuk administrasi daycare. Metode kegiatan menggunakan pendekatan partisipatif melalui workshop, simulasi, dan pendampingan langsung. Hasil kegiatan menunjukkan adanya peningkatan rata-rata sebesar 35% dalam pemahaman peserta terhadap konsep positive parenting dan anti-bullying approach. Selain itu, penerapan sistem digital berhasil meningkatkan efisiensi waktu pencatatan hingga 40%, serta memperkuat komunikasi antara pengelola daycare dan orang tua secara lebih efektif dan transparan. Kegiatan ini memberikan dampak positif terhadap peningkatan kompetensi pengasuh, efisiensi kelembagaan, serta terwujudnya lingkungan pengasuhan yang ramah anak dan adaptif terhadap perkembangan teknologi. Dengan demikian, sinergi antara bidang psikologi dan teknologi informasi dapat menjadi model strategis dalam pengembangan daycare modern yang berorientasi pada kesejahteraan dan tumbuh kembang anak di era digital.
Digitalisasi Sistem Daycare sebagai Solusi Penguatan Layanan Pengasuhan di Daycare Citra Iman Vitriani, Vitriani; Ismanto, Edi; Safitri, Ajeng
Jurnal Pengabdian UntukMu NegeRI Vol. 9 No. 3 (2025): Pengabdian Untuk Mu negeRI
Publisher : LPPM UMRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jpumri.v9i3.10702

Abstract

Kegiatan pengabdian ini bertujuan untuk mengimplementasikan digitalisasi sistem daycare sebagai upaya peningkatan kualitas layanan pengasuhan di Daycare Citra Iman. Proses pelaksanaan mencakup analisis kebutuhan, perancangan sistem, pengembangan aplikasi digital berbasis web/mobile, pelatihan guru asuh, serta implementasi dan evaluasi. Hasil analisis menunjukkan bahwa proses dokumentasi perkembangan anak, absensi, dan komunikasi dengan orang tua sebelumnya masih dilakukan secara manual sehingga berpotensi menimbulkan ketidakteraturan pencatatan dan keterlambatan informasi. Sistem daycare digital yang dikembangkan memuat fitur absensi, catatan harian, dokumentasi kegiatan, catatan kesehatan, dan dashboard informasi orang tua. Pelatihan yang diberikan kepada 13 guru asuh menunjukkan peningkatan pemahaman yang signifikan, yang ditunjukkan oleh kenaikan skor pre-test dari rentang 57–66 menjadi 81–92 pada post-test. Implementasi sistem juga meningkatkan efisiensi alur kerja pengasuhan, akurasi pendokumentasian, serta efektivitas komunikasi antara daycare dan orang tua. Secara keseluruhan, digitalisasi terbukti mampu memperkuat tata kelola layanan pengasuhan di Daycare Citra Iman, meningkatkan literasi digital tenaga pengasuh, serta menyediakan model yang dapat direplikasi oleh lembaga pengasuhan lainnya dalam mengadopsi transformasi digital secara berkelanjutan.
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 : 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.
PREDIKSI HARGA EMAS MENGGUNAKAN ALGORITMA LONG SHORT TERM MEMORY (LSTM) & GATED RECURRENT UNIT (GRU) Hendra, Zana Vania; Ramadhani, Monica Alya; Chintya, Indri; Rahmatullah, Yuvi; Ismanto, Edi
Jurnal Rekayasa Perangkat Lunak dan Sistem Informasi Vol. 6 No. 1 (2026)
Publisher : Department of Information System Muhammadiyah University of Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/seis.v6i1.9809

Abstract

Gold is an asset that has a hedge against inflation and global economic volatility, making it interesting to analyze as an investment instrument. This study aims to compare the performance of Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) models in predicting gold prices using historical data from 2013 to 2022. The data used includes daily gold prices and goes through a preprocessing stage before being divided into training (80%) and testing (20%) data. LSTM and GRU models were trained with epoch and batch size variations, then evaluated using MAE, RMSE, MSE, and MAPE metrics. The results showed that the GRU model with 50 epochs performed best, with MAE 0.0145, RMSE 0.0186, MSE 0.0003, and MAPE 1.9209%, better than LSTM which produced higher errors. The residual graph also shows that GRU produces stable predictions with a random error distribution that is close to zero. These findings confirm that GRU is more accurate and efficient in modeling gold price time series, and has the potential to be implemented in artificial intelligence-based commodity price prediction systems.
Analisis Perbandingan Model Machine Learning dan Deep Learning untuk Peramalan Harga Saham Edi Ismanto; Ahmad Gunawan Dalimunthe; Muhammad Iqbal; Fauza Addinunnisa
Jurnal Ilmu Komputer dan Teknik Informatika Vol. 2 No. 1 (2026): Januari 2026
Publisher : CV. Raskha Media Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64803/juikti.v2i1.114

Abstract

Peramalan harga saham harian masih menjadi tantangan signifikan dalam bidang keuangan dan data science akibat tingginya volatilitas pasar serta pengaruh berbagai faktor eksternal. Penelitian ini menyajikan analisis perbandingan beberapa model Machine Learning (ML) dan Deep Learning (DL) untuk peramalan harga saham berbasis indikator teknikal. Model ML yang dievaluasi meliputi Random Forest, Support Vector Regressor (SVR), dan XGBoost, sedangkan pendekatan DL mencakup Long Short-Term Memory (LSTM) dan Dense Neural Network (DNN). Data yang digunakan berupa data historis harga saham yang diperkaya dengan indikator teknikal seperti Moving Average (MA), Relative Strength Index (RSI), dan Bollinger Bands (BB). Kinerja model dievaluasi menggunakan metrik Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), serta koefisien determinasi (R²). Hasil eksperimen menunjukkan bahwa model Support Vector Regressor menghasilkan kinerja prediksi terbaik, diikuti oleh Random Forest dan XGBoost. Model Deep Learning menunjukkan kinerja yang relatif lebih rendah, yang diduga disebabkan oleh keterbatasan data serta kebutuhan proses tuning hiperparameter yang lebih kompleks. Temuan ini menunjukkan bahwa model berbasis Machine Learning, khususnya SVR, lebih efektif untuk peramalan harga saham dalam kondisi eksperimental penelitian ini.
PENGEMBANGAN MEDIA PEMBELAJARAN AUGMENTED REALITY (AR) BERBASIS ANDROID PADA MATERI STRUKTUR ATOM SMA KELAS X Azzahra Chairunnisa; Rahmad Al Rian; Edi Ismanto
Didaktik : Jurnal Ilmiah PGSD STKIP Subang Vol. 11 No. 03 (2025): Volume 11 No. 03 September 2025 In Build
Publisher : STKIP Subang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36989/didaktik.v11i03.7710

Abstract

Penelitian ini bertujuan untuk merancang dan mengembangkan media pembelajaran berbasis Augmented Reality (AR) pada materi struktur atom, guna meningkatkan pemahaman peserta didik terhadap konsep-konsep kimia yang bersifat abstrak. Media ini disusun untuk memberikan pengalaman belajar yang lebih interaktif, menarik, dan fleksibel dengan memanfaatkan perangkat berbasis Android yang dapat diakses secara luas oleh peserta didik. Kegiatan penelitian dilaksanakan di SMA IT Darul Fiy Azkya dengan menggunakan metode Research and Development (R&D) yang mengacu pada model pengembangan ADDIE, yang terdiri atas lima tahapan: Analisis, Perancangan, Pengembangan, Implementasi, dan Evaluasi. Hasil validasi oleh ahli media menunjukkan tingkat kelayakan sebesar 91,11%, sedangkan hasil validasi oleh ahli materi mencapai skor 98,46%. Keduanya berada dalam kategori "sangat layak". Media pembelajaran yang dikembangkan, dengan nama Automatic Struktur, menyajikan objek tiga dimensi (3D) dari struktur atom, dilengkapi dengan fitur suara dan kuis. Berdasarkan hasil uji coba terhadap peserta didik, media ini dinilai praktis. Dengan demikian dapat disimpulkan bahwa media pembelajaran berbasis Augmented Reality ini sudah layak digunakan dalam pembelajaan.
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): 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.
Peningkatan Akurasi Diagnosis Penyakit Ginjal Kronis melalui Integrasi Algoritma Naive Bayes dan Algoritma Genetika Eka Pandu Cynthia; Edi Ismanto
Jurnal Ilmu Komputer, Teknologi Dan Informasi Vol 4 No 1 (2026): Januari 2026
Publisher : CV. Graha Mitra Edukasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62866/jurikti.v4i1.272

Abstract

Chronic Kidney Disease (CKD) is a significant global health challenge that necessitates early diagnosis to prevent severe organ failure. While machine learning techniques such as Naive Bayes (NB) have been widely implemented for medical classification, their performance is often hindered by redundant and irrelevant features within high-dimensional medical datasets. This study aims to address this limitation by reducing the dimensions of non-contributing medical attributes, thereby minimizing bias and improving classification accuracy. Consequently, this study proposes the integration of Genetic Algorithm (GA) as a feature selection method to optimize the performance of the Naive Bayes (NB) algorithm in diagnosing CKD. The dataset, sourced from the UCI Machine Learning Repository, consists of 400 samples and 24 clinical features. A genetic algorithm was employed to identify the optimal feature subset through a binary evolution mechanism, while NB served both as the classifier and the fitness evaluation function. The results demonstrate that GA successfully reduced the data dimensions by 50%, streamlining the initial 24 features into 12 highly discriminative ones. Evaluation using 10-Fold Cross-Validation revealed a significant increase in accuracy, rising from 92.50% using the standard NB to 98.50% with the integrated GA-NB model. Furthermore, the recall reached 98.40%, indicating the model's high capability in minimizing diagnostic errors for affected patients (false negatives). This research proves that GA-based feature selection effectively enhances diagnostic reliability and model efficiency, presenting substantial potential for implementation in clinical decision support systems for medical professionals.
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 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 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 Safira, Natasya 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 Alrian Rahmad Gunawan Gunawan 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 Safitri, Ajeng Septian Alza Septiawan, Raffi 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 Winson Ardhika Ramadhani Yeeri Badrun