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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
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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Pemanfaatan Digital Marketing untuk Memperluas Strategi Pemasaran Produk Furniture dari Bahan Kayu Rubber Ismanto, Edi; Januar Al Amien; Hammam Zaki; Eka Pandu Cynthia
Jurnal Pengabdian UntukMu NegeRI Vol. 8 No. 1 (2024): Pengabdian Untuk Mu negeRI
Publisher : LPPM UMRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jpumri.v8i1.5720

Abstract

The COVID-19 pandemic, which has affected Indonesia for the past three years, has had a significant negative impact on a number of industries, including the Micro, Medium, and Small Enterprises (MSME) sector, which has been particularly hard hit. Pekanbaru City has 105,445 MSMEs, with data indicating that there are as many as 1,034 MSMEs, which produce a range of goods used by the community, including furniture products and various wood-based office and home furnishings. Of course, if development is carried out for MSME wood craftsmen, this is a potential aspect for the City of Pekanbaru. UMKM Furniqa Woodcraft as a raw material to create furniture items like chairs, tables, cabinets, and various other handicraft products uses rubber wood. However, there has been a significant drop in sales since the Covid-19 pandemic, so a solution must be found. In an effort to increase product marketing, service activities performed include training and assisting with managing Digital Marketing. This activity is implemented using a variety of approaches, including the Interview and Discussion Method, the Training Method, and the Evaluation Method. The evaluation of the implementation of digital marketing training and mentoring showed that employees at Furniqa Woodcraft had increased knowledge competence by 75.875%.
Analisis Perbandingan Model Fully Connected Neural Networks (FCNN) dan TabNet Untuk Klasifikasi Perawatan Pasien Pada Data Tabular Ismanto, Edi; Abdul Fadlil; Anton Yudhana
Computer Science and Information Technology Vol 5 No 3 (2024): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Electronic Health Records (EHR) store tabular data that is rich in information and play a critical role in supporting decision-making within the healthcare field, particularly for patient care classification. This study evaluates the performance of two artificial intelligence models, Fully Connected Neural Networks (FCNN) and TabNet, in processing tabular data for patient care classification tasks. The findings reveal that both models demonstrate strong performance, with TabNet showing a slight advantage. TabNet achieves an accuracy of 0.74, marginally surpassing FCNN's 0.73. Furthermore, TabNet excels in precision (0.74 vs. 0.72), recall (0.72 vs. 0.71), and F1-Score (0.73 vs. 0.71), highlighting its greater reliability in minimizing false positives and accurately detecting positive cases with a better balance between precision and recall. With its architecture specifically tailored for tabular data and its capacity for direct interpretability, TabNet offers enhanced efficiency and ease of implementation compared to FCNN, which demands more complex data preprocessing. For future research, it is suggested to employ larger and more diverse datasets, explore data with higher feature complexity, and conduct comprehensive hyperparameter tuning to further improve the performance of both models.
Efektivitas Sosialisasi Listrik Aman dan Hemat pada Mahasiswa melalui Pretest dan Posttest Menggunakan Google Form Tri Wahono; Ismanto, Edi; Nuraeni, Eneng; Yudhana, Anton; Herman
Jurnal Pengabdian Kepada Masyarakat MEMBANGUN NEGERI Vol. 8 No. 2 (2024): Jurnal Pengabdian Kepada Masyarakat MEMBANGUN NEGERI
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35326/pkm.v8i2.6394

Abstract

Sosialisasi mengenai penggunaan listrik yang aman dan hemat menjadi semakin penting dalam konteks modern, mengingat berbagai tantangan dan kebutuhan yang dihadapi masyarakat saat ini. Beberapa faktor utama yang melatarbelakangi upaya sosialisasi ini meliputi kebutuhan akan keselamatan, efisiensi energi, penghematan biaya. Kurangnya pengetahuan tentang pengetahuan mengenai penggunaan listrik yang benar mengakibatkan insiden kecelakaan listrik. Sosialisasi ini bertujuan untuk menganalisis efektivitas sosialisasi tentang listrik yang aman dan hemat pada mahasiswa pendidikan Universitas Muhammadiyah Riau. Sosialisasi dilakukan untuk meningkatkan pemahaman dan kesadaran mahasiswa terhadap penggunaan listrik yang lebih bijaksana dan aman. Metode penelitian yang digunakan adalah pemberian materi secara langsung kemudian untuk mengukur pemahaman mahasiswa menggunakan pre-test dan post-test. Alat pengumpulan data berupa kuesioner yang disebarkan melalui Google form, yang mencakup pertanyaan terkait pengetahuan dan perilaku dalam penggunaan listrik sebelum dan sesudah sosialisasi. Hasil dari 49 responden yang mengisi kuesioner dengan 15 pertanyaan, analisis statistik mengungkapkan bahwa intervensi yang diberikan memiliki efek positif yang signifikan terhadap pengetahuan atau keterampilan responden. Hal ini dibuktikan dengan nilai p yang lebih kecil dari 0,05. Dapat disimpulksn bahwa kegiataan sosialisasi terbukti efektif dalam meningkatkan pemahaman atau kemampuan responden secara cepat.
Sosialisasi & Edukasi: Optimalisasi Bakat dan Minat Siswa Berbasis Sistem Pakar Dengan Pendekatan Artificial Intelligence Ismanto, Edi; Vitriani; Ajeng Safitri
Jurnal Pengabdian UntukMu NegeRI Vol. 8 No. 3 (2024): Pengabdian Untuk Mu negeRI
Publisher : LPPM UMRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jpumri.v8i3.7947

Abstract

Di tengah era digital, pentingnya pendidikan yang dapat mempersiapkan generasi muda untuk tantangan global sangat nyata, terutama dalam konteks pengembangan potensi individu. Banyak siswa yang kurang mendapatkan bimbingan yang tepat, sehingga kemampuan mereka tidak termanfaatkan secara maksimal. Optimalisasi bakat dan minat siswa berbasis sistem pakar di SMPN 16 Pekanbaru bertujuan untuk mengatasi kesenjangan dalam identifikasi dan pengembangan bakat siswa. Dengan menerapkan sistem pakar berbasis Artificial Intelligence (AI), kegiatan ini menawarkan solusi inovatif untuk mendeteksi dan mengarahkan bakat serta minat siswa secara lebih objektif. Kegiatan mencakup seminar edukasi yang mengenalkan konsep bakat dan minat, serta pelatihan praktis dalam penggunaan sistem pakar. Evaluasi efektivitas program menunjukkan peningkatan pemahaman peserta sebesar 77.5%, dengan analisis t-test yang mengindikasikan dampak positif yang signifikan. Temuan ini mempertegas bahwa integrasi teknologi dalam pendidikan sangat penting untuk mendukung pengembangan bakat siswa, sehingga mereka dapat menjadi individu yang lebih percaya diri dan berdaya saing tinggi di masyarakat global.
Advanced tourist arrival forecasting: a synergistic approach using LSTM, Hilbert-Huang transform, and random forest Mukhtar, Harun; Remli, Muhammad Akmal; Mohamad, Mohd Saberi; Wan Salihin Wong, Khairul Nizar Syazwan; Ridhollah, Farhan; Deprizon, Deprizon; Soni, Soni; Lisman, Muhammad; Amran, Hasanatul Fu'adah; Sunanto, Sunanto; Ismanto, Edi
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 1: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i1.pp517-526

Abstract

An advanced synergistic approach for forecasting tourist arrivals is presented, integrating long short-term memory (LSTM), Hilbert-Huang transform (HHT), and random forest (RF). LSTM is leveraged for its capability to capture long-term dependencies in sequential data. Additional data from Google Trends (GT) is processed with HHT for feature extraction, followed by feature selection using the RF algorithm. The combined HHT-RF-LSTM model delivers highly accurate forecasts. Evaluation employs regression analysis with metrics such as root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and mean square error (MSE), highlighting the effectiveness of this innovative approach in predicting tourist arrivals. This methodology provides a robust framework for handling limited datasets and improving forecast reliability. By incorporating diverse data sources and advanced preprocessing techniques, the model enhances prediction performance, demonstrating the strong performance of RF in feature selection.
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.
Pengembangan Game Edukasi Interaktif Berbasis Android Untuk Mendukung Proses Pembelajaran Siswa Sekolah Menengah An Nikmah Al Islamiyah Kamboja Herdani, Inka Friska; Ismanto, Edi; Novalia, Melly; Syahfutra, Wandi
Jurnal Pendidikan dan Teknologi Indonesia Vol 5 No 4 (2025): JPTI - April 2025
Publisher : CV Infinite Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jpti.818

Abstract

Penelitian ini bertujuan untuk mengembangkan game edukasi berbasis Android sebagai media pembelajaran jaringan komputer dasar di sekolah menengah An-Nikmah Al-Islamiyah Phnom Penh. Pengembangan dilakukan menggunakan model 4D yang terdiri dari tahapan Define, Design, Develop, dan Disseminate. Metode pengujian melibatkan validasi oleh ahli media, ahli materi, dan ahli bahasa serta uji coba kepada siswa dan guru untuk menilai aspek kelayakan dan praktikalitas. Data dikumpulkan melalui observasi, wawancara, dan angket, kemudian dianalisis secara kuantitatif menggunakan skala Likert untuk mengukur tingkat validitas dan efektivitas media pembelajaran.Hasil penelitian menunjukkan bahwa game edukasi yang dikembangkan memiliki tingkat kelayakan sangat tinggi dengan skor validasi ahli media sebesar 87%, ahli materi 79%, dan ahli bahasa 84%. Evaluasi praktikalitas oleh guru dan siswa juga memberikan hasil positif dengan persentase masing-masing 90% dan 81%. Penggunaan game edukasi ini meningkatkan keterlibatan siswa dalam pembelajaran serta memberikan pengalaman belajar yang lebih interaktif dan mandiri. Selain itu, game edukasi ini berkontribusi terhadap pengembangan pendidikan berbasis teknologi dengan menyediakan alternatif pembelajaran digital yang menarik dan mudah diakses.Penelitian ini memberikan dasar bagi pengembangan lebih lanjut dalam integrasi teknologi dalam pembelajaran, terutama dalam meningkatkan pemahaman siswa terhadap konsep jaringan komputer dasar. Pengujian lebih luas dan jangka panjang direkomendasikan untuk mengoptimalkan efektivitas media ini dalam berbagai konteks pendidikan.
Syshunt: Game Quiz Mobile untuk Pengenalan Perangkat Keras Komputer menggunakan Successive Approximation Model Suryadila, Lusi; Ismanto, Edi; Novalia, Melly; Syahfutra, Wandi
Jurnal Pendidikan Informatika (EDUMATIC) Vol 9 No 1 (2025): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v9i1.29773

Abstract

Game-based learning media is an example of ongoing technological advances in education. This media is becoming increasingly popular as an innovative solution in education. This research aims to develop and test the feasibility of mobile-based quiz games as learning media for computer hardware introduction using the Successive Approximation Model (SAM). This research is a kind of development using the SAM model. The three main stages of the SAM model consist of the preparation stage, the iterative design stage, and the iterative development stage. The data collection technique in this study used a questionnaire. Meanwhile, the data analysis technique used descriptive quantitative. The result of our findings is a mobile-based quiz game as a learning medium for computer hardware introduction. The results of media and material expert validation show that this game has a media feasibility of 87.14% and material feasibility of 86%. However, the results of the student practicality test were slightly lower with a score of 79.88%, which may be influenced by limitations in the interface features or the time needed for students to adapt to the game mechanics. Nevertheless, the game proved to be effective in understanding computer hardware and is more interactive and fun compared to conventional learning. With a more engaging learning experience, this game can be a creative alternative that supports the teaching and learning process, overcoming the problems of traditional learning that is often monotonous.
Pengenalan dan Edukasi Kecerdasan Artifisial Generatif untuk Siswa Sekolah Al-Amin Terengganu, Malaysia Ismanto, Edi; Rahmad Al Rian; Vitriani; Melly Novalia; Pratama Benny Herlandy
Jurnal Pengabdian UntukMu NegeRI Vol. 9 No. 2 (2025): Pengabdian Untuk Mu negeRI
Publisher : LPPM UMRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jpumri.v9i2.9784

Abstract

Kecerdasan Artifisial Generatif (Generative Artificial Intelligence/GenAI) merupakan salah satu inovasi teknologi yang berkembang pesat dan memiliki dampak luas di berbagai sektor, termasuk pendidikan. Kegiatan pengabdian ini bertujuan untuk memberikan pengenalan dan edukasi mengenai konsep dasar, manfaat, serta tantangan etis dari GenAI kepada siswa Sekolah Al-Amin Terengganu, Malaysia. Metode yang digunakan meliputi penyampaian materi interaktif, demonstrasi langsung penggunaan aplikasi GenAI seperti ChatGPT dan DALL·E, serta pelaksanaan pre-test dan post-test untuk mengukur peningkatan pemahaman peserta. Hasil kegiatan menunjukkan adanya peningkatan signifikan dalam pengetahuan siswa, dengan rata-rata nilai post-test meningkat sebesar 34,5 poin dibandingkan pre-test. Siswa juga menunjukkan antusiasme tinggi dalam mencoba berbagai aplikasi AI dan mampu mengidentifikasi peluang serta risiko penggunaannya secara kritis. Kegiatan ini membuktikan bahwa literasi AI dapat ditanamkan secara efektif sejak usia sekolah melalui pendekatan edukatif yang partisipatif dan kontekstual. Diharapkan kegiatan ini menjadi langkah awal dalam membentuk generasi yang siap menghadapi tantangan era digital dengan pemahaman teknologi yang mendalam dan etis.
Peramalan Harga Emas Berbasis Time Series Menggunakan Arsitektur LSTM Deep Learning Diva Arifal Adha; Adam Ramadhan; Habil Maulana; Patlan Putra Humala Harahap; 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.v6i2.9980

Abstract

Gold is one of the most influential commodities in the global economy. Its high price volatility poses a significant challenge for investors, financial analysts, and policymakers in formulating effective strategies and making accurate decisions. Therefore, an accurate prediction method is needed to forecast future gold price movements. This study aims to forecast gold prices using a deep learning approach with the Long Short-Term Memory (LSTM) algorithm. The LSTM model is capable of learning long-term dependencies in time-series data, making it highly suitable for modeling complex and dynamic financial data. The data used in this study consists of daily historical gold prices obtained from reliable sources. A preprocessing phase was carried out to clean and normalize the data before training the model. Furthermore, this study compares the performance of the LSTM model with the Multilayer Perceptron (MLP) model to examine differences in prediction accuracy. Evaluation metrics such as Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) were used to assess model performance. The results show that the LSTM model provides more accurate predictions compared to MLP, with lower error values and better model stability. In conclusion, the deep learning approach, particularly the LSTM model, can serve as an effective alternative for gold price forecasting and support data-driven decision-making in the financial sector.
Co-Authors Abdul Fadlil Adam Ramadhan Agus Satria Ajeng Safitri Al Rian, Rahmad Ambiyar, Ambiyar 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 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 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