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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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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.
Peningkatan Kinerja Model Random Forest untuk Deteksi Kecurangan Kartu Kredit Menggunakan RandomizedSearchCV Oriana, Larisa; Dwi Sanggar Wati, Anisa; Putri Ramahdani, Anggi; Nurul Safira, Natasya; Ismanto, Edi
JURNAL FASILKOM Vol. 15 No. 2 (2025): 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.v15i2.9832

Abstract

Pertumbuhan transaksi kartu kredit di Indonesia yang pesat diiringi dengan peningkatan kasus penipuan membutuhkan sistem deteksi yang handal. Penelitian ini mengembangkan model deteksi penipuan dengan mengoptimalkan algoritma Random Forest melalui teknik RandomizedSearchCV. Dataset mencakup 690 transaksi dengan 16 fitur, termasuk 14 variabel prediktor dan label kelas (0 untuk transaksi normal, 1 untuk fraud). Tahapan penelitian meliputi eksplorasi data (EDA) untuk memahami karakteristik dataset, standarisasi fitur, pembagian data (80% pelatihan, 20% pengujian), serta implementasi model Random Forest dalam dua versi: tanpa tuning dan dengan optimasi parameter. Hasil penelitian menunjukkan peningkatan performa model setelah optimasi, dengan akurasi mencapai 90,58% pada data uji (naik dari 89,86%). Model yang dioptimasi juga berhasil meningkatkan precision deteksi fraud dari 0,94 menjadi 0,98 dan mengurangi kesalahan klasifikasi transaksi normal (false positive) dari 3 menjadi 1 kasus. Namun, terdapat sedikit penurunan recall kelas fraud dari 0,82 menjadi 0,80, yang mengindikasikan perlunya penyeimbangan antara akurasi dan sensitivitas deteksi. Temuan ini membuktikan bahwa tuning hyperparameter dengan RandomizedSearchCV dapat meningkatkan kinerja model Random Forest dalam mendeteksi penipuan kartu kredit, khususnya dalam meminimalkan kesalahan klasifikasi
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