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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) PROCESSOR Jurnal Ilmiah Sistem Informasi, Teknologi Informasi dan Sistem Komputer JURIKOM (Jurnal Riset Komputer) JOURNAL V-TECH (VISION TECHNOLOGY) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) KLIK: Kajian Ilmiah Informatika dan Komputer Jurnal Manajemen Teknologi dan Sistem Informasi Jurnal Pengabdian Masyarakat Unama
Suyanti Suyanti
Program Studi Sistem Informasi, Fakultas Ilmu Komputer, Universitas Dinamika Bangsa, Jambi
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering

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PEMANFAATAN FITUR AI GOOGLE GEMINI UNTUK PELATIHAN PENGEMBANGAN MATERI AJAR INTERAKTIF PADA SMAS ISLAM AL FALAH JAMBI Astri, Lola Yorita; Ophelia, Chandy; Wardani , Muhammad; Suyanti; Febri; Siswanto, Agus; Prayitno
Jurnal Pengabdian Masyarakat UNAMA Vol 4 No 2 (2025): JPMU Volume 4 Nomor 2 Oktober 2025
Publisher : LPPM Universitas Dinamika Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33998/jpmu.2025.4.2.2566

Abstract

Pendidikan di era digital menuntut pendekatan pembelajaran yang interaktif dan personal. SMAS Islam Al Falah Jambi menghadapi tantangan dalam mengembangkan materi ajar yang menarik karena keterbatasan literasi digital guru. Kegiatan pengabdian masyarakat ini bertujuan untuk memberikan pelatihan pemanfaatan fitur AI, khususnya Google Gemini, sebagai alat bantu dalam menciptakan materi ajar yang dinamis. Metode pelaksanaan meliputi penyuluhan teori dan praktik langsung kepada 20 orang guru. Hasil kegiatan menunjukkan bahwa peserta mampu memahami manfaat AI dan secara teknis dapat menggunakan Google Gemini untuk membuat materi ajar sesuai bidang studinya masing-masing. Antusiasme peserta yang tinggi selama pelatihan menunjukkan potensi besar untuk penerapan berkelanjutan. Kegiatan ini berhasil menjembatani kesenjangan keterampilan digital guru dan berkontribusi pada peningkatan kualitas pembelajaran di sekolah.
Arsitektur Enterprise Sistem Pemerintahan Berbasis Elektronik (SPBE) Domain Arsitektur Proses Bisnis Pada Desa Sido Rukun Aryani, Lies; Suyanti; Jannah, Siti Raudatul
Jurnal Manajemen Teknologi Dan Sistem Informasi (JMS) Vol 6 No 1 (2026): JMS Vol 6 No 1 MARET 2026
Publisher : LPPM STIKOM Dinamika Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33998/jms.2026.6.1.2762

Abstract

The implementation of the Electronic-Based Government System (SPBE) is essential for achieving efficient, transparent, and accountable village governance. Sido Rukun Village in Merangin Regency, Jambi Province, has begun using several government applications but lacks a structured enterprise architecture aligned with the national SPBE framework. This study aims to develop an enterprise architecture for SPBE in the business process domain at Sido Rukun Village. The research employs the TOGAF ADM (The Open Group Architecture Framework – Architecture Development Method) approach, involving stages such as identifying current business processes, designing a target architecture, and conducting a gap analysis between the as-is and to-be states. The findings include a business process architecture blueprint compliant with Presidential Regulation No. 95 of 2018 and Presidential Regulation No. 132 of 2022 on the National SPBE Architecture. This blueprint encompasses BPMN-based business process models and supporting artifacts that serve as a foundation for integrated information systems at the village level. The study’s implications are significant: it provides Sido Rukun Village with a practical and standardized technical blueprint for implementing a sustainable electronic-based government system, thereby supporting its transformation toward a Smart Village capable of adapting to evolving information and communication technology trends.
Klasifikasi Tumor Otak pada Citra MRI Menggunakan Transfer Learning EfficientNetB1 dan Visualisasi Grad-CAM Suyanti; Ophelia S, Chandy; Aryani, Lies; Saputra, Chindra; Prayitno
Jurnal Manajemen Teknologi Dan Sistem Informasi (JMS) Vol 6 No 1 (2026): JMS Vol 6 No 1 MARET 2026
Publisher : LPPM STIKOM Dinamika Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33998/jms.2026.6.1.2782

Abstract

Magnetic resonance imaging (MRI) provides rich anatomical contrast for brain tumor assessment, yet routine interpretation remains time-intensive and demands high precision. This work develops a pipeline for four-class brain MRI image classification (glioma, meningioma, pituitary tumor, and no tumor) by combining automated brain-region cropping, data augmentation, and transfer learning with EfficientNetB1. Experimental results demonstrate exceptional performance, achieving an overall accuracy of 0.99 (99%) on the test set. Specifically, the model reached an F1-score of 1.00 for the no tumor class, 0.99 for pituitary, and 0.98 for both glioma and meningioma classes. Beyond reporting numerical performance, the study utilizes Grad-CAM heatmaps to verify that predictions rely on clinically plausible regions rather than spurious background cues. These results indicate that an efficiency-oriented backbone, paired with systematic preprocessing, can achieve reliable and interpretable performance for brain tumor classification tasks.
Analisis Prediktif Harga Penutupan Harian Bitcoin Menggunakan Arsitektur Jaringan Saraf Tiruan Long Short-Term Memory Suyanti Suyanti; Chandy Ophelia S; Lies Aryani
Journal of Information System Research (JOSH) Vol 7 No 1 (2025): October 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i1.7485

Abstract

The highly volatile price of Bitcoin makes it difficult for financial market players. This research aims to build a Bitcoin daily closing price prediction model using Long Short-Term Memory (LSTM) neural network. Bitcoin price data from January 1, 2014 to May 9, 2025 was taken from Yahoo Finance, normalized with MinMaxScaler, and divided into 80% training data and 20% testing data. The LSTM model, which consists of two LSTM layers (50 units each) and two dense layers, was trained with Adam optimization and mean squared error loss function. The model uses the 60-day price sequence to predict the next day's price. The evaluation results show high accuracy with Root Mean Squared Error (RMSE) 105.80, Mean Squared Error (MSE) 2,822,880.74, Mean Absolute Error (MAE) 1,103.42, and R-squared (R²) 0.995. This model becomes one of the reliable prediction tools for financial decisions using historical data. This research enriches machine learning-based bitcoin price prediction solutions.
Sistem Pendukung Keputusan Pemilihan Calon Penerima Beasiswa BSM pada SMKN 2 Sarolangun Suyanti
JOURNAL VISION TECHNOLOGY (V-TECH) Vol. 4 No. 2 (2021): JOURNAL V-TECH (VISION TECHNOLOGY)
Publisher : LPPM Universitas Adiwangsa Jambi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35141/njybwf12

Abstract

SMK N 2 Sarolangun memiliki program pemberian beasiswa setiap tahun, salah satunya Beasiswa siswa Miskin (BSM) dari Kopertais Wilayah XIII Jambi. Dalam melakukan seleksi calon penerima beasiswa bagian kemahasiswaan mengalami kesulitan karena banyaknya pemohon beasiswa dan kriteria yang di tetapkan oleh Kopertais Wilayah XIII Jambi, sehingga membutuhkan waktu yang lama dan sering terjadi kesalahan karena banyaknya kriteria yang harus dinilai dan dibandingkan oleh bidang kemahasiswaan. Agar penyeleksian benar-benar selektif dan mengurangi subjektivitas serta dapat menghasilkan informasi yang cepat dan tepat, Sehingga di perlukan metode-metode untuk melalukan penyeleksian calon penerima beasiswa yang membantu bidang kemahasiswaan dalam menyeleksi calon penerima beasiswa agar penyeleksian selektif dan mengurangi human eror serta dapat menghasilkan informasi yang cepat dan tepat sesuai dengan kriteria yang ditentukan. Metode yang digunakan adalah yaitu SAW.
Co-Authors Agus Siswanto Arvita, Yulia Aryani, Lies Beni Irawan Chandy Ophelia S Chindra Saputra Errissya Rasywir Fachruddin Febri Jannah, Siti Raudatul Lies Aryani Lola Yorita Astri, Lola Yorita Moh. Ismail Nurul Marwiyah, Siska Ophelia S, Chandy OPHELIA, CHANDY Prayitno ROBY SETIAWAN Siska Nurul Marwiyah Wardani , Muhammad Yorita Astri, Lola Yovi Pratama Zulfi Karman