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All Journal Information System for Educators and Professionals : Journal of Information System JITK (Jurnal Ilmu Pengetahuan dan Komputer) KOPERTIP: Jurnal Ilmiah Manajemen Informatika dan Komputer Jurnal ICT : Information Communication & Technology Tematik : Jurnal Teknologi Informasi Komunikasi Jurnal Mahasiswa Sistem Informasi (JMSI) INFORMATIKA INFORMATION SYSTEM FOR EDUCATORS AND PROFESSIONALS : Journal of Information System AMMA : Jurnal Pengabdian Masyarakat INTERNAL (Information System Journal)
Dian Ade Kurnia
Manajemen Informatika; STMIK IKMI Cirebon
Humanities Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mechanical Engineering Nursing Other

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Digitalisasi Wisata Desa: Pelatihan E-Ticket Untuk Kemudahan Kunjungan Di Desa Cisantana Dian Ade Kurnia; Kaslani; Cep Lukman Rohmat
AMMA : Jurnal Pengabdian Masyarakat Vol. 1 No. 09 (2022): AMMA : Jurnal Pengabdian Masyarakat
Publisher : CV. Multi Kreasi Media

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Abstract

Local tourism development is an important effort to support economic growth and sustainability at the village level. Cisantana Village, with its abundant natural and cultural riches, has great potential to develop the tourism sector. However, to achieve its potential, efficient and transparent management is needed. One of the proposed solutions is through the use of a tourist e-ticket application. This situation analysis will describe the location of partners, cases that have occurred, social and cultural aspects, special problems faced, service methods or approaches used, as well as the implications of the results of service to the community . Cisantana Village is located in a rural area that has extraordinary natural and cultural tourism potential. Tourists often come to this village to enjoy the natural beauty, interact with local residents, and experience the rich traditional culture. However, in recent years, increasing tourist interest has also led to complex management problems. Cases that have occurred are a surge in tourist visits that are not well organized, ticket sales that are difficult to monitor, and difficulties in understanding the magnitude of the impact of tourism on the community. As a result of this community service, we also held a successful marketing campaign to promote the use of e-ticketing to tourists. Through social media, village websites, and physical promotional materials, the benefits of e-ticketing are clearly communicated. Travelers are provided with information on how e-ticketing can save them time, avoid long queues, and provide a more comfortable visiting experience. The results of this community service are very satisfying, with a significant increase in tourist visits and positive feedback from users. The digitalization of village tourism through e-ticketing training has opened new doors to ease and comfort in enjoying the charm of Cisantana Village.
Rancang Sistem Presensi Online dengan Metode Gamifikasi dan Online Collaborative Learning Dodi Solihudin; Iin Iin; Dian Ade Kurnia
INTERNAL (Information System Journal) Vol. 5 No. 2 (2022)
Publisher : Masoem University

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Abstract

Online lectures are still being held while Covid-19 pandemic is going to end. Suitable system is needed to ensure the quality of online learning. We must combine the teleconference with the Collaborative Learning System to maintenance the interactions between students and their lecturer. The research is to build a system that can implement the Collaborative Learning System in online lectures. The methods used are gamification and Extreme Programming. The results of the research is the Online Presence application with chat system features, real-time polling system, real-time question-answer, and real-time leaderboard. The application can be accessed athttps://ikmiapp.web.id/presline.
SMART ATTENDANCE TRACKING SYSTEM EMPLOYING DEEP LEARNING FOR FACE ANTI-SPOOFING PROTECTION Bani Nurhakim; Ahmad Rifai; Dian Ade Kurnia; Dadang Sudrajat; Ujang Supriatna
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 3 (2025): JITK Issue February 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i3.5992

Abstract

Conventional attendance systems face challenges in accuracy and efficiency, often vulnerable to spoofing and data manipulation. This study addresses these issues by developing a smart attendance system integrating Deep Learning-based facial recognition with anti-spoofing technology. The system ensures secure and reliable attendance authentication while automating and enhancing management processes. Utilizing a convolutional neural network (CNN) architecture, the system processes raw facial images directly without additional feature extraction, improving accuracy and efficiency. A novel training strategy, termed 50 Random Samples-30 Sub-epochs Count-1 Epoch, is introduced to optimize the training process. This strategy involves random sampling during each forward pass and grouping 30 passes as one epoch, enabling the use of complex CNN architectures and automatic dataset expansion. The system achieves 98.90% accuracy in identifying genuine attendance, maintaining a confidence level above 80%, significantly reducing spoofing risks and errors. This innovative solution has significant implications, particularly for educational institutions. It automates attendance tracking, minimizes manual effort, reduces errors, and supports disciplinary enforcement through accurate data. Moreover, its scalability allows for application across various environments, offering benefits to a wide range of institutions. By enhancing data accuracy and operational efficiency, this system sets a foundation for smarter, more reliable attendance management, strengthening administrative practices in education and beyond.
ADAPTIVE CLASS WEIGHTING DAN AUGMENTATION UNTUK KLASIFIKASI BATIK KERATON Witriyani Witriyani; Dian Ade Kurnia; Yudhistira Arie Wijaya; Mulyawan Mulyawan; Irfan Ali
Informatika: Jurnal Teknik Informatika dan Multimedia Vol. 6 No. 1 (2026): MEI : JURNAL INFORMATIKA DAN MULTIMEDIA
Publisher : LPPM Politeknik Pratama Kendal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/informatika.v6i1.1516

Abstract

This study aims to improve the performance of Batik Keraton motif classification on an imbalanced dataset through the integration of adaptive class weighting and data augmentation within a transfer learning framework. The dataset consists of 1,799 images across four classes (Kawung, Mega Mendung, Parang, Truntum), preprocessed to 224×224 pixels and split stratifiedly into training, validation, and test sets (80/10/10). Three transfer learning architectures—ResNet50V2, VGG16, and EfficientNetB0—were evaluated with adaptive class weighting and geometric augmentation to enhance minority-class representation. The results indicate that ResNet50V2 with pretrained weights achieved the best performance, reaching a test accuracy of 92.78%, macro precision of 93.13%, macro recall of 92.79%, and a macro F1-score of 92.83%. Adaptive class weighting improved sensitivity toward minority classes, while augmentation contributed to model stability and generalization. These findings demonstrate that combining adaptive weighting and augmentation effectively enhances Batik Keraton motif classification under imbalanced data conditions.  
Pengembangan Sistem Informasi Learning Analytics untuk Monitoring dan Evaluasi Kompetensi Digital Pelaku UMKM pada Platform SkillUP Denni Pratama; Dian Ade Kurnia; Saeful Anwar
TEMATIK Vol. 13 No. 1 (2026): Tematik : Jurnal Teknologi Informasi Komunikasi (e-Journal) - Juni 2026
Publisher : LPPM POLITEKNIK LP3I BANDUNG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38204/tematik.v13i1.2994

Abstract

Transformasi digital telah mendorong kebutuhan peningkatan kompetensi digital bagi pelaku Usaha Mikro, Kecil, dan Menengah (UMKM) di Indonesia. Platform microlearning SkillUP telah berhasil dikembangkan dan diterima pengguna berdasarkan Technology Acceptance Model (TAM), namun masih belum dilengkapi mekanisme monitoring dan evaluasi kompetensi digital secara komprehensif. Penelitian ini bertujuan mengembangkan Sistem Informasi Learning Analytics (SILA) untuk monitoring dan evaluasi kompetensi digital pelaku UMKM pada platform SkillUP menggunakan metode Design Science Research (DSR). Sistem yang dikembangkan mengintegrasikan data aktivitas pembelajaran ke dalam tiga lapisan pengumpulan data, mesin analitik, dan dashboard. Novelty penelitian mencakup Digital Competency Monitoring Framework dan Digital Competency Progress Index (DCPI) yang dihitung dari Learning Engagement Score (LES), Learning Completion Rate (LCR), Quiz Achievement Score (QAS), dan Competency Achievement Score (CAS). Evaluasi sistem menggunakan standar ISO/IEC 25010 dengan fokus pada Functional Suitability, Usability, dan Performance Efficiency. Hasil evaluasi menunjukkan nilai Functional Suitability sebesar 1,00 (sangat baik), Usability sebesar 87,5 (excellent berdasarkan SUS), dan rata-rata response time 1,87 detik. Sistem ini berkontribusi dalam menyediakan data berbasis bukti untuk pengambilan keputusan strategis terkait pengembangan kompetensi digital UMKM di Indonesia.
Penerapan Model LSTM Univariat dengan Walk-Forward Validation untuk Estimasi Harga Saham Nokia Ahmad Rifai; Roni Saputra; Dian Ade Kurnia; Fatihanursari Dikanandafatiha.dikananda@gmail.com
TEMATIK Vol. 13 No. 1 (2026): Tematik : Jurnal Teknologi Informasi Komunikasi (e-Journal) - Juni 2026
Publisher : LPPM POLITEKNIK LP3I BANDUNG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38204/tematik.v13i1.3000

Abstract

Prediksi harga saham merupakan permasalahan yang kompleks karena karakteristik data deret waktu finansial yang bersifat non-linear, volatil, dan dinamis. Meskipun algoritma Long Short-Term Memory (LSTM) terbukti efektif dalam menangkap pola temporal, banyak penelitian sebelumnya menggunakan pendekatan multivariat yang melibatkan variabel dengan korelasi sangat tinggi sehingga berpotensi menimbulkan redundansi informasi dan meningkatkan kompleksitas model. Penelitian ini mengusulkan model LSTM univariat untuk memprediksi harga saham Nokia Corporation (NOK) dengan menggunakan harga penutupan sebagai variabel masukan tunggal. Data historis harian periode 1 Oktober 2015 hingga 24 Oktober 2025 sebanyak 2.532 observasi diperoleh dari Yahoo Finance. Sebelum proses pemodelan, dilakukan analisis korelasi terhadap variabel Open, High, Low, Close, dan Volume. Hasil analisis menunjukkan bahwa variabel harga memiliki korelasi yang sangat tinggi (r > 0,99), sedangkan variabel Volume memiliki korelasi yang sangat rendah terhadap variabel harga (−0,052 ≤ r ≤ −0,043). Berdasarkan hasil tersebut, harga penutupan dipilih sebagai fitur utama dalam pemodelan. Untuk mengevaluasi performa model pada kondisi prediksi yang realistis, diterapkan metode Walk-Forward Validation (WFV) sebanyak 30 iterasi. Hasil pengujian menunjukkan bahwa model memperoleh nilai MSE sebesar 0,0260, RMSE sebesar 0,1613, MAE sebesar 0,1086, MAPE sebesar 2,75%, dan koefisien determinasi (R²) sebesar 0,9446. Hasil tersebut menunjukkan bahwa model mampu menjelaskan 94,46% variasi harga saham dengan tingkat kesalahan prediksi yang rendah. Penelitian ini menyimpulkan bahwa model LSTM univariat yang didukung oleh proses seleksi fitur yang sistematis dan validasi temporal yang robust mampu menghasilkan prediksi harga saham yang andal dengan kompleksitas yang lebih rendah dibandingkan pendekatan multivariat konvensional.
PENINGKATAN AKURASI KLASIFIKASI KEMATANGAN KELAPA SAWIT BERBASIS CITRA DENGAN ENSEMBLE DEEP LEARNING TEROPTIMASI DIMENSI RASIO Ahmad Rifai Ikhsanudin; Dian Ade Kurnia; Yudhistira Arie Wijaya; Dodi Solihudin; Tati Suprapti
Jurnal Mahasiswa Sistem Informasi (JMSI) Vol. 7 No. 2 (2026): Jurnal Mahasiswa Sistem Informasi (JMSI)
Publisher : Program Studi DIII Sistem Informasi - Universitas Muhammadiyah Metro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24127/jmsi.v7i2.11181

Abstract

Penentuan tingkat kematangan buah kelapa sawit secara manual sering menimbulkan subjektivitas dan menurunkan efisiensi. Penelitian ini mengembangkan metode klasifikasi berbasis citra menggunakan ensemble averaging pada tiga arsitektur MobileNetV2 dengan ukuran input berbeda (224×224, 224×300, dan 300×300) untuk mengurangi varians prediksi akibat variasi dimensi dan rasio aspek citra. Dataset yang digunakan berasal dari Kaggle berjumlah 1.380 citra, dengan pembagian 80% data latih dan 20% data validasi. Proses pengolahan mencakup rescaling, aspect-ratio-aware resizing, augmentasi, serta pelatihan menggunakan transfer learning dengan optimizer Adam dan early stopping. Hasil menunjukkan bahwa model berukuran 300×300 memberikan performa terbaik dengan akurasi 95,22% dan F1-score 0,9523. Ensemble averaging menghasilkan akurasi 94,71% dan F1-score 0,9475, yang meskipun sedikit lebih rendah dari model terbaik, memberikan stabilitas prediksi yang lebih baik dibanding model individual. Temuan ini menunjukkan bahwa resolusi input yang lebih tinggi meningkatkan kualitas ekstraksi fitur, sementara ensemble averaging tetap efektif dalam mereduksi varians dan meningkatkan ketahanan sistem klasifikasi di kondisi lapangan.
Co-Authors Ade Irma Purnamasari Ahmad Rifai Ahmad Rifai Ikhsanudin Ananda Rafly Bani Nurhakim Cep Lukman Rohmat Dadang Sudrajat Denni Pratama Denni Pratama Dodi Solihudin Farids Al Ma’ruf Fatihanursari Dikanandafatiha.dikananda@gmail.com Gifthera Dwilestari Hegarmanah Muhabatin Hilman Rifa'i Iin Irfan Ali Kaslani Leli Oktaviani Martanto . Mochamad Aditya Sunaryo Mubarok Mubarok Muhammad Aji Pratama Mulyawan Ramadhan Ramadhan Roni Saputra Ryan Hamonangan Saeful Anwar Sulistiyana Sulistiyana Tati Suprapti Tri Anelia Ujang Supriatna Witriyani Witriyani Yudhistira Arie Wijaya