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All Journal Nuansa Informatika Jurnal Edukasi dan Penelitian Informatika (JEPIN) JURNAL MEDIA INFORMATIKA BUDIDARMA bit-Tech Jurnal Abdi Insani Jurnal Teknimedia: Teknologi Informasi dan Multimedia Journal of Applied Data Sciences Jurnal Pengabdian Masyarakat Bangsa JGGAG (Journal of Games, Game Art, and Gamification) Bulletin of Intelligent Machines and Algorithms
Dede Husen
Universitas Kuningan
Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Medicine & Pharmacology Nursing Physics Public Health Social Sciences Other

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EVALUASI TEKNIK AUGMENTASI DATA UNTUK KLASIFIKASI TUMOR OTAK MENGGUNAKAN CNN PADA CITRA MRI Dede Husen
TEKNIMEDIA: Teknologi Informasi dan Multimedia Vol. 5 No. 2 (2024): Desember 2024
Publisher : Badan Penelitian dan Pengabdian Masyarakat (BP2M) STMIK Syaikh Zainuddin NW Anjani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46764/teknimedia.v5i2.220

Abstract

Brain tumor classification on Magnetic Resonance Imaging (MRI) scans poses a significant challenge in the fields of radiology and medical technology. To enhance diagnostic accuracy, Convolutional Neural Network (CNN) methods have shown great potential. However, the limitation of having an adequate training dataset remains a major obstacle in developing effective models. This study aims to evaluate the performance of CNN models by applying various data augmentation techniques for brain tumor classification and identifying the most effective augmentation techniques. The augmentation techniques tested include image scaling, random rotation, vertical and horizontal flipping, random brightness adjustments, and combinations of these various techniques. The results indicate that the scaling and vertical and horizontal flipping techniques yield the highest average accuracy of 92.97%, with a maximum accuracy of 100% achieved at the 20th epoch using the vertical and horizontal flipping technique. Thus, it is hoped that the findings of this study can be utilized by other researchers in selecting appropriate augmentation techniques for MRI images.
Peningkatan Kapasitas Teknologi Informasi Kader PKK Desa Kaduagung Melalui Pelatihan Komputer Dasar Dede Husen; Toni Khalimi
Jurnal Pengabdian Masyarakat Bangsa Vol. 3 No. 10 (2025): Desember
Publisher : Amirul Bangun Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59837/jpmba.v3i10.3465

Abstract

Kader PKK memiliki peran strategis dalam pengelolaan kegiatan sosial kemasyarakatan di tingkat desa, termasuk administrasi, pelaporan, dan dokumentasi program. Namun, keterbatasan literasi digital masih menjadi kendala utama dalam pelaksanaan tugas tersebut. Kegiatan pengabdian ini bertujuan untuk meningkatkan kemampuan teknologi informasi kader PKK Desa Kaduagung melalui pelatihan komputer dasar, khususnya Microsoft Word dan Microsoft Excel. Metode pelaksanaan terdiri dari beberapa tahapan, meliputi analisis kebutuhan, sosialisasi program, pelatihan berbasis praktik (hands-on learning), penerapan teknologi, pendampingan, evaluasi, dan keberlanjutan program. Hasil pelatihan menunjukkan peningkatan signifikan pada keterampilan peserta. Sebanyak 90% kader mampu membuat laporan kegiatan menggunakan template Word, sementara 82% mampu menyusun rekap data dan laporan keuangan sederhana menggunakan Excel. Implementasi sistem pengarsipan digital juga meningkatkan efisiensi dan kerapian dokumentasi organisasi PKK. Pendampingan lanjutan membantu kader mempertahankan keterampilan yang diperoleh serta memperkuat kepercayaan diri dalam mengoperasikan teknologi. Pembahasan menunjukkan bahwa pelatihan komputer dasar yang dirancang sesuai kebutuhan peserta memberikan dampak positif terhadap peningkatan literasi digital dan profesionalisme kader PKK. Secara keseluruhan, kegiatan pengabdian ini membuktikan bahwa pelatihan yang terstruktur, relevan, dan berbasis praktik dapat menjadi strategi efektif dalam memperkuat kapasitas digital kader PKK dan mendukung transformasi administratif organisasi menuju sistem digital yang lebih efisien dan mandiri.
Gamified Digital Intervention to Reduce Online Game Gambling Tendency among Youth: A TAM–SDT Evaluation Yulyanto Yulyanto; Erik Kurniadi; Dede Husen; Fahmi Yusuf
Journal of Applied Data Sciences Vol 7, No 1: January 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i1.1095

Abstract

The rapid growth of online gaming has raised concerns about addictive behaviors among young people, particularly with the emergence of loot boxes that resemble gambling mechanisms. This study aims to examine the effectiveness of a gamification-based application as a preventive intervention for online gambling game addiction and to evaluate user acceptance through an extended Technology Acceptance Model (TAM). The research was conducted in two stages. In the pre-test phase, 588 respondents aged 15–25 completed a questionnaire measuring impulsivity, Internet Gaming Disorder (IGD), and loot box exposure. The results identified 169 individuals (28.7%) with addictive tendencies. In the intervention phase, 86 respondents from this group participated in a gamified stimulation program using a specially designed application. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The measurement model met reliability and validity requirements. Structural model analysis confirmed the classic TAM relationships: perceived ease of use significantly influenced perceived usefulness (β = .559, p 0.001), perceived usefulness influenced attitude toward use (β = .385, P= 0.001), and attitude influenced behavioral intention (β = .461, p 0.001). In addition, self-determination theory (SDT) significantly affected both attitude (β = .360, P= 0.003) and behavioral intention (β = .166, P= 0.038). However, affective visual design (AVD) was not significant, and behavioral intention did not reduce addictive behavior (β = -0.109, P= 0.386). The model demonstrated predictive relevance for TAM constructs (Q² 0) but failed to predict addictive behavior (Q² = -0.003). This study contributes theoretically by extending TAM with SDT in the context of digital health interventions and practically by demonstrating the potential of gamification as a preventive tool. However, the short intervention period and limited sample size constrained its effectiveness in reducing addiction. Longer-term interventions and broader contextual factors are recommended for future research.
Predictive Modeling of Student Academic Performance Using Regression Methods Dede Husen
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3391

Abstract

The advancement of digital technologies has strengthened the use of data driven approaches in understanding the factors that shape students’ academic achievement. This study aims to examine how daily habits, lifestyle patterns, and environmental conditions contribute to exam performance using the Student Habits vs Academic Performance dataset from Kaggle, which contains 1,000 student records covering behavioral, health related, and socioenvironmental attributes. Guided by the CRISPDM framework, the research includes data preparation, exploratory analysis, and predictive modeling using two regression techniques: Linear Regression and Random Forest Regressor. The predictive models were developed to estimate exam scores based on several key variables, including study duration, attendance rate, sleep quality, leisure activities, and parental education level. The results show that Linear Regression achieved the highest accuracy, with an MAE of 4.19, an RMSE of 5.15, and an R² of 0.897, indicating that approximately 89.65% of score variability can be explained by the selected features. Meanwhile, the Random Forest model recorded a slightly lower R² of 0.850, suggesting that the dominant relationships in the dataset follow a largely linear pattern. These findings highlight that consistent study routines, regular attendance, adequate sleep, and supportive home environments are strongly associated with improved academic outcomes. The study emphasizes the importance of interpretable machine learning models in educational analytics and offers insights that may support data informed interventions aimed at enhancing student performance.
Implementasi Mekanisme Adaptif Berbasis Q-Learning pada Game Edukasi Matematika Numerasi Awal Krisdiawan, Rio Andriyat; Husen, Dede; Herwanto, Heri
JEPIN (Jurnal Edukasi dan Penelitian Informatika) Vol. 12 No. 2 (2026): Volume 12 No 2
Publisher : Program Studi Informatika

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

Abstract

Pembelajaran numerasi awal membutuhkan media digital yang tidak hanya interaktif, tetapi juga mampu menyesuaikan tingkat tantangan berdasarkan performa peserta didik. Penelitian ini bertujuan mengimplementasikan mekanisme adaptif berbasis Q-Learning pada game edukasi matematika numerasi awal untuk siswa kelas awal sekolah dasar. Penelitian menggunakan pendekatan implementatif-deskriptif dengan fokus pada integrasi alur permainan, gameplay logging, pemodelan state siswa, pemilihan action adaptif, perhitungan reward, pembaruan Q-value, dan transisi tingkat kesulitan. Game dikembangkan dalam enam level aktivitas yang mencakup materi menghitung objek, mengenali simbol bilangan, mengurutkan bilangan, serta memahami nilai tempat sederhana sampai 20. Pengujian dilakukan di SDN 1 Langseb dengan melibatkan 38 siswa dan 2 guru. Hasil implementasi menunjukkan bahwa sistem mencatat 1.430 baris gameplay log yang terdiri atas 683 event jawaban, 253 event bantuan, 228 event penyelesaian level, 228 event keputusan adaptif, dan 38 event penyelesaian permainan. Jumlah 228 keputusan adaptif sesuai dengan enam level yang diselesaikan oleh 38 siswa, sehingga menunjukkan bahwa mekanisme adaptif berjalan pada setiap akhir level. Distribusi action menghasilkan 71 keputusan Naik, 47 Tetap, dan 110 Turun/Penguatan. Rata-rata Q-value meningkat dari 1,115 menjadi 1,199 dengan rata-rata delta Q sebesar 0,084. Hasil uji penerimaan pengguna menunjukkan persentase UAT sebesar 79,21% pada siswa, 80,00% pada guru, dan 79,25% secara keseluruhan, dengan kategori baik. Hasil penelitian menunjukkan bahwa mekanisme adaptif berbasis Q-Learning dapat diintegrasikan ke dalam game edukasi matematika numerasi awal melalui pencatatan aktivitas, pembentukan state, pemilihan action, reward, pembaruan Q-value, dan pengaturan tingkat kesulitan. Penelitian ini dibatasi pada evaluasi implementasi dan penerimaan pengguna, bukan pada pengujian kausal terhadap peningkatan hasil belajar.
Co-Authors Daswa Daswa, Daswa Dede - Sandi Erik Kurniadi Fahmi Yusuf Heri Herwanto Herwanto, Heri Kusnawi Kusnawi Kusrini Nida Amalia Asikin RIO ANDRIYAT KRISDIAWAN Sepriadi - Bumbungan Toni Khalimi Yulyanto Yulyanto