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All Journal bit-Tech Jurnal PETISI (Pendidikan Teknologi Informasi)
Pradana, Handika Dio
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Computer Science & IT Education

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Shooter Flag Finder: Game Edukasi Pemburu Bendera Menggunakan Python PyGame Firdaus, Revina Aurigha; Orvala, Imas Dewi; Pradana, Handika Dio; Santoso, Aditya Ramadhan; Dermawan, Dodik Arwin; Aditia, Dimas Novian
JURNAL PETISI (Pendidikan Teknologi Informasi) Vol 5 No 1 (2024): JURNAL PETISI (Pendidikan Teknologi Informasi)
Publisher : Universitas Pendidikan Muhammadiyah Sorong

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36232/jurnalpetisi.v5i1.5135

Abstract

Game atau permainan tentunya sudah tidak asing lagi bagi masyarakat dari berbagai kalangan mulai dari anak-anak hingga orang dewasa. Terdapat bermacam-macam jenis game mulai dari game yang dimainkan secara online serta game yang dimainkan secara offline. Tidak hanya itu, tema game juga bervariasi, salah satunya game edukasi yang bernama Shooter Flag Finder. Game edukasi ini ditujukan kepada anak-anak usia sekolah dasar untuk membantu anak sekolah memahami bendera negara yang ada di seluruh dunia. Pengetahuan bendera negara ini dikemas semenarik mungkin untuk memberikan warna baru sehingga anak-anak di sekolah dasar tidak merasa jenuh saat pembelajaran berlangsung. Keterbaruan game ini adalah menciptakan sebuah permainan berbasis web agar siswa semakin tertarik dalam mengenal dan mempelajari bendera negara yang ada di seluruh dunia. Dalam pengembangan game edukasi ini dibangun menggunakan library package Pygame dikarenakan membangun konsep ide serta penempatan objek game lebih mudah dilakukan. Selain itu, penggunaan game yang user friendly juga menjadi faktor utama dalam game edukasi ini. Hal ini mampu menghasilkan pengalaman belajar yang menyenangkan dan interaktif bagi pengguna game khususnya anak-anak di sekolah dasar
Personality Prediction Based on Video Using Transfer Learning DeepID Model Pradana, Handika Dio; Nudin, Salamun Rohman
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.2866

Abstract

This research presents an automatic personality prediction system based on the Big Five model openness, conscientiousness, extraversion, agreeableness, and neuroticism by leveraging transfer learning on the DeepID architecture. Video input is first processed with the MTCNN algorithm for robust facial region detection under varying lighting and poses. Extracted features are fed into a modified DeepID model, pre-trained on large-scale face-recognition datasets, to perform spatial encoding. To capture temporal dynamics, Long Short-Term Memory (LSTM) networks model frame-to-frame changes in expression. Training and validation use the ChaLearn LAP dataset of approximately 10,000 annotated videos. Experimental results demonstrate 88.6% overall accuracy, with an average precision of 87.2%, recall of 86.5%, and F1-score of 86.8%, confirming the model’s balanced performance across classes. A minimum loss of 11.3% further underscores effective convergence. The complete pipeline is deployed via Flask, enabling real-time, web-based integration. Beyond academic novelty, this system holds promise for practical applications: in recruitment, it can offer unbiased, rapid personality screening; in mental-health contexts, it may assist clinicians by flagging behavioral cues non-invasively; and in human–computer interaction, adaptive interfaces could personalize responses based on users’ inferred traits. By combining transfer learning with temporal modeling, our approach delivers a scalable, non-invasive tool for automated psychological assessment through visual data, paving the way for ethical, real-time personality analytics in diverse domains.
Co-Authors Aditia, Dimas Novian Dodik Arwin Dermawan Firdaus, Revina Aurigha Orvala, Imas Dewi SALAMUN ROHMAN NUDIN Santoso, Aditya Ramadhan