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All Journal Telematika International Journal for Applied Information Management
Taqwa Hariguna
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Humanities Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Environmental Science Social Sciences

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DONGENG AYAM DAN KELINCI BERSAUDARA BERBASIS ANIMASI 2 DIMENSI Taqwa Hariguna; Adi Wijiono
Telematika Vol 10, No 1: Februari (2017)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (943.389 KB) | DOI: 10.35671/telematika.v10i1.481

Abstract

Dongeng merupakan cerita yang biasanya berisi tentang khayalan dan mengandung pesan moral tinggi. Namun seiring dengan berkembangnya teknologi, sehingga anak-anak lebih suka melihat sinetron, film, main game, internet. Oleh karena itu orang tua zaman sekarang dituntut untuk bisa bercerita atau mendongeng dengan menarik sekaligus menghibur, agar tidak kalah dengan teknologi dan dunia hiburan yang semakin canggih. Salah satu teknologi yang berkembang yaitu animasi. Sehingga tujuan dari penelitian ini adalah merancang dan membuat Dongeng Ayam Dan Kelinci Bersaudara Berbasis Animasi 2 Dimensi agar dapat menyampaikan pesan moral yang terdapat didalamnya. Animasi ini dibuat dengan menggunakan Adobe Flash Profesional CS6. Metode pengumpulan data yang digunakan adalah studi kepustakaan. Sedangkan metode pengembangan data system yang digunakan adalah Multimedia Development Life Cycle (MDLC) yang terdiri dari tahapan pengonsepan, perancangan, pengumpulan material, pembuatan, pengujian, pendistribusian. Hasil penelitian yang dicapai adalah animasi berupa dongeng ayam dan kelinci bersaudara berbasis animasi 2 dimensi yang dikemas secara menarik sehingga  memiliki fungsi sebagai penyampai pesan moral.
An Empirical Study to Understanding Students Continuance Intention Use of Multimedia Online Learning Taqwa Hariguna
International Journal for Applied Information Management Vol. 1 No. 2 (2021): Regular Issue: July 2021
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijaim.v1i2.10

Abstract

The purpose of this study was to assess students' ongoing intentions towards online multimedia learning such as perceived usefulness, ease of use, and flow experience. The sample of this study was 523 students who used off-campus/online learning resources and examined the content of online learning resources and their multimedia aspects. The Extended of Technology Acceptance Model (TAM) was used to predict students' continuing intentions. The results showed that students' intentions were positively influenced by their perceived usefulness, ease of use, and flow experience. It is suggested that the designer of multimedia online learning should be more specific in determining the target users to receive and cultivate a more positive sustainable intention.
Evaluating the Impact of SMOTE-Based Data Balancing on Decision Bias and Algorithmic Fairness in XGBoost-Based Student Dropout Prediction Les Endahti; Taqwa Hariguna; Dhanar Intan Surya Saputra
Telematika Vol 19, No 2: August (2026)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v19i2.3425

Abstract

Student dropout prediction is a key application of Educational Data Mining for supporting early intervention in higher education. However, previous studies have primarily focused on improving predictive accuracy, while the effects of data balancing on decision bias and algorithmic fairness remain underexplored. This study proposes a comprehensive evaluation framework that integrates predictive performance, decision bias, and algorithmic fairness to assess the impact of the Synthetic Minority Over-sampling Technique (SMOTE) on Extreme Gradient Boosting (XGBoost) for student dropout prediction. Experiments were conducted using the publicly available Predict Students Dropout and Academic Success dataset containing 4,424 student records. After excluding the Enrolled class, the dataset was transformed into a binary classification problem consisting of 2,209 Graduate (60.9%) and 1,421 Dropout (39.1%) instances. Two models were compared: a baseline XGBoost classifier and an XGBoost classifier trained with SMOTE. Predictive performance was evaluated using Accuracy, Precision, Recall, F1-score, and ROC-AUC, while decision bias and algorithmic fairness were assessed using the False Negative Rate (FNR), Statistical Parity Difference (SPD), Disparate Impact (DI), Equal Opportunity Difference (EOD), and Average Odds Difference (AOD). The baseline model achieved higher Accuracy (93.11% vs. 92.29%), Precision (91.49% vs. 89.86%), F1-score (91.17% vs. 90.18%), and a lower FNR (0.0915 vs. 0.0951), whereas both models produced comparable ROC-AUC values (0.972). McNemar's test indicated that the difference in predictive performance was not statistically significant (p = 0.264). Although SMOTE did not improve predictive performance, it produced modest reductions in Statistical Parity Difference (0.2310–0.2218), Equal Opportunity Difference (0.0298–0.0233), and Average Odds Difference (0.0295–0.0235), indicating a slight improvement in fairness metrics while maintaining comparable discrimination capability. These findings highlight the trade-off between predictive performance and algorithmic fairness and demonstrate that evaluating predictive performance together with decision bias and fairness provides a more comprehensive assessment of educational machine learning models, supporting the development of responsible AI-based educational decision-support systems.
Co-Authors Adi Wijiono Dhanar Intan Surya Saputra Les Endahti