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All Journal AL MA'ARIEF : JURNAL PENDIDIKAN SOSIAL DAN BUDAYA Bulletin of Computer Science Research PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON DATA SCIENCE AND OFFICIAL STATISTICS
Syukri, Nabila
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Religion Humanities Computer Science & IT Social Sciences

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Pengaruh Game Mobile Legends terhadap Minat Belajar Mahasiswa/i Fakultas Sains dan Teknologi UIN Alauddin Makassar Nawawi, M. Ichsan; Pathuddin, Hikmawati; Syukri, Nabila; Alfidayanti, Alfidayanti; Poppysari, Sartika; Saputri, Saputri; Ramdani, Muhammad; Jun, Muhammad; Marsuki, Ismail
AL MA'ARIEF : Jurnal Pendidikan Sosial dan Budaya Vol 3 No 1 (2021): Al Ma'arief: Jurnal Pendidikan Sosial dan Budaya
Publisher : Program Studi Tadris IPS Institut Agama Islam Negeri (IAIN) Parepare

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35905/almaarief.v3i1.2039

Abstract

This study aims to determine the effect of mobile legends online games on the interest in learning of students of the Faculty of Science and Technology UIN Alauddin Makassar. The research method used in this study is using a correlational quantitative approach, where the population taken is active students who play Mobile Legend online games at FST UINAM. To determine the sample used quota sampling technique by determining the portion of 10 people in each department in the Faculty of Science and Technology. Retrieval of respondent data through google form then the data is processed with the SPSS application. Mobile legend online game is one of the top 5 most played MOBA games on the android market and much favored by students, none other than students. The Mobile Legend online game variable consists of three indicators, 1) Media Exposure, 2) Play Motivation and 3) Imitation Behavior. From the results of the analysis carried out by using a hypothesis test with a calculated F value of 3.620 > F table 2.71, it can be concluded that there is an effect of Media Exposure (X1), Play Motivation (X2) and Imitation Behavior (X3) simultaneously on Learning Interest (Y). The value of R2 (R Square) 0.112 means that the influence of the Mobile Legend Online Game variable on learning interest is 11.2% while the effect is influenced by other factors not examined in this study.
School Accreditation Prediction Based on Literacy and Numeracy: Ordinal Logistic Regression vs KNN Syukri, Nabila; Hiola, Yani Prihantini; Putri, Mega Ramatika; Susetyo, Budi
Bulletin of Computer Science Research Vol. 6 No. 1 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i1.861

Abstract

School accreditation in Indonesia has traditionally relied on administrative inputs and institutional documentation, which often fail to capture the actual quality of student learning. In contrast, the National Assessment provides direct evidence of student literacy and numeracy outcomes, offering a more objective and outcome-based measure of educational quality. Leveraging these results as predictors for accreditation rankings is therefore crucial, as they reflect the competencies most relevant to effective learning delivery. This study aims to develop and evaluate classification models for school accreditation rankings using literacy and numeracy results as predictor variables. The dataset consists of secondary data from the 2023 and 2024 National School Assessments, covering 789 schools across four provinces: DKI Jakarta, Yogyakarta, Bali, and Banten. Two methods were applied, Ordinal Logistic Regression and K-Nearest Neighbors (K-NN) under two scenarios: with and without class imbalance handling. To address imbalance, two techniques were employed: Synthetic Minority Oversampling Technique (SMOTE) and Class Weight. The results indicate that K-NN consistently outperformed Ordinal Logistic Regression in both scenarios. On data without imbalance handling, K-NN achieved Accuracy, Precision, Recall, and F1-Score of 0.803, 0.705, 0.587, and 0.619, respectively. with imbalance treatment using SMOTE, the values were 0.753, 0.619, 0.686, and 0.644. While class balancing did not significantly improve overall accuracy, it enhanced the model’s ability to recognize minority classes. These findings highlight the strong relationship between literacy and numeracy outcomes and school accreditation status, demonstrating that outcome-based measures can complement traditional accreditation instruments, and that conventional statistical approaches are still relevant for modeling school accreditation.
Implementing LSTM-Based Deep Learning for Forecasting Food Commodity Prices with High Volatility: A Case Study in East Java Province Nensi, Andi Illa Erviani; Pangesti, Windi; Syukri, Nabila; Maida, Mahda Al; Notodiputro, Khairil Anwar
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2025 No. 1 (2025): Proceedings of 2025 International Conference on Data Science and Official St
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/icdsos.v2025i1.692

Abstract

Accurate food price forecasting is essential for maintaining market stability and food security. East Java Province was selected as the study area because it is one of Indonesia’s main food production centers and a major contributor to national inflation. This study compares three deep learning architectures LSTM, Bi-LSTM, and hybrid CNN-LSTM to forecast the prices of four key food commodities (red chili, shallots, medium-grade rice, and beef) in East Java. Hyperparameter tuning was performed using grid search, and performance was evaluated using MAPE, MAE, and RMSE. The results show that the Bi-LSTM model consistently provides the best performance compared to LSTM and CNN-LSTM across the four analyzed commodities. Based on MAPE, MAE, and RMSE values, Bi-LSTM achieved the lowest forecasting errors for all commodities. The MAPE values of Bi-LSTM were 1.73% for red chili, 0.60% for shallots, 0.23% for medium-grade rice, and 0.08% for beef, all of which were lower than those of LSTM and CNN-LSTM models. These findings highlight Bi-LSTM’s bidirectional architecture, which leverages contextual information from both past and future data sequences, making it the most robust and effective model for forecasting food prices under varying volatility. The study provides practical insights for policymakers and supply chain stakeholders in supporting price stability and food security.
Co-Authors Alfidayanti, Alfidayanti Budi Susetyo Hiola, Yani Prihantini Jun, Muhammad Maida, Mahda Al Marsuki, Ismail Nawawi, M. Ichsan Nensi, Andi Illa Erviani Pangesti, Windi Pathuddin, Hikmawati Poppysari, Sartika Putri, Mega Ramatika Ramdani, Muhammad Saputri, Saputri