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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Ilmu Komputer dan Informasi Jurnal Teknik Informatika Indonesian Journal of Electrical Engineering and Informatics (IJEEI) JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Teknologi Terpadu International Journal for Educational and Vocational Studies Journal of Sustainable Engineering: Proceedings Series Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Sistem Informasi Jurnal Teknik Elektro dan Komputer Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics
Daniel Febrian Sengkey
Department Of Electrical Engineering, Faculty Of Engineering, Universitas Sam Ratulangi, Jln. Kampus Unsrat, Bahu - Manado 95115, INDONESIA
Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Social Sciences

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Development of Virtual Lab on Collision Dynamics Learning Object with Collision Algorithm Integration Yusupa, Ade; Tarigan, Victor; Sengkey, Daniel F.
Journal of Applied Informatics and Computing Vol. 9 No. 1 (2025): February 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i1.8765

Abstract

The objective of this study is to evaluate the efficacy of a Virtual Lab employing a collision algorithm in enhancing students' conceptual comprehension of collision dynamics, in comparison to traditional pedagogical approaches, within the context of physics education.The methodology employed in this study is as follows: The study employed an experimental approach, comprising a comparison between two groups: an experimental class that used the Virtual Lab, and a control class that utilised traditional teaching methods. Both groups were subjected to pre-tests to ascertain their existing level of understanding, after which post-tests were conducted to evaluate their knowledge after the instruction period. An independent t-test was employed to analyse the differences in post-test outcomes between the two groups.The results are as follows: The findings indicated a significant improvement in the experimental class's understanding, with an average increase from the pre-test to the post-test of 33.89%, in comparison to a 30.74% improvement in the control class. The results of the t-test demonstrated a statistically significant difference (t = 4.32, p < 0.05), indicating that the Virtual Lab was more effective in enhancing conceptual comprehension. In conclusion, the Virtual Lab, based on the collision algorithm, has been demonstrated to be an effective tool for teaching collision dynamics, offering a more interactive and engaging experience than traditional methods. This study highlights the potential of technology-based learning tools to enhance physics education and recommends further development of Virtual Labs with interactive features to increase accessibility and understanding in diverse educational environments.
Attention-based Residual Long Short-Term Memory for Earthquake Return Period Prediction in the Sulawesi Region Bachmid, Muhdad; Sengkey, Daniel; Manoppo, Fabian
Jurnal Ilmu Komputer dan Informasi Vol. 18 No. 2 (2025): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Informatio
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21609/jiki.v18i2.1506

Abstract

Indonesia, particularly the Sulawesi region, experiences significant seismic activity due to its position at the convergence of three major tectonic plates. This study seeks to construct a model for predicting earthquake return periods in the Sulawesi area by employing the Residual Long Short-Term Memory (Residual LSTM) architecture integrated with an attention mechanism. The dataset utilized originates from the United States Geological Survey (USGS), focusing on the Sulawesi Island region within the coordinates of latitude -6.184° to 2.021° and longitude 118.433° to 125.552°, spanning the years 1975 to 2024. The research methodology is structured into three primary phases: (1) data collection and preprocessing, including data cleaning, missing value handling, and normalization, (2) exploratory data analysis to understand seismic data characteristics, and (3) development of the Residual LSTM model with an attention mechanism. The evaluation results show excellent model performance with Train Loss 0.0090, Test Loss 0.0091, Training MAE 0.0698, Testing MAE 0.0717, Training RMSE 0.0947, Testing RMSE 0.0951, and stable Huber Loss of 0.0045 for both training and testing data. The implementation of residual connections successfully addressed the vanishing gradient problem, while the attention mechanism enhanced prediction interpretability. The small discrepancy between the training and testing metrics confirms the model's robust generalization ability, indicating its strong potential for applications in predicting earthquake return periods.
A Survey on Students Interests toward On-line Learning Media Choices: A Case Study Sengkey, Daniel Febrian; Paturusi, Sary Diane Ekawati; Sambul, Alwin Melkie; Gozali, Chyntia Theresa
International Journal for Educational and Vocational Studies Vol. 1 No. 2 (2019): June 2019
Publisher : Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/ijevs.v1i2.1527

Abstract

The advancement of Information Technology alters various aspects of human life, including learning. In the present era, on-line learning facilities are provided by institutions, ranging from formal higher education to open course-ware providers. On-line learning or e-learning is mostly achieved through stored media that widely available. These media take forms in various formats such as text and images, slide that equipped with narration from the lecturer, or a video where the lecturer appears inside the frames. We conducted a research about how students would response to the available learning media. The research was conducted with repetitive measures. Each measurement was a module that divided into three parts, where each part was presented to the student as one out of three media listed above. Hence we had three media types for each module. Each module took one week, and at the next week we gather their responses through evaluation forms. All modules were completed in six consecutive weeks. After all modules were completed, we analyze their responses and found that our samples responded best to the video with the appearance of the instructor/lecturer, then the slide with audio, and finally text and images.
Combinations of Optimization Method and Balancing Technique in Hypertension Classification with Machine Learning Lu'o, Natalia Intan Suryani; Sengkey, Daniel Febrian; Joseph, Victor Florencia Ferdinand
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 2 (2025): May
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v7i2.86

Abstract

Hypertension is a condition in which blood vessels experience continuous pressure higher than normal limits which can cause pain and even death. Hypertension is classified into several classes based on the measured blood pressure. To correctly diagnose hypertension is a critical task that requires medical specialists who are unfortunately not evenly distributed in every region. This research aims to implement Particle Swarm Optimization for hyperparameter tuning in machine learning algorithms in hypertension disease classification. The approach was developed by comparing the performance of Random Forest (RF), Light Gradient Boosting Machine (LGBM), and Extra Trees (ET). Each algorithm was trained using its default hyperparameters, tuned with Grid Search and Cross-validation (GSCV), and the Particle Swarm Optimization with Cross-validation (PSO-CV). We consider recall to be the primary evaluation metric due to the imbalance in the dataset. The experiment results show that the combination of the LGBM and PSO-CV is the best combination of algorithm and hyperparameter optimization method with precision, recall, F1-score, ROC-AUC, and PR-AUC values of 0.22, 0.63, 0.33, 0.79, and 0.24, respectively. The results of this study prove that PSO might positively influence model performance, particularly in the case of unbalanced data.
Investigation on low-performance tuned-regressor of inhibitory concentration targeting the SARS-CoV-2 polyprotein 1ab Sengkey, Daniel Febrian; Regina Masengi, Angelina Stevany; Sambul, Alwin Melkie; Tallei, Trina Ekawati; Unsratdianto Sompie, Sherwin Reinaldo
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i4.pp3003-3013

Abstract

Hyperparameter tuning is a key optimization strategy in machine learning (ML), often used with GridSearchCV to find optimal hyperparameter combinations. This study aimed to predict the half-maximal inhibitory concentration (IC50) of small molecules targeting the SARS-CoV-2 replicase polyprotein 1ab (pp1ab) by optimizing three ML algorithms: histogram gradient boosting regressor (HGBR), light gradient boosting regressor (LGBR), and random forest regressor (RFR). Bioactivity data, including duplicates, were processed using three approaches: untreated, aggregation of quantitative bioactivity, and duplicate removal. Molecular features were encoded using twelve types of molecular fingerprints. To optimize the models, hyperparameter tuning with GridSearchCV was applied across a broad parameter space. The results showed that the performance of the models was inconsistent, despite comprehensive hyperparameter tuning. Further analysis showed that the distribution of Murcko fragments was uneven between the training and testing datasets. Key fragments were underrepresented in the testing phase, leading to a mismatch in model predictions. The study demonstrates that hyperparameter tuning alone may not be sufficient to achieve high predictive performance when the distribution of molecular fragments is unbalanced between training and testing datasets. Ensuring fragment diversity across datasets is crucial for improving model reliability in drug discovery applications.
Co-Authors Ade Yusupa Agustinus Jacobus Alicia A.E. Sinsuw Alwin M. Sambul Alwin M. Sambul Alwin M. Sambul, Alwin M. Alwin Melkie Sambul Alwin Melkie Sambul Angelina Stevany Regina Masengi Arie Lumenta Arthur Morits Rumagit Bachmid, Muhdad Balansa, Kirsten Edward Nangoy, Edward Fabian Johanes Manoppo Feisy Diane Kambey Gozali, Chyntia Theresa Gozali, Chyntia Theresa Heilbert Armando Mapaly Indahsari, Bunga Jimmy Posangi Joseph, Victor Florencia Ferdinand Kainde, Henry Valentino Florensius Kambey, Feisy Diane Kaunang, Stephani Gabriela Kenneth Yosua Rumawas Palilingan Kolang, Indri Claudia Legi, Moudy Lu'o, Natalia Intan Suryani Mamahit, Juliet Merry Eva Mambo, Christi Diana Marthen Theogives Lasut, Marthen Theogives Mawara, Reza Michelly Cantika Nur Fiat, Daffa Onsu, Romario Paturusi , Sary D. E. Paturusi, Sary Diane Ekawati Paturusi, Sary Diane Ekawati Pertiwi, Junita Maja Pinrolinvic Duadelfri Kurnialimki Manembu Purnomo, Nadira Tri Ardianti Rizal Sengkey Salaki, Reynaldo Joshua salvius paulus lengkong Salvius Paulus Lengkong Sambul, Alwin Melkie Sambul, Alwin Melkie Saraun, Astri Sary D.E. Paturusi Sary Diane Ekawati Paturusi Sary Diane Ekawati Paturusi Sary Diane Ekawati Paturusi Sherwin R.U.A. Sompie Sugiarso, Brave Angkasa Suratinoyo, Syifabela Ticoalu, Injilia Tirza TRINA EKAWATI TALLEI Unsratdianto Sompie, Sherwin Reinaldo Victor Tarigan Virginia Tulenan Warouw, Finny Yulianty Sanggelorang