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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Informatics and Communication Technology (IJ-ICT) Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) International Journal of Advances in Intelligent Informatics CESS (Journal of Computer Engineering, System and Science) Proceeding of the Electrical Engineering Computer Science and Informatics Sistemasi: Jurnal Sistem Informasi Jurnal Teknologi dan Sistem Komputer Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) International Journal of Artificial Intelligence Research Knowledge Engineering and Data Science JIKO (Jurnal Informatika dan Komputer) International Journal of Computing and Informatics (IJCANDI) JURNAL REKAYASA TEKNOLOGI INFORMASI ILKOM Jurnal Ilmiah Prosiding SAKTI (Seminar Ilmu Komputer dan Teknologi Informasi) METIK JURNAL JISKa (Jurnal Informatika Sunan Kalijaga) Sains, Aplikasi, Komputasi dan Teknologi Informasi Indonesian Journal of Electrical Engineering and Computer Science JUKI : Jurnal Komputer dan Informatika Jurnal Teknik Informatika (JUTIF) Journal of Applied Data Sciences International Journal of Engineering, Science and Information Technology Insyst : Journal of Intelligent System and Computation International Journal of Advanced Science and Computer Applications Adopsi Teknologi dan Sistem Informasi Information Technology Education Journal Bulletin of Social Informatics Theory and Application Periodicals of Occupational Safety and Health Pengabdian Kepada Masyarakat Bidang Teknologi dan Sistem Informasi The Indonesian Journal of Computer Science
Haviluddin Haviluddin
(SCOPUS ID: 56596793000, Universitas Mulawarman)
Aerospace Engineering Agriculture, Biological Sciences & Forestry Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation

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Handwriting Character Recognition usingVector Quantization Technique Haviluddin, Haviluddin; Alfred, Rayner; Moham, Ni’mah; Pakpahan, Herman Santoso; Islamiyah, Islamiyah; Setyadi, Hario Jati
Knowledge Engineering and Data Science
Publisher : citeus

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Abstract

This paper seeks to explore Learning Vector Quantization (LVQ) processing stage to recognize The Buginese Lontara script from Makassar as well as explaining its accuracy. The testing results of LVQ obtained an accuracy degree of 66.66 %. The most optimal variant of network architecture in the recognition process is a variation of learning rate of 0.02, a maximum epoch of 5000 and a hidden layer of 90 neurons which was the result of recognition based on feature 8. Based on these variations, the obtained performance with a mean square error (MSE) of 0.0306 and the time required during the learning process was quite short, 6 minutes and 38 seconds. Based on the results of the testing, the LVQ method has not been able to provide good recognition results and still requires development to generate better recognition results.
Backpropagation Neural Network with Combination of Activation Functions for Inbound Traffic Prediction Purnawansyah, Purnawansyah; Haviluddin, Haviluddin; Darwis, Herdianti; Azis, Huzain; Salim, Yulita
Knowledge Engineering and Data Science
Publisher : citeus

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Predicting network traffic is crucial for preventing congestion and gaining superior quality of network services. This research aims to use backpropagation to predict the inbound level to understand and determine internet usage. The architecture consists of one input layer, two hidden layers, and one output layer. The study compares three activation functions: sigmoid, rectified linear unit (ReLU), and hyperbolic Tangent (tanh). Three learning rates: 0.1, 0.5, and 0.9 represent low, moderate, and high rates, respectively. Based on the result, in terms of a single form of activation function, although sigmoid provides the least RMSE and MSE values, the ReLu function is more superior in learning the high traffic pattern with a learning rate of 0.9. In addition, Re-LU is more powerful to be used in the first order in terms of combination. Hence, combining a high learning rate and pure ReLU, ReLu-sigmoid, or ReLu-Tanh is more suitable and recommended to predict upper traffic utilization.
Adaptive Neuro-Fuzzy Inference System for Waste Prediction Haviluddin, Haviluddin; Pakpahan, Herman Santoso; Puspitasari, Novianti; Putra, Gubtha Mahendra; Hasnida, Rima Yustika; Alfred, Rayner
Knowledge Engineering and Data Science
Publisher : citeus

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The volume of landfills that are increasingly piled up and not handled properly will have a negative impact, such as a decrease in public health. Therefore, predicting the volume of landfills with a high degree of accuracy is needed as a reference for government agencies and the community in making future policies. This study aims to analyze the accuracy of the Adaptive Neuro-Fuzzy Inference System (ANFIS) method. The prediction results' accuracy level is measured by the value of the Mean Absolute Percentage Error (MAPE). The final results of this study were obtained from the best MAPE test results. The best predictive results for the ANFIS method were obtained by MAPE of 3.36% with a data ratio of 6:1 in the North Samarinda District. The study results show that the ANFIS algorithm can be used as an alternative forecasting method.
Comparative Analysis of BPNN and LVQ for Sundanese Character Recognition Haviluddin, Haviluddin; Pakpahan, Herman Santoso; Nurpadillah, Dinda Izmya; Setyadi, Hario Jati; Taruk, Medi; Alfred, Rayner
Knowledge Engineering and Data Science
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The Sundanese script (Aksara Sunda), an essential part of Sundanese cultural heritage, has been used since the 14th century AD. However, recognizing handwritten Sundanese characters remains challenging due to variations in individual writing styles. This study compares the performance of Backpropagation Neural Network (BPNN) and Learning Vector Quantization (LVQ) for recognizing handwritten Sundanese vowel (Swara) characters. A dataset was collected from 15 individuals, each writing seven Sundanese vowel characters, which were then used for training and testing the recognition models. Experimental results show that BPNN outperforms LVQ, achieving a higher classification accuracy (95.23%), lower Mean Squared Error (MSE), and faster convergence compared to LVQ, which reached a maximum accuracy of 66.66%. Additionally, BPNN demonstrated better generalization and robustness. At the same time, LVQ was highly sensitive to learning rate variations, leading to unstable accuracy and slower training times. The findings highlight that BPNN is a more effective model for Sundanese script recognition, providing a reliable approach for preserving and digitizing traditional scripts. Future research should explore hybrid models, deep learning approaches, and larger datasets to enhance recognition accuracy and system robustness.
Network Traffic Time Series Performance Analysisusing Statistical Methods Purnawansyah, Purnawansyah; Haviluddin, Haviluddin; Alfred, Rayner; Gaffar, Achmad Fanany Onnlita
Knowledge Engineering and Data Science
Publisher : citeus

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This paper presents an approach for a network traffic characterization by using statistical techniques. These techniques are obtained using the decomposition, winter’s exponential smoothing and autoregressive integrated moving average (ARIMA). In this paper, decomposition and winter’s exponential smoothing techniques were used additive and multiplicative model. Then, ARIMA based-on Box-Jenkins methodology. The results of ARIMA (1,0,2) was shown the best model that can be used to the internet network traffic forecasting
Performance Analysis of Ensemble Learning Models Comparing Bagging and Boosting Techniques for Early Preeclampsia Risk Detection in Pregnant Women Prediction Yudhi Saputra; Milkhatun Milkhatun; Aldi Bastiatul Fawait; Zakaria Ahmad Dahlan; Yazeed Al Moaiad; Haviluddin Haviluddin; Rayner Alfred
International Journal of Engineering, Science and Information Technology Vol 6, No 1 (2026)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v6i1.1880

Abstract

Preeclampsia is a major pregnancy complication that substantially contributes to maternal morbidity and mortality worldwide, making early identification of risk factors essential for effective prevention and timely clinical intervention. This study evaluates the performance of ensemble learning models by comparing bagging and boosting techniques to develop an accurate early prediction system for preeclampsia risk using clinical medical record data. The research follows the Cross-Industry Standard Process for Data Mining (CRISP-DM) framework, encompassing data understanding, data preparation, modeling, evaluation, and interpretation. The dataset was obtained from RSUD Inche Abdoel Moeis Samarinda and underwent preprocessing procedures, including data cleaning, transformation, feature encoding, normalization, and Synthetic Minority Over-sampling Technique (SMOTE) to address class imbalance. Six ensemble learning algorithms were evaluated, consisting of Random Forest, Extra Trees, and Rotation Forest as bagging methods, and XGBoost, LightGBM, and CatBoost as boosting methods. Model performance was assessed using accuracy, precision, recall, and weighted F1-score. The experimental results demonstrate that Random Forest achieved the highest predictive performance, with an accuracy of 0.92, precision of 0.93, recall of 0.92, and weighted F1-score of 0.91, indicating superior robustness and generalization capability. Extra Trees achieved comparable accuracy (0.92) but exhibited lower prediction stability across evaluation metrics. Among the boosting algorithms, LightGBM and CatBoost each obtained an accuracy of 0.89, while XGBoost achieved 0.88. Rotation Forest recorded the lowest accuracy (0.62), suggesting limited suitability for this clinical dataset. These findings indicate that bagging-based ensemble methods, particularly Random Forest, outperform boosting techniques for imbalanced clinical data and provide strong empirical support for developing reliable Clinical Decision Support Systems (CDSS) for early preeclampsia screening and risk assessment in healthcare settings
Food Delivery Time Prediction using Tree-Based Ensemble Models: A Comparative Study with Explainable Artificial Intelligence Raihanfitri Adi Kalipaksi; Haviluddin Haviluddin; Anindita Septiarini; Joan Angelina Widians; Novianti Puspitasari
Information Technology Education Journal Vol. 5, No. 3, August (2026)
Publisher : Jurusan Teknik Informatika dan Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/intec.v5i3.12157

Abstract

Purpose – Being able to predict delivery time accurately is important for online food delivery services, both for operational efficiency and customer satisfaction. However, this is not an easy task. Delivery time depends on many factors that are related to each other, such as courier characteristics, delivery distance, and order-related information, and these factors interact in complex ways. This study looks at how well tree-based ensemble learning models can predict food delivery time, and also uses explainable artificial intelligence so the models can still be interpreted properly. Design – This study uses 45,593 delivery records taken from Kaggle. In this study, four tree-based ensemble models were developed, namely Random Forest, Gradient Boosting, XGBoost, and LightGBM, with each model optimized through hyperparameter tuning. The models were evaluated using repeated 5-fold cross-validation with three repetitions, and their performance was assessed using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R². Findings – LightGBM Tuned came out with the best numerical performance, showing an MAE of 5.678 minutes, RMSE of 7.204 minutes, and R² of 0.411. However, based on ANOVA and Tukey's post-hoc test, the difference in performance between the best boosting-based models was not statistically significant. SHAP analysis also showed that courier rating, delivery distance, courier age, and several interaction features were the factors that had the biggest influence on delivery time prediction. Research implications – The findings suggest that boosting-based ensemble learning models can provide moderate predictive performance while offering interpretable insights into the factors contributing to delivery time predictions. Nevertheless, the moderate R² value indicates that additional operational variables, such as traffic conditions, restaurant preparation time, and courier workload, may be required to improve practical prediction reliability. Originality/value – This study combines ensemble learning, feature engineering, statistical validation, and explainable artificial intelligence together to evaluate both the predictive performance and interpretability of models for food delivery time prediction. 
Co-Authors Achmad Fanany Onnilita Gaffar Achmad Fanany Onnilita Gaffar Adnan, Adam Afdal Jamil Tanjung Agus Soepriyadi Ahmad Hijazi, Mohd Hanafi Ahmad Jawahir Ahmad Jawahir Aiman, Ahmad Zuhair Nur Aina Musdholifah Aini, Hijratul Aji Prasetya Wibawa Akhmad Masyudi Albertus Juvensius Pontus Aldi Bastiatul Fawait Fawait Alfiansyah, M Nur Ali Sholihin Allo, Adriati Manuk Anam, M Khairul Anggari, Ricky Anindita Septiarini, Anindita Anton Prafanto Arda Yunianta Arda Yunianta Arif Bramantoro Arif Harjanto Arinda Mulawardani Kustiawan Astuti, Wistiani Aulia Rahman Awang Harsa Kridalaksana Bambang Nur Basuki Bangkit Bekti Nurdianto Basuki, Nur Bambang Brins Leonard Pailan Budiman, Edy Burhandenny, Aji Ery Cahyani, Oktari Indi Cahyani, Oktaria Indi Cellia Auzia Nugraha Chrisman Bonor Sinaga Davina Putri Ananta Dedy Cahyadi Dedy Mirwansyah Delvina Dwiani Samjar Dhanar Intan Surya Saputra Dhanar Intan Surya Saputra Didit Suprihanto, Didit Dinda Izmya Nurpadillah Djoko Setyadi Dwiyanto, Felix Andika Efrizoni, Lusiana Emmilya Umma Azizah Gaffar Fahrul Agus Faizul Anwar Wandi Fatkhul Hani Rumawan Fauzan, Ammar Nabil Faza Alameka Fazma Urmila Jannah Helmi Puadi Firdaus, Ardhifa Firdaus, Muhammad Bambang Fui Fui, Ching Fui, Ching Fui Gaffar, Achmad Fanany Onnlita Gubtha Mahendra Putra Gubtha Mahendra Putra Gultom, Tiopan Hendry Manto Hairah, Ummul Hamdani Hamdani Hasihi, Cholisah Erman Hasnida, Rima Yustika Hatta, Heliza Rahmania Helmi Puadi, Fazma Urmila Jannah Herlina Jayadiyanti Herman Santoso Pakpahan Hersa Safitri Hery Widijanto Hijazi, Mohd Hanafi Ahmad Hijratul Aini Hijratul Aini Huzain Azis Ibrahim, Muhammad Rivani Ifandi, Muhammad Imam Tahyudin Imam Tahyudin Irwan Gani Islamiyah Islamiyah Islamiyah Islamiyah Islamiyah Islamiyah, Islamiyah Iwan Muhamad Ramdan Izdihar, Zahra Nabila Jainuddin Jainuddin Jayadiyanti, Herlina Kesuma, Muhammad Afrizal Kim On, Chin Leong, Jing Mei Lilik Hendrajaya Malani, Rheo Maratus Soleha Medi Taruk Mega Yoalifa Milkhatun, Milkhatun Ming Foey Teng, Ming Foey Moham, Ni’mah Mohd Shahizan Othman Mohd Shahizan Othman Mualin Renaldy Setiabudi Muhammad Bambang Muhammad Rafif Hanif Muhammad Soleh Muhammad Sultan, Muhammad Muhammad Syarif Abdillah Nafalski, Andrew Nataniel Dengen Ngurah Satria Darmawangsa Ni’mah Moham Norazah Yusof Novianti Puspitasari Nugraha, Cellia Auzia Nugroho, Basuki Rahmat Nur Fadhilah Nurfaizi Amin Nurpadillah, Dinda Izmya Olivia Angelica Murtioso Omar Mohammed Barukab Omar Obarukab Norazah Yusof Othman, Mohd Shahizan Paroliyan, Abraham Pradinata, Muhammad Aji Prafanto, Anton Pratama, Arief Ardi Prawira, Muhammad Nanda Purnawansyah Purnawansyah Puspitasari, Novianti Putra, Gubtha Mahendra Putut Pamilih Widagdo, Putut Pamilih Qonita, Adiba Rahayu, Ervina Raihanfitri Adi Kalipaksi Raja, Roesman Ridwan Rayner Alfred Rayner Alfred Rayner Alfred Rayner Alfred Rayner Alfred Rayner Alfred Rendy Ramadhan Revia Oktaviani Rima Yustika Hasnida Salim, Yulita Saputra, Irzan Tri Sarjon Defit Saudi, Azali Setyadi, Hario Jati Simanungkalit, Julius Rinaldi Sitompul, Tua Delima Soepriyadi, Agus Suryani Junita Patandianan Sutikno Sutikno Suwardi Gunawan Tindik, Emmanuel Steward Tommy Trides Triyanna Widiyaningtyas Triyanna Widyaningtyas, Triyanna Utama, Agung Bella Putra Utomo Pujianto Vina Zahrotun Kamila Wandi, Faizul Anwar Wati, Masna Wei, Toh Yin Widians, Joan Angelina Wong, Kelvin Yahya, Fiqri Khaidar Yazeed Al Moaiad Yudhi Saputra Yudi Sukmono Yulita Salim Yunianta, Arda Yusof, Omar Obarukab Norazah Zainal Arifin Zainal Arifin Zakaria Ahmad Dahlan