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All Journal Indonesian Journal of Electrical Engineering and Computer Science Jurnal Teknik Informatika (JUTIF) Jurnal Informatika dan Rekayasa Komputer
Abidin, Dodo Zaenal
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Computer Science & IT

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Experimental of information gain and AdaBoost feature for machine learning classifier in media social data Jasmir, Jasmir; Abidin, Dodo Zaenal; Fachruddin, Fachruddin; Riyadi, Willy
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 2: November 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i2.pp1172-1181

Abstract

In this research, we use several machine learning methods and feature selection to process social media data, namely restaurant reviews. The selection feature used is a combination of information gain (IG) and adaptive boosting (AdaBoost) which is used to see its effect on the classification performance evaluation value of machine learning methods such as Naïve Bayes (NB), K-nearest neighbor (KNN), and random forest (RF) which is the aim of this research. NB is very simple and efficient and very sensitive to feature selection. Meanwhile, KNN is known for its weaknesses such as biased k values, overly complex computation, memory limitations, and ignoring irrelevant attributes. Then RF has weaknesses, including that the evaluation value can change significantly with only small data changes. In text classification, feature selection can improve the scalability, efficiency and accuracy of text classification. Based on tests that have been carried out on several machine learning methods and a combination of the two selection features, it was found that the best classifier is the RF algorithm. RF produces a significant increase in value after using the IG and AdaBoost features. Increased accuracy by 10%, precision by 12.43%, recall by 8.14% and F1-score by 10.37%. RF also produces even accuracy, precision, recall, and F1-score values after using IG and AdaBoost with an accuracy value of 84.5%; precision of 85.58%; recall was 86.36%; and F1-score was 85.97%.
Perancangan Sistem Informasi Administrasi Pembayaran Spp Pada Smk Batanghari Kota Jambi Sujatmiko, Tri Aji; Maulana Hidayat; Pajri; Abidin, Dodo Zaenal
Jurnal Informatika Dan Rekayasa Komputer(JAKAKOM) Vol 4 No 2 (2024): JAKAKOM Vol 4 No 2 September 2024
Publisher : LPPM Universitas Dinamika Bangsa

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Abstract

ABSTRACT Tri Aji Sujatmiko 8020180256, Maulana Hidayat 8020190016, Pajri 8020190025 DESIGN OF A WEB-BASED SPP ADMINISTRATION PAYMENT INFORMATION SYSTEM AT SMK BATANGHARI JAMBI CITY Keywords: Design, System, Information, Payment, Tuition Payment, Website. In today's digital era, efficiency and effectiveness in school administration will definitely be important. One important aspect of school administration is the payment of SPP (Education Development Contribution). SMK Batanghari Jambi City needs a system that can simplify the SPP payment process, reduce data entry errors, and increase transparency. In this research, we will design and develop a web-based tuition payment information system that can be accessed by students, parents, and schools. With a software development method that involves requirements analysis, system design, implementation, and testing. The result of the design is a website that allows users to pay tuition fees online, storage of payment history, and real time financial reports. This system is expected to improve the efficiency of managing tuition payments at SMK Batanghari Jambi City and provide easy access to information to all interested parties.
Improving Term Deposit Customer Prediction Using Support Vector Machine with SMOTE and Hyperparameter Tuning in Bank Marketing Campaigns Abidin, Dodo Zaenal; Rosario , Maria; Sadikin , Ali; Nurhadi, Nurhadi; Jasmir, Jasmir
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 3 (2025): JUTIF Volume 6, Number 3, Juni 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.3.4585

Abstract

Identifying potential customers for term deposit products remains a challenge in the banking industry due to class imbalance in marketing datasets. This study proposes an integrated approach that combines Support Vector Machine (SVM) with the Synthetic Minority Oversampling Technique (SMOTE) and hyperparameter tuning via GridSearchCV to enhance prediction performance. The dataset comprises 45,211 records containing demographic and campaign-related features. Preprocessing steps include categorical encoding, feature scaling, and SMOTE-based resampling. The optimized SVM model achieves an accuracy of 91% and an AUC of 0.96, outperforming the baseline model and demonstrating strong discriminatory ability, particularly for the minority class. This method improves the balance between precision and recall while reducing bias toward the majority class. The findings confirm the effectiveness of combining SMOTE and SVM for imbalanced classification tasks in the financial domain. These results contribute to the advancement of applied machine learning in informatics, particularly in developing robust decision support systems for data-driven banking strategies. Future work may extend this approach to diverse datasets and explore advanced resampling or ensemble techniques to improve model generalization.
Comparison of robust machine learning algorithms on outliers and imbalanced spam data Abidin, Dodo Zaenal; Jasmir, Jasmir; Rasywir, Errisya; Siswanto, Agus
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 2: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i2.pp1130-1144

Abstract

Effective spam detection is essential for data security, user experience, and organizational trust. However, outliers and class imbalance can impact machine learning models for spam classification. Previous studies focused on feature selection and ensemble learning but have not explicitly examined their combined effects. This study evaluates the performance of random forest (RF), gradient boosting (GB), and extreme gradient boosting (XGBoost) under four experimental scenarios: (i) without synthetic minority over-sampling technique (SMOTE) and outliers, (ii) without SMOTE but with outliers, (iii) with SMOTE and without outliers, and (iv) with SMOTE and with outliers. Results show that XGBoost achieves the highest accuracy (96%), an area under the curve-receiver operating characteristic (AUCROC) of 0.9928, and the fastest computation time (0.6184 seconds) under the SMOTE and outlier-free scenario. Additionally, RF attained an AUCROC of 0.9920, while GB achieved 0.9876 but required more processing time. These findings emphasize the need to address class imbalance and outliers in spam detection models. This study contributes to developing more robust spam filtering techniques and provides a benchmark for future improvements. By systematically evaluating these factors, it lays a foundation for designing more effective spam detection frameworks adaptable to real-world imbalanced and noisy data conditions.
A Comprehensive Benchmarking Pipeline for Transformer-Based Sentiment Analysis using Cross-Validated Metrics Abidin, Dodo Zaenal; Afuan, Lasmedi; Toscany, Afrizal Nehemia; Nurhadi, Nurhadi
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.4.4894

Abstract

Transformer-based models have significantly advanced sentiment analysis in natural language processing. However, many existing studies still lack robust, cross-validated evaluations and comprehensive performance reporting. This study proposes an integrated benchmarking pipeline for sentiment classification on the IMDb dataset using BERT, RoBERTa, and DistilBERT. The methodology includes systematic preprocessing, stratified 5-fold cross-validation, and aggregate evaluation through confusion matrices, ROC and precision-recall (PR) curves, and multi-metric classification reports. Experimental results demonstrate that all models achieve high accuracy, precision, recall, and F1-score, with RoBERTa leading overall (94.1% mean accuracy and F1), followed by BERT (92.8%) and DistilBERT (92.1%). All models exceed 0.97 in ROC-AUC and PR-AUC, confirming strong discriminative capability. Compared to prior approaches, this pipeline enhances result robustness, interpretability, and reproducibility. The provided results and open-source code offer a reliable reference for future research and practical deployment. This study is limited to the IMDb dataset in English, suggesting future work on multilingual, cross-domain, and explainable AI integration.
Co-Authors Afrizal Nehemia Toscany Agus Siswanto Fachruddin Fachruddin Jasmir, Jasmir Lasmedi Afuan Maulana Hidayat Nurhadi Pajri Rasywir, Errisya Rosario , Maria Sadikin , Ali Sujatmiko, Tri Aji WILLY RIYADI