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All Journal Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Journal of Nonformal Education Jurnal Teknologi Informasi dan Ilmu Komputer Scientific Journal of Informatics Sistemasi: Jurnal Sistem Informasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) PROCESSOR Jurnal Ilmiah Sistem Informasi, Teknologi Informasi dan Sistem Komputer Jurnal Ilmiah Media Sisfo JATI (Jurnal Mahasiswa Teknik Informatika) Indonesian Journal of Electrical Engineering and Computer Science Jurnal Teknik Informatika (JUTIF) Journal of Social Responsibility Projects by Higher Education Forum Jurnal Sistem Informasi Triguna Dharma (JURSI TGD) Malcom: Indonesian Journal of Machine Learning and Computer Science Jurnal Informatika dan Rekayasa Komputer Jurnal Manajemen Teknologi dan Sistem Informasi Jurnal Manajemen Sistem Informasi PROCEEDING OF INTERNATIONAL CONFERENCE ON BUSINESS MANAGEMENT AND ACCOUNTING Prosiding Seminar Nasional Ilmu Teknik
Jasmir, Jasmir
Teknik Informatika
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mathematics Social Sciences

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Journal : Jurnal Teknik Informatika (JUTIF)

 1 
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.
PATTERN CLASSIFICATION SIGN LANGUAGE USING FEATURES DESCRIPTORS AND MACHINE LEARNING Nurhadi, Nurhadi; Winanto, Eko Arip; Said, Rahaini Mohd; Jasmir, Jasmir; Afuan, Lasmedi
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 2 (2024): JUTIF Volume 5, Number 2, April 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.2.1228

Abstract

Sign language is way of communication for the deaf and speech impaired. In Indonesia, the utilization of a standardized language involves the incorporation of American Sign Language (ASL). ASL is employed for various communication needs, ranging from basic alphanumeric fingerspelling (A-Z and numbers) to the more complex SIBI form (comprising gesture vocabulary) in everyday interactions as well as formal contexts. This surge in the digitization of sign language underscores the ongoing advancements in research and development. The challenge in this research lies in the ability to recognize American Sign Language (ASL) with diverse intensities and invariant backgrounds. Therefore, the study emphasis is on proposing a suitable segmentation method comparison for multi-intensity ASL cases. Subsequently, global feature descriptor methods, including Color Histogram, Hu Moments, and Haralick Texture techniques, are applied for feature extraction. The result of the Logistic Regression method versus the supervised Random Forest checks accuracy and suitability in identifying ASL fingerspelling. The findings of this research is predictive value of logistic regression is 48%, with class Y having the highest precision (0.86), class V having the lowest accuracy (0.16), and class L having the highest recall (0.73). The maximum precision in classes B, F, H, I, K, Y, and Z is 1.00, and the lowest in class U is 0.58, while the highest recall is in class G, which is 1.00. The lowest is in class V, while the predictive value from the random forest is 86 percent. Class H has the greatest f1 score (0.99), while class U has the lowest f1 score (0.64). The Random Forest method outperforms the two methods suggested in the paper, according to the comparison.
Comparison of AdaBoost and Random Forest Methods in Osteoporosis Risk Prediction Based on Machine Learning Parlindungan H, Edwardo; Assegaff, Setiawan; Jasmir, Jasmir
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 1 (2026): JUTIF Volume 7, Number 1, February 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.1.5297

Abstract

This study aims to determine the most effective ensemble machine learning algorithm for osteoporosis risk prediction in resource-constrained healthcare settings, specifically comparing AdaBoost and Random Forest performance on Southeast Asian population data. We implemented nested 5-fold cross-validation on a dataset of 1,958 records with 15 lifestyle and demographic attributes. Both algorithms underwent hyperparameter optimization, and performance was evaluated using accuracy, precision, recall, F1-score, and clinical utility metrics including cost-effectiveness analysis. AdaBoost achieved superior performance with 86.90% accuracy (95% CI: 84.2-89.6%) and perfect precision (1.00) compared to Random Forest's 84.69% accuracy and 0.92 precision. Statistical significance testing confirmed AdaBoost's advantage (p=0.032). Clinical implementation in three health centers demonstrated 60% reduction in unnecessary referrals. This is the first study to compare these algorithms specifically for Southeast Asian populations with clinical validation and cost-effectiveness analysis, providing a ready-to-deploy model for resource-limited healthcare settings.
K-Means Clustering with Elbow Method and Validity Indices for Classifying Student Academic Achievement Based on Knowledge Scores at SDN 48 Kota Jambi Azmi, M. Fikri; Abidin, Dodo Zaenal; Jasmir, Jasmir
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 1 (2026): JUTIF Volume 7, Number 1, February 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.1.5349

Abstract

Student performance evaluation at SDN 48 Kota Jambi has been traditionally conducted manually, which is inefficient and often subjective. This study aims to provide an objective classification of students’ academic achievement using data-driven methods. The research applies the Knowledge Discovery in Databases (KDD) framework, which involves data selection, preprocessing, clustering, and evaluation. The dataset consists of knowledge scores from 152 elementary students across seven subjects, obtained from the Merdeka Curriculum report cards. Data preprocessing included cleaning and normalization to ensure consistency. K-Means clustering was implemented using RapidMiner, with the optimal number of clusters determined through the Elbow Method. Cluster validity was assessed using the Davies–Bouldin Index (1.226) and the Silhouette Coefficient (0.245). The results produced three clusters: high achievers (30.9%), medium achievers (27.0%), and low achievers (42.1%). Centroid analysis indicated that Mathematics and Physical Education were the most discriminative subjects across groups. These findings highlight a substantial proportion of students requiring remedial intervention and support differentiated learning strategies. The contribution of this research lies in applying educational data mining techniques to an elementary school context in Jambi, integrating both quantitative indices and qualitative validation with teachers. The study demonstrates that clustering methods can enhance educational decision-making, providing a basis for adaptive teaching, targeted interventions, and resource allocation in elementary education.
Optimizing Heart Disease Classification Using C4.5, Random Forest, and XGBoost with ANOVA, Chi-Square, and AdaBoost Pratama, Andika; Assegaff, Setiawan; Jasmir, Jasmir; Nurhadi, Nurhadi
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 2 (2026): JUTIF Volume 7, Number 2, April 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.2.5430

Abstract

Heart disease remains one of the leading causes of mortality worldwide, underscoring the need for accurate and scalable prediction models within clinical informatics. This study proposes a leakage-safe machine learning pipeline combining stratified splitting, SMOTE-based imbalance handling, and in-fold feature selection using ANOVA, Chi-Square, and AdaBoost-assisted ranking to enhance classification performance on a large heart-disease dataset consisting of 10,000 samples and 21 attributes. Three widely used algorithms, C4.5, Random Forest, and XGBoost, were evaluated to determine the optimal model-feature selection configuration for structured medical data. The results demonstrate that feature relevance contributes more significantly to predictive performance than increasing model complexity, with Random Forest achieving the highest accuracy, precision, recall, and F1-Score at 98.43% when combined with Chi-Square or ANOVA feature selection. C4.5 showed the greatest relative improvement, rising from 76.52% to 97.57% using AdaBoost-assisted selection, while XGBoost improved from 66.32% to 94.88% after statistical filtering. The dominant features identified such as CRP, BMI, blood pressure, fasting glucose, LDL, triglycerides, and homocysteine align with well-established cardiovascular biomarkers, supporting clinical validity. This research provides an important contribution to computer science by demonstrating an efficient and scalable hybrid FS-boosting framework capable of reducing unnecessary model complexity, improving generalization, and supporting low-latency deployment in clinical decision-support systems. The findings highlight the potential of structured-data machine learning to strengthen digital health diagnostics in resource-limited environments.
Enhancement Of The C4.5 Decision Tree Algorithm With Anova For Predicting Academic Achievement Of Students At Smpn.16 Kota Jambi Osviarni, Rice; Assegaff, Setiawan; Jasmir, Jasmir; Nurhadi, Nurhadi
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 2 (2026): JUTIF Volume 7, Number 2, April 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.2.5431

Abstract

This study aims to improve the accuracy of predicting student academic achievement by integrating the Analysis of Variance (ANOVA) method with the C4.5 Decision Tree algorithm. In the context of information systems, this research holds significant importance for the development of more reliable Decision Support Systems (DSS) or early warning systems in school environments. The research was conducted at SMPN 16 Jambi City using secondary data from three academic years (2022/2023-2024/2025) covering academic variables, attendance, and parental income. The main issue addressed was the limitations of the C4.5 algorithm in handling irrelevant features and unbalanced data, which, at the system implementation level, can lead to inaccurate recommendations or alerts.This research method employed a data mining approach with stages including data cleaning, numeric conversion, missing value imputation, formation of derived variables, and categorization of the target variable "Achievement." The initial C4.5 model produced 72.81% accuracy on the training data and 69.71% accuracy on cross-validation. After feature selection using ANOVA, one insignificant variable was removed, resulting in a hybrid C4.5+ANOVA model with nine key features. Test results showed an increase in accuracy to 80.44% on the training data and 73.66% on the cross-validation data, representing an improvement of 7.63 and 3.95 percentage points, respectively.This improvement in model performance directly translates to an enhancement in the quality of the information system's output, yielding more reliable reports and predictions for teachers and school management.
Co-Authors Abdul Harris Abdul Rahim Abidin, Dodo Zaenal Adi Kusuma Agus Nugroho, Agus Agus Siswanto Agus Siswanto Agustina Ahmad Ahmad Andika Pratama Annisa, Tiko Nur Arvita, Yulia Ayu, Nur Trisna Azmi, M. Fikri Clara Zuliani Syahputri Fachruddin Fachruddin Fachruddin Fachruddin Faiza Rini Fatima Anggraini Fatma, Dhea Fun, Chi-Win Gunardi Gunardi Gustinar, Gustinar Hasanah Arif, Surya Hendri Hendri Heri Santoso Herti Yani, Herti Hetty Rohayani Hidayatullah, Arief Irawan Irawan Jasmir , Jasmir Juliana Juliana Jumairi, Nopri Kurniawansyah, Kevin Lasmedi Afuan Lastari, Widya Margaretta S, Chinthya Marthiawati, Noneng Mega, Meyer Meisak, Despita Muhammad Al-Aziz Sofyan Mulyadi Rusli NAIBAHO, RONALD NOVIANTO, YUDI Nurhadi OSCARIO OSCARIO Osviarni, Rice Pahlevi B, M Riza Pardamean Simanjuntak, Daniel Sintong Pareza Alam Jusia, Pareza Alam Parlindungan H, Edwardo Purnama, Benni Putri, Azhiah Raden Mohamad Herdian Bhakti Rasywir, Errisya Rawal Dewa, Suhendra Riza Pahlevi, Riza Rohaini, Eni Rosario , Maria Sadikin , Ali Said, Rahaini Mohd Sasa Kirana Wulandari Setiawan Assegaf Sharipuddin, Sharipuddin Sidik Praptomo Sophia, Aya Sukoco, Bambang Suryawahyuni Latief Takhir, Said Hambali Trivenna, Priskha WIJAYA, IBNU SANI WILLY RIYADI Yudha, M. Zahran Yudi Novianto Zudyanti Dwi Rahma Sari