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All Journal ComEngApp : Computer Engineering and Applications Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) JURNAL SISTEM INFORMASI BISNIS Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Teknologi Informasi dan Ilmu Komputer KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) JOIN (Jurnal Online Informatika) Jurnal Teknologi dan Sistem Komputer Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Komputasi Sebatik Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Informatika dan Rekayasa Elektronik Jurnal Mnemonic Bubungan Tinggi: Jurnal Pengabdian Masyarakat Computer Science and Information Technologies Jurnal Teknik Informatika (JUTIF) Journal of Data Science and Software Engineering Jurnal INFOTEL Jurnal Informatika Polinema (JIP) Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics
Rudy Herteno
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Health Professions Library & Information Science Materials Science & Nanotechnology Social Sciences Other

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Design of Application Framework for Vital Monitoring Mobile-Based System Rizky Ananda, Muhammad; Faisal, Mohammad Reza; Herteno, Rudy; Nugroho, Radityo Adi; Abadi, Friska
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 2 (2024): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i2.28416

Abstract

In the realm of modern healthcare, continuous monitoring can leverage the affordable wearable devices available on the market to manage costs. However, these devices face several limitations, such as restricted access for other parties, including nurses and doctors, and the need for redevelopment to integrate new devices for data accessibility. This study addresses these challenges by establish an application framework tailored for mobile-based systems, by ensuring accessibility by external parties. The research contribution is encompassing two key aspects: the potential implementation of Feature-Oriented Domain Analysis (FODA) in the domain of mobile-based vital sign monitoring, particularly in the absence of prior studies addressing the same context, and the identification reusable (frozen spots) and adaptable components (hot spots), providing guidance for the development of mobile-based vital sign monitoring. FODA is utilized during the analysis activity. Design patterns are then implemented when creating class diagrams in the design activity. This study finding reveal 7 primary features and 18 sub-features essential that must be incorporated into the application framework. The framework includes 5 hot spots and 7 frozen spots, with the implementation of Strategy and Filter design patterns. In conclusion, the developed application framework represents a significant advancement in vital sign monitoring, particularly within mobile-based systems. This study emphasizing limitations in analysis and design phases. In future research, the focus will shift to the construction and stabilization phases, all crucial for refining the framework. Implementing framework in actual applications can aid in developing vital sign monitoring systems and potentially improving healthcare outcomes.
Effect of SMOTE Variants on Software Defect Prediction Classification Based on Boosting Algorithm Aflaha, Rahmina Ulfah; Herteno, Rudy; Faisal, Mohammad Reza; Abadi, Friska; Saputro, Setyo Wahyu
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 2 (2024): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i2.28521

Abstract

Detecting software defects early on is critical for avoiding significant financial losses. However, building accurate software defect prediction models can be challenging due to class imbalance, where the data for defective modules is much less than for standard modules. This research addresses this issue using the imbalanced dataset NASA MDP. To address this issue, researchers have proposed new methods that combine data level balancing approaches with 14 variations of the SMOTE algorithm to increase the amount of defective module data. An algorithm-level approach with three boosting algorithms, Catboost, LightGBM, and Gradient Boosting, is applied to classify modules as defective or non-defective. These methods aim to improve the accuracy of software defect prediction. The results show that this new method can produce a more accurate classification than previous studies. The DSMOTE and Gradient Boosting pair with 0.9161 has the highest average accuracy (0.9161). The DSMOTE and Catboost model achieved the highest average AUC value (0.9637). The ADASYN kernel and Catboost showed the best ability to perform the average G-mean value (0.9154). The research contribution to software defect prediction involves developing new techniques and evaluating their effectiveness in addressing class imbalance.
Improving with Hybrid Feature Selection in Software Defect Prediction Pratama, Muhammad Yoga Adha; Herteno, Rudy; Faisal, Mohammad Reza; Nugroho, Radityo Adi; Abadi, Friska
JOIN (Jurnal Online Informatika) Vol 9 No 1 (2024)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v9i1.1307

Abstract

Software defect prediction (SDP) is used to identify defects in software modules that can be a challenge in software development. This research focuses on the problems that occur in Particle Swarm Optimization (PSO), such as the problem of noisy attributes, high-dimensional data, and premature convergence. So this research focuses on improving PSO performance by using feature selection methods with hybrid techniques to overcome these problems. The feature selection techniques used are Filter and Wrapper. The methods used are Chi-Square (CS), Correlation-Based Feature Selection (CFS), and Forward Selection (FS) because feature selection methods have been proven to overcome data dimensionality problems and eliminate noisy attributes. Feature selection is often used by some researchers to overcome these problems, because these methods have an important function in the process of reducing data dimensions and eliminating uncorrelated attributes that can cause noisy. Naive Bayes algorithm is used to support the process of determining the most optimal class. Performance evaluation will use AUC with an alpha value of 0.050. This hybrid feature selection technique brings significant improvement to PSO performance with a much lower AUC value of 0.00342. Comparison of the significance of AUC with other combinations shows the value of FS PSO of 0.02535, CFS FS PSO of 0.00180, and CS FS PSO of 0.01186. The method in this study contributes to improving PSO in the SDP domain by significantly increasing the AUC value. Therefore, this study highlights the potential of feature selection with hybrid techniques to improve PSO performance in SDP.
Deep CNN for Wetland Mapping from Satellite Imagery Ramadhan, As`'ary; Herteno, Rudy; Farmadi, Andi
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.5280

Abstract

Wetland loss endangers the ecosystem through loss of biodiversity, carbon sequestration and flood regulation potential. A precise determination of wetlands status is necessary to safeguard for their conservation and ensure sustainable management. Implementation This study aims to assess the performance of deep CNNs in wetland detection using high-resolution Google Earth image data in South Kalimantan province, Indonesia. The work adopts the Chopped Picture Method (CPM) and the use of sliding windows for data augmentation to improve the diversity of the dataset and reduce the computational cost. Two CNN models, VGG-16Net, and LeNet-5, were trained using a dataset comprising 220 satellite images, which we converted into 89,100 patches of 56×56 pixels. Performance was compared using accuracy, precision, recall, and F1-score. Experimental results show good levels of accuracy for the two architectures, but LeNet-5 provided more stable results between test locations, having a F1-score closer to 100% and spending less computational time (≈10s per epoch) than VGG-16Net (≈40s per epoch). These results validate that CPM significantly increases the variety of training data, making it possible for a CNN to correctly identify the vague and irregular shapes of wetlands with high accuracy. In addition to advancing environmental conservation strategies, the study highlights the contribution of informatics to large-scale, automated environmental monitoring, particularly in supporting wetland conservation, sustainable land-use planning, and climate adaptation efforts.
Implementation of Copeland Method on Wrapper-Based Feature Selection Using Random Forest For Software Defect Prediction Aryanti, Agustia Kuspita; Herteno, Rudy; Indriani, Fatma; Nugroho, Radityo Adi; Muliadi, Muliadi
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 1 (2025): February
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/2pgffc67

Abstract

Software Defect Prediction is crucial to ensure software quality. However, high-dimensional data presents significant challenges in predictive modelling, especially identifying the most relevant features to improve model performance. Therefore, efforts are needed to address these issues, and one is to apply feature selection methods. This study introduces a new approach by applying the Copeland ranking method, which aggregates feature weights from multi-wrapper methods, including Recursive Feature Elimination (RFE), Boruta, and Custom Grid Search, using 12 NASA MDP datasets. The study also applies Random Forest classification and evaluates the model using AUC and t-Test. In addition, this study also compares the accuracy and precision values produced by each method. The results consistently show that the Copeland ranking method produces superior results compared to other ranking methods. The average AUC value obtained from the Copeland ranking method is 0.7496, higher than the Majority ranking method with an average AUC of 0.7416 and the Optimal Rank ranking method with an average AUC of 0.7343. These findings confirm that applying the Copeland ranking method in wrapper-based feature selection can enhance classification performance in software defect prediction using Random Forest compared to other ranking methods. The strength of the Copeland method lies in its ability to integrate rankings from various feature selection approaches and identify relevant features. The findings of this research demonstrate the potential of the Copeland ranking method as a reliable tool for ranking features obtained from various wrapper-based feature selection techniques. The implementation of this approach contributes to improved software defect prediction and provides new insights for the development of ranking methods in the future
Hybrid Feature Selection and Balancing Data Approach for Improved Software Defect Prediction Febrian, Muhamad Michael; Saputro, Setyo Wahyu; Saragih, Triando Hamonangan; Abadi, Friska; Herteno, Rudy
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.v7i2.67

Abstract

Software Defect Prediction (SDP) plays a vital role in identifying defects within software modules. Accurate early detection of software defects can reduce development costs and enhance software reliability. However, SDP remains a significant challenge in the software development lifecycle. This study employs Particle Swarm Optimization (PSO) and addresses several challenges associated with its application, including noisy attributes, high-dimensional data, and imbalanced class distribution. To address these challenges, this study proposed a hybrid filter-based feature selection and class balancing method. The feature selection process incorporates Chi-Square (CS), Correlation-Based Feature Selection (CFS), and Correlation Matrix-Based Feature Selection (CMFS), which have been proven effective in reducing noisy and redundant attributes. Additionally, the Synthetic Minority Over-sampling Technique (SMOTE) is applied to mitigate class imbalance in the dataset. The K-Nearest Neighbors (KNN) algorithm is employed as the classification model due to its simplicity, non-parametric nature, and suitability for handling the feature subsets produced. Performance evaluation is conducted using the Area Under Curve (AUC) metric with a significance threshold of 0.05 to assess classification capability.  The proposed method achieved an AUC of 0.872, demonstrating its effectiveness in enhancing predictive performance. The proposed method was also superior to other combinations such as PSO SMOTE (0.0043), PSO SMOTE CS (0.0091), PSO SMOTE CFS (0.0111), and PSO SMOTE CFS CMFS (0.0007). The findings of this study show that the proposed method significantly enhances the efficiency and accuracy of PSO in software defect prediction tasks. This hybrid strategy demonstrates strong potential as a robust solution for future research and application in predictive software quality assurance.
Enhancing Software Defect Prediction: HHO-Based Wrapper Feature Selection with Ensemble Methods Fauzan Luthfi, Achmad; Herteno, Rudy; Abadi, Friska; Adi Nugroho, Radityo; Itqan Mazdadi, Muhammad; Athavale, Vijay Anant
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/f2140043

Abstract

The growing complexity of data across domains highlights the need for effective classification models capable of addressing issues such as class imbalance and feature redundancy. The NASA MDP dataset poses such challenges due to its diverse characteristics and highly imbalanced classes, which can significantly affect model accuracy. This study proposes a robust classification framework integrating advanced preprocessing, optimization-based feature selection, and ensemble learning techniques to enhance predictive performance. The preprocessing phase involved z-score standardization and robust scaling to normalize data while reducing the impact of outliers. To address class imbalance, the ADASYN technique was employed. Feature selection was performed using Binary Harris Hawk Optimization (BHHO), with K-Nearest Neighbor (KNN) used as an evaluator to determine the most relevant features. Classification models including Random Forest (RF), Support Vector Machine (SVM), and Stacking were evaluated using performance metrics such as accuracy, AUC, precision, recall, and F1-measure. Experimental results indicated that the Stacking model achieved superior performance in several datasets, with the MC1 dataset yielding an accuracy of 0.998 and an AUC of 1.000. However, statistical significance testing revealed that not all observed improvements were meaningful; for example, Stacking significantly outperformed SVM but did not show a significant difference when compared to RF in terms of AUC. This underlines the importance of aligning model choice with dataset characteristics. In conclusion, the integration of advanced preprocessing and metaheuristic optimization contributes positively to software defect prediction. Future research should consider more diverse datasets, alternative optimization techniques, and explainable AI to further enhance model reliability and interpretability.
Analysis of the Effect of Feature Extraction on Sentiment Analysis using BiLSTM: Monkeypox Case Study on X/Twitter Noryasminda; Saragih, Triando Hamonangan; Herteno, Rudy; Faisal, Mohammad Reza; Farmadi, Andi
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.v7i2.73

Abstract

The monkeypox outbreak has again become a global concern due to its widespread spread in various countries. Information related to the disease is widely shared through social media, especially Twitter which is a major source of public opinion. However, the complexity of language and the diverse viewpoints of users often pose challenges in accurately analyzing sentiment. Therefore, sentiment analysis of tweets about monkeypox is important to understand public perception and its impact on the dissemination of health information. This research contributes to identifying the most effective word embedding-based feature extraction method for sentiment analysis of health issues on social media. The purpose of this study is to compare the performance of word embedding methods namely Word2Vec, GloVe, and FastText in sentiment analysis of tweets about monkeypox using the BiLSTM model. Data totaling 1511 tweets were collected through a crawling process using the Twitter API. After the data is collected, manual labeling is done into three sentiment categories, namely positive, negative, and neutral. Furthermore, the data is processed through a preprocessing stage which includes data cleaning, case folding, tokenization, stopword removal, and stemming. The evaluation results show that FastText with BiLSTM produces the highest accuracy of 90%, followed by Word2Vec at 89%, and GloVe at 87%. FastText proved to be more effective in reducing classification errors, especially in distinguishing between negative and positive sentiments due to its ability to capture subword information and broader context. These findings suggest that the use of FastText can improve the accuracy of sentiment analysis, especially on health issues that develop on social media, so that it can support data-driven decision making by relevant parties in handling information dissemination. 
Characteristics ransomware stop/djvu remk and erqw variants with static-dinamic analysis Nugrahadi, Dodon Turianto; Abadi, Friska; Herteno, Rudy; Muliadi, Muliadi; Alkaff, Muhammad; Alfando, Muhammad Alvin
Computer Science and Information Technologies Vol 6, No 3: November 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v6i3.p283-293

Abstract

Ransomware has developed into various new variants every year. One type of ransomware is STOP/DJVU, containing more than 240+ variants. This research to determine changes in differences characteristics and impact between ransomware variants STOP/DJVU remk, which is a variant from 2020, and the erqw variant from 2023, through a mixed-method research approach. Observation, simulation using mixing static and dynamic malware analysis methods. Both variants are from the Malware Bazaar site. The total characteristics based on dynamic analysis, the remk variant has 177, and the erqw variant has 190, which increased by 1.8%. The total characteristics based on static analysis, the remk variants have 586, and the erqw variants have 736, which increased by 5.7%. All characteristics from remk to erqw increasing in dynamic analysis, except the number of payloads that decreased about 20%. In static analysis, all characteristics from remk to erqw increase except the number of sections decreased about 1.5%. It can be the affected CPU performance, because the remk variant affects performance by increasing CPU work by 3.74%, while the erqw variant affects performance by reducing CPU work by 1.18%, both compared with normal CPU. which will affect the ransomware's destructive work and require changes in its handling.
Optimasi Recursive Feature Elimination menggunakan Shapley Additive Explanations dalam Prediksi Cacat Software dengan klasifikasi LightGBM Hartati, Hartati; Herteno, Rudy; Faisal, Mohammad Reza; Indriani, Fatma; Abadi, Friska
JURNAL INFOTEL Vol 17 No 1 (2025): February 2025
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v17i1.1159

Abstract

Software defect refers to issues where the software does not function properly. The mistakes in the software development process are the reasons for software defects. Software defect prediction is performed to ensure the software is defect-free. Machine learning classification is used to classify defects in software. To improve the classification model, it is necessary to select the best features from the dataset. Recursive Feature Elimination (RFE) is a feature selection method. Shapley Additive Explanations (SHAP) is a method that can optimize feature selection algorithms to produce better results. In this research, the popular boosting algorithm LightGBM will be selected as a classifier to predict software defects. Meanwhile, RFE-SHAP will be used for feature selection to identify the best subset of features. The results and discussion show that RFE-SHAP feature selection slightly outperforms RFE, with average AUC values of 0.864 and 0.858, respectively. Moreover, RFE-SHAP produces more significant results in feature selection compared to RFE. The RFE feature selection T-Test results are Pvalue = 0.039 < α = 0.05 and tcount = 3.011 > ttable = 2.776. On the contrary, the RFE-SHAP feature selection T-Test results are Pvalue = 0.000 < α = 0.05 and tcount = 11.91 > ttable = 2.776.
Co-Authors Abdullayev, Vugar Achmad Zainudin Nur Adawiyah, Laila Adela Putri Ariyanti Aflaha, Rahmina Ulfah Ahmad Juhdi Ahmad Rusadi Akhtar, Zarif Bin Al Ghifari, Muhammad Akmal Al Habesyah, Noor Zalekha Alfando, Muhammad Alvin Andi - Farmadi Andi Farmadi Andi Farmadi Andi Farmadi Angga Maulana Akbar Antoh, Soterio Arifin Hidayat Aryanti, Agustia Kuspita Athavale, Vijay Anant Azizah, Azkiya Nur Azizah, Siti Roziana Bahriddin Abapihi Dendy Fadhel Adhipratama Dendy Dodon Turianto Nugrahadi Dwi Kartini Dwi Kartini, Dwi Emma Andini Faisal, M. Reza Fatma Indriani Fauzan Luthfi, Achmad Favorisen R. Lumbanraja Fayyadh, Muhammad Naufaldi Febrian, Muhamad Michael Friska Abadi Ghinaya, Helma Hartati Hartati Hermiati, Arya Syifa Huynh, Phuoc-Hai Irwan Budiman Irwan Budiman Itqan Mazdadi, Muhammad Junaidi, Ridha Fahmi Lilies Handayani Lisnawati M Kevin Warendra Mariana Dewi Miftahul Muhaemen Muflih Ihza Rifatama Muhammad Alkaff Muhammad Anshari Muhammad Azmi Adhani Muhammad Denny Ersyadi Rahman Muhammad Itqan Mazdadi Muhammad Noor Muhammad Reza Faisal, Muhammad Reza Muhammad Rizky Mubarok Muhammad Sholih Afif Muhammad Syahriani Noor Basya Basya Muliadi Muliadi MULIADI -, MULIADI Muliadi Muliadi Muliadi Muliadi Muliadi Muliadi Nabella, Putri Nafis Satul Khasanah Ngo, Luu Duc Noor Hidayah Noryasminda Nur Hidayatullah, Wildan Nurdiansyah Nurdiansyah Nursyifa Azizah Oni Soesanto Pratama, Muhammad Yoga Adha Putri Nabella Putri, Nitami Lestari Radityo Adi Nugroho Rahmad Ubaidillah Rahmat Ramadhani Raidra Zeniananto Ramadhan, As`'ary Reza Faisal, Mohammad Rizky Ananda, Muhammad Rozaq, Hasri Akbar Awal Saputro, Setyo Wahyu Saragih, Triando Hamonangan Setyo Wahyu Saputro Siti Aisyah Solechah Suci Permata Sari Suryadi, Mulia Kevin Tri Mulyani Ulya, Azizatul Vina Maulida, Vina Wahyu Ramadansyah Wahyu Saputro, Setyo Zaini Abdan Zamzam, Yra Fatria