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All Journal International Journal of Electrical and Computer Engineering IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Speed - Sentra Penelitian Engineering dan Edukasi Jurnal Teknologi Informasi dan Ilmu Komputer Telematika JUITA : Jurnal Informatika KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) JOIN (Jurnal Online Informatika) Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Komputasi Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Sebatik Journal of Computer Science and Informatics Engineering (J-Cosine) Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Mnemonic International Journal of Advances in Data and Information Systems Computer Science and Information Technologies Jurnal Teknik Informatika (JUTIF) Journal of Data Science and Software Engineering Journal of Embedded Systems, Security and Intelligent Systems Journal of Computing Theories and Applications Jurnal Informatika Polinema (JIP) Scientific Journal of Informatics Jurnal Pengabdian Masyarakat Tekno Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Komputasi
Friska Abadi
Universitas Lambung Mangkurat
Aerospace Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Health Professions Library & Information Science Materials Science & Nanotechnology

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Metrics Based Feature Selection for Software Defect Prediction Radityo Adi Nugroho; Friska Abadi; M. Reza Faisal; Rudy Herteno; Rahmat Ramadhani
Jurnal Komputasi Vol. 8 No. 2 (2020)
Publisher : Jurusan Ilmu Komputer Fakultas MIPA Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/komputasi.v8i2.2670

Abstract

Nowadays, software is very influential on various sectors of life, both to solve business needs, as well as personal needs. To have a Software with high quality, testing is needed to avoid software defect. Research on software defects involving Machine Learning is currently being carried out by many researchers. This method contains one important step, which is called feature selection. In this study, researchers conducted a feature selection based on the software metric category to determine the level of accuracy of the prediction of software defects by utilizing 13 (thirteen) datasets from NASA MDP namely CM1, JM1, KC1, KC3, KC4, MC1, MC2, MW1, PC1, PC2, PC3, PC4, and PC5. To classify, the researchers involved 5 (five) classifiers, namely Naive Bayes, Decision Trees, Random Forests, K-Nearest Neighbor, and Support Vector Machines. The research result shows that each attribure on software metric categories has effect on each dataset. Naive Bayes Algorithm and Random Forest Algorithm can give better performance than other algorithm in classifieng software defect with feature selection based on metrics. On the other hand, the best metrics category on each classifier algorithm is metric Misc. From average AUC value, it can be concluded that metrics category which can give best performance is metric LoC, followed by metric Misc. Both categories have achieved highest AUC value in Random Forest classifier.
Analisis Komparasi Implementasi Steganografi White-Space dan White-Space Modified pada Artikel Terenkripsi AES dalam HTML5 Rudy Herteno; Dodon Turianto Nugrahadi; Muhammad Sholih Afif; M Reza Faisal; Friska Abadi
Jurnal Komputasi Vol. 8 No. 1 (2020)
Publisher : Jurusan Ilmu Komputer Fakultas MIPA Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/komputasi.v8i1.2525

Abstract

The level of internet usage continues to increase until now.  information exchange requires security that cannot be predicted by others.  one technique for securing information is steganography.  Steganography techniques are the science and art of hiding information.  This technique can hide the content of information in media that cannot be guessed by ordinary people, so as not to arouse suspicion of the people who see it.  One of the media that can implement the white-space modified steganography method is HTML pages.  in addition, AES (Advanced Encryption Standard) is a lighter encryption security algorithm compared to other algorithms. In this study, plain text that has been encrypted into cipher text is then inserted with white-space and white-space modification steganography techniques. Data changes have occurred but only less than 1 percent.  In experiments that have been implemented on Google Chrome and Mozilla Firefox are the same except in Internet Explorer, which changes the data slightly larger.The implementation of AES encryption and stegano white-space original, has 100% success but the 80% decryption process is successful, but the decryption results contain additional binaries. This happen because the use of tabulation (tabs) instead of spaces in HTML5 articles, and this is often found in HTML articles. while the implementation of AES encryption and stegano whitespace modified, has a success of 100% and the decryption process of 90% succeeded without any changes. 1 article failed because the number of articles is too small compared to the amount of space provided. The conclusion that implementation of AES encryption and white-space modified is more appropriate to be implemented in HTML5 articles, and than the use of tabulation and the number of characters also consequences on the implementation.Keywords: Information, Steganography, White-space modified, Security, AES, Web Browser 
Implementation of PPCA Imputation, SMOTE-N Class Balancing in Hepatitis Classification Using Naïve Bayes Siti Fathmah; Dwi Kartini; Friska Abadi; Irwan Budiman; Muhammad Itqan Mazdadi
JUITA: Jurnal Informatika JUITA Vol. 12 No. 2, November 2024
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v12i2.21528

Abstract

The availability of complete data in research is crucial, especially in the initial stages. The Hepatitis data used in this study encountered issues such as missing data and class imbalance, which hindered its optimal utilization. The method employed to address missing data was the PPCA imputation method. After filling in the missing data, the data was balanced using the SMOTE-N class balancing method and classified using Gaussian Naïve Bayes. The aim of this research was to compare the classification evaluation of hepatitis disease using Naive Bayes with the PPCA imputation approach and SMOTE-N class balancing. The best results from each scenario yielded an AUC value of 0.833 in the first scenario with an 80:20 data split for training and testing, and 0.875 in the second scenario with a 90:10 data split. The highest AUC value was obtained in the application of PPCA imputation with SMOTE-N class balancing using Naive Bayes classification. This demonstrates that the implementation of PPCA imputation with SMOTE-N class balancing has a better impact on the performance of Naïve Bayes classification.
Quantifying the Impact of Text Preprocessing on IndoBERT Fine-Tuning for Indonesian Informal Culinary Sentiment Analysis Rahmat Budianoor; Setyo Wahyu Saputro; Friska Abadi; Radityo Adi Nugroho; Andi Farmadi
Journal of Computing Theories and Applications Vol. 3 No. 4 (2026): JCTA 3(4) 2026
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.15980

Abstract

Indonesian culinary comments on social media platforms such as Instagram are characterized by informal spelling, regional language mixing, slang expressions, and emojis, posing substantial challenges for automated sentiment classification. While IndoBERT has demonstrated strong performance across Indonesian natural language processing tasks, the contribution of individual preprocessing components to fine-tuning performance on informal text remains underexplored, particularly in the culinary domain. This study addresses this gap by conducting a systematic preprocessing ablation study on IndoBERT-Base fine-tuning for Indonesian culinary sentiment classification, accompanied by a comparative evaluation against Naive Bayes with TF-IDF, SVM with TF-IDF, and BiLSTM as representative baselines. A dataset of 3,500 manually labeled Instagram culinary comments across three sentiment classes was used, with a stratified 80/10/10 split. Six preprocessing variants were evaluated under identical experimental conditions to isolate the contribution of each component. The results show that slang normalization is the most impactful single preprocessing step, yielding a macro F1-score gain of +0.0609 over the no-preprocessing baseline, while the full pipeline achieves an accuracy of 0.8800 and a macro F1-score of 0.8465. IndoBERT-Base with the full pipeline outperforms all baselines across all evaluation metrics. Per-class analysis reveals that the negative class achieves the lowest F1-score of 0.7600, with sarcastic expressions and Banjar regional vocabulary identified as primary sources of misclassification. These findings indicate that preprocessing decisions have a measurable and non-uniform effect on IndoBERT fine-tuning performance. In this study, slang normalization provides the most substantial individual contribution in bridging the vocabulary gap between informal user-generated text and the model’s pre-training distribution.
Metaheuristic-Based Hyperparameter Optimization Analysis of Deep Neural Network for Cross-Project Defect Prediction in Mobile Applications Abdul Rahman, Maulana; Herteno, Rudy; Adi Nugroho, Radityo; Abadi, Friska; Wahyu Saputro, Setyo
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 8 No. 2 (2026): May
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v8i2.340

Abstract

Software Defect Prediction (SDP) plays a strategic role in identifying software defects during the early stages of development, thereby enabling more efficient allocation of testing resources, particularly in the rapidly evolving mobile application domain characterized by fast release cycles. The commonly used Within-Project Defect Prediction (WPDP) approach is often constrained by the limited availability of historical data, especially in projects at early stages of development. As an alternative, Cross-Project Defect Prediction (CPDP) leverages historical data from other projects as training sources. Moreover, the performance of the Deep Neural Network (DNN) used in SDP is highly dependent on accurate hyperparameter configurations, where manual tuning requires substantial time and computational resources without guaranteeing optimal results. To address this issue, this study analyzes and compares the effectiveness of three metaheuristic algorithms, namely Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Grey Wolf Optimizer (GWO), in optimizing DNN hyperparameters within a CPDP framework. This study utilizes 14 open-source Android mobile application projects and employs the Leave-One-Out Cross-Validation technique. The performance of each combination is evaluated using ROC-AUC as the primary metric. The Wilcoxon Signed-Rank Test with a Bonferroni correction is used to assess the statistical significance of the observed performance differences. The experimental results demonstrate that GWO-DNN achieves the best performance, with an average ROC-AUC of 0.721, and is the only combination that remains statistically significant after Bonferroni correction, with a small effect size based on Cliff’s delta. Overall, the findings of this study indicate that metaheuristic-based hyperparameter tuning is a sufficiently effective approach for improving the capability of DNN in cross-project software defect prediction within the mobile application domain, although the observed improvements remain moderate.
Evaluating CNN Robustness for Face Mask Classification under Environmental Variations Bagaskara Ridho Vandio; Fatma Indriani; Andi Farmadi; Dodon Turianto Nugrahadi; Friska Abadi
Journal of Embedded Systems, Security and Intelligent Systems Vol 7 No 2 (2026): June 2026
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v7i2.2617

Abstract

Purpose – This study aims to analyze and compare the performance of ResNet50 and MobileNetV3 for multi-class face mask classification under various environmental conditions. Design/methods/approach – ResNet50 and MobileNetV3 are trained using transfer learning for three-class face mask classification and evaluated under normal conditions and environmental variations, including illumination changes, blur, low compression, and rotation. Findings – Experimental results show that ResNet50 achieves an accuracy of 94.32% under normal conditions, slightly outperforming MobileNetV3 at 94.10%. Under environmental variations, the largest performance degradation is observed under darkening and blur conditions, while low compression and rotation have relatively minor effects. ResNet50 demonstrates higher robustness across most perturbation settings, whereas MobileNetV3 provides competitive performance with substantially better computational efficiency. Research implications/limitations – This study is limited to a controlled evaluation using synthetic environmental perturbations on a single dataset and does not consider broader dataset diversity. Therefore, the findings should be interpreted within the evaluated experimental conditions. Originality/value – This study provides a comparative analysis of model robustness under controlled environmental perturbations, highlighting the trade-off between robustness and computational efficiency for face mask classification systems.
Characteristics ransomware stop/djvu remk and erqw variants with static-dinamic analysis Dodon Turianto Nugrahadi; Friska Abadi; Rudy Herteno; Muliadi Muliadi; Muhammad Alkaff; Muhammad Alvin Alfando
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.
Co-Authors A.A. Ketut Agung Cahyawan W AA Sudharmawan, AA Abdullayev, Vugar Achmad Zainudin Nur Adi Mu'Ammar, Rifqi Aflaha, Rahmina Ulfah Ahmad Juhdi Amalia, Raisa Andi Farmadi Andi Farmadi Andi Farmandi Arif, Nuuruddin Hamid Athavale, Vijay Anant Bagaskara Ridho Vandio budiman, irwan Deni Kurnia Dodon Turianto Nugrahadi Dwi Kartini Dwi Kartini, Dwi Emma Andini Fatma Indriani Fauzan Luthfi, Achmad Febrian, Muhamad Michael Halimah Herteno, Rudy Herteno, Rudy Indriani, Fatma Irwan Budiman Irwan Budiman Irwan Budiman Itqan Mazdadi, Muhammad Kartika, Najla Putri M Kevin Warendra Mafazy, Muhammad Meftah Martalisa, Asri Maulana Abdul Rahman Mera Kartika Delimayanti Muhamad Fawwaz Akbar Muhammad Adika Riswanda Muhammad Alkaff Muhammad Alkaff Muhammad Alvin Alfando Muhammad Azmi Adhani Muhammad Denny Ersyadi Rahman Muhammad Fikri Muhammad Haekal Muhammad Itqan Mazdadi Muhammad Khairin Nahwan Muhammad Mirza Hafiz Yudianto Muhammad Nazar Gunawan Muhammad Noor Muhammad Reza Faisal, Muhammad Reza Muhammad Sholih Afif Muliadi Muliadi Muliadi Aziz Muliadi Muliadi Muliadi Muliadi Nabella, Putri Nor Indrani Nugrahadi, Dodon Nurlatifah Amini Nursyifa Azizah Prastya, Septyan Eka Pratama, Muhammad Yoga Adha Putri Nabella Radityo Adi Nugroho Rahman Hadi Rahman Rahmat Budianoor Rahmat Ramadhani Rahmawati, Nanda Putri Rahmayanti Rahmayanti Ramadhan, Muhammad Rizky Aulia Reina Alya Rahma Rezeki, Abdillah Rinaldi Riza Susanto Banner Rizal, Muhammad Nur Rizky Ananda, Muhammad Rizky, Muhammad Hevny Rudy Herteno Rudy Herteno Rudy Herteno SALLY LUTFIANI Saragih, Triando Hamonangan Sarah Monika Nooralifa Sa’diah, Halimatus Septyan Eka Prastya Setyo Wahyu Saputro Siti Fathmah Siti Napi'ah Tri Mulyani Ulya, Azizatul Umar Ali Ahmad Vina Maulida, Vina Wahyu Dwi Styadi Yunida, Rahmi