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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) ComEngApp : Computer Engineering and Applications Journal Jurnal Pendidikan Karakter TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Bulletin of Electrical Engineering and Informatics Annual Research Seminar Bulletin of Electrical Engineering and Informatics Proceeding of the Electrical Engineering Computer Science and Informatics Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Journal of Information Systems and Informatics Indonesian Journal of Electrical Engineering and Computer Science Computer Science and Information Technologies Sriwijaya Journal of Informatics and Applications Jurnal Generic Journal of Applied Data Sciences Journal of Innovation and Research in Primary Education Jurnal Indonesia Sosial Teknologi Jurnal Pendidikan Indonesia (Japendi)
Deris Stiawan
Universitas Sriwijaya
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Social Sciences Other

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Neural network models selection scheme for health mobile app development Yaya Sudarya Triana; Mohd Azam Osman; Adji Pratomo; Muhammad Fermi Pasha; Deris Stiawan; Rahmat Budiarto
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 3: September 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i3.pp1191-1203

Abstract

Mobile healthcare application (mHealth app) assists the frontline health worker in providing necessary health services to the patient. Unfortunately, existing mHealth apps continue to have accuracy issues and limited number of disease detection systems. Thus, an intelligent disease diagnostics system may help medical staff as well as people in poor communities in rural areas. This study proposes a scheme for simultaneously selecting the best neural network models for intelligent disease detection systems on mobile devices. To find the best models for a given dataset, the proposed scheme employs neural network models capable of evolving altered neural network architectures. Eight neural network models are developed simultaneously and then implemented on the Android Studio platform. Mobile health applications use pre-trained neural network models to provide users with disease prediction results. The performance of the mobile application is measured against the existing available datasets. The trained neural network engines perform admirably, detecting 7 out of 8 diseases with high accuracy ranging from 86% to 100% and a low detection accuracy of 63%. The detection times vary from 0.01 to 0.057 seconds. The developed mHealth app may be used by health workers and patients to improve resource-poor community health services and patients' healthcare quality.
The incorporation of stacked long short-term memory into intrusion detection systems for botnet attack classification Heryanto, Ahmad; Stiawan, Deris; Hermansyah, Adi; Firnando, Rici; Pertiwi, Hanna; Bin Idris, Mohd Yazid; Budiarto, Rahmat
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i3.pp3657-3670

Abstract

Botnets are a common cyber-attack method on the internet, causing infrastructure damage, data theft, and malware distribution. The continuous evolution and adaptation to enhanced defense tactics make botnets a strong and difficult threat to combat. In light of this, the study's main objective was to find out how well techniques like principal component analysis (PCA), synthetic minority oversampling technique (SMOTE), and long short-term memory (LSTM) can help find botnet attacks. PCA shows the ability to reduce the feature dimensions in network data, allowing for a more efficient and effective representation of the patterns contained. The SMOTE addresses class imbalances in the dataset, enhancing the model's ability to recognize suspicious activity. Furthermore, LSTM classifies sequential data, understanding complex network patterns and behaviors often used by botnets. The combination of these three methods provided a substantial improvement in detecting suspicious botnet activities. We also evaluated the effectiveness using performance metrics such as accuracy, precision, recall, and F1-score. The results showed an accuracy of 96.77%, precision of 88.95%, recall of 88.58%, and F1-score of 88.64%, indicating that the proposed model was reliable in detecting botnet traffic compared to other deep learning models. Furthermore, LSTM can classify sequential data, understanding complex network patterns and behaviors often used by botnets.
Proposed threshold-based and rule-based approaches to detecting duplicates in bibliographic database Amin, M. Miftakul; Stiawan, Deris; Ermatita, Ermatita; Budiarto, Rahmat
Bulletin of Electrical Engineering and Informatics Vol 13, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i3.7665

Abstract

Bibliographic databases are used to measure the performance of researchers, universities and research institutions. Thus, high data quality is required and data duplication is avoided. One of the weaknesses of the threshold-based approach in duplication detection is the low accuracy level. Therefore, another approach is required to improve duplication detection. This study proposes a method that combines threshold-based and rule-based approaches to perform duplication detection. These two approaches are implemented in the comparison stage. The cosine similarity function is used to create weight vectors from the features. Then, the comparison operator is used to determine whether the pair of records are grouped as duplication or not. Three research databases: Web of Science (WoS), Scopus, and Google Scholar (GS) on the Science and Technology Index (SINTA) database are investigated. Rule 4 and Rule 5 provide the best performance. For WoS dataset, the accuracy, precision, recall, and F1-measure values were 100.00%. For Scopus dataset, the accuracy and precision values were 100.00%, recall: 98.00%, and the F1-measure value is 98.00%. For GS dataset, the accuracy value was 100.00%, precision: 99.00%, recall: 97.00%, and the F1-measure value is 98.00%. The proposed method is potential tool for accurate detection on duplication records in publication databases.
User behavior analysis for insider attack detection using a combination of memory prediction model and recursive feature elimination algorithm Triana, Yaya Sudarya; Osman, Mohd Azam; Stiawan, Deris; Budiarto, Rahmat
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i2.pp1793-1804

Abstract

Existing defense tools against the insider attacks are rare, not in real time fashion and suffer from low detection accuracy as the attacks become more sophisticated. Thus, a detection tool with online learning ability and better accuracy is required urgently. This study proposes an insider attack detection model by leveraging entity behavior analysis technique based on a memory prediction model combined with the recursive feature elimination (RFE) feature selection algorithm. The memory-prediction model provides ability to perform online learning, while the RFE algorithm is deployed to reduce data dimensionality. Dataset for the experiment was created from a real network with 150 active users, and mixed with attacks data from publicly available dataset. The dataset is simulated on a testbed network environment consisting of a server configured to run 4 virtual servers and other two computers as traffic generator and detection tool. The experimental results show 94.01% of detection accuracy, 95.64% of precision, 99.28% of sensitivity, and 96.08% of F1-score. The proposed model is able to perform on-the-fly learning to address evolving nature of the attacks. Combining memory prediction models with the RFE for user behavior analysis is a promising approach, and achieving high accuracy is definitely a positive outcome.
Malware Detection in Portable Document Format (PDF) Files with Byte Frequency Distribution (BFD) and Support Vector Machine (SVM) Saputra, Heru; Stiawan, Deris; Satria, Hadipurnawan
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 4 (2023): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i4.27559

Abstract

Portable Document Format (PDF) files as well as files in several other formats such as (.docx, .hwp and .jpg) are often used to conduct cyber attacks. According to VirusTotal, PDF ranks fourth among document files that are frequently used to spread malware in 2020. Malware detection is challenging partly because of its ability to stay hidden and adapt its own code and thus requiring new smarter methods to detect. Therefore, outdated detection and classification methods become less effective. Nowadays, one of such methods that can be used to detect PDF files infected with malware is a machine learning approach. In this research, the Support Vector Machine (SVM) algorithm was used to detect PDF malware because of its ability to process non-linear data, and in some studies, SVM produces the best accuracy. In the process, the file was converted into byte format and then presented in Byte Frequency Distribution (BFD). To reduce the dimensions of the features, the Sequential Forward Selection (SFS) method was used. After the features are selected, the next stage is SVM to train the model. The performance obtained using the proposed method was quite good, as evidenced by the accuracy obtained in this study, which was 99.11% with an F1 score of 99.65%. The contributions of this research are new approaches to detect PDF malware which is using BFD and SVM algorithm, and using SFS to perform feature selection with the purpose of improving model performance. To this end, this proposed system can be an alternative to detect PDF malware.
Detection of android malware with deep learning method using convolutional neural network model Maulana, Reza; Stiawan, Deris; Budiarto, Rahmat
Computer Science and Information Technologies Vol 6, No 1: March 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v6i1.p68-79

Abstract

Android malware is an application that targets Android devices to steal crucial data, including money or confidential information from Android users. Recent years have seen a surge in research on Android malware, as its types continue to evolve, and cybersecurity requires periodic improvements. This research focuses on detecting Android malware attack patterns using deep learning and convolutional neural network (CNN) models, which classify and detect malware attack patterns on Android devices into two categories: malware and non-malware. This research contributes to understanding how effective the CNN models are by comparing the ratio of data used with several epochs. We effectively use CNN models to detect malware attack patterns. The results show that the deep learning method with the CNN model can manage unstructured data. The research results indicate that the CNN model demonstrates a minimal error rate during evaluation. The comparison of accuracy, precision, recall, F1 Score, and area under the curve (AUC) values demonstrates the recognition of malware attack patterns, reaching an average of 92% accuracy in data testing. This provides a holistic understanding of the model's performance and its practical utility in detecting Android malware.
Machine learning model approach in cyber attack threat detection in security operation center Saputra, Muhammad Ajran; Stiawan, Deris; Budiarto, Rahmat
Computer Science and Information Technologies Vol 6, No 1: March 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v6i1.p80-90

Abstract

The evolution of technology roles attracted cyber security threats not only compromise stable technology but also cause significant financial loss for organizations and individuals. As a result, organizations must create and implement a comprehensive cybersecurity strategy to minimize further loss. The founding of a cybersecurity surveillance center is one of the optimal adopted strategies, known as security operation center (SOC). The strategy has become the forefront of digital systems protection. We propose strategy optimization to prevent or mitigate cyberattacks by analyzing and detecting log anomalies using machine learning models. This study employs two machine learning models: the naïve Bayes model with multinomial, Gaussian, and Bernoulli variants, and the support vector machine (SVM) model with radial basis function (RBF), linear, polynomial, and sigmoid kernel variants. The hyperparameters in both models are then optimized. The models with optimized hyperparameters are subsequently trained and tested. The experimental results indicate that the best performance is achieved by the RBF kernel SVM model, with an accuracy of 79.75%, precision of 80.8%, recall of 79.75%, and F1-score of 80.01%; and the Gaussian naïve Bayes model, with an accuracy of 70.0%, precision of 80.27%, recall of 70.0%, and F1-score of 70.66%. Overall, both models perform relatively well and are classified in the very good category (75%‒89%).
Sentiment and Emotion Classification Model Using Hybrid Textual and Numerical Features: A Case Study of Mental Health Counseling Ramayanti, Indri; Hermawan, Latius; Syakurah, Rizma Adlia; Stiawan, Deris; Meilinda, Meilinda; Negara, Edi Surya; Fahmi, Muhammad; Ghiffari, Ahmad; Rizqie, Muhammad Qurhanul
Journal of Applied Data Sciences Vol 6, No 3: September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i3.632

Abstract

Mental health issues among individuals, particularly in counseling contexts, require practical tools to understand and address emotional states. This study explores the application of machine learning models for emotion detection in mental health counseling conversations, focusing on four algorithms: Bernoulli Naive Bayes, Decision Tree, Logistic Regression, and Random Forest. The dataset, derived from transcribed counseling sessions, underwent preprocessing, including stemming, stopword removal, and TF-IDF vectorization to create structured inputs for classification. Emotional categories such as "Depresi" (Depression), "Kecewa" (Dissapointed), "Senang" (Happy), "Bingung" (Confused) and "Stres" (Stress) were analyzed to evaluate model performance. Results indicated that Logistic Regression achieved the highest accuracy at 82%, showcasing its reliability and scalability, followed closely by Random Forest with 81%, demonstrating robustness in handling complex data structures. Bernoulli Naive Bayes performed competitively at 80%, excelling in computational efficiency, while Decision Tree recorded the lowest accuracy at 70%, reflecting its limitations in managing overlapping features and high-dimensional data. These findings highlight the potential of machine learning in addressing the increasing demand for scalable mental health support tools. The study underscores the importance of model selection, balanced datasets, and feature engineering to improve classification accuracy. Future work includes developing AI-driven chatbots for real-time emotion detection and integrating multimodal data to enhance interpretability. This research contributes to advancing automated solutions for mental health care, offering new pathways for timely and personalized interventions.
Classification and similarity detection of Indonesian scientific journal articles Cahyani, Nyimas Sabilina; Stiawan, Deris; Abdiansah, Abdiansah; Afifah, Nurul; Permana, Dendi Renaldo
Computer Science and Information Technologies Vol 6, No 2: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v6i2.p147-158

Abstract

The development of technology is accelerating in finding references to scientific articles or journals related to research topics. One of the sources of national aggregator services to find references is Garba Rujukan Digital (GARUDA), developed by the Ministry of Education, Culture, Research, and Technology (Kemendikbudristek) of the Republic of Indonesia. The naïve Bayes method classifies articles into several categories based on titles and abstracts. The system achieves an F1-score of 98%, which indicates high classification accuracy, and the classification process takes less than 60 minutes. Article similarity detection is done using the cosine similarity method, and a similarity score of 0.071 reflects the degree of similarity between the title and the abstract that has been concatenated, while a score close to 1 indicates a higher similarity. Searching for similar scientific articles based on title and abstract, sort articles based on the results of the highest similarity score are the most similar articles, and generating article categories. The results of the research show that the proposed method significantly improves the classification and search processes in GARUDA, as well as accurate and efficient similarity detection.
Early Mental Health Detection with Machine Learning : A Practical Approach to Model Development and Implementation Hermawan, Latius; Syakurah, Rizma Adlia; Meilinda, Meilinda; Stiawan, Deris; Negara, Edi Surya; Ramayanti, Indri; Fahmi, Muhammad; Rizqie, Muhammad Qurhanul; Hermanto, Dedy
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 13, No 2: June 2025
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v13i2.6111

Abstract

Academic pressures, lifestyle changes, and socio-economic factors significantly impact mental health, a critical determinant of academic success and well-being. Early detection and intervention are crucial to mitigate severe outcomes like academic underperformance and suicidal tendencies. Leveraging tools like the DASS-42, this study examines mental health patterns using Support Vector Machine (SVM) models, achieving accuracies of 88% for depression, 71% for stress, and 57% for anxiety. While the model excels in identifying "Normal" cases, its performance for "Mild," "Moderate," and "Severe" cases highlights limitations due to class imbalance and feature representation. The findings reveal that anxiety is the most volatile and severe condition, with peaks in 2018 and 2022, while stress remains manageable and depression moderately stable. Gender and program-specific differences emphasize the need for tailored interventions. Addressing challenges related to data quality, algorithmic transparency, and ethical concerns is essential for real-world applications. This study highlights the potential of machine learning in early detection and intervention for mental health issues. Future research should explore advanced feature engineering techniques and develop more interpretable models to enhance clinical decision-making.
Co-Authors Abd Rahim, Mohd Rozaini Abdiansah, Abdiansah Abdul Hadi Fikri Abdul Hanan Abdullah Abdul Harris Adi Hermansyah, Adi Adi Sutrisman Aditya Putra Perdana Prasetyo Aditya Putra Perdana Prasetyo Adji Pratomo Agung Juli Anda Agus Eko Minarno Ahmad Fali Oklilas Ahmad Firdaus Ahmad Ghiffari Ahmad Heryanto Ahmad Heryanto Ahmad Heryanto Ahmad Heryanto, Ahmad Ahmad Zarkasi Ahmad Zarkasi Albertus Edward Mintaria Ali Bardadi Ali Firdaus Alshaflut, Ahmed Anto Saputra, Iwan Pahendra Bedine Kerim Bedine Kerim Bhakti Yudho Suprapto Bhakti Yudho Suprapto Bhakti Yudho Suprapto Bin Idris, Mohd Yazid Cahyani, Nyimas Sabilina Darmawijoyo, Darmawijoyo Dasuki, Massolehin Dedy Hermanto Desak Putu Dewi Kasih Dewi Bunga Dian Palupi Rini Dwi Budi Santoso Edi Surya Negara Eko Arip Winanto Endang Lestari Ruskan Ermatita - Erwin, Erwin Fachrudin Abdau Fakhrurroja, Hanif Ferdiansyah Ferdiansyah Fikri, Abdul Hadi Firdaus Firdaus Firdaus, Firdaus Firnando, Rici Firsandaya Malik, Reza Gonewaje gonewaje Habibullah, Nik Mohd Hadipurnawan Satria Harris, Abdul Huda Ubaya Huda Ubaya Huda Ubaya I Gede Yusa Idris, Mohd. Yazid Idris, Mohd. Yazid Imam Much Ibnu Subroto Indradewa, Rhian Iswari, Rosada Dwi John Arthur Jupin Juli Rejito Kemahyanto Exaudi Kurniabudi, Kurniabudi Latius Hermawan Lelyzar Siregar Lina Handayani M. Miftakul Amin M. Ridwan Zalbina Majzoob K. Omer Makmum Raharjo Mardhiyah, Sayang Ajeng Marisya Pratiwi Marita, Raini Massolehin Dasuki Mehdi Dadkhah Meilinda Meilinda Meilinda, Meilinda Mintaria, Albertus Edward Mohamed S. Adrees Mohamed Shenify Mohammad Davarpanah Jazi Mohammed Y. Alzahrani Mohd Arfian Ismail Mohd Azam Osman Mohd Faizal Ab Razak Mohd Rozaini Abd Rahim Mohd Saberi Mohamad Mohd Yazid Bin Idris Mohd Yazid bin Idris Mohd Yazid Idris Mohd Yazid Idris Mohd. Yazid Idris Mohd. Yazid Idris Mohd. Yazid Idris Muhammad Afif Muhammad Fahmi MUHAMMAD FAHMI Muhammad Fermi Pasha Muhammad Qurhanul Rizqie Muhammad Sulkhan Nurfatih Munawar A Riyadi Munawar Agus Riyadi Naufal Semendawai, Jaka Negara, Edi Surya Ni Ketut Supasti Dharmawan Nik Mohd Habibullah Nur Sholihah Zaini Nuzulastri, Sari Osama E. Sheta Osman, Mohd Azam Osvari Arsalan Pahendra, Iwan Permana, Dendi Renaldo Pertiwi, Hanna Prabowo, Christian Purnama, Benni Putra Perdana Prasetyo, Aditya Rahmat Budiarto Rahmat Budiarto Rahmat Budiarto Rahmat Budiarto Rahmat Budiarto Rahmat Budiarto Raja Zahilah Md Radzi Ramayanti, Indri Ramayanti, Indri Reza Firsandaya Malik Reza Maulana Riyadi, Munawar A Rizki Kurniati Rizma Adlia Syakurah Rizqie, Muhammad Qurhanul Rossi Passarella Samsuryadi Samsuryadi Saparudin Saparudin Saparudin, Saparudin Saputra, Muhammad Ajran Sari Sandra Sarmayanta Sembiring Sarmayanta Sembiring Sasut A Valianta Sasut Analar Valianta Semendawai, Jaka Naufal Shahreen Kasim Sharipuddin, Sharipuddin Sidabutar, Alex Onesimus Siti Hajar Othman Siti Nurmaini Sri Arttini Dwi Prasetyawati Sri Desy Siswanti Susanto Susanto Susanto Susanto Susanto, Susanto Sutarno Sutarno Syakurah, Rizma Adlia Syamsul Arifin, M. Agus Tasmi Salim tasmi salim Tole Sutikno Wan Isni Sofiah Wan Din Yaya Sudarya Triana Yazid Idris, Mohd. Yazid Idris, Mohd. Yesi Novaria Kunang Yoga Yuniadi Yudho Suprapto, Bhakti Yundari, Yundari Zulhipni Reno Saputra Els