Scopus Citation Analysis

Last update: June 12, 2023

List of Quartile 1 (Q1) and Quartile 2 (Q2) journals that have cited articles published in International Journal of Advances in Data and Information Systems (IJADIS).

Quartile 1
Expert Systems with Applications
IEEE Transactions on Network Science and Engineering
International Journal of Production Research
ISA Transactions
Multimedia Tools and Applications
IEEE Access
Journal of Mechanical Design
Neural Computing and Applications
Waste Management
Cancers
Remote Sensing
Health promotion international
Sensors
Journal of Business Research


Quartile 2
Symmetry
Current Psychology
Future Internet
Electronics
Mathematical Problems in Engineering
Wireless Communications and Mobile Computing
AgBioForum
Entropy
Journal of Financial Services Marketing

The International Journal of Advances in Data and Information Systems (IJADIS)'s articles are cited by 84 articles in the SCOPUS database since published in 2020. Citation Detail:

Adriyendi, A., & Melia, Y. (2021). Multi-Attribute Decision Making using Hybrid Approach based on Benefit-Cost Model for Sustainable Fashion. International Journal of Advances in Data and Information Systems, 2(1), 9-24.

Cited By

  1. Saghari, A., Budinská, I., Hosseinimehr, M., & Rahmani, S. (2023). A Robust-Reliable Decision-Making Methodology Based on a Combination of Stakeholders’ Preferences Simulation and KDD Techniques for Selecting Automotive Platform Benchmark. Symmetry, 15(3), 750.
  2. Linger, M. A., & Bogale, T. M. (2023). Parameters optimization of tungsten inert gas welding process on 304L stainless steel using grey based Taguchi method. Engineering Research Express, 5(1), 015013.
  3. Linger, M. A., & Bogale, T. M. (2023). Parameters optimization of tungsten inert gas welding process on 304L stainless steel using grey based Taguchi method. Engineering Research Express, 5(1), 015013.
  4. Pena, J. C., Nápoles, G., & Salgueiro, Y. (2022). Normalization method for quantitative and qualitative attributes in multiple attribute decision-making problems. Expert Systems with Applications, 198, 116821.



Rerung, R. R., Fauzan, M., & Hermawan, H. (2020). Website quality measurement of higher education services institution region IV using Webqual 4.0 method. International Journal of Advances in Data and Information Systems, 1(2), 89-102.

Cited By

  1. Nugraha, A., Kuswantoro, I. G., & Utama, D. M. (2022, July). Mobile news applications quality analysis using web quality and importance performance analysis. In AIP Conference Proceedings (Vol. 2453, No. 1, p. 020046). AIP Publishing LLC.
  2. Sanjaya, L. A., Bunyamin, M. A. H., Meganingtyas, D. E. W., & Haeruman, L. D. (2022, November). Trends of Web-Based Instructional (WBI) in Physics Learning: A Literature Review. In Journal of Physics: Conference Series (Vol. 2377, No. 1, p. 012082). IOP Publishing.
  3. Soetikno, Y. J. W., Layuk, N. S., Patasik, M., Wahyuni, S., & Mambu, J. Y. (2021, October). E-Learning Vs Google Classroom using WebQual 4.0 Modification: Quality of Learning Services During COVID-19 Pandemic. In 2021 3rd International Conference on Cybernetics and Intelligent System (ICORIS) (pp. 1-7). IEEE.
  4. Saleh, A. H., Yusoff, R. C. M., Bakar, N. a. A., & Ibrahim, R. (2022). Systematic literature review on university website quality. Indonesian Journal of Electrical Engineering and Computer Science, 25(1), 511. https://doi.org/10.11591/ijeecs.v25.i1.pp511-520
  5. Soetikno, Y. J. W. (2020, October). Using WebQual 4.0 for measuring quality of E-learning services during COVID-19 pandemic. In 2020 8th International Conference on Cyber and IT Service Management (CITSM) (pp. 1-7). IEEE.
  6. Rhazal, A., Ajana, L., & Khouna, J. (2022). Are Moroccan Free School Support Websites Effective for Learners During the Covid-19 Pandemic?: A Study Based on an Evaluation Grid. International Journal of Information and Communication Technology Education (IJICTE), 18(1), 1-21.
  7. Afif, M. (2023). Exploring the quality of the higher educational institution website using data mining techniques. Decision Science Letters, 12(2), 279-290.



Rumetna, M., Renny, E. E., & Lina, T. N. (2020). Designing an Information System for Inventory Forecasting:(Case Study: Samsung Partner Plaza, Sorong City). International Journal of Advances in Data and Information Systems, 1(2), 80-88.

Cited By

  1. Jauhar, S. K., Jani, S. M., Kamble, S. S., Pratap, S., Belhadi, A., & Gupta, S. (2023). How to use no-code artificial intelligence to predict and minimize the inventory distortions for resilient supply chains. International Journal of Production Research, 1-25.

 

Rumetna, M. S., Lina, T. N., Rajagukguk, I. S., Pormes, F. S., & Santoso, A. B. (2022). Payroll Information System Design Using Waterfall Method. International Journal of Advances in Data and Information Systems, 3(1), 1-10.

Cited By

  1. Trianda, M., Novita, R., Mustakim, N., & Afdal, M. (2022). Centralized Information System for Data Services of the Pekanbaru High Court Decisions. 2022 2nd International Conference on Intelligent Cybernetics Technology &Amp; Applications (ICICyTA). https://doi.org/10.1109/icicyta57421.2022.10038216
  2. Ahmed, A. M., Mohammed, C. N., & Ahmad, A. M. (2023). Web-based payroll management system: design, implementation, and evaluation. Journal of Electrical Systems and Information Technology, 10(1), 17.

 

Wayahdi, M. R., Ginting, S. H. N., & Syahputra, D. (2021). Greedy, A-Star, and Dijkstra’s algorithms in finding shortest path. International Journal of Advances in Data and Information Systems, 2(1), 45-52.

Cited By

  1. Liao, H., Patil, V., Dong, X., Shanbhag, D., Fallon, E., Hogan, T., ... & Kara, L. B. (2023). HIERARCHICAL AUTOMATIC MULTILAYER POWER PLANE GENERATION WITH GENETIC OPTIMIZATION AND MULTILAYER PERCEPTRON. Journal of Mechanical Design, 1-16.
  2. Jason, Siever, M., Valentino, A., Suryaningrum, K. M., & Yunanda, R. (2023). Dijkstra's algorithm to find the nearest vaccine location.
  3. Siever, M., Valentino, A., Suryaningrum, K. M., & Yunanda, R. (2023). Dijkstra's algorithm to find the nearest vaccine location. Procedia Computer Science, 216, 5-12.
  4. Idoko, G. O. (2023, March). Drive Path Optimization for Electric Vehicles Using Particle Swarm Optimization. In 2023 13th International Symposium on Advanced Topics in Electrical Engineering (ATEE) (pp. 1-6). IEEE.
  5. Zhang, Z., Jiang, J., Wu, J., & Zhu, X. (2023). Efficient and optimal penetration path planning for stealth unmanned aerial vehicle using minimal radar cross-section tactics and modified A-Star algorithm. ISA transactions, 134, 42-57.
  6. Liao, H., Patil, V., Dong, X., Shanbhag, D., Fallon, E., Hogan, T., ... & Kara, L. B. (2022, August). Automatic Power Plane Generation With Genetic Optimization and Multilayer Perceptron. In International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (Vol. 86229, p. V03AT03A003). American Society of Mechanical Engineers.
  7. Delfianti, R., Nusyura, F., Priyadi, A., Abadi, I., & Soeprijanto, A. (2022). Optimizing the Price of Electrical Energy Transactions on the Microgrid System Using the Shortest Path Solution. International Review on Modelling and Simulations, 15(4), 279-286.
  8. Gunawan, E. P., & Tho, C. (2021, August). Development of an application for tourism route recommendations with the Dijkstra algorithm. In 2021 International Conference on Information Management and Technology (ICIMTech) (Vol. 1, pp. 343-347). IEEE.
  9. Susanto, W., Dennis, S., Handoko, M. B. A., & Suryaningrum, K. M. (2021, October). Compare the path finding algorithms that are applied for route searching in maps. In 2021 1st International Conference on Computer Science and Artificial Intelligence (ICCSAI) (Vol. 1, pp. 178-183). IEEE.
  10. Idoko, G. O. (2023, March). Drive Path Optimization for Electric Vehicles Using Particle Swarm Optimization. In 2023 13th International Symposium on Advanced Topics in Electrical Engineering (ATEE) (pp. 1-6). IEEE.
  11. Siever, M., Valentino, A., Suryaningrum, K. M., & Yunanda, R. (2023). Dijkstra's algorithm to find the nearest vaccine location. Procedia Computer Science, 216, 5-12.
  12. Singh, N., Srujan, C., Baruah, D. J., Sharma, D., & Kushwaha, R. (2022). Light Weight Approach for Agnostic Optimal Route Selection. In Proceedings of Emerging Trends and Technologies on Intelligent Systems: ETTIS 2022 (pp. 415-427). Singapore: Springer Nature Singapore.
  13. Liao, H., Patil, V., Dong, X., Shanbhag, D., Fallon, E., Hogan, T., ... & Kara, L. B. (2022, August). Automatic Power Plane Generation With Genetic Optimization and Multilayer Perceptron. In International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (Vol. 86229, p. V03AT03A003). American Society of Mechanical Engineers.

 

Tembusai, Z. R., Mawengkang, H., & Zarlis, M. (2021). K-nearest neighbor with k-fold cross validation and analytic hierarchy process on data classification. International Journal of Advances in Data and Information Systems, 2(1), 1-8.

Cited By

  1. Chhabra, M., Ravulakollu, K. K., Kumar, M., Sharma, A., & Nayyar, A. (2023). Improving automated latent fingerprint detection and segmentation using deep convolutional neural network. Neural Computing and Applications, 35(9), 6471-6497.
  2. Inyang, U. G., Ijebu, F. F., Osang, F. B., Afolorunso, A. A., Udoha, S. S., & Eyoha, I. J. A Dataset-Driven Parameter Tuning Approach for Enhanced K-Nearest Neighbour Algorithm Performance.
  3. Handa, D., & Rai, K. (2022). Comparative Analysis of KNN Classifier with K-Fold Cross-Validation in Acoustic-Based Gender Recognition. In Next Generation of Internet of Things: Proceedings of ICNGIoT 2022 (pp. 399-404). Singapore: Springer Nature Singapore.
  4. Paulauskaite-Taraseviciene, A., Raudonis, V., & Sutiene, K. (2022). Forecasting municipal solid waste in Lithuania by incorporating socioeconomic and geographical factors. Waste Management, 140, 31-39.
  5. Kalra, V., Kashyap, I., & Kaur, H. (2022, May). Effect of Ensembling over K-fold Cross-Validation with Weighted K-Nearest Neighbour for Classification in Medical Domain. In 2022 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COM-IT-CON) (Vol. 1, pp. 796-800). IEEE.
  6. Moldovanu, S., Damian Michis, F. A., Biswas, K. C., Culea-Florescu, A., & Moraru, L. (2021). Skin lesion classification based on surface fractal dimensions and statistical color cluster features using an ensemble of machine learning techniques. Cancers, 13(21), 5256.
  7. Utami, N. (2021, December). Data Distribution Modelling in Supervised Learning Algorithm is for The classification of Prospective Recipient Candidate. In 2021 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI) (pp. 593-597). IEEE.
  8. Inyang, U. G., Ijebu, F. F., Osang, F. B., Afolorunso, A. A., Udoha, S. S., & Eyoha, I. J. A Dataset-Driven Parameter Tuning Approach for Enhanced K-Nearest Neighbour Algorithm Performance. International Journal on Advanced Science, Engineering and Information Technology, 13(1), pp. 380-391
  9. Divyasri, S. R., Saranya, R., & Kathiravan, P. (2023). Comprehensive analysis of Classical Machine Learning models and Ensemble methods for predicting Crime in urban society.

 

Bourequat, W., & Mourad, H. (2021). Sentiment analysis approach for analyzing iPhone release using support vector machine. International Journal of Advances in Data and Information Systems, 2(1), 36-44.

Cited By

  1. Sutoyo, E., Rifai, A. P., Risnumawan, A., & Saputra, M. (2022). A comparison of text weighting schemes on sentiment analysis of government policies: a case study of replacement of national examinations. Multimedia Tools and Applications, 81(5), 6413-6431.
  2. Ligiarta, M. A., & Ruldeviyani, Y. (2022, November). Customer Satisfaction Analysis of Mobile Banking Application Based on Twitter Data. In 2022 2nd International Conference on Electronic and Electrical Engineering and Intelligent System (ICE3IS) (pp. 322-327). IEEE.
  3. Patil, R., Shukla, D., Kumar, A., Rajanak, Y., & Singh, Y. P. (2022, November). Machine Learning for Sentiment Analysis and Classification of Restaurant Reviews. In 2022 3rd International Conference on Computing, Analytics and Networks (ICAN) (pp. 1-5). IEEE.
  4. Altaf, A., Anwar, M. W., Jamal, M. H., Hassan, S., Bajwa, U. I., Choi, G. S., & Ashraf, I. (2022). Deep Learning Based Cross Domain Sentiment Classification for Urdu Language. IEEE Access, 10, 102135-102147.
  5. Yunita, S., Amaliah, Y., Indriani, A., & Fadlan, M. (2021, October). Sentiment Analysis Of Practice Service's Questionnaire Using Naïve Bayes Classifier Method. In 2021 3rd International Conference on Cybernetics and Intelligent System (ICORIS) (pp. 1-6). IEEE.
  6. Callista, A. S., Pratiwi, O. N., & Sutoyo, E. (2021, September). Questions Classification Based on Revised Bloom's Taxonomy Cognitive Level using Naive Bayes and Support Vector Machine. In 2021 4th International Conference of Computer and Informatics Engineering (IC2IE) (pp. 260-265). IEEE.
  7. Ramadhani, A., Sutoyo, E., & Widartha, V. P. (2021, November). LSTM-based deep learning architecture of tourist review in Tripadvisor. In 2021 Sixth International Conference on Informatics and Computing (ICIC) (pp. 1-6). IEEE.
  8. Rusandi, M. R., Sutoyo, E., & Widartha, V. P. (2021, November). Convolutional Neural Network for Predicting Sentiment: Case Study in Tourism. In 2021 Sixth International Conference on Informatics and Computing (ICIC) (pp. 1-5). IEEE.
  9. Sofian, N. B., Keikhosrokiani, P., & Asl, M. P. (2022). Opinion Mining and Text Analytics of Reader Reviews of Yoko Ogawa's The Housekeeper and the Professor in Goodreads. In Handbook of Research on Opinion Mining and Text Analytics on Literary Works and Social Media (pp. 240-262). IGI Global.
  10. Es-Sabery, F., Es-Sabery, I., Hair, A., Sainz-De-Abajo, B., & Garcia-Zapirain, B. (2022). Emotion processing by applying a fuzzy-based vader lexicon and a parallel deep belief network over massive data. IEEE Access, 10, 87870-87899.
  11. Oetama, R. S., Yanfi, Y., UP, M. M. I. A., & Isa, S. M. (2023, February). Sentiment Analysis in Indonesian Trading using Lexicon-based and Support Vector Machine. In 2023 International Conference on Computer Science, Information Technology and Engineering (ICCoSITE) (pp. 744-749). IEEE.

 

Pratama, I. P. A. E. (2021). Self-Diagnosis of Web-Based Pregnancy and Childbirth Disorders Using Forward Chaining Methods. International Journal of Advances in Data and Information Systems, 2(1), 25-35.

Cited By

  1. Dairoh, D., Sasmito, G. W., & Raharjo, G. (2023, May). Implementation of an expert system in diagnosing children's mental disorders using the forward chaining method. In AIP Conference Proceedings (Vol. 2706, No. 1). AIP Publishing.
  2. Sidik, I. J., & Permana, S. D. H. (2021, September). Identification of Feed in Determining the Quality of Cow's Milk using Forward Chaining. In 2021 4th International Conference of Computer and Informatics Engineering (IC2IE) (pp. 362-368). IEEE.



Jacob, O. N. (2020). Impact of COVID-19 pandemic school close down on the research programme of higher institutions. International journal of advances in data and information systems, 1(1), 40-49.

Cited By

  1. Asiamah, N., Opuni, F. F., Muhonja, F., Danquah, E., Agyemang, S. M., Agyemang, I., ... & Manu, C. A. (2022). The relationship between job components, neighbourhood walkability and African academics' physical activity: a post-COVID-19 context. Health promotion international, 37(2), daab090.
  2. Asiamah, N., Muhonja, F., Omisore, A., Opuni, F. F., Mensah, H. K., Danquah, E., ... & Manu, C. A. (2021). The association between core job components, physical activity, and mental health in African academics in a post-COVID-19 context. Current Psychology, 1-17.
  3. Saghari, A., Budinská, I., Hosseinimehr, M., & Rahmani, S. (2023). A Robust-Reliable Decision-Making Methodology Based on a Combination of Stakeholders’ Preferences Simulation and KDD Techniques for Selecting Automotive Platform Benchmark. Symmetry, 15(3), 750.



Khairani, N. A., & Sutoyo, E. (2020). Application of k-means clustering algorithm for determination of fire-prone areas utilizing hotspots in West Kalimantan Province. Int. J. Adv. Data Inf. Syst, 1(1), 9-16.

Cited By

  1. Kumalawati, R., Dewi, A., & Danarto, W. P. (2023, May). Mapping of fire level using S-NPP VIIRS as disaster management reference in Banjarbaru City. In IOP Conference Series: Earth and Environmental Science (Vol. 1180, No. 1, p. 012018). IOP Publishing.
  2. Cardone, B., Di Martino, F., & Miraglia, V. (2022). A GIS-Based Hot and Cold Spots Detection Method by Extracting Emotions from Social Streams. Future Internet, 15(1), 23.
  3. Lasaponara, R., Abate, N., Fattore, C., Aromando, A., Cardettini, G., & Di Fonzo, M. (2022). On the Use of Sentinel-2 NDVI Time Series and Google Earth Engine to Detect Land-Use/Land-Cover Changes in Fire-Affected Areas. Remote Sensing, 14(19), 4723.
  4. Cardone, B., & Di Martino, F. (2022). Fuzzy-Based Spatiotemporal Hot Spot Intensity and Propagation—An Application in Crime Analysis. Electronics, 11(3), 370.
  5. Sutoyo, E., Rifai, A. P., Risnumawan, A., & Saputra, M. (2022). A comparison of text weighting schemes on sentiment analysis of government policies: a case study of replacement of national examinations. Multimedia Tools and Applications, 81(5), 6413-6431.
  6. Liu, J. (2022, September). The Application of K-Means Clustering Algorithm in the Quality Analysis of College English Teaching. In 2022 International Conference on Education, Network and Information Technology (ICENIT) (pp. 1-4). IEEE.
  7. Haekal, F. M., & Sutedja, I. (2022, October). Region Grouping Based on Sales Results Using K-Means Algorithm at PT RMK. In 2022 4th International Conference on Cybernetics and Intelligent System (ICORIS) (pp. 1-6). IEEE.
  8. Mimboro, P., Kusrini, K., & Laksito, A. D. (2022, August). Spatial Hotspot Data and Weather for Forest Fire Data Clustering. In 2022 5th International Conference on Information and Communications Technology (ICOIACT) (pp. 160-165). IEEE.
  9. Haekal, F. M., & Sutedja, I. (2022, August). Region Grouping Based On Sales Results Using K-Medoids Algorithm At PT RMK. In 2022 International Conference on Information Management and Technology (ICIMTech) (pp. 178-183). IEEE.
  10. Ma, J., Li, Z., Liu, Z., Zhang, S., & Lai, W. (2022). Influence of Management Education on Enterprise Scientific and Technological Innovation Based on K-Means Clustering Algorithm. Mathematical Problems in Engineering, 2022.
  11. Zhu, G., Zhu, G., & Lv, G. (2022). Application Research of the-Means Clustering Algorithm in the Construction of Ideological and Political Education Resource Database in Colleges and Universities Based on 5G. Wireless Communications and Mobile Computing, 2022.
  12. Najib, M. K., Nurdiati, S., & Sopaheluwakan, A. (2021, October). Quantifying the joint distribution of drought indicators in Borneo fire-prone area. In IOP Conference Series: Earth and Environmental Science (Vol. 880, No. 1, p. 012002). IOP Publishing.
  13. Fattore, C., Abate, N., Faridani, F., Masini, N., & Lasaponara, R. (2021). Google earth engine as multi-sensor open-source tool for supporting the preservation of archaeological areas: the case study of flood and fire mapping in Metaponto, Italy. Sensors, 21(5), 1791.
  14. Afitah, I., & Isra, E. P. (2021). Analysis of forest and land fire with hotspot modis on various types of land cover in Central Kalimantan Province. AgBioForum, 23(2), 13-21.
  15. Di Martino, F., & Sessa, S. (2021). Fuzzy Entropy-Based Spatial Hotspot Reliability. Entropy, 23(5), 531.
  16. Ramadhan, E. R., Sutoyo, E., Musnansyah, A., & Belgaman, H. A. (2020, September). Analysis of hotspot data for drought clustering using K-means algorithm. In Proceedings of the 2021 International Conference on Engineering and Information Technology for Sustainable Industry (pp. 1-6).
  17. Sutoyo, E., & Almaarif, A. (2020). Twitter sentiment analysis of the relocation of Indonesia's capital city. Bulletin of Electrical Engineering and Informatics, 9(4), 1620-1630.

 

Suryati, O. T., & Budiono, A. (2020). Impact analysis of malware based on call network API with heuristic detection method. International Journal of Advances in Data and Information Systems, 1(1), 1-8.

Cited By

  1. Ibrahim, W. N., Anuar, M. S., Selamat, A., & Krejcar, O. (2022). BOTNET DETECTION USING INDEPENDENT COMPONENT ANALYSIS. IIUM Engineering Journal, 23(1), 95-115.
  2. Sadhu, P. K., Yanambaka, V. P., & Abdelgawad, A. (2022). Internet of Things: Security and Solutions Survey. Sensors, 22(19), 7433.
  3. Purnama, B., Stiawan, D., Hanapi, D., Idris, M. Y., Sharipuddin, S., Afifah, N., & Budiarto, R. (2022). Time Efficiency on Computational Performance of PCA, FA and TSVD on Ransomware Detection. Indonesian Journal of Electrical Engineering and Informatics (IJEEI), 10(1), 102-111.
  4. Mira, F. (2022, March). A Futuristic Appraisal of Malware Detection by Employing Data Mining. In 2022 5th International Conference on Computing and Informatics (ICCI) (pp. 204-208). IEEE.
  5. Elkhail, A. A., Refat, R. U. D., Habre, R., Hafeez, A., Bacha, A., & Malik, H. (2021). Vehicle security: A survey of security issues and vulnerabilities, malware attacks and defenses. IEEE Access, 9, 162401-162437.

 

Andwika, V. R., & Witjaksono, R. W. (2020). Analysis of user acceptance of ERP system on after sales function using unified theory of acceptance and use of technology (UTAUT) model. International Journal of Advances in Data and Information Systems, 1(1), 26-33.

Cited By

  1. Roffia, P., & Mola, L. (2022). Is COVID-19 enough? Which underestimated conditions characterise the adoption of complex information infrastructures in small and medium-sized enterprises. Journal of Business Research, 144, 1249-1255.
  2. Santosa, A. D., Taufik, N., Prabowo, F. H. E., & Rahmawati, M. (2021). Continuance intention of baby boomer and X generation as new users of digital payment during COVID-19 pandemic using UTAUT2. Journal of Financial Services Marketing, 26(4), 259-273.
  3. Emhmed, S., Al-Sanjary, O. I., Jaharadak, A. A., Aldulaimi, S. H., & HazimAlkawaz, M. (2021, March). Technical and Organizational Facilitating Conditions—The Antecedent Factors and Impact on the Intention to Use ERP System in Libyan Universities. In 2021 IEEE 17th International Colloquium on Signal Processing & Its Applications (CSPA) (pp. 41-46). IEEE.

 

Hermawan, S. S., & Saedudin, R. R. (2020). Design of Cooling and Air Flow System Using NDLC Method Based on TIA-942 Standards in Data Center at CV Media Smart Semarang. International Journal of Advances in Data and Information Systems, 1(1), 34-39.

Cited By

  1. Utomo, S. W., Saedudin, R. R., & Widjajarto, A. (2018). Analisa dan desain data center building facilities berdasarkan humidity monitoring system di rumah sakit islam muhammadiyah sumberrejo menggunakan standar TIA-942 dengan metode PPDIOO life-cycle approach. vol, 5, 3055-3066.



Setiyani, L., Syamsudin, A., Gintings, A., & Arifin, D. (2020). The analysis of functional needs on undergraduate thesis information system management: a case study in Indonesian universities. International Journal of Advances in Data and Information Systems, 1(2), 50-59.

  1. Gómez-Quispe, O., Benites, R. M., Contreras, W., & Ibañez, V. (2022). Tesis desarrolladas, tiempo de realización y publicación como artículos científicos en una facultad de medicina veterinaria y zootecnia de Perú. Revista de Investigaciones Veterinarias del Perú, 33(4), e23345-e23345.

 

Muhtadi, A. F., & Almaarif, A. (2020). Analysis of malware impact on network traffic using behavior-based detection technique. International Journal of Advances in Data and Information Systems, 1(1), 17-25.

  1. Zhang, Z., Li, Y., Dong, H., Gao, H., Jin, Y., & Wang, W. (2020). Spectral-based directed graph network for malware detection. IEEE Transactions on Network Science and Engineering, 8(2), 957-970.
  2. Mira, F. (2022, March). A Futuristic Appraisal of Malware Detection by Employing Data Mining. In 2022 5th International Conference on Computing and Informatics (ICCI) (pp. 204-208). IEEE.
  3. Yuan, M. (2021, September). Smart Terminal Malware Detection Technology Based on Abnormal Network Behavior. In Journal of Physics: Conference Series (Vol. 2037, No. 1, p. 012038). IOP Publishing.
  4. Ibrahim, W. N., Anuar, M. S., Selamat, A., & Krejcar, O. (2022). BOTNET DETECTION USING INDEPENDENT COMPONENT ANALYSIS. IIUM Engineering Journal, 23(1), 95-115.

 

Yunus, W., & Lasulika, M. E. (2022). Security System Analysis Against Flood Attacks Using TCP, UDP, and ICMP Protocols on Mikrotik Routers. International Journal of Advances in Data and Information Systems, 3(1), 11-19.

  1. Xueqiang YAN, Guanjie CHENG, Shuiguang DENG, Jianjun WU, Lu LU, Mingyu ZHAO, Yan XI, Chao LIU, Weiyuan LI, Lei FENG, Tong ZHANG. Data services and data plane for 6G mobile communication network[J]. Chinese Journal on Internet of Things, 2023, 7(1): 60-72.

 

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