Parameter tuning in KNN for software defect prediction: an empirical analysis

Modinat Abolore Mabayoje; Abdullateef Olwagbemiga Balogun; Hajarah Afor Jibril; Jelili Olaniyi Atoyebi; Hammed Adeleye Mojeed; Victor Elijah Adeyemo

doi:10.14710/jtsiskom.7.4.2019.121-126

DOI: https://doi.org/10.14710/jtsiskom.7.4.2019.121-126

Parameter tuning in KNN for software defect prediction: an empirical analysis

Modinat Abolore Mabayoje¹, Abdullateef Olwagbemiga Balogun¹

, Hajarah Afor Jibril¹, Jelili Olaniyi Atoyebi², Hammed Adeleye Mojeed¹, Victor Elijah Adeyemo¹

¹Department of Computer Science, University of Ilorin, Nigeria

²Department of Computer Science and Engineering, Obafemi Awolowo University, Nigeria

Received: 27 Jan 2019; Revised: 31 Jul 2019; Accepted: 10 Aug 2019; Available online: 3 Oct 2019; Published: 31 Oct 2019.

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

BibTex Citation Data :

@article{JTSISKOM13262,
    author = {Modinat Abolore Mabayoje and Abdullateef Olwagbemiga Balogun and Hajarah Afor Jibril and Jelili Olaniyi Atoyebi and Hammed Adeleye Mojeed and Victor Elijah Adeyemo},
    title = {Parameter tuning in KNN for software defect prediction: an empirical analysis},
    journal = {Jurnal Teknologi dan Sistem Komputer},
  volume = {7},
    number = {4},
    year = {2019},
    keywords = {software defect prediction; parameter tuning; k-nearest neighbor; distance function; distance weighting},
    abstract = {Software Defect Prediction (SDP) provides insights that can help software teams to allocate their limited resources in developing software systems. It predicts likely defective modules and helps avoid pitfalls that are associated with such modules. However, these insights may be inaccurate and unreliable if parameters of SDP models are not taken into consideration. In this study, the effect of parameter tuning on the k nearest neighbor (k-NN) in SDP was investigated. More specifically, the impact of varying and selecting optimal k value, the influence of distance weighting and the impact of distance functions on k-NN. An experiment was designed to investigate this problem in SDP over 6 software defect datasets. The experimental results revealed that k value should be greater than 1 (default) as the average RMSE values of k-NN when k>1(0.2727) is less than when k=1(default) (0.3296). In addition, the predictive performance of k-NN with distance weighing improved by 8.82% and 1.7% based on AUC and accuracy respectively. In terms of the distance function, kNN models based on Dilca distance function performed better than the Euclidean distance function (default distance function). Hence, we conclude that parameter tuning has a positive effect on the predictive performance of k-NN in SDP.},
   issn = {2338-0403},   pages = {121--126}  doi = {10.14710/jtsiskom.7.4.2019.121-126},
    url = {https://jtsiskom.undip.ac.id/article/view/13262}
}

Citation Format:

Abstract

Software Defect Prediction (SDP) provides insights that can help software teams to allocate their limited resources in developing software systems. It predicts likely defective modules and helps avoid pitfalls that are associated with such modules. However, these insights may be inaccurate and unreliable if parameters of SDP models are not taken into consideration. In this study, the effect of parameter tuning on the k nearest neighbor (k-NN) in SDP was investigated. More specifically, the impact of varying and selecting optimal k value, the influence of distance weighting and the impact of distance functions on k-NN. An experiment was designed to investigate this problem in SDP over 6 software defect datasets. The experimental results revealed that k value should be greater than 1 (default) as the average RMSE values of k-NN when k>1(0.2727) is less than when k=1(default) (0.3296). In addition, the predictive performance of k-NN with distance weighing improved by 8.82% and 1.7% based on AUC and accuracy respectively. In terms of the distance function, kNN models based on Dilca distance function performed better than the Euclidean distance function (default distance function). Hence, we conclude that parameter tuning has a positive effect on the predictive performance of k-NN in SDP.

Fulltext View|Download Email colleagues

Keywords: software defect prediction; parameter tuning; k-nearest neighbor; distance function; distance weighting

Funding: University of Ilorin; Obafemi Awolowo University

Article Metrics:

Article Info

Section: Original Research Articles

Language : EN

In Volume 7, Issue 4, Year 2019 (October 2019)

Most viewed articles

Perancangan Sistem Informasi Akademik Berbasis Mobile Web Studi Kasus di Program Studi Sistem Komputer Universitas Diponegoro Pemanfaatan Augmented Reality untuk Pembelajaran Pengenalan Alat Musik Piano Teka-teki Unsur Kimia sebagai Media Pembelajaran Kimia Interaktif bagi siswa SMA Kelas X Berbasis Android Perancangan Game Math Adventure Sebagai Media Pembelajaran Matematika Berbasis Android Decision Support System for Thesis Graduation Recommendation Using AHP-TOPSIS Method More articles

Most cited articles

Android Application of Expert System for Gastroenteritis Detection Mobile Application of Expert System for Meningitis Early Diagnosis Perancangan Papan Informasi Digital Berbasis Web pada Raspberry pi Perancangan Aplikasi Multimedia Untuk Pembelajaran Gerbang Logika Menggunakan Augmented Reality Pengembangan Sistem Pakar Untuk Diagnosis Penyakit Hepatitis Berbasis Web Menggunakan Metode Certainty Factor More cited articles

M. M. Ali, S. Huda, J. Abawajy, S. Alyahya, H. Al-Dossari, and J. Yearwood, "A parallel framework for software defect detection and metric selection on cloud computing," Cluster Computing, vol. 20, no. 3, pp. 2267-2281, 2017. doi: 10.1007/s10586-017-0892-6
H. B. Yadav and D. K. Yadav, "A fuzzy logic based approach for phase-wise software defects prediction using software metrics," Information and Software Technology, vol. 63, pp. 44-57, 2015. doi: 10.1016/j.infsof.2015.03.001
Huda et al., "A framework for software defect prediction and metric selection," IEEE access, vol. 6, pp. 2844-2858, 2018. doi: 10.1109/ACCESS.2017.2785445
Z. Li, X.-Y. Jing and X. Zhu, "Progress on approaches to software defect prediction," IET Software, vol. 12, no. 3, pp. 161-175, 2018. doi: 10.1049/iet-sen.2017.0148
M. Tan, L. Tan, S. Dara, and C. Mayeux, "Online defect prediction for imbalanced data," in the 37th IEEE International Conference on Software Engineering, Florence, Italy, May 2015, pp. 99-108. doi: 10.1109/ICSE.2015.139
C. Tantithamthavorn, S. McIntosh, A. E. Hassan, and K. Matsumoto, "An empirical comparison of model validation techniques for defect prediction models," IEEE Transactions on Software Engineering, vol. 43, no. 1, pp. 1-18, 2017. doi: 10.1109/TSE.2016.2584050
X.-Y. Jing, F. Wu, X. Dong, and B. Xu, "An improved SDA based defect prediction framework for both within-project and cross-project class-imbalance problems," IEEE Transactions on Software Engineering, vol. 43, no. 4, pp. 321-339, 2017. doi: 10.1109/TSE.2016.2597849
H. Tong, B. Liu, and S. Wang, "Software defect prediction using stacked denoising autoencoders and two-stage ensemble learning," Information and Software Technology, vol. 96, pp. 94-111, 2017. doi: 10.1016/j.infsof.2017.11.008
Ö. F. Arar and K. Ayan, "Software defect prediction using cost-sensitive neural network," Applied Soft Computing, vol. 33, pp. 263-277, 2015. doi: 10.1016/j.asoc.2015.04.045
F. Zhang, Q. Zheng, Y. Zou, and A. E. Hassan, "Cross-project defect prediction using a connectivity-based unsupervised classifier," in the 38th International Conference on Software Engineering, Austin, USA, May 2016, pp. 309-320. doi: 10.1145/2884781.2884839
A. O. Balogun, S. Basri, S. J. Abdulkadir, and A. S. Hashim, "Performance analysis of feature selection methods in software defect prediction: a search method approach," Applied Sciences, vol. 9, no. 13, pp. 1-20, 2019. doi: 10.3390/app9132764
S. Herbold, A. Trautsch, and J. Grabowski, "A comparative study to benchmark cross-project defect prediction approaches," IEEE Transactions on Software Engineering, vol. 44, no. 9, pp. 811-833, 2017. doi: 10.1109/TSE.2017.2724538
Y. Kamei, T. Fukushima, S. McIntosh, K. Yamashita, N. Ubayashi, and A. E. Hassan, "Studying just-in-time defect prediction using cross-project models," Empirical Software Engineering, vol. 21, no. 5, pp. 2072-2106, 2016. doi: 10.1007/s10664-015-9400-x
R. Malhotra, "A systematic review of machine learning techniques for software fault prediction," Applied Soft Computing, vol. 27, pp. 504-518, 2015. doi: 10.1016/j.asoc.2014.11.023
C. Tantithamthavorn, S. McIntosh, A. E. Hassan, and K. Matsumoto, "Automated parameter optimization of classification techniques for defect prediction models," in the IEEE/ACM 38th International Conference on Software Engineering, Austin, USA, May 2016, pp. 321-332. doi: 10.1145/2884781.2884857
A. O. Balogun, S. Basri, S. J. Abdulkadir, and A. S. Hashim, "A hybrid multi-filter wrapper feature selection method for software defect predictors," International Journal of Supply Chain Management, vol. 8, no. 2, pp. 916-922, 2019
W. Fu, T. Menzies, and X. Shen, "Tuning for software analytics: Is it really necessary?," Information and Software Technology, vol. 76, pp. 135-146, 2016. doi: 10.1016/j.infsof.2016.04.017
Y. Jiang, B. Cukic, and T. Menzies, "Can data transformation help in the detection of fault-prone modules?," in the 2008 Workshop on Defects in Large Software Systems, Seattle, USA, Jul. 2008, pp. 16-20. doi: 10.1145/1390817.1390822
A. Tosun and A. Bener, "Reducing false alarms in software defect prediction by decision threshold optimization," in the 2009 3rd International Symposium on Empirical Software Engineering and Measurement, Florida, USA, Oct. 2009, pp. 477-480. doi: 10.1109/ESEM.2009.5316006
A. G. Koru and H. Liu, "An investigation of the effect of module size on defect prediction using static measures," in the 2005 Workshop on Predictor Models in Software Engineering, New York, USA, May 2005, pp. 1-5. doi: 10.1145/1083165.1083172
T. Mende, "Replication of defect prediction studies: problems, pitfalls and recommendations," in the 6th International Conference on Predictive Models in Software Engineering, Timisoara, Romania, Sept. 2010, pp. 1-10. doi: 10.1145/1868328.1868336
T. Hall, S. Beecham, D. Bowes, D. Gray, and S. Counsell, "A systematic literature review on fault prediction performance in software engineering," IEEE Transactions on Software Engineering, vol. 38, no. 6, pp. 1276-1304, 2012. doi: 10.1109/TSE.2011.103
A. G. Akintola, A. O. Balogun, F. Lafenwa-Balogun, and H. A. Mojeed, "Comparative analysis of selected heterogeneous classifiers f analysis of gray code number system in image security or software defects prediction using filter-based feature selection methods," FUOYE Journal of Engineering and Technology, vol. 3, no. 1, pp. 134-137, 2018
R. Jimoh, A. Balogun, A. Bajeh, and S. Ajayi, "A PROMETHEE based evaluation of software defect predictors," Journal of Computer Science and Its Application, vol. 25, no. 1, pp. 106-119, 2018
M. A. Mabayoje, A. O. Balogun, S. M. Bello, J. O. Atoyebi, H. A. Mojeed, and A. H. Ekundayo, "Wrapper feature selection based heterogeneous classifiers for software defect prediction," Adeleke University Journal of Engineering and Technology, vol. 2, no. 1, pp. 1-11, 2019
A. O. Balogun, R. O. Oladele, H. A. Mojeed, B. Amin-Balogun, V. E. Adeyemo, and T. O. Aro, "Performance analysis of selected clustering techniques for software defects prediction," African Journal of Computing & ICT, vol. 12, no. 2, pp. 30-42, 2019
T. G. Grbac, G. Mausa, and B. D. Basic, "Stability of software defect prediction in relation to levels of data imbalance," in the 2nd Workshop on Software Quality Analysis, Monitoring, Improvement, and Applications, Novi Sad, Serbia, Sept. 2013, pp. 1-10
Q. Yu, S. Jiang, and Y. Zhang, "The performance stability of defect prediction models with class imbalance: an empirical study," IEICE Transactions on Information and Systems, vol. 100, no. 2, pp. 265-272, 2017
S. Bibi, G. Tsoumakas, I. Stamelos, and I. P. Vlahavas, "Software defect prediction using regression via classification," in IEEE International Conference on Computer Systems and Applications, Dubai, UAE, Mar. 2006, pp. 330-336. doi: 10.1109/AICCSA.2006.205110
P. Singh and S. Verma, "Automated tool for extraction of software fault data," in Advances in Data and Information Sciences: Springer, 2018, pp. 29-37. doi: 10.1007/978-981-10-8360-0_3
M. Tan, L. Tan, S. Dara, and C. Mayeux, "Online defect prediction for imbalanced data," in the 37th Internation Conference on Software Engineering, Florence, Italy, May 2015, pp. 99-108
G. I. Salama, M. Abdelhalim, and M. A.-e. Zeid, "Breast cancer diagnosis on three different datasets using multi-classifiers," International Journal of Computer and Information Technology, vol. 1, no. 1, pp. 36-43, 2012
Y. A. Christobel and P. Sivaprakasam, "A new classwise k nearest neighbor (CKNN) method for the classification of diabetes dataset," International Journal of Engineering and Advanced Technology, vol. 2, no. 3, pp. 396-200, 2013
Y. Liao and V. R. Vemuri, "Use of k-nearest neighbor classifier for intrusion detection," Computers & Security, vol. 21, no. 5, pp. 439-448, 2002. doi: 10.1016/S0167-4048(02)00514-X
M. Mabayoje, A. Balogun, A. Bajeh, and B. Musa, "Software defect prediction: effect of feature selection and ensemble methods," FUW Trends in Science & Technology Journal, vol. 3, no. 2, pp. 518-522, 2018
P. Hall, B. U. Park, and R. J. Samworth, "Choice of neighbor order in nearest-neighbor classification," The Annals of Statistics, vol. 36, no. 5, pp. 2135-2152, 2008. doi: 10.1214/07-AOS537
R. J. Samworth, "Optimal weighted nearest neighbour classifiers," The Annals of Statistics, vol. 40, no. 5, pp. 2733-2763, 2012. doi: 10.1214/12-AOS1049
T. M. Kodinariya and P. R. Makwana, "Review on determining number of cluster in k-means clustering," International Journal of Advanced Research in Computer Science and Management Studies, vol. 1, no. 6, pp. 90-95, 2013
L. Song, L. L. Minku, and X. Yao, "The impact of parameter tuning on software effort estimation using learning machines," in the 9th International Conference on Predictive Models in Software Engineering, Maryland, USA, Oct. 2013, pp. 1-10. doi: 10.1145/2499393.2499394

Last update:

Optimising classification in sport: a replication study using physical and technical-tactical performance indicators to classify competitive levels in rugby league match-play
Victor Elijah Adeyemo, Anna Palczewska, Ben Jones, Dan Weaving, Sarah Whitehead. Science and Medicine in Football, 8 (1), 2024. doi: 10.1080/24733938.2022.2146177
Proceedings of the 2nd International Conference on Emerging Technologies and Intelligent Systems
Abdullah B. Nasser, Waheed Ghanem, Antar Shaddad Hamed Abdul-Qawy, Mohammed A. H. Ali, Abdul-Malik Saad, Sanaa A. A. Ghaleb, Nayef Alduais. Lecture Notes in Networks and Systems, 573 , 2023. doi: 10.1007/978-3-031-20429-6_18
Sampling-based novel heterogeneous multi-layer stacking ensemble method for telecom customer churn prediction
Fatima E. Usman-Hamza, Abdullateef O. Balogun, Ramoni T. Amosa, Luiz Fernando Capretz, Hammed A. Mojeed, Shakirat A. Salihu, Abimbola G. Akintola, Modinat A. Mabayoje. Scientific African, 24 , 2024. doi: 10.1016/j.sciaf.2024.e02223
International Conference on Emerging Applications and Technologies for Industry 4.0 (EATI’2020)
Abdullateef O. Balogun, Fatimah B. Lafenwa-Balogun, Hammed A. Mojeed, Fatimah E. Usman-Hamza, Amos O. Bajeh, Victor E. Adeyemo, Kayode S. Adewole, Rasheed G. Jimoh. Lecture Notes in Networks and Systems, 254 , 2021. doi: 10.1007/978-3-030-80216-5_4
Binary Grey Wolf Optimizer with K-Nearest Neighbor classifier for Feature Selection
Ranya Al-wajih, Said Jadid Abdulakaddir, NorShakirah Bt A Aziz, Qasem Al-tashi. 2020 International Conference on Computational Intelligence (ICCI), 2020. doi: 10.1109/ICCI51257.2020.9247792
Computational Science and Its Applications – ICCSA 2021
Abdullateef O. Balogun, Noah O. Akande, Fatimah E. Usman-Hamza, Victor E. Adeyemo, Modinat A. Mabayoje, Ahmed O. Ameen. Lecture Notes in Computer Science, 12957 , 2021. doi: 10.1007/978-3-030-87013-3_12
Computational Science and Its Applications – ICCSA 2022 Workshops
Abdullateef O. Balogun, Babajide J. Odejide, Amos O. Bajeh, Zubair O. Alanamu, Fatima E. Usman-Hamza, Hammid O. Adeleke, Modinat A. Mabayoje, Shakirat R. Yusuff. Lecture Notes in Computer Science, 13381 , 2022. doi: 10.1007/978-3-031-10548-7_27
Intelligent Algorithms in Software Engineering
Abdullateef O. Balogun, Shuib Basri, Said A. Jadid, Saipunidzam Mahamad, Malek A. Al-momani, Amos O. Bajeh, Ammar K. Alazzawi. Advances in Intelligent Systems and Computing, 1224 , 2020. doi: 10.1007/978-3-030-51965-0_43
Bio-Inspired Optimization Algorithm in Machine Learning and Practical Applications
Shallu Juneja, Harsh Taneja, Ashish Patel, Yogesh Jadhav, Anita Saroj. SN Computer Science, 5 (8), 2024. doi: 10.1007/s42979-024-03412-0
Empirical analysis of tree-based classification models for customer churn prediction
Fatima E. Usman-Hamza, Abdullateef O. Balogun, Salahdeen K. Nasiru, Luiz Fernando Capretz, Hammed A. Mojeed, Shakirat A. Salihu, Abimbola G. Akintola, Modinat A. Mabayoje, Joseph B. Awotunde. Scientific African, 23 , 2024. doi: 10.1016/j.sciaf.2023.e02054
Proceedings of International Conference on Advanced Computing Applications
Favour Onotse Momoh, Sandip Rakshit, Narasimha Rao Vajjhala. Advances in Intelligent Systems and Computing, 1406 , 2022. doi: 10.1007/978-981-16-5207-3_48
When less is more: on the value of “co-training” for semi-supervised software defect predictors
Suvodeep Majumder, Joymallya Chakraborty, Tim Menzies. Empirical Software Engineering, 29 (2), 2024. doi: 10.1007/s10664-023-10418-4
Smell-Aware Bug Classification
Khyber, Sikandar Ali, Fazli Wahid, Samad Baseer, Ahmed Alkhayyat, Akram M. Al-Radaei. IEEE Access, 12 , 2024. doi: 10.1109/ACCESS.2023.3335175
Optimization of k value and lag parameter of k-nearest neighbor algorithm on the prediction of hotel occupancy rates
Agus Subhan Akbar, R. Hadapiningradja Kusumodestoni. Jurnal Teknologi dan Sistem Komputer, 8 (3), 2020. doi: 10.14710/jtsiskom.2020.13648
Advances in Computing and Data Sciences
Yakub Kayode Saheed, Olumide Longe, Usman Ahmad Baba, Sandip Rakshit, Narasimha Rao Vajjhala. Communications in Computer and Information Science, 1440 , 2021. doi: 10.1007/978-3-030-81462-5_29
Detecting block ciphers generic attacks: An instance-based machine learning method
Yazan Ahmad Alsariera. International Journal of ADVANCED AND APPLIED SCIENCES, 9 (5), 2022. doi: 10.21833/ijaas.2022.05.007
A trustworthy hybrid model for transparent software defect prediction: SPAM-XAI
Mohd Mustaqeem, Suhel Mustajab, Mahfooz Alam, Fathe Jeribi, Shadab Alam, Mohammed Shuaib, Hikmat Ullah Khan. PLOS ONE, 19 (7), 2024. doi: 10.1371/journal.pone.0307112
Software Engineering Perspectives in Systems
Babajide J. Odejide, Amos O. Bajeh, Abdullateef O. Balogun, Zubair O. Alanamu, Kayode S. Adewole, Abimbola G. Akintola, Shakirat A. Salihu, Fatima E. Usman-Hamza, Hammed A. Mojeed. Lecture Notes in Networks and Systems, 501 , 2022. doi: 10.1007/978-3-031-09070-7_49
An Optimized LSTM Neural Network for Accurate Estimation of Software Development Effort
Anca-Elena Iordan. Mathematics, 12 (2), 2024. doi: 10.3390/math12020200
Impact of Feature Selection Methods on the Predictive Performance of Software Defect Prediction Models: An Extensive Empirical Study
Abdullateef O. Balogun, Shuib Basri, Saipunidzam Mahamad, Said J. Abdulkadir, Malek A. Almomani, Victor E. Adeyemo, Qasem Al-Tashi, Hammed A. Mojeed, Abdullahi A. Imam, Amos O. Bajeh. Symmetry, 12 (7), 2020. doi: 10.3390/sym12071147
Software Engineering Methods Design and Application
Aminat T. Bashir, Abdullateef O. Balogun, Matthew O. Adigun, Sunday A. Ajagbe, Luiz Fernando Capretz, Joseph B. Awotunde, Hammed A. Mojeed. Lecture Notes in Networks and Systems, 1118 , 2024. doi: 10.1007/978-3-031-70285-3_4
Machine Learning and Big Data Analytics
Narasimha Rao Vajjhala, Kenneth David Strang. Springer Proceedings in Mathematics & Statistics, 401 , 2023. doi: 10.1007/978-3-031-15175-0_2
Computational Science and Its Applications – ICCSA 2020
Abdullateef O. Balogun, Fatimah B. Lafenwa-Balogun, Hammed A. Mojeed, Victor E. Adeyemo, Oluwatobi N. Akande, Abimbola G. Akintola, Amos O. Bajeh, Fatimah E. Usman-Hamza. Lecture Notes in Computer Science, 12254 , 2020. doi: 10.1007/978-3-030-58817-5_45

Last update: 2024-11-29 17:13:20

Heterogeneous Ensemble with Combined Dimensionality Reduction for Social Spam Detection
Oladepo A.G.. International Journal of Interactive Mobile Technologies, 15 (17), 2021. doi: 10.3991/ijim.v15i17.19915
Data Sampling-Based Feature Selection Framework for Software Defect Prediction
Balogun A.O.. Lecture Notes in Networks and Systems, 127 , 2021. doi: 10.1007/978-3-030-80216-5_4
Binary Grey Wolf Optimizer with K-Nearest Neighbor classifier for Feature Selection
Ranya Al-wajih, Said Jadid Abdulakaddir, NorShakirah Bt A Aziz, Qasem Al-tashi. 2020 International Conference on Computational Intelligence (ICCI), 2020. doi: 10.1109/ICCI51257.2020.9247792
Search-Based Wrapper Feature Selection Methods in Software Defect Prediction: An Empirical Analysis
Balogun A.O.. Advances in Intelligent Systems and Computing, 127 (7), 2020. doi: 10.1007/978-3-030-51965-0_43
SMOTE-Based Homogeneous Ensemble Methods for Software Defect Prediction
Balogun A.O.. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformat, 127 (7), 2020. doi: 10.1007/978-3-030-58817-5_45
Impact of Feature Selection Methods on the Predictive Performance of Software Defect Prediction Models: An Extensive Empirical Study
Abdullateef O. Balogun, Shuib Basri, Saipunidzam Mahamad, Said J. Abdulkadir, Malek A. Almomani, Victor E. Adeyemo, Qasem Al-Tashi, Hammed A. Mojeed, Abdullahi A. Imam, Amos O. Bajeh. Symmetry, 12 (7), 2020. doi: 10.3390/sym12071147

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Starting from 2021, the author(s) whose article is published in the JTSiskom journal attain the copyright for their article and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. By submitting the manuscript to JTSiskom, the author(s) agree with this policy. No special document approval is required.

The author(s) guarantee that:

their article is original, written by the mentioned author(s),
has never been published before,
does not contain statements that violate the law, and
does not violate the rights of others, is subject to copyright held exclusively by the author(s), is free from the rights of third parties, and the necessary written permission to quote from other sources has been obtained by the author(s).

The author(s) retain all rights to the published work, such as (but not limited to) the following rights:

Copyright and other proprietary rights related to the article, such as patents,
The right to use the substance of the article in its own future works, including lectures and books,
The right to reproduce the article for its own purposes,
The right to archive all versions of the article in any repository, and
The right to enter into separate additional contractual arrangements for the non-exclusive distribution of published versions of the article (for example, posting them to institutional repositories or publishing them in a book), acknowledging its initial publication in this journal (Jurnal Teknologi dan Sistem Komputer).

Suppose the article was prepared jointly by more than one author. Each author submitting the manuscript warrants that all co-authors have given their permission to agree to copyright and license notices (agreements) on their behalf and notify co-authors of the terms of this policy. JTSiskom will not be held responsible for anything arising because of the writer's internal dispute. JTSiskom will only communicate with correspondence authors.

Authors should also understand that their articles (and any additional files, including data sets and analysis/computation data) will become publicly available once published. The license of published articles (and additional data) will be governed by a Creative Commons Attribution-ShareAlike 4.0 International License. JTSiskom allows users to copy, distribute, display and perform work under license. Users need to attribute the author(s) and JTSiskom to distribute works in journals and other publication media. Unless otherwise stated, the author(s) is a public entity as soon as the article is published.

Parameter tuning in KNN for software defect prediction: an empirical analysis

EDITORIAL OFFICE OF JURNAL TEKNOLOGI DAN SISTEM KOMPUTER