The Influence of Level of Education, Training and Work Discipline on the Performance of Forestry Officials in Makassar

Herman Jelatu (1), Lucas Maria (2), Jayshree Martin (3)
(1) Sekolah Tinggi Ilmu Ekonomi Amkop Makassar, Indonesia,
(2) Katholieke Universiteit Leuven, Belgium,
(3) Bakhtar University, Afghanistan

Abstract

This study aims to determine the effect partially and simultaneously the level of education, training, and work discipline partially affect the performance of employees in the Makassar Forestry Service. To determine the level of education, training, work discipline on the performance of employees in the Makassar Forestry Service. This study uses a quantitative approach. Data analysis techniques in this study used validity tests, reliability tests, multiple linear regression analysis, partial tests, and simultaneous tests. From the results of the validity test, the data shows valid with the R-count greater than the R-table. Based on the multiple linear regression analysis the results of the analysis are: Y = 42.460 + 0.050X1 + 0.830X2 + 0.857X3. It is known that the level of education, training and work discipline has a partial effect on the performance of forestry service employees in Makassar. Where each variable t-count value is greater than t-table, education level (2.362 > 1.699), training (1.974 > 1.699), work discipline (2.232 > 1.699) and simultaneous test scores show f-count greater than f -table (2.986 > 2.96). This means that the level of education, training, and work discipline simultaneously affect the performance of forestry service employees in Makassar.

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References

Abbasimehr, H., Shabani, M., & Yousefi, M. (2020). An optimized model using LSTM network for demand forecasting. Computers & Industrial Engineering, 143, 106435. https://doi.org/10.1016/j.cie.2020.106435

Abbass, K., Qasim, M. Z., Song, H., Murshed, M., Mahmood, H., & Younis, I. (2022). A review of the global climate change impacts, adaptation, and sustainable mitigation measures. Environmental Science and Pollution Research, 29(28), 42539–42559. https://doi.org/10.1007/s11356-022-19718-6

Bjornson, E., & Sanguinetti, L. (2020). Making Cell-Free Massive MIMO Competitive With MMSE Processing and Centralized Implementation. IEEE Transactions on Wireless Communications, 19(1), 77–90. https://doi.org/10.1109/TWC.2019.2941478

Carter, L., & Rossi, A. (2019). Embodying Strength: The Origin, Representations, and Socialization of the Strong Black Woman Ideal and its Effect on Black Women’s Mental Health. Women & Therapy, 42(3–4), 289–300. https://doi.org/10.1080/02703149.2019.1622911

Deschênes, S. S., Kivimaki, M., & Schmitz, N. (2021). Adverse Childhood Experiences and the Risk of Coronary Heart Disease in Adulthood: Examining Potential Psychological, Biological, and Behavioral Mediators in the Whitehall II Cohort Study. Journal of the American Heart Association, 10(10), e019013. https://doi.org/10.1161/JAHA.120.019013

Domingo, E., & Perales, C. (2019). Viral quasispecies. PLOS Genetics, 15(10), e1008271. https://doi.org/10.1371/journal.pgen.1008271

Dror, A. A., Eisenbach, N., Taiber, S., Morozov, N. G., Mizrachi, M., Zigron, A., Srouji, S., & Sela, E. (2020). Vaccine hesitancy: The next challenge in the fight against COVID-19. European Journal of Epidemiology, 35(8), 775–779. https://doi.org/10.1007/s10654-020-00671-y

Duan, S.-B., Li, Z.-L., Li, H., Göttsche, F.-M., Wu, H., Zhao, W., Leng, P., Zhang, X., & Coll, C. (2019). Validation of Collection 6 MODIS land surface temperature product using in situ measurements. Remote Sensing of Environment, 225, 16–29. https://doi.org/10.1016/j.rse.2019.02.020

Erdo?an, S., Y?ld?r?m, D. Ç., & Gedikli, A. (2020). Natural resource abundance, financial development and economic growth: An investigation on Next-11 countries. Resources Policy, 65, 101559. https://doi.org/10.1016/j.resourpol.2019.101559

Eysenbach, G. (2020). How to Fight an Infodemic: The Four Pillars of Infodemic Management. Journal of Medical Internet Research, 22(6), e21820. https://doi.org/10.2196/21820

Ferentinos, K. P. (2018). Deep learning models for plant disease detection and diagnosis. Computers and Electronics in Agriculture, 145, 311–318. https://doi.org/10.1016/j.compag.2018.01.009

Gollakota, A. R. K., Volli, V., & Shu, C.-M. (2019). Progressive utilisation prospects of coal fly ash: A review. Science of The Total Environment, 672, 951–989. https://doi.org/10.1016/j.scitotenv.2019.03.337

Grossman, C., Grossman, E., & Goldbourt, U. (2019). Uric acid variability at midlife as an independent predictor of coronary heart disease and all-cause mortality. PLOS ONE, 14(8), e0220532. https://doi.org/10.1371/journal.pone.0220532

Hernaus, T., Cerne, M., Connelly, C., Poloski Vokic, N., & Škerlavaj, M. (2019). Evasive knowledge hiding in academia: When competitive individuals are asked to collaborate. Journal of Knowledge Management, 23(4), 597–618. https://doi.org/10.1108/JKM-11-2017-0531

Hu, G., Ou, Q., Si, G., Wu, Y., Wu, J., Dai, Z., Krasnok, A., Mazor, Y., Zhang, Q., Bao, Q., Qiu, C.-W., & Alù, A. (2020). Topological polaritons and photonic magic angles in twisted ?-MoO3 bilayers. Nature, 582(7811), 209–213. https://doi.org/10.1038/s41586-020-2359-9

Karolidis, D., & Vouzas, F. (2019). From PSM to Helping Behavior in the Contemporary Greek Public Sector: The Roles of Organizational Identification and Job Satisfaction. Public Performance & Management Review, 42(6), 1418–1447. https://doi.org/10.1080/15309576.2019.1592762

Key, N. S., Khorana, A. A., Kuderer, N. M., Bohlke, K., Lee, A. Y. Y., Arcelus, J. I., Wong, S. L., Balaban, E. P., Flowers, C. R., Francis, C. W., Gates, L. E., Kakkar, A. K., Levine, M. N., Liebman, H. A., Tempero, M. A., Lyman, G. H., & Falanga, A. (2020). Venous Thromboembolism Prophylaxis and Treatment in Patients With Cancer: ASCO Clinical Practice Guideline Update. Journal of Clinical Oncology, 38(5), 496–520. https://doi.org/10.1200/JCO.19.01461

Kumar, K., Srivastav, S., & Sharanagat, V. S. (2021). Ultrasound assisted extraction (UAE) of bioactive compounds from fruit and vegetable processing by-products: A review. Ultrasonics Sonochemistry, 70, 105325. https://doi.org/10.1016/j.ultsonch.2020.105325

Li, H., & Wu, X.-J. (2019). DenseFuse: A Fusion Approach to Infrared and Visible Images. IEEE Transactions on Image Processing, 28(5), 2614–2623. https://doi.org/10.1109/TIP.2018.2887342

Li, M., Lu, J., Ji, X., Li, Y., Shao, Y., Chen, Z., Zhong, C., & Amine, K. (2020). Design strategies for nonaqueous multivalent-ion and monovalent-ion battery anodes. Nature Reviews Materials, 5(4), 276–294. https://doi.org/10.1038/s41578-019-0166-4

Li, Y., Ding, L., Yin, S., Liang, Z., Xue, Y., Wang, X., Cui, H., & Tian, J. (2020). Photocatalytic H2 Evolution on TiO2 Assembled with Ti3C2 MXene and Metallic 1T-WS2 as Co-catalysts. Nano-Micro Letters, 12(1), 6. https://doi.org/10.1007/s40820-019-0339-0

Martin, M., Karenberg, A., & Fangerau, H. (2022). Legalisierte Entrechtung: Zur juristischen Konstruktion von Entlassung und Vertreibung im Nationalsozialismus. Der Nervenarzt, 93(S1), 9–15. https://doi.org/10.1007/s00115-022-01308-z

Mastracci, S., & Adams, I. (2019). Is Emotional Labor Easier in Collectivist or Individualist Cultures? An East–West Comparison. Public Personnel Management, 48(3), 325–344. https://doi.org/10.1177/0091026018814569

Maves, R. C., Downar, J., Dichter, J. R., Hick, J. L., Devereaux, A., Geiling, J. A., Kissoon, N., Hupert, N., Niven, A. S., King, M. A., Rubinson, L. L., Hanfling, D., Hodge, J. G., Marshall, M. F., Fischkoff, K., Evans, L. E., Tonelli, M. R., Wax, R. S., Seda, G., … Christian, M. D. (2020). Triage of Scarce Critical Care Resources in COVID-19 An Implementation Guide for Regional Allocation. Chest, 158(1), 212–225. https://doi.org/10.1016/j.chest.2020.03.063

Meng, X., & Karniadakis, G. E. (2020). A composite neural network that learns from multi-fidelity data: Application to function approximation and inverse PDE problems. Journal of Computational Physics, 401, 109020. https://doi.org/10.1016/j.jcp.2019.109020

Qiu, S. (Charles), Jiang, J., Liu, X., Chen, M.-H., & Yuan, X. (2021). Can corporate social responsibility protect firm value during the COVID-19 pandemic? International Journal of Hospitality Management, 93, 102759. https://doi.org/10.1016/j.ijhm.2020.102759

Robertson, E., Reeve, K. S., Niedzwiedz, C. L., Moore, J., Blake, M., Green, M., Katikireddi, S. V., & Benzeval, M. J. (2021). Predictors of COVID-19 vaccine hesitancy in the UK household longitudinal study. Brain, Behavior, and Immunity, 94, 41–50. https://doi.org/10.1016/j.bbi.2021.03.008

Rossi, S., Antal, A., Bestmann, S., Bikson, M., Brewer, C., Brockmöller, J., Carpenter, L. L., Cincotta, M., Chen, R., Daskalakis, J. D., Di Lazzaro, V., Fox, M. D., George, M. S., Gilbert, D., Kimiskidis, V. K., Koch, G., Ilmoniemi, R. J., Lefaucheur, J. P., Leocani, L., … Hallett, M. (2021). Safety and recommendations for TMS use in healthy subjects and patient populations, with updates on training, ethical and regulatory issues: Expert Guidelines. Clinical Neurophysiology, 132(1), 269–306. https://doi.org/10.1016/j.clinph.2020.10.003

Sim, J., & Waterfield, J. (2019). Focus group methodology: Some ethical challenges. Quality & Quantity, 53(6), 3003–3022. https://doi.org/10.1007/s11135-019-00914-5

Soled, D., Goel, S., Barry, D., Erfani, P., Joseph, N., Kochis, M., Uppal, N., Velasquez, D., Vora, K., & Scott, K. W. (2020). Medical Student Mobilization During a Crisis: Lessons From a COVID-19 Medical Student Response Team. Academic Medicine, 95(9), 1384–1387. https://doi.org/10.1097/ACM.0000000000003401

Tashman, P., Marano, V., & Kostova, T. (2019). Walking the walk or talking the talk? Corporate social responsibility decoupling in emerging market multinationals. Journal of International Business Studies, 50(2), 153–171. https://doi.org/10.1057/s41267-018-0171-7

Tian, Y., Yang, G., Wang, Z., Wang, H., Li, E., & Liang, Z. (2019). Apple detection during different growth stages in orchards using the improved YOLO-V3 model. Computers and Electronics in Agriculture, 157, 417–426. https://doi.org/10.1016/j.compag.2019.01.012

Wang, H., Liu, Y., He, R., Xu, D., Zang, J., Weeranoppanant, N., Dong, H., & Li, Y. (2020). Cell membrane biomimetic nanoparticles for inflammation and cancer targeting in drug delivery. Biomaterials Science, 8(2), 552–568. https://doi.org/10.1039/C9BM01392J

Xu, H., Yao, L., Li, Z., Liang, X., & Zhang, W. (2019). Auto-FPN: Automatic Network Architecture Adaptation for Object Detection Beyond Classification. 2019 IEEE/CVF International Conference on Computer Vision (ICCV), 6648–6657. https://doi.org/10.1109/ICCV.2019.00675

Authors

Herman Jelatu
Hermanjelatu2612@gmail.com (Primary Contact)
Lucas Maria
Jayshree Martin
Author Biographies

Herman Jelatu, Sekolah Tinggi Ilmu Ekonomi Amkop Makassar

 

 

Lucas Maria, Katholieke Universiteit Leuven

 

 

Jayshree Martin, Bakhtar University

 

 

Jelatu, H., Maria, L., & Martin, J. (2023). The Influence of Level of Education, Training and Work Discipline on the Performance of Forestry Officials in Makassar. Journal Markcount Finance, 1(1), 47–59. https://doi.org/10.55849/jmf.v1i1.67

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