RESOLVER PROPERTI EFEKTIF UNTUK PEMERIKSAAN FORMAT SKRIPSI BERBASIS ATURAN DENGAN EVALUASI INJEKSI KESALAHAN
DOI:
https://doi.org/10.31539/vhz8q840Abstract
Kepatuhan terhadap format skripsi umumnya diperiksa secara manual oleh dosen pembimbing, dosen penguji, dan program studi. Proses ini dapat menghasilkan penilaian yang tidak konsisten serta menyebabkan pelanggaran format tidak terdeteksi hingga skripsi disetujui. Perangkat pemeriksa yang tersedia umumnya hanya mengandalkan format yang ditetapkan secara langsung atau definisi gaya (style), tanpa memperhitungkan mekanisme pewarisan berlapis yang menentukan properti format efektif sebagaimana ditampilkan dalam Microsoft Word. Penelitian ini mengembangkan sistem pemeriksa format skripsi berbasis aturan yang memproses berkas Microsoft Word (.docx) secara langsung menggunakan Office Open XML. Sistem ini mengintegrasikan resolver properti efektif yang mengikuti hierarki pewarisan format Microsoft Word, representasi dokumen perantara, 44 aturan yang dikelompokkan ke dalam sembilan kategori, serta profil konfigurasi yang memisahkan nilai persyaratan institusi dari kode aplikasi. Antarmuka berbasis web menyajikan bukti pendukung untuk setiap pelanggaran yang terdeteksi. Evaluasi dilakukan di Program Studi Teknik Informatika Universitas Maarif Hasyim Latif melalui dua prosedur, yaitu verifikasi kartu bukti oleh ketua program studi terhadap 17 skripsi yang telah disetujui serta injeksi kesalahan secara terkendali pada dua dokumen acuan yang menghasilkan 164 dokumen mutan. Kumpulan aturan yang diharapkan untuk setiap mutan ditetapkan sebelum sistem dijalankan. Setelah satu siklus revisi yang telah diverifikasi, aturan deterministik mencapai presisi 1,000 (90/90) pada skripsi aktual. Pada pengujian dengan injeksi kesalahan, sistem mencapai presisi 0,985, recall 0,985, dan skor F1 sebesar 0,985. Penghilangan resolver menurunkan recall pada mutan dengan format yang ditetapkan secara langsung dari 1,000 menjadi 0,773 serta menurunkan spesifisitas dari 1,000 menjadi 0,693. Pelanggaran format yang masih tersisa, yang merepresentasikan estimasi batas bawah, ditemukan pada 15,9% pasangan dokumen–aturan deterministik dalam skripsi yang sebelumnya telah melalui pemeriksaan dosen pembimbing dan penguji. Temuan ini menunjukkan bahwa resolusi properti format efektif merupakan faktor penting dalam meningkatkan akurasi pemeriksaan otomatis, sedangkan evaluasi berbasis bukti memungkinkan klaim akurasi diperiksa dan direproduksi secara lebih transparan.
References
Andrews, J. H., Briand, L. C., & Labiche, Y. (2005). Is mutation an appropriate tool for testing experiments? In Proceedings of the 27th International Conference on Software Engineering (pp. 402–411). https://doi.org/10.1145/1062455.1062530
Arocena, P. C., Glavic, B., Mecca, G., Miller, R. J., Papotti, P., & Santoro, D. (2015). Messing up with BART: Error generation for evaluating data-cleaning algorithms. Proceedings of the VLDB Endowment, 9(2), 36–47.
Aulia, D. S., & Haviana, S. F. C. (2026). Sistem pengecekan format tugas akhir menggunakan rule-based system dan similarity judul. Jurnal Rekayasa Sistem Informasi dan Teknologi, 3(4), 685–696. https://doi.org/10.70248/jrsit.v3i4.3822
Azzahra, F., Anggraeni, D., & Hayat, M. A. (2025). Penerapan natural language processing dan regular expressions dalam validasi artikel ilmiah. Jurnal Muhammadiyah's Applied Technology, 4(3), 335–342. https://doi.org/10.26618/dkjw7933
Barr, E. T., Harman, M., McMinn, P., Shahbaz, M., & Yoo, S. (2015). The oracle problem in software testing: A survey. IEEE Transactions on Software Engineering, 41(5), 507–525. https://doi.org/10.1109/TSE.2014.2372785
Binmakhashen, G. M., & Mahmoud, S. A. (2019). Document layout analysis: A comprehensive survey. ACM Computing Surveys, 52(6), 1–36. https://doi.org/10.1145/3355610
Bloch, L., Rückert, J., & Friedrich, C. M. (2025). Towards automatic formal feedback on scientific documents. In Proceedings of the 20th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2025) (pp. 334–344). Association for Computational Linguistics. https://doi.org/10.18653/v1/2025.bea-1.26
Brown, L. D., Cai, T. T., & DasGupta, A. (2001). Interval estimation for a binomial proportion. Statistical Science, 16(2), 101–133. https://doi.org/10.1214/ss/1009213286
DeMillo, R. A., Lipton, R. J., & Sayward, F. G. (1978). Hints on test data selection: Help for the practicing programmer. Computer, 11(4), 34–41. https://doi.org/10.1109/C-M.1978.218136
Ecma International. (2016). ECMA-376: Office Open XML file formats—Part 1: Fundamentals and markup language reference (5th ed.).
Fröhlich, T., & Schlippe, T. (2025). RubiSCoT: A framework for AI-supported academic assessment (arXiv:2510.17309). arXiv. https://doi.org/10.48550/arXiv.2510.17309
Hlukhov, V. S., & Sydorko, D. S. (2023). Algorithms and software for verification of scientific and technical text documents. Applied Aspects of Information Technology, 6(3), 304–317. https://doi.org/10.15276/aait.06.2023.21
Jia, Y., & Harman, M. (2011). An analysis and survey of the development of mutation testing. IEEE Transactions on Software Engineering, 37(5), 649–678. https://doi.org/10.1109/TSE.2010.62
Just, R., Jalali, D., Inozemtseva, L., Ernst, M. D., Holmes, R., & Fraser, G. (2014). Are mutants a valid substitute for real faults in software testing? In Proceedings of the 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering (pp. 654–665). https://doi.org/10.1145/2635868.2635929
Kapoor, S., & Narayanan, A. (2023). Leakage and the reproducibility crisis in machine-learning-based science. Patterns, 4(9), Article 100804. https://doi.org/10.1016/j.patter.2023.100804
Knight, S., Shibani, A., Abel, S., Gibson, A., Ryan, P., Sutton, N., Wight, R., Lucas, C., Sándor, Á., Kitto, K., Liu, M., Mogarkar, R. V., & Buckingham Shum, S. (2020). AcaWriter: A learning analytics tool for formative feedback on academic writing. Journal of Writing Research, 12(1), 141–186. https://doi.org/10.17239/jowr-2020.12.01.06
Lei, Y., Tian, Y., Li, N., Liang, Q., & Zhao, W. (2016). Identification methods for bibliographic reference entries in editable scientific and technical documents. In Proceedings of the 2016 International Conference on Computer Science and Electronic Technology (CSET 2016) (pp. 172–178). Atlantis Press. https://doi.org/10.2991/cset-16.2016.42
Martsinkevich, V., Tereshchenko, V., Larionova, G., Berezhkov, A., Kobets, E., Nasyrov, N., & Gorlushkina, N. (2023). Web tool for automated document formatting verification. In Proceedings of the 33rd Conference of Open Innovations Association FRUCT (pp. 375–381). FRUCT Oy. https://fruct.org/publications/volume-33/acm33/files/Mar.pdf
Nosek, B. A., Ebersole, C. R., DeHaven, A. C., & Mellor, D. T. (2018). The preregistration revolution. Proceedings of the National Academy of Sciences, 115(11), 2600–2606. https://doi.org/10.1073/pnas.1708274114
Papadakis, M., Kintis, M., Zhang, J., Jia, Y., Le Traon, Y., & Harman, M. (2019). Mutation testing advances: An analysis and survey. Advances in Computers, 112, 275–378. https://doi.org/10.1016/bs.adcom.2018.03.015
Program Studi Teknik Informatika UMAHA. (2021). Buku pedoman proposal dan tugas akhir. Universitas Maarif Hasyim Latif.
Putra, A. D., Pambudhi, T., Muhisom, & Afwan, B. (2026). Pemetaan tipologi kesalahan teknis dalam naskah skripsi mahasiswa program studi Pendidikan Guru Sekolah Dasar (PGSD). Paedagogie, 21(1), 1463–1471. https://doi.org/10.31603/paedagogie.v21i1.16306
python-openxml. (n.d.). Text-related objects. python-docx documentation. Retrieved October 11, 2026, from https://python-docx.readthedocs.io/en/latest/api/text.html
Ramadhan, H. F. (2025). Development and optimization of an automated thesis format checking system for undergraduate students at Universitas Negeri Makassar. Journal of Embedded Systems Security and Intelligent Systems, 6(2). https://doi.org/10.59562/jessi.v6i2.9211
Solihin, W., & Eastman, C. (2015). Classification of rules for automated BIM rule checking development. Automation in Construction, 53, 69–82. https://doi.org/10.1016/j.autcon.2015.03.003
Svintsytska, O., Korotun, O., Marchuk, D., & Zhelizko, V. (2025). Algorithms for automated verification of academic document parameters. Infocommunication and Computer Technologies, 2(10), 119–127. https://doi.org/10.36994/2788-5518-2025-02-10-15
Sydorko, D., & Hlukhov, V. S. (2024). Program implementation of methods for analysis and verification of technical reports. Computing Systems and Networks, 6(2), 208–221. https://doi.org/10.23939/csn2024.02.208
Tkaczyk, D., Collins, A., Sheridan, P., & Beel, J. (2018). Machine learning vs. rules and out-of-the-box vs. retrained: An evaluation of open-source bibliographic reference and citation parsers. In Proceedings of the 18th ACM/IEEE Joint Conference on Digital Libraries (pp. 99–108). https://doi.org/10.1145/3197026.3197048
Verbovyi, D. S., & Saiapina, I. O. (2025). Method for automatic document verification for compliance with regulatory requirements. Systems and Technologies, 3(158), 181–187. https://doi.org/10.34185/1562-9945-3-158-2025-18
Voorhees, E. M. (2000). Variations in relevance judgments and the measurement of retrieval effectiveness. Information Processing & Management, 36(5), 697–716. https://doi.org/10.1016/S0306-4573(00)00010-8
Wilson, E. B. (1927). Probable inference, the law of succession, and statistical inference. Journal of the American Statistical Association, 22(158), 209–212. https://doi.org/10.1080/01621459.1927.10502953
Zhang, J., Sun, Q., Wang, Q., Zhang, W., Xiao, Z., Zhang, X., Ren, J., & Liu, J. (2025). PEMUTA: Pedagogically-enriched multi-granular undergraduate thesis assessment (arXiv:2507.19556). arXiv. https://doi.org/10.48550/arXiv.2507.19556
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