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Perkembangan Pengetahuan Anak Usia Dini Melalui Permainan Komputer Edukatif (Studi Kasus TK Aisyiyah 3 Salatiga)

Published in Jurnal KomuniTi, 2011

Pelaksanaan penelitian ini bertujuan untuk mengetahui pengaruh pemanfaatan teknologi komputer terhadap peningkatan kecerdasan dan kreatifitas anak didik TK dengan berbagai jenis permainan komputer edukatif.

Recommended citation: Nugroho, Y.S. (2011). "Perkembangan Pengetahuan Anak Usia Dini Melalui Permainan Komputer Edukatif (Studi Kasus TK Aisyiyah 3 Salatiga)". KomuniTi. 3(1). http://journals.ums.ac.id/index.php/komuniti/article/view/2966/1901

Seleksi Sekolah Menengah Lanjutan Menggunakan Analytic Hierarchy Process

Published in Jurnal KomuniTi, 2013

Penelitian ini dilakukan dalam rangka mengembangkan suatu aplikasi sistem pendukung keputusan untuk membantu orang tua dalam memilih sekolah yang sesuai bagi anak dan memenuhi kriteria yang diinginkan oleh orang tua.

Recommended citation: Nugroho, Y.S., Ulinuha, A., Aji, N.N.S. (2013). "Seleksi Sekolah Menengah Lanjutan Menggunakan Analytic Hierarchy Process". KomuniTi. 5(2). https://publikasiilmiah.ums.ac.id/handle/11617/4496

Analisa Kecepatan Proxy Squid, Safesquid dan Polipo Pada Ubuntu Server

Published in Jurnal Emitor, 2013

Penelitian ini untuk membandingkan beberapa sistem operasi pada proxy server yaitu squid, safesquid dan polipo untuk mendapatkan sistem operasi proxy server yang cocok pada jaringan yang dirancang.

Recommended citation: Al Irsyadi, F.Y., Nugroho, Y.S., Wuryanto, D. (2013). "Analisa Kecepatan Proxy Squid, Safesquid dan Polipo Pada Ubuntu Server". Emitor. 13(2). https://publikasiilmiah.ums.ac.id/xmlui/handle/11617/4581

Klasifikasi Masa Studi Mahasiswa Fakultas Komunikasi dan Informatika UMS Menggunakan Algoritma C4.5

Published in Jurnal KomuniTi, 2014

Penelitian ini dilakukan untuk memanfaatkan data-data yang melimpah di UMS sebagai sumber informasi strategis bagi fakultas untuk mengklasifikasi masa studi mahasiswa dengan menggunakan teknik data mining. Klasifikasi masa studi terhadap data lulusan mahasiswa FKI UMS menggunakan metode Decision Tree dengan algoritma C4.5.

Recommended citation: Nugroho, Y.S., Setyawan. (2014). "Klasifikasi Masa Studi Mahasiswa Fakultas Komunikasi dan Informatika UMS Menggunakan Algoritma C4.5". KomuniTi. 6(1). http://journals.ums.ac.id/index.php/komuniti/article/view/2946/1881

Aplikasi Pemrediksi Masa Studi dan Predikat Kelulusan Mahasiswa Informatika Universitas Muhammadiyah Surakarta Menggunakan Metode Naive Bayes.

Published in Jurnal Khazanah Informatika, 2015

Penelitian ini dilakukan untuk membuat suatu aplikasi untuk melakukan prediksi terhadap lama studi dan predikat kelulusan mahasiswa dengan menerapkan metode Naive Bayes.

Recommended citation: Nurrohmat, M.A., Nugroho, Y.S. (2015). "Aplikasi Pemrediksi Masa Studi dan Predikat Kelulusan Mahasiswa Informatika Universitas Muhammadiyah Surakarta Menggunakan Metode Naive Bayes". Khazanah Informatika. 1(1). http://journals.ums.ac.id/index.php/khif/article/view/1179/1028

Implementasi Data Warehouse Dan Data Mining Untuk Pengembangan Sistem Rekomendasi Pemilihan SMA

Published in Jurnal Khazanah Informatika, 2016

Pengembangan sistem rekomendasi pemilihan sekolah menengah atas dengan teknik data warehouse dan data mining.

Recommended citation: Nugroho, Y.S., Salma, T.D., Rokhanudin, S. (2016). "Implementasi Data Warehouse Dan Data Mining Untuk Pengembangan Sistem Rekomendasi Pemilihan SMA". Khazanah Informatika. 2(2). http://journals.ums.ac.id/index.php/khif/article/view/2333/1857

Sistem Klasifikasi Variabel Tingkat Penerimaan Konsumen Terhadap Mobil Menggunakan Metode Random Forest

Published in Jurnal Teknik Elektro, 2017

Pengembangan sistem pengklasifikasi tingkat penerimaan mobil oleh konsumen menggunakan metode Random Forest (RF).

Recommended citation: Nugroho, Y.S., Emiliyawati, N. (2017). "Sistem Klasifikasi Variabel Tingkat Penerimaan Konsumen Terhadap Mobil Menggunakan Metode Random Forest". Jurnal Teknik Elektro. 9(1). https://journal.unnes.ac.id/nju/index.php/jte/article/view/10452/6660

Identifying Algorithm Names in Code Comments

Published in -, 2019

For recent machine-learning-based tasks like API sequence generation, comment generation, and document generation, large amount of data is needed. When software developers implement algorithms in code, we find that they often mention algorithm names in code comments. Code annotated with such algorithm names can be valuable data sources. In this paper, we propose an automatic method of algorithm name identification. The key idea is extracting important N-gram words containing the word `algorithm' in the last. We also consider part of speech patterns to derive rules for appropriate algorithm name identification. The result of our rule evaluation produced high precision and recall values (more than 0.70). We apply our rules to extract algorithm names in a large amount of comments from active FLOSS projects written in seven programming languages, C, C++, Java, JavaScript, Python, PHP, and Ruby, and report commonly mentioned algorithm names in code comments.Ê

Recommended citation: Klainongsuang, J., Nugroho, Y.S., Hata, H., Manaskasemsak, B., Rungsawang, A., Leelaprute, P., & Matsumoto, K. (2019). Identifying Algorithm Names in Code Comments. arXiv preprint arXiv:1907.04557. https://arxiv.org/abs/1907.04557

How different are different diff algorithms in Git? Use –histogram for code changes

Published in Empirical Software Engineering, 2019

Automatic identification of the differences between two versions of a file is a common and basic task in several applications of mining code repositories. Git, a version control system, has a diff utility and users can select algorithms of diff from the default algorithm Myers to the advanced Histogram algorithm. From our systematic mapping, we identified three popular applications of diff in recent studies. On the impact on code churn metrics in 14 Java projects, we obtained different values in 1.7% to 8.2% commits based on the different diff algorithms. Regarding bug-introducing change identification, we found 6.0% and 13.3% in the identified bug-fix commits had different results of bug-introducing changes from 10 Java projects. For patch application, we found that the Histogram is more suitable than Myers for providing the changes of code, from our manual analysis. Thus, we strongly recommend using the Histogram algorithm when mining Git repositories to consider differences in source code.

Recommended citation: Nugroho, Y. S., Hata, H., & Matsumoto, K. (2019). How Different Are Different diff Algorithms in Git? Use --histogram for Code Changes. Empirical Software Engineering. https://doi.org/10.1007/s10664-019-09772-z https://doi.org/10.1007/s10664-019-09772-z

From Academia to Software Development: Publication Citations in Source Code Comments

Published in -, 2019

Academic publications have been evaluated with the impact on research communities based on the number of citations. On the other hand, the impact of academic publications on industry has been rarely studied. This paper investigates how academic publications contribute to software development by analyzing publication citations in source code comments in open source software repositories. We propose an automated approach of detecting academic publications based on Named Entity Recognition, and achieve 0.90 in F1 as detection accuracy. We conduct a large-scale study of publication citations with 319,438,977 comments collected from active 25,925 repositories written in seven programming languages. Our findings indicate that academic publications can be knowledge sources of software development, and there can be potential issues of obsoleting knowledge.

Recommended citation: Inokuchi, A., Nugroho, Y.S., Konishi, F., Hata, H., Monden, A., & Matsumoto, K. (2019). From Academia to Software Development: Publication Citations in Source Code Comments. arXiv preprint arXiv:1910.06932. https://arxiv.org/abs/1910.06932

A Topological Analysis of Communication Channels for Knowledge Sharing in Contemporary GitHub Projects

Published in The Journal of Systems and Software, 2019

With over 28 million developers, success of the GitHub collaborative platform is highlighted through an abundance of communication channels among contemporary software projects. Knowledge is broken into two forms and its sharing (through communication channels) can be described as externalization or combination by the SECI model. Such platforms have revolutionized the way developers work, introducing new channels to share knowledge in the form of pull requests, issues and wikis. It is unclear how these channels capture and share knowledge. In this research, our goal is to analyze these communication channels in GitHub. First, using the SECI model, we are able to map how knowledge is shared through the communication channels. Then in a large-scale topology analysis of seven library package projects (i.e., involving over 70 thousand projects), we extracted insights of the different communication channels within GitHub. Using two research questions, we explored the evolution of the channels and adoption of channels by both popular and unpopular library package projects. Results show that (i) contemporary GitHub Projects tend to adopt multiple communication channels, (ii) communication channels change over time and (iii) communication channels are used to both capture new knowledge (i.e., externalization) and updating existing knowledge (i.e., combination).

Recommended citation: Jirateep Tantisuwankul, Yusuf Sulistyo Nugroho, Raula Gaikovina Kula, Hideaki Hata, Arnon Rungsawang, Pattara Leelaprute, Kenichi Matsumoto, A Topological Analysis of Communication Channels for Knowledge Sharing in Contemporary GitHub Projects, Journal of Systems and Software, 2019, 110416, ISSN 0164-1212, https://doi.org/10.1016/j.jss.2019.110416. https://doi.org/10.1016/j.jss.2019.110416

How different are different diff algorithms in Git? Use –histogram for code changes

Published in Empirical Software Engineering, Vol. 25, No. 1, pp. 790-823, January 2020., 2020

Automatic identification of the differences between two versions of a file is a common and basic task in several applications of mining code repositories. Git, a version control system, has a diff utility and users can select algorithms of diff from the default algorithm Myers to the advanced Histogram algorithm. From our systematic mapping, we identified three popular applications of diff in recent studies. On the impact on code churn metrics in 14 Java projects, we obtained different values in 1.7% to 8.2% commits based on the different diff algorithms. Regarding bug-introducing change identification, we found 6.0% and 13.3% in the identified bug-fix commits had different results of bug-introducing changes from 10 Java projects. For patch application, we found that the Histogram is more suitable than Myers for providing the changes of code, from our manual analysis. Thus, we strongly recommend using the Histogram algorithm when mining Git repositories to consider differences in source code.

Recommended citation: Nugroho, Y. S., Hata, H., & Matsumoto, K. (2020). How Different Are Different diff Algorithms in Git?, Empirical Software Engineering., Vol. 25, No. 1, pp. 790-823. https://doi.org/10.1007/s10664-019-09772-z

talks

teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

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Teaching experience 2

Workshop, University 1, Department, 2015

This is a description of a teaching experience. You can use markdown like any other post.