Penerapan Metode Profile Matching Untuk Penentuan Siswa Berprestasi Pada MTS NU Miftahul Falah Kudus
DOI:
https://doi.org/10.31000/jika.v7i2.7480Abstrak
Prestasi belajar siswa merupakan prestasi yang diperoleh siswa dalam hal pengetahuan, keterampilan, dan sikap serta pengalaman dan latihan yang telah dilalui oleh setiap individu. Penilaian dilakukan dengan memberikan nilai yang dibuat oleh guru. Namun saat ini dalam penentuan siswa berprestasi di MTS NU Miftahul Falah hanya berdasarkan dari nilai akademik saja. Dalam menentukan rekomendasi siswa berprestasi pihak sekolah juga masih menggunakan proses manual dengan pencatatan data untuk menyeleksi siswa berprestasi dengan menuliskan di buku induk bagian administrasi tata usaha. Hal tersebut dapat menimbulkan masalah antara lain waktu yang diperlukan dalam prosesnya relatif lama dan hasil yang di dapat belum optimal.
Oleh sebab itu, berdasarkan gambaran masalah yang sudah dijelaskan diatas maka penulis memberikan sebuah solusi yaitu Penerapan Metode Profile Matching untuk Penentuan Siswa Berprestasi pada MTS NU Miftahul Falah Kudus dengan kriteria diantaranya nilai akademik, karya siswa, ekstra kurikuler dan kemampuan bahasa inggris. Dengan tujuan memudahkan pihak sekolah dalam menentukan siswa berprestasi agar hasil yang di dapat lebih optimal.Referensi
Amri, Irman, and Afif Praseto Aji. 2018. “Rancang Bangun Sistem Aplikasi Penerimaan Siswa Baru Menggunakan Metode Agile Di SMK Modellink Kabupaten Sorong.†Jurnal Teknik Informasi Dan Keamanan 4(2):51–57.
Apriana, Veti. 2019. “Penerapan Profile Matching Untuk Menentukan Pemberian Beasiswa Pada Siswa Sekolah Menengah Atas.†Jurnal Mantik Penusa 3(1):15–21.
APRIYANI, DWI DANI. 2021. “Sistem Pendukung Keputusan Pemilihan Siswa Berprestasi Menggunakan Metode Profile Matching.†Faktor Exacta 14(1):44. doi: 10.30998/faktorexacta.v14i1.9057.
Dahriansah, Andri Nata, and Indra Ramadona Harahap. 2020. “Sistem Pendukung Keputusan Pemilihan Siswa Berprestasi Pada Aliyah Aras Kabu Agung Tanjungbalai Menggunakan Metode AHP.†3(1):86–95.
Diana. 2018. Metode & Aplikasi Sistem Pendukung Keputusan. Deepublish.
Fahmi, Zulfahmi, and Faradika Dika. 2019. “Sistem Pendukung Keputusan Pemilihan Siswa Berprestasi; Metode Profile Matching.†Jurnal Teknologi Dan Sistem Informasi Bisnis 1(1):30–37. doi: 10.47233/jteksis.v1i1.4.
Ferdiansyah, Devy. 2018. “Penerapan Konsep Model View Controller Pada Rancang Bangun Sistem Informasi Klinik Kesehatan Berbasis Web.†Jurnal Kajian Ilmiah 18(2):195. doi: 10.31599/jki.v18i2.289.
Hartono, Jogiyanto. 2005. Analisa Dan Desain Sistem Informasi. Yogyakarta: Andi Offset.
Ismaya, Nina, Akhmad Khanif Zyen, 2022. “PENENTUAN SISWA BERPRESTASI MENGGUNAKAN METODE K-MEANS CLUSTERING DI SMP TAKHASSUS AL QUR ’ AN.†1(1):64–68.
Kristanto, Andri. 2018. Perancangan Sistem Informasi Dan Aplikasinya (Edisi Revisi). Yogyakarta: Gava Media.
Latif, Lita Asyriati, Mohamad Jamil, and Said HI Abbas. 2018. Buku Ajar: Sistem Pendukung Keputusan Teori Dan Implementasi. Deepublish.
Sukamto, Rosa Ariani, and M. Shalahuddin. 2018. Rekayasa Perangkat Lunak Terstruktur Dan Berorientasi Objek Edisi Revisi. Bandung: Informatika.
Susanto, Ahmad. 2018. BIMBINGAN Dan KONSELING Di Sekolah (Konsep, Teori, Dan Aplikasinya). Jakarta: Prenadamedia Group.
Tristianto, Chrisantus. 2018. “Penggunaan Metode Waterfall Untuk Pengembangan Sistem Monitoring Dan Evaluasi Pembangunan Pedesaan.†Jurnal Teknologi Informasi ESIT, Vol. XII No.01.
Tutiasri, Ririn Puspita, Wahyu Santoso, and Aulia Rahmawati. 2021. “Pemanfaatan Whatsapp Grup Sebagai Media Komunikasi Di Daerah Pedesaan.†Jurnal Ilmu Komunikasi 11(1):79–92. doi: 10.15642/jik.2021.11.1.79-92.
Unduhan
Diterbitkan
Terbitan
Bagian
Lisensi
License and Copyright Agreement
In submitting the manuscript to the journal, the authors certify that:
- They are authorized by their co-authors to enter into these arrangements.
- That it is not under consideration for publication elsewhere,
- That its publication has been approved by all the author(s) and by the responsible authorities – tacitly or explicitly – of the institutes where the work has been carried out.
- They secure the right to reproduce any material that has already been published or copyrighted elsewhere.
- They agree to the following license and copyright agreement.
Copyright
Authors who publish with International Journal of Advances in Intelligent Informatics agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (CC BY-SA 4.0) that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.Â
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
Licensing for Data Publication
International Journal of Advances in Intelligent Informatics use a variety of waivers and licenses, that are specifically designed for and appropriate for the treatment of data:
Open Data Commons Attribution License, http://www.opendatacommons.org/licenses/by/1.0/ (default)
Creative Commons CC-Zero Waiver, http://creativecommons.org/publicdomain/zero/1.0/
Open Data Commons Public Domain Dedication and Licence, http://www.opendatacommons.org/licenses/pddl/1-0/
Other data publishing licenses may be allowed as exceptions (subject to approval by the editor on a case-by-case basis) and should be justified with a written statement from the author, which will be published with the article.
Open Data and Software Publishing and Sharing
The journal strives to maximize the replicability of the research published in it. Authors are thus required to share all data, code or protocols underlying the research reported in their articles. Exceptions are permitted but have to be justified in a written public statement accompanying the article.
Datasets and software should be deposited and permanently archived inappropriate, trusted, general, or domain-specific repositories (please consult http://service.re3data.org and/or software repositories such as GitHub, GitLab, Bioinformatics.org, or equivalent). The associated persistent identifiers (e.g. DOI, or others) of the dataset(s) must be included in the data or software resources section of the article. Reference(s) to datasets and software should also be included in the reference list of the article with DOIs (where available). Where no domain-specific data repository exists, authors should deposit their datasets in a general repository such as ZENODO, Dryad, Dataverse, or others.
Small data may also be published as data files or packages supplementary to a research article, however, the authors should prefer in all cases a deposition in data repositories.
