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All Journal Jurnal Edukasi dan Penelitian Informatika (JEPIN) JURNAL MEDIA INFORMATIKA BUDIDARMA J-SAKTI (Jurnal Sains Komputer dan Informatika) KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) Journal of Computer System and Informatics (JoSYC) Jurnal Sistem Komputer dan Informatika (JSON) JUKI : Jurnal Komputer dan Informatika TIN: TERAPAN INFORMATIKA NUSANTARA Brahmana : Jurnal Penerapan Kecerdasan Buatan Jurnal Tunas Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) RESOLUSI : REKAYASA TEKNIK INFORMATIKA DAN INFORMASI KLIK: Kajian Ilmiah Informatika dan Komputer J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Pengabdian Kepada Masyarakat BEES: Bulletin of Electrical and Electronics Engineering Bulletin of Data Science Journal of Artificial Intelligence and Engineering Applications (JAIEA) JOMLAI: Journal of Machine Learning and Artificial Intelligence Journal of Informatics, Electrical and Electronics Engineering Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen)
Okprana, Harly
STIKOM Tunas Bangsa, Pematangsiantar, Indonesia
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Journal : TIN: TERAPAN INFORMATIKA NUSANTARA

 1 
Pengelompokan Pembiayaan Nasabah Klaim Asuransi Pengguna Kendaraan Bermotor dengan Metode K-Medoids Aulanda, Lulu; Windarto, Agus Perdana; Okprana, Harly
TIN: Terapan Informatika Nusantara Vol 2 No 4 (2021): September 2021
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

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Abstract

In general, insurance is providing risk coverage to the insurer, namely the insurance company for a predetermined period and agreements. Insurance or coverage is an agreement between two or more parties, in which the insurer binds himself to the insured, by receiving an insurance premium, to provide compensation to the insured due to loss, damage or loss. The k-medoids method is one of several clustering methods in data mining which is part of partitional clustering. This method uses objects in a collection of objects to represent a cluster. The k-medoids clustering method can be applied to customer financing data for insurance claims on motor vehicle users, so that the financing grouping can be seen based on these data. From the grouping data, the characteristics can be seen so that it is known that the cluster is low, cluster is medium and cluster is high
Analisis Metode Backpropragation DalamMemprediksi Kelulusan Mahasiswa Studi Kasus STIKOM Tunas Bangsa Nasution, Selvi Salsabillah; Okprana, Harly; Saragih, Ilham Syahputra
TIN: Terapan Informatika Nusantara Vol 2 No 5 (2021): Oktober 2021
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

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Abstrak− Prediksi kelulusan Mahasiswa STIKOM Tunas Bangsa diperlukan untuk meninjau sejauh mana tingkat pemaha.man Siswa. Backpropagation merupakan salah satu teknik yang baik digunakan untuk prediksi, Metode yang digunakan adalah metode Backpropagation. Dengan metode ini dapat dilakukan pengolahan data menggunakan nilai input serta target yang ingin dihasilkan. Sehingga dapat memprediksi kelulusan Mahasiswa dalam uji kompetensi keahlian. Selanjutnya data yang akan dikelola adalah rekap nilai rata-rata kejuruan jurusan sistem komputer dari semester 1 sampai semester 5 dengan aspek pengetahuan pada target siswa Tahun Pelajaran 2019 dan Tahun Pelajaran 2020 yang diperoleh dari penjumlahan seluruh mata pelajaran pada setiap semester. Hasil dari perhitungan dengan metode Backpropagation dengan aplikasi Matlab akan menjadi prediksi dalam menghasilkan nilai tingkat kelulusan siswa di masa yang akan datang. Sehingga penelitian ini menjadi indikator dalam pengembangan prediksi Mahasiswa dimasa yang akan datang. Kata Kunci : Jaringan Syaraf Tiruan, Backpropagation, Prediksi Mahasiswa STIKOM Tunas Bangsa Abstract− Predictions of student bud stikom graduation nations are needed to look at the level of insia. Man student. Backpropagation is one of the techniques used for prediction, the method used is the method of backpropagation. This method will allow data processing to use input values and targets to be produced. So it can predict student graduation in competence expertise tests. Furthermore, the data will be managed is the vocational recap of the computer system's department of department from semester 1 to semester 5 with knowledge on student targets of lesson year 2019 and 2020 school years that are generated from a total of entire subjects each semester. The result of calculating the backpropagation method with the matlab application will be the prediction in producing a student's grade level of graduation in the future. So this research should be an indicator of future student development predictions. Keywords: artificial nerve tissue, backpropagation, predictive female to the nation's bud.
Algoritma C4.5 Dalam Data Mining Untuk Menentukan Klasifikasi Penerimaan Calon Mahasiswa Baru Haryoto, Parawystia Prabasini; Okprana, Harly; Saragih, Ilham Syahputra
TIN: Terapan Informatika Nusantara Vol 2 No 5 (2021): Oktober 2021
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

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Abstract

Conducting a graduation classification of prospective freshmen at a college should be best not only by the written exam value criteria but also the interview test, and other things that can serve as assessment parameters. In this study an increased parameters for classifying potential students in the scarlet hyperlinate's post-collegiate bud with deep learning methods using a c4 algorithc.5. With this method of classification, it is hoped to help academicians determine the criteria of active and exemplary freshman candidates. From experiment with the rapidminer's software on the data of students who have registered from 2016 to 2020, obtained an active student classification defined by the value of the interview being the first node, coupled with the value of an academic potential test. In the meantime, it is found that what can affect a student's performance is the school's origin and duration of college. Students who continue studying => 4 years tend to have grades and achievements under those who continue to study at three to four years. Based on research, score of accuracy at 81.32%.
Strategi SEO Berbasis WEB untuk Pengoptimalan Pemasaran UMKM Berbasis Digital: Memanfaatkan Peluang Ekonomi Digital Okprana, Harly; Darma, Surya
TIN: Terapan Informatika Nusantara Vol 5 No 6 (2024): November 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v5i6.5999

Abstract

This research aims to optimize digital marketing for Micro, Small, and Medium Enterprises (MSMEs) by applying web-based SEO (Search Engine Optimization) methods to capitalize on digital economic opportunities. The main issue faced by MSMEs is low visibility on search engines, which impacts organic traffic and online market competitiveness. The proposed solution is an SEO strategy that includes content optimization, website structure, relevant keywords, quality backlinks, and search engine algorithm analysis. The system used involves a case study at Primecom Store in Pematangsiantar, North Sumatra, with the implementation of SEO steps such as on-page and off-page optimization, and monitoring using Google Analytics. Over six months of implementation, the research results show an increase in organic traffic from 200 to 523 visits per month, an average keyword ranking improvement of 35%, and page visits rising from 100 to 417 per month. Additionally, quality backlinks increased from 10 to 40, while page load time decreased from 4.5 seconds to 2.8 seconds. These results demonstrate that the implementation of a web-based SEO strategy can enhance MSMEs' visibility and competitiveness in the digital market. This research provides practical recommendations for MSMEs to effectively leverage digital economic opportunities through an integrated SEO strategy.
Co-Authors Agus Perdana Windarto Ambariyanto Ambariyanto Andi Sahputra Annas Prasetio Aulanda, Lulu Bahrudi Efendi Damanik Cici Astria Dea Dwi Rizki Tampubolon Dedy Hartama Eka Irawan Eka Irawan F Fitriyani GS , Achmad Daengs Hadinata, Jaya Tata Halimatusakdiah Pohan Haryoto, Parawystia Prabasini Hasudungan Siahaan Hendro Sanjaya Heru Satria Tambunan Heru Satria Tambunan Ilham Syahputra Saragih Ina Syafitri Ingkana Devi Cahaya Irfan Sudahri Damanik Jaya Tata Hardinata Millah Sari Muhammad Zarlis Nasution, Rizki Alfadillah Nasution, Selvi Salsabillah Nasution, Siti Andry Yani Ni Luh Wiwik Sri Rahayu Ginantra Poningsih Poningsih Poningsih Poningsih, Poningsih Primatua Sitompul Rizky Khairunnisa Sormin Robiansyah, Wendi Sepridho, Jaka Sonia Tarigan Surya Darma Syahfitri, Retno Ayu Syahputra, Muhammad Riza Teuku Afriliansyah Tri Ayu Lestari Tri Novika Wanto, Anjar Widodo Saputra Winanjaya, Riki Yuegilion Pranayama