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All Journal Jurnal Teknik Komputer AMIK BSI METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi JIPN (Journal of Informatics Pelita Nusantara) METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Jurnal Nasional Komputasi dan Teknologi Informasi Jurnal Informatika Kaputama (JIK) TRIDARMA: Pengabdian Kepada Masyarakat (PkM) Journal of Intelligent Decision Support System (IDSS) INFOKUM Jurnal Ilmu Komputer dan Sistem Informasi (JIKOMSI) Journal of Computer Networks, Architecture and High Performance Computing Jurnal Pengabdian kepada Masyarakat Nusantara KLIK: Kajian Ilmiah Informatika dan Komputer INFOSYS (INFORMATION SYSTEM) JOURNAL Jurnal Media Informatika Jurnal Info Sains : Informatika dan Sains
Jijon Raphita Sagala
STMIK Pelita Nusantara, Medan
Aerospace Engineering Arts Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Neuroscience Social Sciences Transportation

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Penerapan Data Mining Untuk Pengelompokan Siswa Berdasarkan Nilai Akademik dengan Algoritma K-Means Penda Sudarto Hasugian; Jijon Raphita Sagala
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 3 No. 3 (2022): Desember 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v3i3.627

Abstract

The data mining process by applying the K-Means algorithm is carried out to group data into one or more groups, where data that has representative similarities is grouped into one group and data that has differences is included in another group. Grouping student data is done to facilitate schools in facilitating students based on differences in their ability to learn and participate in learning which consists of groups or classes of superior students, medium and low groups. The data application used for the calculation process is student data based on a centralized assessment in presenting reports on student learning outcomes using the results of report cards, namely the rapid miner. This assessment forms the basis of the attributes used in the calculation process to determine superior, medium and low class students. The purpose of this study is to manage centralized assessment data in presenting reports on student learning outcomes and grouping students in superior classes by implementing the K-means algorithm and conducting tests using the rapidminer application. So that student data can be managed and grouped effectively and efficiently
SISTEM PENDUKUNG KEPUTUSAN PEMBERIAN BEASISWA DI SMAN 1 BANGUN PURBA MENGGUNAKAN METODE SIMPLE MULTI ATTRIBUTE RATING TECHNIQUE (SMART) Nicko Permana Putra; Jijon Raphita Sagala
Jurnal Informatika Kaputama (JIK) Vol 6 No 1 (2022): Volume 6, Nomor 1, Januari 2022
Publisher : STMIK KAPUTAMA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59697/jik.v6i1.141

Abstract

Beasiswa merupakan pemberian bantuan kepada seseorang untuk keberlangsungan pendidikan. SMAN 1 Bangun Purba sebagai salah satu lembaga pendidikan formal yang memberikan beasiswa bagi siswa yang kurang mampu dan berprestasi. Penerimaan beasiswa selama ini menerapkan sistem manual dengan cara mencatat dan menjumlahkan nilai siswa dan membandingkan nilai satu persatu. Bersadarkan permasalahan tersebut penelitian ini dibuat menggunakan Sistem Pendukung Keputusan (SPK) dengan menerapkan metode Simple Multi Attribute Rating Technique (SMART) yaitu metode yang mampu menyelesaikan masalah dengan multikriteria. Kriteria yang digunakan dalam penelitian ini adalah nilai rata-rata ujian semester, penghasilan orang tua, prestasi, sikap, tanggungan orang tua dan absensi. Sistem yang dibangun pada SPK ini menggunakan bahasa pemrograman PHP dan database MySQL. Hasil dari penelitian ini berupa laporan rekomendasi penerima beasiswa yang dirangking dengan urutan nilai terbesar. Sistem yang dibuat dapat memberikan hasil yang akurat dan tepat untuk memudahkan pihak sekolah mementukan penerima beasiswa.
Alumni Data Grouping Using the K-Means Clustering Method for Study Program Curriculum Development Penda Sudarto Hasugian; Jijon Raphita Sagala; Lela Dwi Ani
Jurnal Info Sains : Informatika dan Sains Vol. 13 No. 02 (2023): Jurnal Info Sains : Informatika dan Sains , Edition September  2023
Publisher : SEAN Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Application of Datamining by applying the k-means clustering method to classify STMIK Pelita Nusantara alumni data as a basis for developing study program curricula that are more relevant to the needs of the world of work or industrial needs. Where the K-Means Clustering Method is used to group alumni based on similar characteristics they have, such as personal data, academic achievement, areas of expertise, and job information after graduating from college. The research data source used is graduate data for the 2021/2022 academic year. The data collection method was carried out by distributing questionnaires directly to alumni. The application of the k-means method is carried out by forming 2 groups (clusters), namely C1 = Liner and C2 = Not Linear. Data testing is also carried out using the rapid miner application. So that by grouping alumni data, it is hoped that tertiary institutions can identify the needs and preferences of alumni for the study programs followed so that they can develop study program curricula that are more targeted and in accordance with the needs of the job market.Application of Datamining by applying the k-means clustering method to classify STMIK Pelita Nusantara alumni data as a basis for developing study program curricula that are more relevant to the needs of the world of work or industrial needs. Where the K-Means Clustering Method is used to group alumni based on similar characteristics they have, such as personal data, academic achievement, areas of expertise, and job information after graduating from college. The research data source used is graduate data for the 2021/2022 academic year. The data collection method was carried out by distributing questionnaires directly to alumni. The application of the k-means method is carried out by forming 2 groups (clusters), namely C1 = Liner and C2 = Not Linear. Data testing is also carried out using the rapid miner application. So that by grouping alumni data, it is hoped that tertiary institutions can identify the needs and preferences of alumni for the study programs followed so that they can develop study program curricula that are more targeted and in accordance with the needs of the job market.
Penerapan Aplikasi Sistem Informasi Forkomling Pada Perumahan Bekala Asri Jijon Raphita Sagala; Penda Sudarto Hasugian; Martua Sitorus; Melisa Van Breukelen; W. S. Usha Nantheni
Jurnal Pengabdian kepada Masyarakat Nusantara Vol. 5 No. 1 (2024): Jurnal Pengabdian kepada Masyarakat Nusantara (JPkMN)
Publisher : Cv. Utility Project Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55338/jpkmn.v5i1.2917

Abstract

Perkembangan Teknologi yang begitu pesat sekarang ini, masyarakat tentu tidak asing lagi dengan internet. Internet merupakan akses masyarakat untuk melihat atau mencari informasi yang dibutuhkan masyarakat. Melalui Penerapan Aplikasi Sistem Informasi Forkomling Pada Perumahan Bekala Asri, didapatkan hasil pengumpulan data serta analisis kebutuhan yang telah dilakukan pada Perumahan Bekala Asri, didapatkan hasil bahwa Aplikasi Sistem Informasi Forkomling yang telah dibangun dapat menyelesaikan pengolahan keamanan, kebersihan, pengutipan iuran dan lainya sehingga dapat berjalan dengan efektif dan menghasilkan informasi yang lebih akurat dan informatif sehingga memberikan manfaat dalam proses transparansi dan keterbukaan laporan keuangan iuran warga tersebut. Selain oleh petugas, semua warga juga sekarang dapat mengakses laporan keuangan warga secara langsung dan terbuka. Dalam Penelitian ini dibagun Aplikasi Sistem Informasi Forkomling di Perumahan Bekala Asri berbasis web mengunakan PHP dan HTML sebagai bahasa pemogramannya, MYSQL sebagai database, XAMPP sebagai server lokal, agar pengelolaan kemanan dan kebersihan bisa berjalan dengan baik.
Sistem Pendukung Keputusan Pemilihan Tentor Terbaik Dengan Metode Technique For Order Preference By Similarity To Ideal Solution (Topsis) Larisma Situmorang; Jijon Raphita Sagala
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 3, No 3 (2020): Desember 2020
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v3i3.2418

Abstract

Abstrak— Tentor adalah Sumber daya manusia yang merupakan bagian yang sangat terpenting bagi tumbuh kembangnya sebuah Bimbingan Belajar. Bimbingan Belajar yang berkembang dengan baik sangatlah  dipengaruhi oleh kualitas sumber daya manusia, yang dalam hal ini adalah tentor  yang bekerja di dalam sebuah Bimbingan Belajar tersebut. Oleh karena itu, dilakukan pemilihan tentor terbaik menggunakan Metode sistem Keputusan Pemilihan Tentor Terbaik di Bimbingan Manna adalah Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Sistem Pendukung Keputusan yang dibangun  dapat membantu serta memudahkan pihak bimbingan belajar manna terutama ketua dalam mengambil sebuah keputusan pemilihan tentor terbaik yang dirancang dengan aplikasi berbasis website dan dengan database phpmyadmin. Form  nilai matrik adalah form yang paling penting, karena didalam form  ini dilakukan perhitungan dengan langkah-langkah perhitungan metode Topsis mulai dari awal, Nilai Matrik, Nilai Matrik Normalisasi, Nilai Bobot Normalisasi, Matrik Ideal Positif/Negatif, Jarak Solusi Ideal Positif/Negatif, Nilai Preferensi.
PENGAJUAN KREDIT SEPEDA MOTOR MENGGUNAKAN ANALYTICAL HIERARCHY PROCESS (STUDI KASUS SHOWROOM YOYO) Nuri Latifa Efrata; Jijon Raphita Sagala
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 1, No 2 (2018): OKTOBER 2018
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v1i2.776

Abstract

The company sets policies in granting credit, among others, sets the Standard for accepting or rejecting credit risk, namely determining who has the right to receive credit that has fulfilled the five C requirements, what is the character of the customer, capacity to repay credit, the ability of capital owned by the customer (capital), collateral owned by the customer to bear credit risk (collateral) and the customer's financial condition (condition). Decisions are reached after consideration is made by choosing one possible choice. Decisions are categorized as feasible and not feasible. The criteria set by the company in applying for credit are personality, advances, abilities, guarantees, conditions. With the AHP method, from the criteria determined the intensity of the importance / priority scale on the criteria. After doing the calculation process, the credit decision was obtained from the ranking of the AHP method: 0.258652 (feasible), 0.2569579 (feasible), 0.2445319 (not feasible), 0.2398582 (not feasible).
Co-Authors Amran Sitohang Devi Permata Sari Sianturi Dwi Putri Rahayu Fenda Sudarto Hasugian Hasanul Fahmi Hasanul Fahmi Hasugian , Paska Marto Hasugian, Penda Sudarto Insan Astuti Laphita Sari Simanjuntak Larisma Situmorang Lela Dwi Ani Martua Sitorus Mawaddah Arrosty Melisa Van Breukelen Mufida Khairani Murni Marbun Nicko Permana Putra Nuri Latifa Efrata Nurjamiyah, Nurjamiyah Nurmawaty Hutabarat Permata Sari Tarigan Sarah Afriani Sihombing Simangunsong, Agustina Sulindawaty, Sulindawaty Syahrul Fahri W. S. Usha Nantheni Yessy F A Lubis Yuda Perwira Zanma Ameta Tarigan