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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Jurnal Komputer Terapan IT JOURNAL RESEARCH AND DEVELOPMENT INTECOMS: Journal of Information Technology and Computer Science Journal of Applied Engineering and Technological Science (JAETS) INFOKUM Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Jurnal UNITEK Jurnal Sistem Informasi dan Teknologi Jaringan Jurnal J-PEMAS JITER-PM (Jurnal Inovasi Terapan - Pengabdian Masyarakat) JI-Tech Jurnal Testing dan Implementasi Sistem Informasi JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Khairul Umam Syaliman
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Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Social Sciences

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Journal : Jurnal Sistem Informasi dan Teknologi Jaringan

 1 
Sistem Prediksi Keberhasilan Siswa Menggunakan Metode Nearest Cluster Classifier Khairul Umam Syaliman; Edwil Jafri
Jurnal Sistem Informasi dan Teknologi Jaringan (SISFOTEKJAR) Vol 2 No 2 (2021): September : 2021
Publisher : Pustaka Timur Publisher

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Abstract

According to present curriculum, every unit of elementary education conduct 4 (four) evaluation stages. The first stage is the first midterm evaluation which is done at the first quarter year. Second, the first semester evaluation is done at the second quarter year. Third, the second midterm evaluation is done at the third quarter year. The last is the second semester evaluation which is done at the fourth quarter year. The first midterm evaluation, the first semester evaluation and the second midterm evaluation are done to see the students’ ability in learning. Moreover, to determine the students’ success or failure in learning process at each grade is the score of the second semester which is compared to their minimum passing grade. If the students’ score is higher than the minimum passing grade decided, the students are determined to be successful. In contrary, if the students’ score is lower than minimum passing grade, they will be determined to be failed in learning process.by considering the above condition, the writer is interested in designing a system which can predict the potential failure of students earlier. This system is built by analyzing the first midterm score, the first semester score and the second midterm score by using Nearest Cluster Classifier (NCC) Method. By the result of this prediction, parents and teachers still have time for about 3 months to help and guide students who are predicted to be potentially failed in learning process.
Seleksi Fitur Menggunakan Pendekatan k-Nearest Neighbor (k-NN) Khairul Umam Syaliman; Yuliska; Nina Fadilah Najwa
Jurnal Sistem Informasi dan Teknologi Jaringan (SISFOTEKJAR) Vol 3 No 1 (2022): Maret : 2022
Publisher : Pustaka Timur Publisher

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Abstract

Data preprocessing merupakan salah satu tahapan yang penting dalam proses data mining. Salah satu proses pada tahapan ini adalah seleksi fitur atau disebut juga dengan seleksi variabel. Seleksi fitur adalah proses pemilihan fitur yang paling relevan serta membuang fitur yang tidak relevan, ambigu, redudan dan noisy feature. Proses seleksi fitur sangat menentukan performa metode pada tahapan mining, artinya salah dalam memilih metode pada proses seleksi fitur tidak akan menghasilkan pengetahuan yang benar dan sesuai dengan harapan. Ada banyak metode yang dapat digunakan untuk melakukan seleksi fitur, diantranya Principal Component Analysis (PCA), Informasi Gain dan Gain Ratio, akan tetapi banyak metode dari seleksi fitur yang memiliki kompleksitas dan computation cost yang tinggi.Karena hal tersebut penelitian kali ini menyarankan seleksi fitur dengan pendekatan k-Nnearesst Neighbor (k-NN). Adapun hasil dari penelitian ini terbukti bahwa data yang telah melwati seleksi fitur dengan pendekatan k-NN mampu memberikan hasil akurasi yang lebih baik.
Co-Authors Aldi Abdillah Nababan Ardianto Wibowo Ardiyanto Ardiyanto Ause Labellapansa, Ause Dadang Syarif Sihabudin Sahid Dini Hidayatul Qudsi Disa Putri, Tiara Ayu Dwi Gunawan, Dwi Dwi Putra, Nanda Edwil Jafri Fikri, Asyrul Gina Rahmadhany Hanifah, Puja Hartanto, Boby Hidayat, Erzi Ibnu Surya Ibnu Surya Jan Alif Kreshna Juanda Hakim Lubis Kartina Diah K. W. Lidia Maysofa M. Zulfahmi Mona Elviyenti Muhammad Crystandy Najwa, Nina Fadila Najwa, Nina Fadilah Nina Fadhilah Najwa Oktavian, Kurnia Eka Ramdani Hadi Nst, Andika Sapriadi Sapriadi Sapriadi, Sapriadi Satria Perdana Arifin Surya, Ibnu Yohana Dewi Lulu Widyasari Yuliska Yuliska Yuliska Yuliska Yuliska Yuliska Yuliska Yuliska Yuliska, Yuliska