Article Per Year (5 Year)
p-Index From 2019 - 2024
0.659
P-Index
This Author published in this journals
All Journal Jurnal Tekinkom (Teknik Informasi dan Komputer)
Sahat Sonang
Unknown Affiliation
Computer Science & IT

Published : 4 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 4 Documents
Search

 1 
IMPLEMENTASI TOPSIS DALAM PEMBERIAN REWARD GURU TERBAIK PADA SMK SWASTA 1 HKBP PEMATANGSIANTAR Sahat Sonang; Arifin Tua Purba
Jurnal Tekinkom (Teknik Informasi dan Komputer) Vol 4 No 1 (2021): Jurnal Tekinkom
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v4i1.226

Abstract

Reward is a form of appreciation received by an individual for the achievement of a work performance that aims to motivate the individual to have high productivity. In managerial terms, giving a warning is a good and reasonable thing to do by the leader to his subordinates. Likewise with the Private Vocational School 1 HKBP Pematangsiantar. This research was conducted to provide solutions to problems that occur through the design of a decision support system software for giving the best teacher reward with the Technique for Order Preferences by Similarity to Ideal Solution (TOPSIS) method with assessment components including (1) discipline, (2) involvement, (3 ) attendance, (4) leadership, (5) training and (6) social. The Decision Support System for providing teacher rewards is able to accommodate subjective problems, lack of transparency, and doubts about its accuracy and the output is very credible. Thus the decision-making process for giving the best teacher reward is efficient, effective and objective and can be used as a recommendation for school principals in determining which teachers deserve rewards. From the 10 data samples used, the results of the analysis using the Decision Support System show that the highest recommendation value is 0.7149 on behalf of A8, while the lowest recommendation value is 0.2851 on behalf of A10.
PENGELOMPOKAN JUMLAH PENDUDUK BERDASARKAN KATEGORI USIA DENGAN METODE K-MEANS Sahat Sonang; Arifin Tua Purba; Ferri Ojak Imanuel Pardede
Jurnal Tekinkom (Teknik Informasi dan Komputer) Vol 2 No 2 (2019)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (458.123 KB) | DOI: 10.37600/tekinkom.v2i2.115

Abstract

Control of population is one of the tasks of the government in Indonesia. The increase and movement of population in each region makes a certain area to defeat changes in population surging, and this can affect the economic level of the area. This study aims to process the population of Pematangsiantar City in 2018 which is divided into age groups, namely: Toddlers, Young Children, Early Adolescents, Late Adolescents, Early Adolescents, Late Adulthood, Early Adulthood, Elderly, Late Elderly, and Upper Seniors. Data processing is done by using K-Means method clustering in accordance with the population of Pematangsiantar City per district. With this grouping, we can see that the number of population in each sub-district is based on each age group so that we can implement programs that are more appropriate in improving human resources.
SISTEM PENDUKUNG KEPUTUSAN KELAYAKAN PEMBERIAN PINJAMAN KREDIT MENGGUNAKAN METODE TOPSIS PADA CUM CARITAS HKBP PEMATANGSIANTAR Sahat Sonang; Arifin Tua Purba; Victor Marudut Mulia Siregar
Jurnal Tekinkom (Teknik Informasi dan Komputer) Vol 3 No 1 (2020)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v3i1.131

Abstract

This research was conducted to solve the problem of decision making to determine the feasibility of giving loans to CUM Caritas HKBP Pematangsiantar. The applicant's assessment includes an administrative assessment and the survey is still being conducted without using a computer-based decision support system. To simplify the decision making to determine the feasibility of giving the loan, a decision support system was designed using the Topsis method (Technique for Order Preference by Similarity to Ideal Solution). The criteria used consist of: Income, Dependent, Loan Size, Guarantee Value and Number of Installments. The results of the calculation of the case of this study that are eligible for a loan in order are NA2 with a value of 0.6390, NA1 with a value of 0.5737, the third is NA3 with a value of 0.5053, and the last is NA4 with a value of 0.3102. The results of this study can be concluded that the Topsis method can answer or resolve problems faced by CUM Caritas HKBP Pematangsiantar in determining creditworthiness.
IMPLEMENTASI K-MEANS DALAM EVALUASI KINERJA DOSEN POLITEKNIK BISNIS INDONESIA Sahat Sonang
Jurnal Tekinkom (Teknik Informasi dan Komputer) Vol 1 No 1 (2018)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1000.676 KB) | DOI: 10.37600/tekinkom.v1i1.49

Abstract

Dalam rangka meningkatkan mutu Pendidikan Tinggi di Indonesia, Pemerintah mengatur Standar Nasional Pendidikan Tinggi melalui Peraturan Menteri Pendidikan dan Kebudayaan Republik Indonesia No.49 Tahun 2014. Dimana dosen merupakan salah satu bagian yang sangat penting dalam meningkatkan mutu pendidikan tinggi. Untuk mengetahui kualitas Dosen, maka pihak kampus akan melakukan evaluasi kepada setiap dosen yang mengajar. Pada penelitian ini, penulis mencoba membangun sebuah sistem untuk mengevaluasi kinerja Dosen berdasarkan kriteria: Kesesuaian waktu mengajar, Ketepatan masuk naskah ujian, Ketepatan masuk koreksi nilai, Evaluasi proses belajar mengajar, Penelitian, dan pengabdian kepada Masyrakat. Untuk memperoleh hasil evaluasi kinerja Dosen penulis menggunakan metode Algoritma K-Means. Dari hasil pengolahan data 20 orang dosen sebagai data sampel maka diperoleh hasil kinerja dosen yang dibagi dalam 4 kelompok yaitu: Sangat Baik, Baik, Cukup, Kurang. Dari hasil tersebut dapat disimpulkan proses pengambilan keputusan sangat optimal dengan menggunakan metode K-Means.
Co-Authors Arifin Tua Purba Ferri Ojak Imanuel Pardede Victor Marudut Mulia Siregar