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All Journal Jurnal Medika Veterinaria Indonesian Journal of Computer Science JURNAL MEDIA INFORMATIKA BUDIDARMA
Abdul Harris
Jurusan Peternakan, Fakultas Pertanian, Universitas Syiah Kuala
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Health Professions

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KADAR GLUKOSA DARAH ANJING PEMBURU (Canis familiaris) DI KENAGARIAN MUNGO KECAMATAN LUAK KABUPATEN LIMA PULUH KOTA PROVINSI SUMATERA BARAT (Blood Glucose Levels on Beagle (Canis familiaris) in Kenagarian Mungo, Luak District Lima Puluh Kota West Sumatera) Arisman, Afrio; Sugito, Sugito; N.A, Zuhrawati; Hasan, M; Harris, Abdul; Azhar, Azhar
Jurnal Medika Veterinaria Vol 9, No 2 (2015): J. Med. Vet.
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (135.588 KB) | DOI: 10.21157/j.med.vet..v9i2.3982

Abstract

This research aimed to determine glucose levels of beagle maintained by Sports Association of Pig Hunting (PORBI) in Kenagarian Mungo Sub-district Luak District Lima Puluh Kota, West Sumatera. Blood samples were collected from 20 beagles. Blood sampling was performed during fasting time and two hours postprandial. Data were analyzed with analysis of Chisquare (x2). The results showed that mean glucose level in the fasting state beagle was 63.45±11.84 mg/dl and two hours postprandial was 93.45±12.52 mg/dl. The statistical test results showed no significant difference (P0.05) between the frequency of feeding and supplementary feeding on blood glucose levels. To conclude, the average of beagle blood glucose levels is still in normal range. There is a difference in blood glucose between two hours postprandial and a fasting blood glucose levels and the difference between hunting frequency and giving multivitamins injection on blood glucose levels, but there was no difference on blood glucose levels between the frequency feeding and supplemental feeding.Key words: beagle, glucose levels, hunting, multivitamins
Evaluasi Kualitas Layanan Jaringan Komputer pada Jaringan Komputer STIKOM Dinamika Bangsa Jambi Novianto, Yudi; Harris, Abdul; Astri, Lola Yorita
Indonesian Journal of Computer Science Vol. 8 No. 1 (2019): April 2019
Publisher : STMIK Indonesia Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1319.861 KB) | DOI: 10.33022/ijcs.v8i1.164

Abstract

STIKOM Dinamika Bangsa Jambi is an institution engaged in education. It's utilize computer networks to carry out institutional and other data management activities. Network service quality / Quality of Service (QOS) is used to measure the level of performance of internet network connections which aims to improve the quality of internet services for institutions. The method used is the action reseach where in this method before evaluating the quality of the existing network, first step in the diagnosis, planning and retrieving the output of the Axence NetTools5 application. The Quality of Service (QOS) parameters that will be seen through this application include delay, packet loss, bandwidth (throughput). Evaluation is done by comparing the catch with Tiphon's standard where for the delay output obtained between 0-1 ms so that based on the tiphon standard includes a very good category. For packet loss the output obtained is 0% and includes a very good category. As for throughput, the output obtained is at least 75.5287%, so it can be concluded that the quality of output on the network is good.
Komparasi Performa Tree-Based Classifier Untuk Deteksi Anomali Pada Data Berdimensi Tinggi dan Tidak Seimbang Kurniabudi, Kurniabudi; Harris, Abdul; Veronica, Veronica
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 1 (2022): Januari 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i1.3473

Abstract

Anomaly detection is one solution to overcome the issue of data network traffic security, but is faced with the challenge of high data dimensionality and imbalanced data. High-dimensional and imbalanced data can affect the performance of the detection system. Therefore we need a feature selection technique that can reduce the dimensionality of the data by eliminating irrelevant features. In addition, the selected features need to be validated with the right classification algorithm to produce high anomaly detection performance. The purpose of this study is to produce a combination of feature selection techniques and appropriate classification algorithms to produce a system that is able to detect attacks on high-dimensional and imbalanced data. Chi-square feature selection technique was used to eliminate irrelevant features. To determine the ideal classification algorithm, in this study, a comparison of the performance of the tree-based classifer algorithm was carried out. This study also examines the performance of classification techniques in detecting traffic on high-dimensional and unbalanced data. Several Tree-based classification algorithms such as REPTree, J48, Random Tree and Random Forest were tested and compared. Testing with the best performance as a recommendation for the ideal combination of feature selection techniques and classification algorithms. This research produces an anomaly detection system that has high performance. For experimental data, the CICIDS-2017 dataset is used, which has high data dimensionality and contains unbalanced data. The test results show that Random Tree has an accuracy of 99.983% and Random Forest 99.984%.
Co-Authors Afrio Arisman, Afrio Azhar Azhar Kurniabudi, Kurniabudi Lola Yorita Astri, Lola Yorita M Hasan N.A, Zuhrawati Novianto, Yudi Sugito Sugito VERONICA VERONICA