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TeknoIS : Jurnal Ilmiah Teknologi Informasi dan Sains
Published by Sekolah Tinggi Ilmu Komputer Binaniaga
ISSN : 20873891     EISSN : 25978918     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Decision Sciences, Operations Research & Management Engineering
TEKNOIS : Jurnal Ilmiah Teknologi - Informasi & Sains Publish by the STIKOM Binaniaga. TeknoIS published twice a year, in May and November. TeknoIS includes Research in the field of Information Technology, Information System, Computer Science and Other. Editors invite research lecturers the reviewer, practitioners industry, and observers to contribute to this journal.
Arjuna Subject : -
Articles 145 Documents
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Application of the SAW Method for Recommendations for Determining Recruitment of Backend Engineers Mellyza Rismawan; Muhamad Miftahudin; Rajib Ghaniy
TeknoIS : Jurnal Ilmiah Teknologi Informasi dan Sains Vol 13, No 2 (2023): July
Publisher : Universitas Binaniaga Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36350/jbs.v13i2.217

Abstract

Determining the acceptance of candidate backend engineers will go through various stages and an assessment process based on certain predetermined criteria or requirements. However, the problem faced at this time is the result of an assessment that has almost the same score weight, close to the same or even exactly same, of course this will make it difficult for the manager concerned to make a decision. Therefore, the implementation of a Decision Support System (DSS) using the Simple Additive Weighting (SAW) method on this problem is one of the right and effective ways. The decision support system created in this study has successfully implemented the SAW method based on candidate backend engineer data which has been processed using 8 criteria, namely Golang, Test Results, Code Readability, Documentation, Unit Tests, Tech Experience, Design System, Code Scalability. And the ranking results have been tested using Spearman Rank correlation calculations and yielded a value of 0.98, which means that the SAW method can be used to determine recommendations for accepting backend engineer candidates
Penerapan Algoritma Naïve Bayes Untuk Penentuan Diagnosa Obesitas Pada Peserta Sosialisasi Deteksi Dini Penyakit Tidak Menular (PTM) Ida Maryani; Irmayansyah Irmayansyah
TeknoIS : Jurnal Ilmiah Teknologi Informasi dan Sains Vol 13, No 2 (2023): July
Publisher : Universitas Binaniaga Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36350/jbs.v13i2.200

Abstract

Obesity is excess fat accumulation due to an imbalance between energy intake and energy expenditure for a long time. Obesity can cause various non-communicable diseases, including heart disease, stroke, diabetes, high blood pressure, gallstones, respiratory problems, cancer, osteoarthritis, including infertility. The Regional Government through Posbindu Non-Communicable Diseases (PTM) in every UPTD Puskesmas carries out socialization as an early detection of PTM risk. This activity is carried out every two or three months, by conducting routine health checks for employees. Measurement of obesity diagnosis is usually by using the Body Mass Index (BMI). BMI is a measurement method by calculating the height in meters and weight in kilograms. However, BMI also has drawbacks, namely it cannot distinguish between muscle mass and fat mass. Apart from BMI, other factors also affect the diagnosis of obesity, including gender, age, abdominal circumference, risky behaviors such as lack of physical activity, eating patterns of excess sugar, excess salt, excess fat, not eating enough fruits and vegetables. With so many determinants of the diagnosis of obesity, the Naïve Bayes Algorithm is used to predict the diagnosis of obesity more effectively and accurately. The application built is in the form of a prototype that utilizes the PHP programming language. This study used data from the health examination of participants in the socialization of PTM risk early detection with a total of 60 participants. There are 10 variables used, namely Gender, Age, Height, Weight, Abdominal Circumference, Lack of Physical Activity, Eating Patterns of Excess Sugar, Excess Salt, Excess Fat and Undereating and Fruit while there are 2 classes, namely obesity and normal diagnoses. Based on the results of calculating accuracy with the Confusion Matrix, an accuracy value of 86.6% is obtained.
Penerapan Metode Topsis Untuk Rekomendasi Penetapan Siswa Berprestasi Penerima Penghargaan Tahunan Di Tingkat Sekolah Menengah Pertama Cindy Shentia; Lis Utari
TeknoIS : Jurnal Ilmiah Teknologi Informasi dan Sains Vol 13, No 2 (2023): July
Publisher : Universitas Binaniaga Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36350/jbs.v13i2.220

Abstract

In a school, the ranking of outstanding students is usually only chosen based on their report card scores, as well as in SMP PGRI 16. The selection of outstanding students at SMP PGRI 16 is only based on grades (raport) which are ranked 1 to 3 only. In the selection process there are many opportunities to make wrong decisions because the student selection process is only based on one aspect, namely student scores (raport), while the values of attitudes and achievements obtained outside of school are not taken into consideration as additional criteria for determining students who are considered outstanding and receive annual awards. This means that most likely the selected outstanding students do not reach the desired standard and do not get the best candidates. The purpose of this research is as an alternative way for schools to determine outstanding students so that they are not based solely on academic scores, and there is no cheating in the selection of outstanding students. The method used in this study is the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. The results of this study are that the TOPSIS method can provide the best results from the criteria and weights that have been determined. From the calculation results with the TOPSIS method, it was obtained by a student named MRF with a preference value of 0.9742
Penerapan Metode Per Connection Classifier dan Failover Untuk Meningkatkan Optimasi Koneksi Jaringan Internet Aris Aditya Nugraha; Arif Harbani
TeknoIS : Jurnal Ilmiah Teknologi Informasi dan Sains Vol 13, No 2 (2023): July
Publisher : Universitas Binaniaga Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36350/jbs.v13i2.209

Abstract

The need for internet access is currently very high considering the need to search for information, articles or the latest knowledge. Many agencies or companies have integrated the internet network into their main needs in carrying out a goal in order to achieve the vision or mission of the agency or company that must be completed in accordance with the business processes that have been made. Basically, all companies want an internet connection that is effective and efficient in its use. Therefore, a solution arises from the existing problem to use two Internet Service Providers and make the proxy router a Load Balancer. The mechanism is that the proxy balances the load on an existing network. Based on the system development method used, namely the Network Development Life Cycle (NDLC), before determining the load balancing method used, an analysis of the traffic conditions running on a network is carried out and monitoring to obtain history on a network. The selection of Per Connection Classifier Load Balancing can meet the criteria because it optimizes existing connections so that there is no buildup or overload. Application of the Failover Technique, that is, if one connection from the ISP is lost, it will automatically look for a gateway that is used as a backup so that it can back up all network traffic. In addition to Load Balancing and Failover, bandwidth management is also implemented according to the needs and characteristics of user usage. In the results obtained from the evaluation for the Network Expert Test, there are results with a feasibility presentation of 100%, which means it is categorized as "Very Eligible". all statements submitted are Valid and Reliable.
Sistem Pakar Diagnosa Kerusakan Hardware Laptop Menggunakan Metode Forward Chaining Rahmatia Wulan Dari; Sopi Sapriadi
TeknoIS : Jurnal Ilmiah Teknologi Informasi dan Sains Vol 13, No 2 (2023): July
Publisher : Universitas Binaniaga Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36350/jbs.v13i2.201

Abstract

Hardware damage to computers is a common problem. Proper and accurate diagnosis is essential to repair damage effectively and avoid unnecessary costs. Therefore, system experts are considered as an effective solution to help diagnose hardware damage to computers. Expert System for Diagnosing Computer Hardware Damage Using the Forward Chaining Method is an expert system built to assist in diagnosing computer hardware damage. The forward chaining method is used in this system to predict possible hardware damage based on the symptoms reported by the user. This expert system uses a structured knowledge base to perform a diagnosis. This knowledge base contains information about the symptoms associated with hardware damage to a computer, as well as possible solutions to fix them. The diagnostic process in this expert system begins with the user reporting the symptoms he is experiencing. Then, the system will achieve these symptoms with the knowledge base to find the most probable solution. This process is performed automatically by the system and does not require the intervention of a computer technician. The diagnostic results produced by this system are quite accurate, namely 85% and can help users to save time and money spent on repairing computers. In addition, this expert system can also assist computer technicians in diagnosing and repairing damage to computer hardware.

Page 15 of 15 | Total Record : 145

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