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IJISCS (International Journal of Information System and Computer Science)

ijiscs
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Published by Institut Bakti Nusantara

ISSN : 25980793 EISSN : 2598246X DOI : -

Core Subject : Science, Engineering,

Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

The IJISCS (International Journal of Information System and Computer Science) is a publication for researchers and developers to share ideas and results of software engineering and technologies. These journal publish some types of papers such as research papers reporting original research results, technology trend surveys reviewing an area of research in software engineering and technologies, survey articles surveying a broad area in software engineering and technologies. The scope covers all areas of software engineering methods and practices, object-oriented systems, rapid prototyping, software reuse, cleanroom software engineering, stepwise refinement/enhancement, ambiguity in software development, impact of CASE on software development life cycle, knowledge engineering methods and practices, formal methods of specification, deductive database systems,logic programming, reverse engineering in software design, expert systems, knowledge-based systems, distributed knowledge-based systems, knowledge representations, knowledge-based systems in language translation & processing, software and knowledge-ware maintenance, Software Specification and Modeling, Embedded and Real-time Software (ERTS), and applications in various domains of interest.

Arjuna Subject : Ilmu Komputer (semua kategori) - Sistem Informasi

Articles 4 Documents

Search results for , issue "Vol 6, No 3 (2022): IJISCS (International Journal of Information System and Computer Science)" : 4 Documents clear

THE COMPARISON USING EXPECTATION-MAXIMIZATION ALGORITHM AND C4.5 ALGORITHM TO PREDICT THE RESULT OF BIOGAS PRODUCTION AS A POWER PLANT AT PT BUDI STARCH & SWEETENER (BSSW) Eriska Vivian Astuti eriska; Nurmayanti Nurmayanti; Rima Mawarni; Asep Afandi; Aris Munandar
IJISCS (International Journal of Information System and Computer Science) Vol 6, No 3 (2022): IJISCS (International Journal of Information System and Computer Science)
Publisher : Bakti Nusantara Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56327/ijiscs.v6i3.1323

Biogas is the result of the development of alternative energy that has formed through the decomposition of organic matter through an anaerobic fermentation process (without oxygen) that produces gas in the form of methane gas (CH4) which has burned. Biogas is a kind of renewable energy because it has a high methane content and calorific value. Methane has one carbon in each chain, which can produce combustion that is more environmentally friendly when compared to fuels that have long carbon chains using specific calculation techniques or methods, a data mining process has been carried out to locate interesting patterns or information in selected data to manipulate the data into more valuable information by extracting significant patterns from the database.

MODELING AND IMPLEMENTATION OF AN ECO-OPTIMIZED NETWORK BASED ON VLANS FOR THE REDUCTION OF CARBON FOOTPRINTS Bopatriciat Boluma Mangata; Evariste Sindani Mbuta; Patience Ryan Tebua Tene; Blanchard Kangulumba Mutanga
IJISCS (International Journal of Information System and Computer Science) Vol 6, No 3 (2022): IJISCS (International Journal of Information System and Computer Science)
Publisher : Bakti Nusantara Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56327/ijiscs.v6i3.1267

The present work proposes a model of responsible digitalization in the Democratic Republic of Congo, by implementing an eco-optimized network based on VLANs while reducing the carbon footprint, the technologies to be adopted and the energy and ecological efficiency measurement indicators to minimize the greenhouse gas emissions in DRC. The goals of such an architecture are to simplify the infrastructure, increase virtualization, save hardware and software, rationalize the use of the information system, reduce energy consumption and finally reduce the carbon footprint of the information system. With regard to the design of an optimal local network within the Directorate General of Taxes (DGI), we opted to set up a virtual local network (VLAN) that will be adapted to all DGI services. The architecture for the implementation of the optimal network within the DGI is composed of the following elements Seven VLANs for the Central Directorates; One VLAN for the operational services; One core switch (Cisco manageable switch), which enabled us to manage our network; One router with firewall for managing inter-VLAN traffic, and incoming and outgoing traffic for Internet access; Eight distribution switches that can connect equipment in the same VLAN.

THE EFFECTS OF FEATURE SELECTION METHODS ON THE CLASSIFICATIONS OF IMBALANCED DATASETS Femi Dwi Astuti; Indra Yatini Buryadi
IJISCS (International Journal of Information System and Computer Science) Vol 6, No 3 (2022): IJISCS (International Journal of Information System and Computer Science)
Publisher : Bakti Nusantara Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56327/ijiscs.v6i3.1279

imbalanced data often results in less than optimal classification. Also, datasets with a large number of attributes tends to make the classification results not too good, and in order get better classification accuracy results, one thing that could be done is to perform pre-processing to select the features to be used in the classification. This research uses information gain and gain ratio feature selection algorithms for the pre-processing stage prior to classification, and Naïve Bayes algorithm for the classification. The test is performed to determine the values of accuracy, precision, recall from the classification process without feature selection; accuracy value with information gain feature selection; accuracy value with gain ratio; and accuracy value with CBFS feature selection. The results are then compared to determine which feature selection algorithm gives the best results when applied to data with imbalanced classes. The results showed that the classification accuracy on the default of credit card client dataset using Nave Bayes algorithm was 64.27%. The information gain feature selection was able to increase the accuracy by 5.27% (from 64.27% to 69.54%), while the gain ratio feature selection was able to increase the accuracy by 14.19% (from 64.27% to 78.46%). In this case, the gain ratio is more suitable for data with greatly varied attribute values.

DISEASE PREDICTION FROM COVID-19 MEDICAL DATA USING DATA MINING ALGORITHM Nafis Md Zawad
IJISCS (International Journal of Information System and Computer Science) Vol 6, No 3 (2022): IJISCS (International Journal of Information System and Computer Science)
Publisher : Bakti Nusantara Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56327/ijiscs.v6i3.1312

The study was designed to introduce a technique for disease prediction by using a data mining algorithm. Here in this paper, a significant discussion has been made on the Novel Corona Virus and the creation of a model for disease prediction. The novel Coronavirus (COVID-19) pandemic has created chaos in the world. People from both developed and developing countries are facing many death tolls due to insufficient ways to prevent COVID-19. It is observed that the environment requires a quick and effective way to control the spread of COVID-19 across the globe. The use of non-clinical methods like data mining techniques can be an effective way to combat the spreading of Covid-19. To minimize the immense pressure on the healthcare system, improved ways of patients’ detection and diagnosis of the nature of the Covid-19 pandemic need to be ensured. In this study, an epidemiological dataset, and data mining models were applied for forecasting the extent of Covid-19 patients. To construct the models, the decision tree and logistic regression were used. Besides, a random forest algorithm was applied to the dataset by using ‘Python Programming Language’. The results reveal that the model created with a ‘Random Forest Data Mining Algorithm’ is more effective in predicting the likelihood of Covid virus-infected patients with the correctness (accuracy) of up to eighty percent (80%).

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Contact Name
Muhamad Muslihudin

Contact Email
ijiscs@ftikomibn.ac.id

Phone
+6272922240

Journal Mail Official
ijiscs@ftikomibn.ac.id

Editorial Address
Editor IJISCS (International Journal of Information System and Computer Science) Bakti Nusantara Institute Street Wisma Rini No.09 Pringsewu, Lampung Phone: 0729-22240

Location
Kab. pringsewu,

Lampung

INDONESIA

Abstract

Abstract

Abstract

Abstract

Home Page

OAI Link

Editorial Team

Contact

Reviewer

Google Scholar

Contact Name Muhamad Muslihudin

Contact Email ijiscs@ftikomibn.ac.id

Phone +6272922240

Journal Mail Official ijiscs@ftikomibn.ac.id

Editorial Address Editor IJISCS (International Journal of Information System and Computer Science) Bakti Nusantara Institute Street Wisma Rini No.09 Pringsewu, Lampung Phone: 0729-22240

Location Kab. pringsewu, Lampung INDONESIA

Abstract

Abstract

Abstract

Abstract

Contact Name
Muhamad Muslihudin

Contact Email
ijiscs@ftikomibn.ac.id

Phone
+6272922240

Journal Mail Official
ijiscs@ftikomibn.ac.id

Editorial Address
Editor IJISCS (International Journal of Information System and Computer Science) Bakti Nusantara Institute Street Wisma Rini No.09 Pringsewu, Lampung Phone: 0729-22240

Location
Kab. pringsewu,

Lampung

INDONESIA