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IJISCS (International Journal Of Information System and Computer Science)

ijiscs
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Published by STMIK Pringsewu Lampung

ISSN : 25980793 EISSN : 2598246x DOI : -

Core Subject : Science,

Computer Science & IT

The International Journal Information System and Computer Science (IJISCS) 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.

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Articles 7 Documents

Search results for , issue "Vol 6, No 3 (2022): IJISCS (International Journal of Information System and Computer Science)" : 7 Documents clear

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 : STMIK Pringsewu Lampung

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%).

DESIGNING AN ANDROID-BASED DIGITAL MUSEUM APPLICATION AS A VIRTUAL VISIT TO THE MUSEUM Yuliska Yuliska; Asyrul Fikri
IJISCS (International Journal of Information System and Computer Science) Vol 6, No 3 (2022): IJISCS (International Journal of Information System and Computer Science)
Publisher : STMIK Pringsewu Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56327/ijiscs.v6i3.1260

The Sang Nila Utama Museum is one of the most comprehensive museums in Riau Province. Currently, the Sang Nila Utama Museum has more than 4000 collections divided into ten classifications: Geology, Biology, Ethnography, Archeology, Historics, Numismatics, Philology, Ceramics, Arts, and Technology. From founded in 1991 to 2018, visits to Sang Nila Utama Museum can be done offline; however, since the COVID-19 pandemic began to enter Pekanbaru City, Sang Nila Utama Museum has been closed to the public. With the closure of the Sang Nila Museum, the museum's function as an educational tour, cultural and heritage tourism has been inhibited. The digital museum application is a solution to make museum visits no longer limited to offline visits but can also be done virtually. The application exhibits all museum collections, which consist of ten classifications, with a total collection of nearly 300 collections. Based on the test results, the digital museum application got good responses from the users and has met the five aspects of usability testing with an average usability value above four which is above the median value on a scale of five.

EMOTION DETECTION IN ARABIC TEXT USING MACHINE LEARNING METHODS Fatimah Aljwari
IJISCS (International Journal of Information System and Computer Science) Vol 6, No 3 (2022): IJISCS (International Journal of Information System and Computer Science)
Publisher : STMIK Pringsewu Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56327/ijiscs.v6i3.1322

Emotions are essential to any or all languages and are notoriously challenging to grasp. The textual data with embedded emotions has increased considerably with the Internet and social networking platforms. This study aims to tackle the challenging problem of emotion detection in Arabic text. Recent studies found that dialect diversity and morpho-logical complexity in the Arabic language, with limited access to annotated training datasets for Arabic emotions, pose the foremost significant challenges to Arabic emotion detection. The previous few years have seen a giant increase in interest in text emotion detection. The study of Arabic emotions might be a result of the Arab world’s considerable influence on global politics and thus the economy. There are numerous uses for the automated recognition of emotions within the textual content on Facebook and Twitter, including company development, program design, content generation, and emergency response. Hence, we shall develop a machine-learning model for emotion detection from Arabic textual data on social platforms. This study categorizes the texts supported emotions, anger, joy, sadness, and fear, using supervised machine learning approaches. We used five different machine learning algorithms, namely Decision Tree (DT), K-Nearest Neighbor (KNN), Naive Bayes (NB), Multinomial Naive Bayes (NB), and Support Vector Machine (SVM) to classify emotions in Arabic tweets. These results found that the selection Tree and K- Nearest Neighbor classifiers have the simplest accomplishment regarding the accuracy, 0.74, While the NB and Multinomial NB classifiers acquired 0.69, and also the SVM obtained 0.63.

STUDY AND IMPROVEMENT OF PERFORMANCE OF NoSQL DATABASES: MongoDB, HBase and OrientDB. Noel Bila; Bopatriciat Boluma Mangata; Eugène MBUYI MUKENDI; Parfum BUKANGA CHRISTIAN
IJISCS (International Journal of Information System and Computer Science) Vol 6, No 3 (2022): IJISCS (International Journal of Information System and Computer Science)
Publisher : STMIK Pringsewu Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56327/ijiscs.v6i3.1262

This dissertation adds to the various research works in the field of NoSQL "Not only SQL" databases. These new models propose a new way of organizing and storing data designed mainly to remedy the constraints imposed by the ACID properties on relational models. Our objective was to develop a comparative performance study, between three NoSQL solutions widely used in the market, namely: MongoDB, HBase and OrientDB, to propose to decision makers, elements of information for possible choices of the best appropriate solution for their companies. The Benchmark used to decide between these solutions is the Yahoo Cloud Serving Benchmark.

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 : STMIK Pringsewu Lampung

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 : STMIK Pringsewu Lampung

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.

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 : STMIK Pringsewu Lampung

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.

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Contact Name Muhamad Muslihudin

Contact Email muslihudinstmikpsw@gmail.com

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Abstract

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Abstract

Contact Name
Muhamad Muslihudin

Contact Email
muslihudinstmikpsw@gmail.com

Phone
-

Journal Mail Official
lppm@stmikpringsewu.ac.id

Editorial Address
-

Location
Kab. pringsewu,

Lampung

INDONESIA