cover
Contact Name
Taqwa Hariguna
Contact Email
taqwa@amikompurwokerto.ac.id
Phone
-
Journal Mail Official
info@ijiis.org
Editorial Address
Puri Mersi Baru, Jl.Martadireja II, Gang Sitihingil 3 Blok A No 2, Purwokerto Timur, Jawa Tengah
Location
Unknown,
Unknown
INDONESIA
International Journal of Informatics and Informations Systems
Published by Bright Publisher
ISSN : -     EISSN : 25797069     DOI : doi.org/10.47738/ijiis
Core Subject : Science,
The IJIIS is an international journal that aims to encourage comprehensive, multi-specialty informatics and information systems. The Journal publishes original research articles and review articles. It is an open access journal, with free access for each visitor (ijiis.org/index.php/IJIIS/); meanwhile we have set up a robust online platform and use an online submission system to ensure the international visibility and the rigid peer review process. The journal staff is committed to a quick turnaround time both in regards to peer-review and time to publication.
Articles 25 Documents
Sales Transaction Data Analysis using Apriori Algorithm to Determine the Layout of the Goods Hariguna, Taqwa; Hasanah, Uswatun; Susanti, Nindi Nofi
IJIIS: International Journal of Informatics and Information Systems Vol 1, No 1: September 2018
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v1i1.19

Abstract

In a shop, usually apply a sales strategy in order. The sales strategy can be in the form of determining the layout of goods so that they are close to one another. Determining the layout of items can be based on items that are often purchased simultaneously. Searching for items that are often purchased together can be done using data mining techniques, which is processing data to become more useful information. Sales transaction data processing can be done using apriori algorithm. Apriori algorithm is the most famous algorithm for finding high-frequency patterns and generating association rules. From the results of the discussion and data analysis, there were 3 (three) association rules formed, namely "If you buy Milo Active 18 grm, then buy ABC Kopi Susu 31G" with support 0.36% and 75% confidence, "If you buy Dancow 1 + Honey 200 grm, then buy Ice Cream Corneto" wit H Support 0.36% and confidence 60%, "If you buy SIIP Roasted 6.5 grm, then buy Davos Strong 10 grm" with support 0.36% and 75% confidence. From the association's rules can be used as decision making to determine the layout of goods that are likely to be purchased simultaneously by the buyer
Classification of Low Birth Weight Baby Under Anthropometry uses Algorithms K-Means Clustering on Maternity Hospital Santiko, Irfan; Kurniawan, Deni
IJIIS: International Journal of Informatics and Information Systems Vol 3, No 1: March 2020
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v3i1.5

Abstract

LBW infants with birth weight less than 2500 grams regardless gestation period. Low birth weight is the weight of a baby who weighed within 1 hour after birth. World Health Organization (WHO) since 1961 states that all newborns are underweight or equal to 2,500 g called low birth weight infant (low birth weight). According to WHO. Statistically, morbidity and mortality in neonates in developing countries is high, with the main causes is associated with LBW. To facilitate medical personnel in determining the risk of LBW. From the testing that has been done by the author, the k-means clustering algorithm has accuracy in classifying LBW babies by spacing the proximity between variables and the similarities in the test data,
Naive Bayes Algorithm Using Selection of Correlation Based Featured Selections Features for Chronic Diagnosis Disease Santiko, Irfan; Honggo, Ikhsan
IJIIS: International Journal of Informatics and Information Systems Vol 2, No 2: September 2019
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v2i2.14

Abstract

Chronic kidney disease is a disease that can cause death, because the pathophysiological etiology resulting in a progressive decline in renal function, and ends in kidney failure. Chronic Kidney Disease (CKD) has now become a serious problem in the world. Kidney and urinary tract diseases have caused the death of 850,000 people each year. This suggests that the disease was ranked the 12th highest mortality rate. Some studies in the field of health including one with chronic kidney disease have been carried out to detect the disease early, In this study, testing the Naive Bayes algorithm to detect the disease on patients who tested positive for negative CKD and CKD. From the results of the test algorithm accuracy value will be compared against the results of the algorithm accuracy before use and after feature selection using feature selection Featured Correlation Based Selection (CFS), it is known that Naive Bayes algorithm after feature selection that is 93.58%, while the naive Bayes without feature selection the result is 93.54% accuracy. Seeing the value of a second accuracy testing Naive Bayes algorithm without using the feature selection and feature selection, testing both these algorithms including the classification is very good, because the accuracy value above 0.90 to 1.00. Included in the excellent classification. higher accuracy results.
Comparative Analysis of Database Query Storage Performance Between Stored Procedure and Function Septiadi, Abednego Dwi; Bae, Lee Jeong
IJIIS: International Journal of Informatics and Information Systems Vol 3, No 2: September 2020
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v3i2.66

Abstract

This research was conducted to measure the data storage time carried out by the DBMS on data that has been prepared with an increasing number of data, the data provided is consistent student data which will be stored with Stored Procedure and Function. This study uses the action research method which has 4 stages, starting with planning, action, observation and reflection. From the results of the experiments that have been carried out, it appears that Stored Procedure is able to outperform Function in data storage time. The data provided are 2 different types of data, each of which consists of 500, 2500, 4500 and 6500 data stages. This study also compares data storage that is differentiated by the computer between the data provider computer and the data storage computer or server, the result of which is the Stored Procedure. able to outperform Function in data storage speed.
Expert System for Diagnosing Early Childhood Developmental Disorders with Certainty Factor Method Hermanto, Nandang; Ramadhan, Zulfiqar Shertian
IJIIS: International Journal of Informatics and Information Systems Vol 1, No 1: September 2018
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v1i1.18

Abstract

The purpose of this research is to design and build an expert system to diagnose the type of developmental disorder in children early using the certainty factor method. The method of data collection used in this study is observation, interviews, and library studies. The system was built with the Waterfall System Development Method. The stage of the Waterfall method is analysis, design, coding, and testing. This expert system is built using the PHP programming language and MySQL database. The result of this research was to successfully build an expert system to diagnose the type of developmental disorder in children early using the Certainty factor method to facilitate the user in diagnosing developmental disorders in the child quickly, efficiently, and without having to consult a pediatrician.
Comparison of Cart and Naive Bayesian Algorithm Performance to Diagnose Diabetes Mellitus Santiko, Irfan; Subarkah, Pungkas
IJIIS: International Journal of Informatics and Information Systems Vol 2, No 1: March 2019
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v2i1.9

Abstract

Based on Indonesia's health profile in 2008, Diabetes Mellitus is the cause of the ranking of six for all ages in Indonesia with the proportion of deaths of 5.7% under stroke, TB, hypertension, injury and perinatal. This is reinforced by WHO (2003), Diabetes Mellitus disease reached 194 million people or 5.1 percent of the world's adult population and in 2025 is expected to increase to 333 million inhabitants. In particular, in Indonesia, people with Diabetes Mellitus are increasing. In 2000, Diabetes Mellitus sufferers have reached 8.4 million people and it is estimated that the prevalence of Diabetes Mellitus in 2030 in Indonesia reaches 21.3 million people.This allows researchers and practitioners to focus their attention on detecting/diagnosing diabetes mellitus and to prevent it because the disease can cause complications. The method used in this research was problem identification, data collection, pre-processing stage, classification method, validation and evaluation and conclusion. The algorithm used in this research was CART and Naïve Bayes using dataset taken from UCI Indian Pima database repository consisting of clinical data ofpatients who detected positive and negative diabetes mellitus. Validation and evaluation method used was 10-crossvalidation and confusion Matrix for the assessment of precision, recall and F-Measure. The result of calculation has been done, got the accuracy result on CART algorithm equaled to 76.9337% with precision 0.764%, recall 0.769%, and F-Measure 0.765%. Whilethe diabetes dataset was tested with the Naïve Bayes algorithm, got an accuracy of 73.7569% with precision 0.732%, recall 0.738%, and F-Measure 0.734%. From these results it can be concluded that to diagnose diabetes mellitus disease it is suggested to use CART algorithm.
Analysis of Customer Transaction Data Associations Based on The Apriori Algorithm Astuti, Tri; Puspita, Bella
IJIIS: International Journal of Informatics and Information Systems Vol 3, No 1: March 2020
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v3i1.4

Abstract

UD Dian Pertiwi is one of the small and medium enterprises engaged in materials with the main product is building materials. This business experiences large amounts of transactions every day, the data obtained becomes increasingly large, and it will only be limited to a pile of useless data or commonly called junk. By utilizing a data mining approach apriori algorithm technique, the data can be utilized to support the sales process and achieve a target of UD Dian Pertiwi. Based on research and data mining that has been done using association analysis and apriori algorithms by applying a minimum of support = 1% and a minimum of confidence = 70% resulted in the ten strongest association rules can be used by UD Dian Pertiwi in the process of applying a sales strategy including determining interrelationships, in short, the product has the potential to be purchased at the same time, increasing the amount of product stock and conducting promotions.
Sentiment Analysis of Product Reviews as A Customer Recommendation Using the Naive Bayes Classifier Algorithm Hariguna, Taqwa; Baihaqi, Wiga Maulana; Nurwanti, Aulia
IJIIS: International Journal of Informatics and Information Systems Vol 2, No 2: September 2019
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v2i2.13

Abstract

In an e-commerce Shopee, the process of selling and buying continues to run every day, and the comments given by consumers will increase more and more. Comments given by consumers will be the reference/review of a product that has been purchased by consumers. Consumers freely provide a review containing positive comments and negative comments in the Comments field listed on the Shopee e-commerce website. With the above problems, researchers will do a research with the method of sentiment analysis to distinguish classes in product review comments that include positive comment class or negative comment class using a combination of K-means and naive Bayes classifier. K-means used to determine the grouping of classes; naive Bayes classifier used to get the value of accuracy. The results obtained based on clustering K-means include getting 116 negative comments on product reviews and 37 negative comments product reviews. Accuracy results obtained from product review comment data of 77.12%. Thus, the accuracy value using K-means and naive Bayes classifier without manual data get a higher accuracy value is compared using K-means, Naive Bayes classifier, and manual data get results lower accuracy of 56.86%. From the results above the most comments is a negative comment of 116 data review comments product, from the results of the study can be concluded that one of the products of Spatuafa named high heels women know the Ribbon Ikat FX18 the condition of the product is not good enough due to the high negative comments compared to positive comments
Property Rental Price Prediction Using the Extreme Gradient Boosting Algorithm Kokasih, Marco Febriadi; Paramita, Adi Suryaputra
IJIIS: International Journal of Informatics and Information Systems Vol 3, No 2: September 2020
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v3i2.65

Abstract

Online marketplace in the field of property renting like Airbnb is growing. Many property owners have begun renting out their properties to fulfil this demand. Determining a fair price for both property owners and tourists is a challenge. Therefore, this study aims to create a software that can create a prediction model for property rent price. Variable that will be used for this study is listing feature, neighbourhood, review, date and host information. Prediction model is created based on the dataset given by the user and processed with Extreme Gradient Boosting algorithm which then will be stored in the system. The result of this study is expected to create prediction models for property rent price for property owners and tourists consideration when considering to rent a property. In conclusion, Extreme Gradient Boosting algorithm is able to create property rental price prediction with the average of RMSE of 10.86 or 13.30%.
Expert System for Simulation of Pest and Disease Diagnosis in Onion Plant Using Putty Shafer Method and Rule-Based Approach Dianingrum, Melia; Hermanto, Nandang; Rifa'i, Mohamad Iqbal
IJIIS: International Journal of Informatics and Information Systems Vol 2, No 1: March 2019
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v2i1.8

Abstract

The expert system is trying to adopt a system of human knowledge into a computer so that the computer can solve problems like the experts. The expert system is well designed in order to solve a particular problem by mimicking the work of the expert. The development of an expert system is expected to be resolved problems with the help of experts. The problems addressed by an expert not only the problems that rely on algorithms but sometimes elusive problems. An expert with knowledge and experience can overcome these problems. The application of an expert system in this study is made to diagnose pests and diseases in onion plants based on the web. The Data Collection method used is literature studies, interviews and observation. The stages of research used are literature review, data processing analyst, and Onion analyzed and photographed which then is uploaded and analyzed, Dempster Shafer method, application development, evaluation. In the last stage is the pilot study conducted using a Blackbox method and testing to the user. The result of the research is in the form of an expert system application that can diagnose pests and diseases of onion as many as 7 types of diseases. The output system is in the form of onion disease searching result obtained based on the symptoms inputted by the user. The result of Blackbox Testing is all functions of the application successfully run well. Testing to the users rated well both appearance and information of the application.

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