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All Journal Syntax Jurnal Informatika Format : Jurnal Imiah Teknik Informatika Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Mantik Penusa J-SAKTI (Jurnal Sains Komputer dan Informatika) IJISTECH (International Journal Of Information System & Technology) JISICOM (Journal of Information System, Infomatics and Computing) Journal of Information System, Applied, Management, Accounting and Research ICIT (Innovative Creative and Information Technology) Journal CICES (Cyberpreneurship Innovative and Creative Exact and Social Science) Community Development Journal: Jurnal Pengabdian Masyarakat SENADA : Semangat Nasional Dalam MengabdI IJISTECH J-SAKTI (Jurnal Sains Komputer dan Informatika) SENADA : Semangat Nasional Dalam Mengabdi Jurnal Pengabdian Masyarakat dan Riset Pendidikan
Embun Fajar Wati
Universitas Bina Sarana Informatika
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Medicine & Pharmacology Public Health Social Sciences Other

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Journal : IJISTECH

 1 
The Combination of AHP (Analytic Hierarchy Process) and SAW (Simple Additive Weighting) Methods in the Selection of Business Locations Embun Fajar Wati
IJISTECH (International Journal of Information System and Technology) Vol 5, No 3 (2021): October
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v5i3.147

Abstract

Choosing a strategic business location is the dream of entrepreneurs. Site selection can also consider several criteria, namely the bustling market, rental prices, and building area. These criteria become important guidelines in choosing the number of business locations offered. It takes one or several methods used in the decision support system to assist in determining the location of the business. The method used is a combination of AHP (Analytic Hierarchy Process) and SAW (Simple Additive Weighting) which can rank business locations based on predetermined criteria weights. The first step in these methods is the weighting of the criteria based on their level of importance, then proceed with selecting the best alternative by ranking obtained from the results of calculations with both methods. The AHP (Analytic Hierarchy Process) method is used to calculate the weight of each criterion which will be calculated using the normalization matrix of the SAW (Simple Additive Weighting) method. The result of combining these two methods is the best location with the first ranking, namely Poris with a preference value of 0.96. The next ranking is Gondrong, Sipon, Royal, Dadap, Teluk Naga, and the last ranking is Rawa.
Application of Naive Bayes Method For Diagnosis of Pregnancy Disease Embun Fajar Wati; Budi Sudrajat
IJISTECH (International Journal of Information System and Technology) Vol 6, No 1 (2022): June
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v6i1.216

Abstract

The risk of pregnancy can be known by early detection of pregnancy with risk factors, so that health workers can know more about treatment. In diagnosing a disease in the field of medicine requires tools such as the application of artificial intelligence, one of which is an expert system. One method that can be applied in expert systems is naive bayes. In this study, naive bayes for the process of diagnosing the disease during pregnancy was done by including symptoms that appear in pregnant women. The stage of research is the collection of data from previous research journal articles with the same theme, but different methods and other journal articles with the same theme and different from the naive bayes method. The next stage is data analysis with naive bayes calculations of patient symptoms and validation, namely comparing the results of naive bayes calculations with expert calculations. The results obtained were 14 patients out of 20 patients, which is 70% have the same results between experts with calculations with naive bayes. The results showed that the calculation of symptoms with naive bayes was sufficient to give valid and feasible results to use
Determination of Business Location by Using Analytical Hierarchy Process (AHP) and Weighted Product (WP) Embun Fajar Wati; Elvi Sunita Perangin-Angin
IJISTECH (International Journal of Information System and Technology) Vol 6, No 3 (2022): October
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v6i3.253

Abstract

Information technology that is currently developing provides opportunities for business actors to develop their business. One of the factors that make a business grow is the location of the business. It is not easy to determine the appropriate business location, so various selections are needed so as to be able to measure the feasibility of the location. The existence of a decision support system can assist in making decisions about determining the location. The method chosen is the AHP method combined with the WP method. To get the value calculated by the AHP method, data collection by interview and observation was used. The literature study is used in the calculation stages with the AHP and WP methods. The use of a combination of AHP and WP methods in determining the location of the business gives a ranking result, with the highest score achieved by the Royal location of 0.617 and the lowest value achieved by the Poris location of 0.094. After observing the new location, Royal for 3 months, there was an increase in sales in the first month by 3 million/15%, in the 2nd month by 4 million/19% and in the 3rd month by 7 million/30%
Pregnancy Disease Diagnostic Expert System With Certainty Factor Method Embun Fajar Wati; Elvi Sunita Perangin-Angin; Budi Sudrajat
IJISTECH (International Journal of Information System and Technology) Vol 6, No 6 (2023): April
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/.v6i6.292

Abstract

Lack of knowledge and information about diseases in pregnancy can delay pregnant women from knowing there are diseases in their pregnancy. Diseases that attack a woman's womb need to be examined by an expert, while experts for this disease are still rare and require a lot of money. In order for the initial diagnosis to be carried out by pregnant women, a solution is proposed in the form of an expert system for diagnosing pregnant women's diseases using the Certainty Factor (CF) method based on the symptoms felt by pregnant women. The research stages used in this study used 4 steps, namely data collection consisting of disease data and symptom data, disease data and symptoms, as well as patient data, symptoms and weights, data analysis using the certainty factor method, validation and evaluation. Diagnostic results that are not in accordance with the CF calculation of around 37.5%, while the results in accordance with the CF calculation were 11 patients or 62.5%.
Improved Naive Bayes Algorithm with Particle Swarm Optimization to Predict Student Graduation Wati, Embun Fajar; Perangin-Angin, Elvi Sunita; Sari, Anggi Puspita
IJISTECH (International Journal of Information System and Technology) Vol 7, No 6 (2024): The April edition
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v7i6.338

Abstract

Timely graduation is very important for educational institutions such as universities, especially for students. Because it can prove that the University and students are able to undergo the learning process theoretically and practically. But many students do not pay attention to graduation, especially those who are already working or married. Therefore, analysis is needed to predict student graduation so that solutions can be found by the University. Data mining was chosen as a method to process data to get new information. The algorithm used in data mining is Naïve Bayes. The research stages include loading data into excel, cleaning empty data, selecting databases related to graduation and taking data from 300 students majoring in Informatics Engineering. The next stage is data transformation by categorizing student data, namely personal data attributes (gender, age, marital status, job status) and academic data (grade). Data testing, application of Naïve Bayes algorithm and accuracy testing were carried out with Rapis Miner software version 10.3.001. The results of data processing with Rapid Miner using the Naïve Bayes algorithm are shown with the Confusion Matrix and ROC Curve. The results of confusion matrix from data processing with Naïve Bayes in the form of accuracy, precision, and recall have the same result of 100%. The percentage of the Confusion Matrix indicates that the model created can classify correctly and accurately. The ROC curve depicted with AUC yields a value of 1, which means that the test showed excellent results
Modelling of C4.5 Algorithm for Graduation Classification Wati, Embun Fajar; Sudrajat, Budi; Nasution, Raudah
IJISTECH (International Journal of Information System and Technology) Vol 8, No 1 (2024): The June edition
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v8i1.345

Abstract

Student admissions in universities every year become a routine thing to do, some even do student admissions every semester. That way, the number of students will continue to grow. Especially if there are students who graduate late, it will increase the number of students in the university. There are many things that can affect graduation, namely personal data (gender, age, marital status, job status) and academic data (grade). Before making a decision, universities must analyze the number of students and the factors that most influence student graduation. Analysis by classifying graduation using C4.5 algorithms. The research method used consists of selection to ensure the data used in the KDD process is appropriate and quality data. Then preprocessing by means of data cleaning, data reduction, and data normalization. The next method is transformation for age attributes to young and old, grade attributes to large and small. The last method is C4.5 algorithm modeling with rapid miner and evaluation. Through the calculation process using the classification method and C4.5 algorithm with the attributes described earlier, the results were obtained that the accuracy of the graduation classification reached 97.00%, the precision value was 91.79%, and the recall value was 100.00%, and the AUC value was 0.978. This means that the model has a very high level of accuracy and has an excellent ability to separate samples from both target classes.
Prediction of Student Graduation using the K-Nearest Neighbors Method Wati, Embun Fajar; Perangin-Angin, Elvi Sunita; Sari, Anggi Puspita
IJISTECH (International Journal of Information System and Technology) Vol 7, No 3 (2023): The October edition
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v7i3.318

Abstract

Predictions on the accuracy of student graduation are designed to support study programs in guiding students so that they can graduate on time. The number of student graduations will influence the university's accreditation score. Graduation predictions can provide very useful information in decision-making; therefore, research was conducted on student graduation data. This data will be processed using the K-Nearest Neighbor method. The dataset used consisted of 150 students majoring in informatics engineering. The variables included gender, age, marital status, grade, and job status. The research methodology used in this study consists of 6 stages: Data Collection, Data Selection, Preprocessing, Transformation, Testing, and Evaluation. In the preprocessing or cleaning stage, the data can be fully utilized because all fields have been filled in correctly. Meanwhile, in the transformation stage, the data is categorized as follows: age (young: 19-24, old: 25-50) and grade (large: 3-4, small: 1-2.9). The K-Nearest Neighbor (KNN) method can predict student graduation rates. The KNN method, processed with the RapidMiner 9.9 tool, obtained an average accuracy of 100%. Based on the results of 100% accuracy and an AUC value of 1, it can be concluded that the KNN method is highly accurate in classifying graduation data for the 150 students.
Customer Loyalty Classification with Comparison of Naive Bayes, C4.5, and KNN Methods Wati, Embun Fajar; Perangin-Angin, Elvi Sunita; Indriyani, Luthfi
IJISTECH (International Journal of Information System and Technology) Vol 8, No 3 (2024): The October edition
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v8i3.361

Abstract

Customer loyalty is indispensable for the survival of a company. Customer loyalty needs to be maintained in order to return to visit and transact with the Company. Customer data consisting of age, annual income, purchase amount, region, purchase frequency, and loyalty score features can produce new information, namely analyzing customers who have high loyalty. Data processing is carried out using three data mining algorithms, namely Naïve Bayes, C4.5 or Decision Tree, and KNN. The stages carried out in data processing consist of data selection, preprocessing, transformation, and modelling. The customer data used amounted to 238. Modelling is carried out using Rapid Miner Software. Customer loyalty classification can be easily done with the three algorithms, namely Naive Bayes, and C4.5 or Decision Tree, and KNN which is validated by the 10-fold cross-validation method so as to produce the highest percentage of accuracy and the similarity of the accuracy value of the Naive Bayes and C4.5 algorithms, which is 96.67%. In the AUC value, it can be seen that the Naive Bayes algorithm is superior to the C4.5 algorithm or Decision Tree and KNN. The result of the highest AUC value is 0.997, the highest precision percentage is 98.92% achieved by the Naive Bayes algorithm. The result of the highest recall percentage is C4.5 of 100%. The results of the AUC value and accuracy percentage on the three algorithms prove that the performance of the three algorithms is very good.
Comparison of Naive Bayes and C4.5 Methods with Particle Swarm Optimization on Customer Loyalty Classification Wati, Embun Fajar; Perangin-Angin, Elvi Sunita; Indriyani, Luthfi
IJISTECH (International Journal of Information System and Technology) Vol 8, No 6 (2025): The April edition
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v8i6.382

Abstract

The Company attaches great importance to customer loyalty for the sustainability of the Company. Loyal customers will buy many times and provide great profits. In this study, the decision tree method or C4.5 and naïve bayes were used with PSO optimization for customer classification which aims to design a strategy in decision-making towards disloyal customers. Some of the stages carried out are data load into MS. Excel, data cleaning from noise, data selection as many as 238 obtained from previous research with several attributes, including, namely age, annual income, purchase amount, region, purchase frequency, and loyalty score, as well as data transformation, namely each attribute is grouped into 2 with their own criteria, data testing by modeling data through Rapidminer, Data evaluation by examining the values of accuracy, precision, recall, and AUC. Both methods have the same accuracy value of 96.67% and the same recall value of 100%. For the precision value, there is a difference of 0.6% and the precision decision tree value is higher than the naïve Bayes which is 96.16%. As for the AUC value, it is higher naïve bayes, which is 0.922 with the difference from the decision tree of 0.059. It can be concluded that the two methods in processing customer loyalty data in this study have the same accuracy, so both methods are equally good.
Co-Authors Anggi Puspita Sari Anggi Puspita Sari Anggi Puspitasari, Anggi Arief Fadholi Arvin Anggoro Kusumo Budi Sudrajat Budi Sudrajat Elvi Sunita Perangin-angin Enok Tuti Alawiah Istikharoh, Istikharoh Jaya, Tri Ika Kiki Indayani Lukman Hakim Luthfi Indriyani Martua Hami Siregar Maryadi, Dedi Mike Indarsih Mike Indarsih, Mike Nur Apriyani Nur Indah Kurniawati Perangin-Angin, Elvi Sunita Raudah Nasution Rudianto, Biktra Sari, Anggi Puspita SUSANTI Tri Ika Jaya Tri Ika Jaya Kusumawati Tuslaela Yunan Maulana Putra