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All Journal JUMANJI (Jurnal Masyarakat Informatika Unjani) Enrichment: Journal of Multidisciplinary Research and Development
Ade Kania Ningsih, Ade Kania
Universitas Jenderal Achmad Yani
Humanities Computer Science & IT Economics, Econometrics & Finance Engineering Health Professions Social Sciences

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STUDENT GRADUATION PREDICTION SYSTEM BASED ON ACADEMIC AND NON-ACADEMIC (EQ) DATA USING C4.5 ALGORITHM Hanafi, Willy; Chrisnanto, Yulison Herry; Ningsih, Ade Kania
JUMANJI (Jurnal Masyarakat Informatika Unjani) Vol 7 No 1 (2023): Jurnal Masyarakat Informatika Unjani
Publisher : Jurusan Informatika Universitas Jenderal Achmad Yani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26874/jumanji.v7i1.272

Abstract

The graduation profile is an important element for higher education accreditation standards. It reflects the performance of the adopted education system within a certain period. The better the profile graduation, the better the value of the accreditation. Some students are unable to complete their studies on time or even fail to complete their studies because they exceed the specified time limit, which is seven years, and it negatively affects institutions' accreditation. To prevent this from happening, it is necessary to know what obstacles that cause these students could not complete their studies on time. by knowing this information, prevention can be done for students who are potentially unable to complete their studies on time. The purpose of this study was to make a system that can predict the graduation timeline and the factors that influence it. The data used was graduation data from undergraduate students majoring in psychology from 2015 to 2017 at a university in Cimahi. The data had a total record of 461 students, 44 subject value attributes, 13 psychotest attributes, and class attributes. We generated the result by using decission tree method with C4.5 algorithm, which produces 90.32% accuracy. The depth of the tree can also influence the accuracy of the algorithm. This study also found that academic and non-academic (EQ) scores can affect students’ graduation time.
Grouping Education Students at Pusdikjas Institutions of The TNI-AD's Disjasad Using the K-Means Clustering Method Pramudjianto, Imam Wibowo; Ningsih, Ade Kania; Komarudin, Agus
Enrichment: Journal of Multidisciplinary Research and Development Vol. 1 No. 7 (2023): Enrichment: Journal of Multidisciplinary Research and Development
Publisher : International Journal Labs

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55324/enrichment.v1i7.64

Abstract

The Military Physical Education Center, known by the abbreviation Pusdikjasmil, is an implementation body that is directly under the TNI AD. Pusdikjas has the task of providing education in the military physical field to support the duties of the Indonesian Army. At the Pusdikjas agency, student data is obtained from the addition of students attending education each year. The process of admitting students to Pusdikjas institutions produces abundant value data and is repeated every year. By using data mining techniques, abundant student data can be grouped and analyzed to find information that is useful for Pusdikjas agencies. The algorithm used in this research is K-means clustering. The data used from 2018 - 2022 was 1428 data on coaching education students, with the variables used being NRP, attitude & and behavior, knowledge & and skills, and physical. Processed using the JavaScript programming language, this research produces optimal clusters, namely 2 clusters. cluster 1 with a total data of 751 students in cluster 1 describes a very satisfactory category of students, and cluster 2 with total data of 676 students. Cluster 2 describes the satisfactory student category. As for testing the quality of the clusters contained in the system using silhouette with results reaching 0.6543347543126016.
Consumer segmentation using K-Medians algorithm on transaction data based on LRFMP (length, recency, frequency, monetary, periodecity) Maulana, Akbar Dena; Ningsih, Ade Kania; Abdillah, Gunawan
Enrichment: Journal of Multidisciplinary Research and Development Vol. 1 No. 8 (2023): Enrichment: Journal of Multidisciplinary Research and Development
Publisher : International Journal Labs

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55324/enrichment.v1i8.70

Abstract

Consumer loyalty has a crucial role for companies, especially in conditions of competition between companies. Success in retaining loyal customers is crucial. For this reason, customer loyalty analysis is needed to identify the level of consumer compliance with the company. In this case, consumer segmentation is also an important step to group consumers with similar characteristics to facilitate the management process. One of the analysis methods used is the LRFMP (Length, Recency, Frequency, Monetary, Periodecity) model, which examines consumer purchasing patterns based on various factors such as relationship length, last transaction time span, number of transactions, total money spent, and purchase regularity. The K-Medians grouping method was also used in this study. The data used is the history of purchase transactions in e-commerce for 373 days. From the application of LRFMP analysis and the K-Medians method, 4 clusters were obtained. The number of consumers in cluster 1 is 1183, cluster 2 is 1221, cluster 3 is 1206, and cluster 4 is 1102. The interpretation of the LRFMP model shows that 25.1% of consumers have high loyalty potential, 25.9% of consumers have low loyalty potential, 25.6% of consumers have high loyalty potential, and 23.4% of consumers have medium loyalty potential.
Classification of Sentiment Towards BPJS Services Using the C50 Algorithm Cahyaningrum, Amellia Fahezha; Chrisnanto, Yulison Herry; Ningsih, Ade Kania
Enrichment: Journal of Multidisciplinary Research and Development Vol. 1 No. 8 (2023): Enrichment: Journal of Multidisciplinary Research and Development
Publisher : International Journal Labs

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55324/enrichment.v1i8.71

Abstract

This public health insurance program for all Indonesian people is supervised by the Social Security Administering Body (BPJS) for Health, an Air-Owned Enterprise. Thus, it will be easier for the public to find information about what policies the government has implemented to regulate BPJS. One of them is that people can find information on the social network Twitter. Due to its ease and simplicity of use, the number of tweets can easily grow quickly, which is why Twitter is more popular among Indonesians for communicating. Twitter is widely used as a promotional medium as well as a means of expressing opinions regarding criticism, suggestions, issues, and opinions of a public nature such as the views of netizens on new government policies and so on. One of them is in BPJS services, the large number of BPJS users causes BPJS to provide feedback services to users to find out how many good and bad responses to BPJS services. Sentiment classification is a branch of text mining. Sentiment classification is very basic in the evaluation process of a topic problem. Then the sentiment classification has the main objective of finding out the polarity of positive, and negative sentiment. The c50 algorithm method is one of the methods that can be used in the classification of BPJS service sentiment. In this research, the classification of BPJS service sentiment through Twitter media was carried out using the C50 algorithm method.
Co-Authors Achmad Zainuddin Arif Rahman Hakim Cahyaningrum, Amellia Fahezha Euis Julaeha Faizal Hermanto Gunawan Abdillah, Gunawan Hanafi, Willy Komarudin, Agus Lilis Siti Aisyah, Lilis Siti Maulana, Akbar Dena Pramudjianto, Imam Wibowo Tati Herlina Tri Reksa Saputra, Tri Reksa Unang Supratman Yenny Febriani Yun, Yenny Febriani Yulison Herry Chrisnanto