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All Journal Dinamik JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Brilliance: Research of Artificial Intelligence SIGMA: Information Technology Journal VIDHEAS: Jurnal Nasional Abdimas Multidisiplin Jurnal Pelita Pengabdian Welfare: Jurnal Pengabdian Masyarakat
Siswandi, Arif
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Prediction of Employee Assessments for Contract Extensions at PT Sagateknindo Sejati Using the Naïve Bayes Algorithm Naya, Candra; Siswandi, Arif; Butsianto, Sufajar; Febriyanti, Febriyanti
Brilliance: Research of Artificial Intelligence Vol. 4 No. 1 (2024): Brilliance: Research of Artificial Intelligence, Article Research May 2024
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v4i1.4170

Abstract

Companies must be selective in conducting employee assessments in order to retain employees with the best performance. When assessing employee performance, it is seen from their perseverance and discipline. However, in reality, good employee performance sometimes gets bad reviews and even gets reprimanded by their superiors. This is caused by the employee assessment monitoring system used, namely only personal assessment without using an assessment system and the data collected is less than optimal. This research uses the Naive Bayes method to process data using a data mining algorithm to obtain predictions that can be used as additional references in making employee performance assessment decisions. Aims to predict employee assessments of contract extensions at PT Sagateknindo Sejati. This research is important because it helps in making more accurate decisions regarding employee contract extensions based on existing historical data. Naive Bayes is a data processing algorithm that is classified as a calculation that is easy to understand but its accuracy results are reliable. It is used because it is efficient in managing data with various attributes and is able to produce predictions based on the probability of each existing attribute. The data used in this research includes various variables, using the Rapidminer supporting application to test the accuracy of the system created. Testing was carried out by preparing 320 data and testing 50 randomly selected data. Test data will be analyzed using the Rapidminer supporting application. The test results produced an accuracy of 83.96%.
Application of the C 4.5 Algorithm to Classify Customer Characteristics at PT. Bayer Indonesia Siswandi, Arif; Anwar, M. Syaibani; Susilo, Arif; Hasibuan, Sultan
Brilliance: Research of Artificial Intelligence Vol. 4 No. 1 (2024): Brilliance: Research of Artificial Intelligence, Article Research May 2024
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v4i1.4174

Abstract

PT. Bayer Indonesia is a company engaged in drug production. In running its business, companies need to know customer characteristics in determining what actions to take next. This research aims to apply the C 4.5 algorithm in classifying customer characteristics at PT. Bayer Indonesia. The C 4.5 algorithm is a decision tree algorithm that is often used in data mining for classification purposes. This research was conducted to make it easier to find out customer characteristics. Starting with collecting data, then selecting the attributes that will be used. Then the data is separated using split data, the initial comparison used is 60% train data and 40% test data. Then training data is carried out using the C4.5 algorithm. Next, the classification results were evaluated using the confusion matrix method. The data used was 200 data with 9 attributes, obtained an accuracy of 86.25%, precision of 86.25% and recall of 54.55%. Then change the data split parameters to 70% : 30%, 80% : 20% and 90% : 10%. The best accuracy is 100%. The research results show that the C 4.5 algorithm has good performance in classifying the characteristics of PT customers. Bayer Indonesia. The resulting model can be used by companies for more effective marketing strategies and personalized customer service.
Analysis Prediksi Wilayah Rawan Banjir dengan Algoritma K-Means Effendi, Muhammad Makmun; Inka, Inka; Siswandi, Arif
Journal of Information System Research (JOSH) Vol 5 No 2 (2024): Januari 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v5i2.4770

Abstract

Along with the high amount of rainfall in Bekasi -West Java, floods have started to inundate several areas of Bekasi , one of the causes is the high rainfall factor. According to (Regional Disaster Management Agency) BPBD, the most flood points are in the Bekasi area, causing several activities of the surrounding community to be disrupted, transportation hampered, and also the emergence of disease problems such as skin diseases, diarrhea, and so on. The problem of flooding is a shared responsibility that requires a solution. also the role of technology to help facilitate the provision of information to the public regarding flood-prone areas in the Bekasi area. One technique that can be used is using the K-Means Clustering Algorithm to group flood-prone areas. The flood dataset was processed using the RapidMiner application, for the dataset taken to carry out this analysis from January to December 2022, there were 24 data from areas affected by flooding from various sub-districts and villages in the city of Bekasi. The results of the research produced 3 clusters, namely, the high flood, medium flood and low flood categories, which received a Davies Bouldin index value of -0.452.
Pelatihan dan Pendampingan Learning Management System (LMS) Pada SMK Negeri 1 Tambelang Kabupaten Bekasi Fauzi, Ahmad; Miharja, Muhammad Najamuddin Dwi; Siswandi, Arif; Wangsadanureja, Miftah; Maulana Majid, Annisa
Welfare : Jurnal Pengabdian Masyarakat Vol. 1 No. 1 (2023): Welfare : March 2023
Publisher : Fakultas Ekonomi dan Bisnis Islam, IAIN Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30762/welfare.v1i1.334

Abstract

The industrial revolution 4.0 has an impact on every field, one of which is the development of the world of education. Developments in the world of education can be seen in learning methods, including teaching and learning processes that are currently easily accessible for teachers and students. In the field of education, new learning methods such as the Moodle-based Learning management system are used to facilitate the teaching and learning process, but there is no implementation of a learning management system to manage online learning activities at SMK Negeri 1 Tambelang, Bekasi Regency. In this service, the implementation of the learning management system was carried out at SMK Negeri 1 Tembelang, Bekasi Regency. The Learning Management System is proven to facilitate the teaching and learning process for teachers and students at SMK Negeri 1 Tambelang, Bekasi Regency.
Co-Authors Abdul Ghofir Afrianto, Irfan Andriani Anwar, M. Syaibani ARIF SUSILO Butsianto, Sufajar Effendi, Muhammad Makmun Elkin Rilvani Ermanto Fachrial Banyu Asmoro Fauzi Ahmad Muda febriyanti febriyanti Gitawijaya, Dhea Hasibuan, Sultan Ikhsan Romli Inka, Inka Maulana Majid, Annisa Miharja, Muhammad Najamuddin Dwi muhidin, asep Naya, Candra Pradini, Purnama Sakhrial Purnama Sakhrial Setyawan, Wisnu Surojudin, Nurhadi Turmud Zy, Ahmad Wangsadanureja, Miftah