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All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA KLIK: Kajian Ilmiah Informatika dan Komputer
Muhammad Ihsan Jambak
Universitas Sriwijaya, Palembang
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Pengelompokan Cuaca Kota Palembang Menggunakan Algoritma K-Means Clustering Untuk Mengetahui Pola Karakteristik Cuaca Shanaz Khairunnisa; Muhammad Ihsan Jambak
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 4 (2022): Oktober 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i4.4810

Abstract

Weather related information is one of the things that is very important and has a big influence on all kinds of life activities such as in public safety, socio-economics, agriculture, aviation, and so on.The weather in each place or region is different, this happens because of the different weather elements in each place/region. By using data mining clustering techniques, weather clustering will be carried out in the city of Palembang. K-means is the algorithm chosen for clustering the weather in the city of Palembang. The test was carried out using daily weather data for 2020-2021 from BMKG by utilizing rapidminer application as learning techniques for data. So that we will get a group of weather characteristics of Palembang city based on similarities and dissimilarities. From the test results, the best k was obtained at k=3 with the parameters  Measure Types ( NumericalMeasure ) and Divergences ( DynamicTimeWarpingDistance ) as well as a local random seed of 2500 seen from the results of the Davies-Bouldin Index (DBI). This weather grouping can later provide information on how the weather character is and reduce the impact of sudden changes in weather conditions.
Pemanfaatan Algoritma Decision Tree ID3 Bagi Manajemen Bimbel Untuk Menentukan Faktor Kelulusan Pada Sekolah Kedinasan Juwita Tetra Marani Aliyah Nazanah; Muhammad Ihsan Jambak
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 3 No. 6 (2023): Juni 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v3i6.791

Abstract

At present official tutoring is an option for students to support their preparation for the next level of education, not only limited to guiding study, usually tutoring management prepares certain strategies so that students can pass the selection at the intended official service. Official schools in Indonesia are currently quite popular and in demand by many students, this is due to the advantages of official schools, namely the cost of education is relatively cheaper and even free, under the auspices of state institutions and have a greater opportunity to work immediately after graduation. This high interest causes high competition to enter service schools. One of the most popular services today is the STIS Statistics Polytechnic. The New Student Selection Selection (SPMB) at the STIS Statistics Polytechnic went through many stages. So that this is of interest to the tutoring management to find out what factors determine the passing of the selection. Ignorance of tutoring management in knowing the passing factor can lead to a lack of effective strategies and learning processes for tutoring students. Therefore, in this study the application of data mining science was carried out, namely classifying data from the 2022 STIS Statistics Polytechnic SPMB results using the ID3 decision tree algorithm which aims to find out the main factors that determine which students graduate. Then, the results of the research can be a support for tutoring management decisions in making strategies and future evaluations. So that tutoring students get the most appropriate and appropriate coaching strategy based on the results of this study. The dataset was analyzed by Data Mining using the ID3 Decision Tree Algorithm. Based on the research conducted on the data, the Kappa value is 1,000, the Accuracy is 100%, the Recall is 100,000%, the Classification Error is 0.00 and the Precision is 100%
Klasifikasi Tindakan Persalinan Pada Pasien Ibu Bersalin Menggunakan Metode Decision Tree C4.5 Rahmat Fitra Arkamil; Muhammad Ihsan Jambak
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 1 (2023): Agustus 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i1.1168

Abstract

Childbirth is the process of delivering a baby, placenta, and amniotic sac from the uterus to the outside world. According to data from the World Health Organization, there are at least 303 thousand women worldwide who die on the verge of or during the childbirth process. Childbirth methods can vary, it could be through normal delivery or cesarean delivery, which are usually based on the health conditions of the mother and baby. Therefore, the selection of the appropriate childbirth method can increase the safety of the mother and baby. Hence, through this research, childbirth methods need to be examined more deeply with the aim of finding out what factors influence them, and then determine the childbirth method based on those factors. In grouping childbirth methods based on childbirth factors, a data mining method is used, namely classification. The Decision Tree C4.5 method is used in this research because of its ability to produce a classification model that is easy to understand and interpret. This model is built based on historical data from Banyuasin Regional General Hospital that includes various health variables and childbirth methods. Testing was conducted using childbirth method data from January 1, 2020 to December 31, 2020. This research produced 20 decision branch patterns or rules that form the basis for determining the label or class data with an accuracy rate of 99.26%.
Evaluasi Proses Bisnis Pendaftaran Nikah Menggunakan Metode Business Process Improvement (BPI) di KUA Amanda Julia Dela Siska; Pacu Putra; Dinna Yunika Hardiyanti; Muhammad Ihsan Jambak
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 3 (2023): Desember 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i3.1482

Abstract

The Indralaya District Religious Affairs Office is a government agency authorized to carry out some of the duties of the Ministry of Religious Affairs. Registration and recording of marriages is one of the duties of the Sub-district KUA. However, in carrying out its duties, there are several obstacles faced in the current flow of marriage registration, such as the length of the marriage registration process, and a complicated process due to the many documents that must be taken care of to various agencies. Therefore, it is necessary to conduct periodic evaluations to find potential problems that hinder the current business process and optimize the quality of services of the sub-district KUA. The current marriage registration flow and the recommended marriage registration flow will be modeled using a Business Process Model and Notation (BPMN) diagram. Then, each activity in the current marriage registration flow is evaluated using the Failure Mode and Effects Analysis (FMEA) approach. The results of the FMEA evaluation are used for business process improvement. Business process improvement is carried out using the Business Process Improvement (BPI) method. BPI is a method used to improve the quality of business processes to make them more effective and efficient without having to reconstruct ongoing business processes radically. After that, the design of business process recommendations will be proposed using tools from the third phase of BPI, namely streamlining. Furthermore, simulations will be carried out using Bizagi Modeler to test time, process validation, and resource analysis. The results obtained are an increase in process time from 28 days 2 hours 56 minutes 30 seconds to 10 days 9 hours 27 minutes 15 seconds or an increase of 62.80%.
Klasifikasi Customer Churn pada Telekomunikasi Industri Untuk Retensi Pelanggan Menggunakan Algoritma C4.5 Stevan Desena Damanik; Muhammad Ihsan Jambak
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 3 No. 6 (2023): Juni 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v3i6.829

Abstract

Ignorance of telecommunications companies regarding the reasons and characteristics of customer churn causes telecommunications companies to suffer huge losses. This makes customer churn a big problem for telecommunications companies. This study uses data mining with classification techniques as a solution to analyze customer churn characteristics. This research will use Rapid Miner and the C4.5 algorithm to carry out the data mining process. . The purpose of this research is to find out what are the characteristics of customer churn so that companies can make policies that can retain customers and increase customer retention. This research is based on CRISP-DM. Data taken from kaggle.com with 21 attributes and 7034 rows of data and data preparation will be carried out. From the research results it is known that there are 5 attributes that have a considerable influence on customer churn, namely contracts, InternetService, TotalChares, tenure, PaperlessBilling, MultipleLines, StreamingMovies. And from the results of this study has an accuracy rate of 79.53%.
Komparasi Penerapan Algoritma C4.5, K-Nearest Neighbor, dan Naïve Bayes untuk Keberlangsungan Pasien Gagal Jantung Muhammad Fakhri Rizqullah; Naura Tri Raihana; Muhammad Ihsan Jambak
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 5 (2024): April 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i5.1788

Abstract

The total number of deaths worldwide due to heart failure continues to show an increase. Classifying patients with the best accuracy can help improve preventive measures based on clinical information. This study compares classification algorithms including C4.5, K-Nearest Neighbor, and Naïve Bayes based on CRISP-DM with the 10-fold cross-validation model evaluation technique and pairwise t-test using RapidMiner software. The research obtained the highest accuracy value of 0.779 with a standard deviation of approximately 0.046.. The research results indicate that the C4.5 algorithm performs the best, followed by the Naïve Bayes algorithm with a statistically insignificant difference, and lastly, the K-Nearest Neighbor algorithm with the smallest value, thus considered less suitable for implementation in the dataset.
Co-Authors Amanda Julia Dela Siska Dinna Yunika Hardiyanti Juwita Tetra Marani Aliyah Nazanah Muhammad Fakhri Rizqullah Naura Tri Raihana Pacu Putra Rahmat Fitra Arkamil Shanaz Khairunnisa Stevan Desena Damanik