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Darmansah Darmansah
Universitas Putera Batam, Batam
Computer Science & IT Decision Sciences, Operations Research & Management

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Penerapan Algoritma C.45 Dalam Pengklasifikasian Data Penjualan Gerai Makanan Cepat Saji Berbasis Website Darmansah Darmansah; Willi Novrian; Tomy Nanda Putra; Budy Satria
Journal of Information System Research (JOSH) Vol 5 No 1 (2023): Oktober 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v5i1.4405

Abstract

With the development of technology, there are many methods that can be used to analyze various kinds of problems, both in the world of education, social and even the business world. One method that can be used is the data mining method. Fast food is one example of a business that is being built by many people today, especially in the city of Batam. With so many fast food outlets in the city of Batam, this study aims to classify the types of food that are in demand and not in demand in the city of Batam. To assist the writer in classifying the best-selling and not-selling food types, in this study the authors used the data mining method, namely applying the C.45 Algorithm approach. Apart from using the C.45 Algorithm to test the calculation results, the author uses a website-based information system. From the research that has been carried out, the results obtained are that the best-selling food for children is the Original Burger (BO) and for adults the best-selling type of food is Kebab (K), while the less popular type of food for all ages is Burger Crispy.
Perbandingan K-Means dan DBSCAN dalam Analisis Pola Pergerakan Kapal Menggunakan Data Automatic Identification System (AIS) Darmansah Darmansah; Koko Handoko; Novri Adhiatma; Pastima Simanjuntak
Journal of Information System Research (JOSH) Vol 7 No 3 (2026): April 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i3.9363

Abstract

Batam waters are one of the busiest shipping lanes in Indonesia, with high ship traffic density and complex movement patterns. This condition requires data analysis techniques that can accurately identify and adapt ship movement patterns. The purpose of this study is to study ship movement patterns using Automatic Identification System (AIS) data, and also to see how the K-Means and DBSCAN algorithms work in the data clustering process. The AIS data used includes geographic coordinates, observation time, speed, and direction of ship movement in Batam waters. This study includes the application of the K-Means and DBSCAN algorithms, feature extraction and normalization, and data pre-processing to improve data quality. Internal validation metrics used to assess cluster quality are the Silhouette Score and the Davies–Bouldin Index. The results of the study show that the DBSCAN algorithm has a better level of cluster cohesion and separation between clusters than K-Means. The K-Means algorithm produces a Silhouette Score value of 0.48 and a Davies–Bouldin Index value of 0.91, while the DBSCAN algorithm produces a Silhouette Score value of 0.62 and a Davies–Bouldin Index value of 0.67. In addition, DBSCAN can find sound data of 19.96% of the data set, which indicates abnormal ship movements or does not form a certain density pattern. The results show that the DBSCAN algorithm analyzes ship movement patterns with AIS data in the Batam waters better than K-Means. This research is expected to be the basis for the development of maritime information systems that help monitor ship traffic, make decisions about safety, and manage waters.
Co-Authors Budy Satria Koko Handoko Novri Adhiatma Pastima Simanjuntak Tomy Nanda Putra Willi Novrian Willi Novrian