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All Journal Bulletin of Electrical Engineering and Informatics Sinkron : Jurnal dan Penelitian Teknik Informatika International Journal of Artificial Intelligence Research Jurnal Mantik Penusa Cetta: Jurnal Ilmu Pendidikan Jurnal Teknik Informatika UNIKA Santo Thomas JIPN (Journal of Informatics Pelita Nusantara) MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Nasional Komputasi dan Teknologi Informasi JURIKOM (Jurnal Riset Komputer) Jurnal Mantik Jusikom: Jurnal Sistem Informasi Ilmu Komputer Jurnal Sains dan Teknologi TRIDARMA: Pengabdian Kepada Masyarakat (PkM) INFOKUM JUKI : Jurnal Komputer dan Informatika BERNAS: Jurnal Pengabdian Kepada Masyarakat Login : Jurnal Teknologi Komputer MEANS (Media Informasi Analisa dan Sistem) Jurnal Ilmu Komputer dan Sistem Informasi (JIKOMSI) Journal of Computer Networks, Architecture and High Performance Computing Jurnal Pengabdian kepada Masyarakat Nusantara Jurnal Media Informatika Jurnal Info Sains : Informatika dan Sains DISTANCE: Journal of Data Science, Technology, and Computer Science Jurnal Teknik Informatika Unika Santo Thomas (JTIUST) Ulead : Jurnal E-pengabdian Jurnal Pendidikan LLDIKTI Wilayah I (JUDIK) Multidisiplin Pengabdian Kepada Masyarakat (M-PKM) Jurnal Komputer Indonesia (JU-KOMI) Pengabdian Kepada Masyarakat Indonesia SEAN (ABDIMAS SEAN) "Journal of Data Science Journal Majelis Paspama
Hasugian , Paska Marto
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Journal : "Journal of Data Science

 1 
Testing the C45 Algorithm with Rapid Miner for Stock Selection (Case Study: Toko Usaha Muda) Hasugian, Paska Marto
Journal Of Data Science Vol. 1 No. 02 (2023): Journal Of Data Science, September 2023
Publisher : Sean Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58471/jds.v1i02.2836

Abstract

One of the keys to the success of a retail company is good stock management. Intuition-based methods are often not enough because customer demands are always changing. This research concentrates on the use of the C4.5 decision tree algorithm on the RapidMiner platform to optimize the selection of goods in the Toko Usaha Muda. This algorithm is used to predict future stock requirements by looking at previous sales patterns in stores and historical sales data. The results show a significant increase in the accuracy of stock predictions and a decrease in the probability of loss due to excess or stockouts. This implementation not only enhances the operations of the Toko Usaha Muda, but also provides a framework that other retail businesses can use to increase their profits through better stock management.
Comparison and Evaluation of Euclidean Distance and Dice Distance in the K-Means Adaptive Algorithm for Clustering Composite Indexes of Food Security and Vulnerability Maps Emma Romasta Naulina Nainggolan; Paska Marto Hasugian
Journal Of Data Science Vol. 3 No. 02 (2025): Journal Of Data Science, September 2025
Publisher : Sean Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58471/jds.v3i2.6941

Abstract

This study aims to compare and evaluate the effectiveness of two distance measurement methods, namely Euclidean Distance and Dice Distance, in the K-Means Adaptive algorithm for clustering Food Security and Vulnerability Composite Index data. The dataset used includes index data from 2022 to 2024, comprising 305 entries, which were then cleaned to 298 entries. The evaluation was conducted manually using a sample dataset and automatically using the entire dataset via Google Colab with Python. The algorithm's performance was assessed using the Silhouette Score metric to measure the quality of the resulting clusters. The evaluation results showed that the Euclidean method produced an average Silhouette Score of 0.3082, indicating an suboptimal cluster structure. This study concludes that the choice of distance method significantly influences clustering results, and selection should be tailored to the characteristics of the data.
Ground Acceleration Clustering Using Self-Organizing Map Method Siska Simamora; Amran Manalu; Paska Marto Hasugian
Journal Of Data Science Vol. 3 No. 02 (2025): Journal Of Data Science, September 2025
Publisher : Sean Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58471/jds.v3i2.7281

Abstract

Peak Ground Acceleration (PGA) is an important parameter in seismic studies because it is directly related to the level of shaking felt on the earth's surface. Analysis of ground acceleration data is needed to identify patterns, group regions based on their seismic characteristics, and support earthquake disaster mitigation efforts. This study uses the Self-Organizing Map (SOM) method, which is an unsupervised learning approach based on artificial neural networks that can map high-dimensional data into a two-dimensional map representation without losing its topological structure. The ground acceleration dataset used in this study consists of key seismic parameters such as depth, magnitude, source distance, and PGA values. The SOM learning process is carried out iteratively to produce a cluster map that groups earthquake data into several groups with different ground acceleration characteristics. The results show that the SOM method is able to identify ground acceleration distribution patterns more clearly than conventional methods, by producing clusters that represent variations in PGA from low to high. These findings can provide important contributions to earthquake risk mapping, regional spatial planning, and the formulation of more accurate disaster mitigation strategies.
Co-Authors Agustinus Parmazatule Laia Al Hashim, Safa Ayoub Alex Rikki Amran Manalu Angelia M Manurung Anju Eliarsyam Lubis Annas Prasetio Arvind Roy Baehaqi Batubara, Muhammad Iqbal Betti Mastaria Br Sembiring Bobby Aris Sandy Bosker Sinaga Bosker Sinaga, Bosker Sinaga Br Ginting, Anirma Kandida Br Sembiring, Betti Mastaria Cinthya Agatha Sinaga Damianus Daha Devlin Iskandar Saragih Dewi Lasmiana Panjaitan Dharma Rajen Kartighaiyab Dharma Rajen Kartighaiyan Efendi, Syahril Emma Romasta Naulina Nainggolan Endang Utari Endra A.P Marpaung Fenius Halawa Ferdiansyah, Rahmat Fristi Riandari Fristy Riandari Giawa, Martinus Hanum, Rahmadiah Harefa, Ade May Luky Harpingka Sibarani Hasugian, Penda Sudarto Hengki Tamando Sihotang Herman Mawengkang Hidayati, Wenika Hutahaean, Harvei Desmon Hutahaean, Harvei Desmon Insan Taufik Ira Mayang Sari Jijon R. Sagala Jijon R. Sagala Jijon Raphita Sagala John Foster Marpaung Kristian Siregar Logaraj Logaraj Logaraj, Logaraj Logaraz Logaraz Lubis, Anju Eliarsyam Makmur Tarigan Manurung, Jonson Martinus Giawa Mathelinea, Devy Maya Theresia Br. Barus MIFTAHUL JANNAH Nababan, Adli Abdillah NASUTION, ATIKA AINI Ndruru, Risnamawati Nera Mayana Br.Tarigan Nico Setiawan Nurayni Sinabang Pandi Barita Nauli Simangunsung Penda Sudarto Hasugian Penda Sudarto Hasugian Poltak Sihombing Prawita Ardella R. Mahdalena Simanjorang Rahmat Ferdiansyah Riana Risnamawati Ndruru Ritha Zahara Tarigan Rizki Manullang Romanus Damanik Romauli Sianipar Sandy, Bobby Aris Sethu Ramen Sethu Ramen, Sethu Ramen Setiawan, Nico Siagian, Novriadi Antonius Sihotang, Jonhariono Sijabat, Petti Indrayati Simamora, Siska Simangunsong, Pandi Barita Nauli sinaga, lotar mateus Sinaga, Sony Bahagia Sinaga, Sony Bahagia Sinta Novianti, Sinta Sipayung, Sardo Sipayung, Sardo Pardingotan Siregar, Vanessa Sitanggang, Sarinah Situmorang, Caesar Juanda Theodorus Sri Wahyuni TONNI LIMBONG Uzitha Ram Vanessa Siregar Venentius Purba Vina Winda Sari Wenika Hidayati Widia Putri Yosapat Sembiring Yuda Perwira Yusi Tri Utari Panggabean