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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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Integration Of Pca And K-Means Clustering For Staple Food Segmentation In Support Of National Food Policy Sipayung, Sardo; Hasugian, Paska Marto
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 4 (2025): Articles Research October 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i4.15343

Abstract

This study aims to develop cross-provincial staple-food segmentation by integrating Principal Component Analysis (PCA) and K-Means to support policy formation. The dataset comprises 2023 staple-food consumption for 34 Indonesian provinces across six indicators from BPS/SUSENAS. All indicators were standardized using z-score, reduced via PCA, and the resulting component scores were used as inputs to K-Means. Three components (PC1–PC3) explained 73.86% of the variance and captured shifts between sweet/animal-based vs. plant foods, fatty or animal-based grains, and the energy contribution of fat. The optimal number of clusters was determined as k = 3, yielding Silhouette = 0.466 and DBI = 0.733, indicating sufficiently compact and well-separated groups. The results reveal three segments: the first group consists of 11 provinces that are predominantly plant-based with low sugar and low animal-based consumption; the second group includes 13 provinces characterized by high animal-based and high-fat consumption; and the third group comprises 10 provinces with low-fat diets and fresh plant-based consumption. Stability checks on initialization and a leave-one-feature-out procedure confirmed consistent assignments. This fills an empirical gap: to our knowledge, no prior research integrates PCA with K-Means for cross-provincial staple-food segmentation in Indonesia while also reporting internal validation. Practically, the study provides operational segmentation to support food-security interventions moving beyond composite indices toward actionable targeting for production support, supply/price stabilization, and improved nutritional access thereby reframing IKP/FSVA from index-ranking to evidence-based segmentation.
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.
Development of distance formulation for high-dimensional data visualization in multidimensional scaling Marto Hasugian, Paska; Mawengkang, Herman; Sihombing, Poltak; Efendi, Syahril
Bulletin of Electrical Engineering and Informatics Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i2.8738

Abstract

This research aims to produce a new method called pasca-multidimensional scaling (pasca-MDS) by modifying the multidimensional scaling (MDS) method, the developed model comes as a solution to overcome the problem of data complexity by reducing its description dimension without losing important information. This model, offers an innovative approach in dealing with these problems. Pasca-MDS not only focuses on reducing the dimensionality of data, but also retains the essence of relevant information from each data point. As such, it allows for easier and more efficient analysis without compromising the accuracy of the information conveyed. The main advantage of pasca-MDS lies in its ability to produce simpler visual representations while maintaining the original structure of complex data. This provides clarity and ease in understanding the patterns or relationships hidden within. By using adjustment techniques after the MDS process, this model can provide more optimized results. This process allows the adjustment of data points to achieve a better representation in a lower dimensional space, resulting in a more intuitive and easy-to-understand interpretation. The developed distance formula has the ability to minimize stress compared to other distance formulas in MDS space, with the aim of improving the accuracy of high-dimensional data visualization.
Information Technology Resource Framework Hasugian, Paska Marto
Journal Majelis Paspama Vol. 2 No. 01 (2024): Journal Majelis Paspama, January 2024
Publisher : Journal Majelis Paspama

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

This research is a literature review that aims to investigate and analyze concepts and frameworks related to information technology resources. Through a literature review approach, this research collects, organizes, and analyzes previous research that has been conducted in this area. The main focus of this research is to gain an understanding of the role, components, and characteristics of the Information Technology Resource framework, as well as its impact on company performance. The method used in this research involved the search and selection of relevant scholarly articles, journals, and publications relating to the topic. After a careful selection process, literature studies that met the inclusion criteria were analyzed in detail, and important relevant information was retrieved for further analysis. The results of this literature review present various existing frameworks in the information technology resources domain. These frameworks cover important aspects such as information technology resource management, integration of information technology in business strategy, information technology performance measurement, and development of information technology competencies in organizations. In this context, this study identifies key concepts, theoretical perspectives, and recent trends in the development of information technology resource frameworks.
Performance of K-Means Algorithm for Ground Acceleration Clustering Siska Simamora; Amran Manalu; Paska Marto Hasugian
Journal Majelis Paspama Vol. 2 No. 2 (2024): Journal Majelis Paspama, July 2024
Publisher : Journal Majelis Paspama

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Indonesia is one of the most seismically active regions in the world due to the convergence of the Indo-Australian, Eurasian, and Pacific tectonic plates. This condition exposes the country to frequent earthquakes with varying magnitudes and intensities that may cause severe structural damage and pose risks to human safety. Ground acceleration, particularly Peak Ground Acceleration (PGA), is a key parameter for evaluating earthquake impacts and is strongly influenced by geological conditions, hypocentral depth, and epicentral distance. However, the complexity and large volume of ground acceleration data often hinder manual interpretation. This study applies the K-Means clustering algorithm to classify ground acceleration data obtained from seismic records at several observation points. Prior to clustering, data preprocessing was performed through data cleaning and min–max normalization to ensure quality and comparability across variables. The optimal number of clusters was determined using the Elbow method and Silhouette Score. The results reveal distinct distribution patterns of ground acceleration, which are closely related to local seismic conditions. These findings are expected to contribute to the development of preliminary ground acceleration zonation, providing valuable insights for earthquake hazard mapping and risk mitigation efforts in Indonesia.
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