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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Pendidikan Vokasi Jurnal Sains dan Teknologi Jurnal Sarjana Teknik Informatika Jurnal Teknologi Informasi dan Ilmu Komputer Sistemasi: Jurnal Sistem Informasi Jurnal Pengabdian UntukMu NegeRI Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Sinkron : Jurnal dan Penelitian Teknik Informatika JURNAL MEDIA INFORMATIKA BUDIDARMA Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control JITK (Jurnal Ilmu Pengetahuan dan Komputer) JOURNAL OF APPLIED INFORMATICS AND COMPUTING ILKOM Jurnal Ilmiah Compiler KACANEGARA Jurnal Pengabdian pada Masyarakat Martabe : Jurnal Pengabdian Kepada Masyarakat JURTEKSI Jurnal Pemberdayaan: Publikasi Hasil Pengabdian Kepada Masyarakat Jambura Journal of Informatics Building of Informatics, Technology and Science JISKa (Jurnal Informatika Sunan Kalijaga) Mobile and Forensics Aviation Electronics, Information Technology, Telecommunications, Electricals, Controls (AVITEC) Jurnal Informatika dan Rekayasa Perangkat Lunak International Journal of Advances in Data and Information Systems Jurnal REKSA: Rekayasa Keuangan, Syariah dan Audit Innovation in Research of Informatics (INNOVATICS) Humanism : Jurnal Pengabdian Masyarakat Jurnal Pendidikan dan Teknologi Indonesia Prima Abdika: Jurnal Pengabdian Masyarakat Bulletin of Pedagogical Research Jurnal Pengabdian Pada Masyarakat Jurnal Pengabdian Informatika (JUPITA) Bulletin of Social Informatics Theory and Application Sabangka Abdimas Jurnal Pengabdian Masyarakat Sabangka Mohuyula : Jurnal Pengabdian Kepada Masyarakat Scientific Journal of Informatics
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Journal : ILKOM Jurnal Ilmiah

Evaluation of K-Means Clustering Using Silhouette Score Method on Customer Segmentation Yulisasih, Baiq Nikum; Herman, Herman; Sunardi, Sunardi; Yuliansyah, Herman
ILKOM Jurnal Ilmiah Vol 16, No 3 (2024)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v16i3.2325.330-342

Abstract

Customer segmentation is a critical process in businesses to understand and meet the diverse needs of customer. This study focused on the challenges of managing large and complex volumes of customer data and identifying the right segments to personalize marketing strategieshow about if I . K-Means Clustering has been widely utilized for its ability to group multidimensional data, but this method often generated broad clusters that lack detailed insights. Therefore, cluster evaluation with the Silhouette Score method became essential to ensure the optimality and validity of the generated groupings. The purpose of this study was to evaluate the quality of K-Means Clustering using the Silhouette Score method on customer segmentation. This research began with the acquisition of a dataset comprising 2,000 data points characterized with 7 attributes: sex, marital status, age, education, income, occupation, and settlement size. The data then underwent pre-processing by checking missing values and normalizing data. K-Means Clustering was then applied to group data into several clusters based on their proximity to the cluster center (centroid). The results of the clusters were assessed using the Silhouette Score method to determine the most optimal number of clusters. The results of this study consisted of manual calculations using Microsoft Excel on 27 data points to facilitate understanding of the logic, steps, methods and practical foundations before implementation on the complete dataset. Furthermore, the results of the Python calculation in 2000 data points showed that the optimal number of clusters (close to the value of 1) between k = 2 to k = 7 was the k = 4 cluster with a Silhouette Score value of 0.43, categorized as a weak structure. Although this value indicated a weak cluster structure, it was the highest value in the test, indicating that the division of data into four clusters (k = 4) was better than the number of other clusters. However, the quality of this cluster indicates the need for futher improvement. Future work should review the used attributes, data normalization methods, or consider other clustering algorithms to achieve a more robust structure and more meaningful interpretation.
File carving Analyze of Foremost and Autopsy on external SSD mSATA using the Association of Chief Police Officer Method Dahlan, Khoirul Anam; Yudhana, Anton; Yuliansyah, Herman
ILKOM Jurnal Ilmiah Vol 16, No 3 (2024)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v16i3.2360.283-295

Abstract

File carving is a method for recovering files using software such as Foremost and Autopsy. The recovery is conducted for deleted files or formatted devices. Popularity Solid State Drive (SSD) has outperformed Hard Disk Drive (HDD) because SSD is faster, more efficient, and shock resistant. However, recovering SSD devices have a lower probability success rate than HDD because the security system often hampers files recovered on SSD. Based on previous research, the success rate of Security Digital High Capacity (SDHC) only achieved 50% more than SSD, whereas SSD can only return 85.7% of its success. Forensics Digital is a part of Forensics Knowledge for deliver valid digital evidence for law investigation. This research aims to increase the success rate of recovery files using two different software: Foremost and Autopsy. The research uses a 512GB Eaget brand SSD with a New Technology File System (NTFS). The file carving is also conducted using the Association of Chief Police Officers (ACPO) method. APCO has several stages: Planning, Capture, Analysis, and Presentation. The experiment results show that Autopsy software with deep recover mode returned 81 out of 88 files (92%), whereas Foremost software run on Debian to make sure no virus on device that could damage computer especially windows system. First attempt recovery can only return 46 out of 88 files (52%). The findings show that the Autopsy software has a higher successful return rate and can be used for evidence in law enforcement and digital forensics investigations.
Co-Authors Abdul Fadlil Adhi Prahara, Adhi Agus Setiawan, Hisyam ALYA MASITHA Anton Yudhana Apriliani, Evinda Ardiansyah, Ricy Arief Ghozali, Fanani Asti Mulasari, Surahma Ayu Laksmi Pandhita, Ayu Laksmi Bambang Sudarsono Bella Okta Sari Miranda Bidinnika, Muhammad Kunta darmanto darmanto Destriana, Rachmat Dewi Soyusiawaty Eko Hari Rachmawanto Fatwa Tentama Febiyan, Rifal Firdaus, Muhammad Khysam Fitriani Mutmainah, Nur Fitriani, Isah Ghozali, Fanani Arief Habie, Khairul Fathan Hafin, Aqid Fahri Hazar, Siti Herminarto Sofyan Hidayat, Muhammad Taufiq Hildayanti, Ica Kurnia Hildayanti, Ica Kurnia Ika Arfiani Imam Riadi Irfan, Syahid Al Jayawarsa, A.A. Ketut Jefree Fahana Jumaedi Nasir, Ardiansyah Khoirul Anam Dahlan Khoirunnisa, Itsnaini Irvina Kintung Prayitno, Kintung Lifa, Lifa Lina Handayani Listyaningrum, Prabandari M. Yogi Riyantama Isjoni Mahiruna, Adiyah Muhammad Dzikrullah Suratin, Muhammad Dzikrullah Muhammad Fahmi Mubarok Nahdli Muhammad Kunta Biddinika Muhammad Ridwan Murinto Murinto Murinto Mutmainah, Nur Fitri Nafiati, Lu'lu' Nafiati, Lu’lu’ NGATIMIN, NGATIMIN Nia Ekawati, Nia Nisa Novianti, Tria Novitasari, Isda Desy Nur Rochmah Dyah Pujiastuti Pamungkas, Gilang Pamungkas, Gilang Pratama, Ridho Haikal Pratama, Wegig Putro, Aldibangun Pidekso R. Hafid Hardyanto, Settings Rachmaliany, Nur Rahmawan, Jihad Rahmawati, Rahmawati Raihan, Habib Aulia Rajunaidi, Rajunaidi Razak, Farhan Radhiansyah Rusydi Umar Salji, Rinday Zildjiani Sri Winiarti Subardjo Subardjo, Subardjo Sukesi , Tri Wahyuni Sulistyawati , Sulistyawati Sulistyawati Sulistyawati Sunardi Sunardi Sunardi Surahma Asti Mulasari Tri Wahyuni Sukesi Ulumiyah, Iftitah Dwi Wahyuni Sukesi, Tri Wala, Jihan Wan Ali, Wan Nur Syamilah Yohanni Syahra Yulianto, Dinan Yulisasih, Baiq Nikum