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All Journal Jurnal Ilmu Komputer Jurnal Transformatika Jurnal Edukasi dan Penelitian Informatika (JEPIN) INFORMAL: Informatics Journal ITEj (Information Technology Engineering Journals) Sistemasi: Jurnal Sistem Informasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING JOURNAL OF APPLIED INFORMATICS AND COMPUTING METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi ILKOM Jurnal Ilmiah Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) JOISIE (Journal Of Information Systems And Informatics Engineering) INFOKUM Jurnal Pengabdian kepada Masyarakat Nusantara Jurnal Computer Science and Information Technology (CoSciTech) International Journal of Engineering, Science and Information Technology Jurnal Informatika dan Teknologi Komputer ( J-ICOM) Multica Science and Technology jeti Jurnal Minfo Polgan (JMP) Jurnal Pengabdian Masyarakat : Pemberdayaan, Inovasi dan Perubahan TECHSI - Jurnal Teknik Informatika Sisfo: Jurnal Ilmiah Sistem Informasi Journal of Information Technology (JINTECH) International Journal of Information System & Innovative Technology Jurnal Pengabdian Masyarakat Bangsa Jurnal Malikussaleh Mengabdi Journal of Advanced Computer Knowledge and Algorithms Gameology and Multimedia Expert International Journal of Information System and Innovative Technology Smatika Jurnal : STIKI Informatika Jurnal Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN) Proceedings of Malikussaleh International Conference on Multidisciplinary Studies (MICoMS)
Sujacka Retno
Universitas Malikussaleh
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Physics Public Health Social Sciences Transportation Other

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Journal : International Journal of Engineering, Science and Information Technology

 1 
Application of Fuzzy C-Means and Borda in Clustering Crime–Prone Areas and Predicting Crime Rates Using Long Short Term Memory in Northern Aceh Regency Lubis, Syahrul Andika; Ula, Munirul; Retno, Sujacka
International Journal of Engineering, Science and Information Technology Vol 5, No 1 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i1.747

Abstract

North Aceh is a district with diverse geographical conditions, ranging from vast lowland areas in the north stretching from west to east, to mountainous areas in the south. The average altitude in North Aceh is 125 meters. The district covers an area of 2,694.66 km² with a population of 614,640 people in 2022. The issue of crime in North Aceh District has caused significant discomfort among the community. According to data from the Central Bureau of Statistics (BPS) of Aceh Province, the number of criminal cases increased from 6,651 cases in 2022 to 10,137 cases in 2023. Using the Fuzzy C-Means clustering method, the data was grouped into three clusters: cluster 1 represents safe areas, cluster 2 represents moderately vulnerable areas, and cluster 3 represents vulnerable areas. For ranking using the Borda method, the Dewantara Police Sector ranked first for the physical aspect, while the Muara Batu Police Sector ranked first for the item aspect. As for predictions using the LSTM model, almost all subdistricts achieved MAPE values below 20%, indicating that the LSTM model is quite effective in predicting crime-prone areas. For example, Baktiya District recorded a MAPE value of 15.85% for the physical aspect, while the best result was achieved by Simpang Keramat District for the item aspect with a MAPE value of 0.00%. However, in Syamtalira Bayu District, the item aspect reached a MAPE value of 20.07%. Although the MAPE value for the item aspect in Syamtalira Bayu is relatively high, it is still considered acceptable as it remains below 50%.
Comparison of K-Medoids and K-Means Result for Regional Clustering of Capture Fisheries in Aceh Province Salsabila, Thifal; Nurdin, Nurdin; Retno, Sujacka
International Journal of Engineering, Science and Information Technology Vol 5, No 2 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i2.829

Abstract

This research aims to develop a web-based application that can categorize areas of capture fisheries in Aceh Province. The methods used in this research are K-Means and K-Medoids. The methods used in this research are K-Means and K-Medoids, a clustering technique used to group districts/cities based on high and low catch areas. This application will use data from the Marine and Fisheries Service (KKP) of Aceh Province, covering the period 2017 to 2023. This research will analyze variables such as production (tons), number of vessels, sub-districts, villages, and fish species. The system is developed using the PHP programming language to facilitate implementation and data access by stakeholders. Stakeholders. As an evaluation tool for clustering results, the Davies-Bouldin Index (DBI) is used to measure the quality of clustering results. The results of this study are expected to provide an overview of areas with high catches and assist policymakers in designing a more strategic approach to fishing—policymakers in developing more effective strategies to increase fishing, especially in districts with low fish catch. In addition, this application also provides an interactive platform for users to analyze fisheries data quickly and efficiently.
Hiace Transportation Departure Scheduling Information System in Lhokseumawe With Genetic Algorithm Taskia, Narita; Dinata, Rozzi Kesuma; Retno, Sujacka
International Journal of Engineering, Science and Information Technology Vol 5, No 3 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i3.878

Abstract

This study aims to address challenges in transportation scheduling by employing a suitable algorithm to ensure the scheduling process operates efficiently and effectively. One algorithm identified as appropriate for this task is the Genetic Algorithm, which is widely recognized for its robust capabilities in optimization tasks. Known for its adaptability and robustness, the Genetic Algorithm is well-suited for scheduling applications, including academic timetabling, as it can handle complex problems involving multiple criteria and objectives. Inspired by principles of biological evolution and natural selection, this algorithm iteratively explores solutions to approach optimal outcomes, refining the schedule in each iteration until an effective solution is achieved. Based on the analysis of experimental results using real-world data and evaluation of the system's design, the study concludes that the Hiace transportation departure scheduling system was successfully developed using a web-based approach. This web-based system offers significant advantages, as it facilitates more efficient management of departure schedules and eliminates the need for manual checks. As a result, it reduces the risk of human error and allows for better resource allocation. The integration of Genetic Algorithms into the development of the Hiace transportation scheduling system demonstrates the potential of evolutionary computation in solving practical, real-life scheduling problems. The resulting system is supported by internet-based technologies, providing easy access to passengers and system administrators. Despite the positive outcomes achieved, the current implementation is not without limitations. Further refinement and continued development are essential to enhance system performance, increase reliability, and ensure it can adapt to evolving needs and operational complexities, ensuring its long-term effectiveness.
Evaluating the Quality of Agglomerative Hierarchical Clustering on Crime Data in Indonesia Rizkya, Dini Dara; Retno, Sujacka; Yunizar, Zara
International Journal of Engineering, Science and Information Technology Vol 5, No 2 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i2.863

Abstract

This study evallualtes the quallity of ALgglomeraltive Hieralrchicall Clustering with single linkalge, complete linkalge, alveralge linkalge, alnd walrd linkalge on the daltalset of the number of criminall calses in Indonesial (20ll0ll0ll-20ll23). The analysis compares clustering performance on the original and normalized datasets using the Davies-Bouldin Index (DBI), Silhouette Score (SS), Normalized Mutual Information (NMI), Adjusted Rand Index (ARI), and Callinski-Harabasz Index (CH). The results showed that Ward Linkage provided the best clustering results, with the highest CH increasing from 65.826 to 66.873, clear cluster separation, and a stable structure (NMI = 0.5855, ARI = 0.6298). Single Linkage experienced a chaining effect, although it showed improvement in DBI from 0l.1793 to 0l.1765 and SS from 0l.6271 to 0l.640l0l, with NMI and ARI stable at 0l.4537 and 0l.5865, but CH decreased from 21.731 to 21.0l72. Complete Linkage was too aggressive in separating the data, shown by an increase in DBI from 0.5327 to 0.7116 and a decrease in SS from 0.6336 to 0.5830, although CH increased from 64.244 to 66.873. Average Linkage showed stable results, with NMI = 0l.6481 and ARI = 0l.7993 remaining, but a slight decrease in DBI from 0l.3874 to 0l.40l91, SS from 0l.6839 to 0l.6825, and CH from 42.358 to 40l.251. Data normalization generally helps to improve clustering quality by reducing the influence of feature scale differences. Several metrics showed improved cluster separation on normalized data, although the impact varied depending on the linkage method. Overall, Ward Linkage with normalization is recommended as the best method to produce more accurate clustering in Indonesia's crime data analysis. 
Co-Authors Abdul Azis Alfika, Selly Andra Munandar Angga Pratama Ardi Wirya Indarto Asmaul Husna Asrianda Asrianda Aulia, Faizul Azrai Putra Barumun Daulay Bustami Bustami Bustami Cut Agusniar Devi, Salma EDI YUSUF, EDI Fadlisyah Fadlisyah Fahrizal, Effan Fajri, T Irfan Fiasari, Fiasari Fikran, Rifzan Fortilla, Zeny Arsya Gadis Ayu Sofiana Gilang Wahyu Ramadhan Gilang Hakimi, Musawer Haried Novriando Hidayatsyah Hidayatsyah ilham - sahputra Ilham Sahputra Ima Pratiwi Irvan Na’syakban Lidya Rosnita Lubis, Syahrul Andika Maghfirah, Riezka Mahsa, Masithah Maida, Eka Maryana Maryana Maryana Maryana Maryana, Maryana Muhammad Al Imran Muhammad Daud Muhammad Fikry Muhammad Ikhwanus Muhammad Nurfahmi Muhammad, Muhammad Munirul Ula Mutammimul Ula Mutasar Nadia Saphira Nasrul ZA, Nasrul Nisa Ul Fadila Nisa, Hayatun Novia Hasdyna Nur Faliza Nurdin Nurdin Panjaitan, Cherlina Helena Purnamasari Putra Barumun Daulay, Azrai Rahma Fitria, Rahma Reza Pahlevi Ginting Richki Hardi Rijal, Himmatur Rini Meiyanti Rizky Putra Fhonna Rizkya, Dini Dara Rozzi Kesuma Dinata Safriandi, Safriandi Safwandi Safwandi, Safwandi Sahputra, Ilham Said Fadlan Anshari Salsabila, Thifal Sayed Fachrurrazi Sinambela, Ilmi Suciani Siti Fatimatun Zahro Siti Wahyuni Sudirman Sudirman T Irfan Fajri Taskia, Narita Teuku Zulkarnaen Tsania Asha Fadilah Daulay Utari, Sylva Putri Veri Ilhadi Wahdana, Aldi Wahyu Isnanda Nasution Wibowo, Patmono Yafis, Balqis Yanti, Riski Yesy Afrillia Yopy Anfelia Zara Yunizar Zulfadl, Zulfadl Zulfia , Anni