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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 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
Sujacka Retno
Universitas Malikussaleh
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Journal : Journal of Advanced Computer Knowledge and Algorithms

 1 
Implementation of K-NN Algorithm to classify the Scholarship Recipients of Aceh Carong at Universitas Malikussaleh Yanti, Riski; Retno, Sujacka; Yafis, Balqis
Journal of Advanced Computer Knowledge and Algorithms Vol 1, No 1 (2024): Journal of Advanced Computer Knowledge and Algorithms - January 2024
Publisher : Department of Informatics, Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jacka.v1i1.14534

Abstract

In an effort to increase the efficiency of the scholarship selection process, this research aims to implement the K-Nearest Neighbors (K-NN) algorithm in the classification of scholarship recipients. The research method involves collecting data on scholarship receipts from several previous years based on predetermined criteria such as father's job, mother's job, parent's income, number of parents working, father's last education, and mother's last education. Next, the K-NN algorithm is applied to classify prospective scholarship recipients based on the similarity of their profiles to students who have received previous scholarships. The results of this research indicate that the implementation of the K-NN algorithm in the classification of scholarship admissions at Malikussaleh Aceh Carong University can increase selection accuracy. The experimental results of the accuracy values obtained show that using the K-Nearest Neighbors algorithm with the Euclidean Distance approach and a value of K = 3 produces an algorithm accuracy level of 87.55%. Thus, the K-NN algorithm can be a useful method for scholarship selectors to support more precise and objective decision making.
K-NN with Purity Algorithm to Enhance the Classification of the Air Quality Dataset Retno, Sujacka; Hasdyna, Novia; Yafis, Balqis
Journal of Advanced Computer Knowledge and Algorithms Vol 1, No 2 (2024): Journal of Advanced Computer Knowledge and Algorithms - April 2024
Publisher : Department of Informatics, Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jacka.v1i2.15890

Abstract

The large number of attributes in a large dataset can cause a decrease in the level of classification accuracy. Attribute reduction can be a solution to improve classification performance, especially in the K-NN algorithm. This research discusses the classification results of K-NN with attribute reduction using Purity. Based on the results of testing carried out on the Air Quality Dataset, the level of accuracy obtained after attribute reduction was 70.71%, while the level of accuracy obtained before attribute reduction was 56.44%, the increase in accuracy obtained from testing this dataset was equal to 14.27%. The proposed Purity method for attribute reduction can increase the accuracy level of the K-NN classification process.
Comparison of Chen's Fuzzy Time Series and Triple Exponential Smoothing in Forecasting Medicine Stocks at the Blang Cut Kuala Community Health Center Devi, Salma; Yunizar, Zara; Retno, Sujacka
Journal of Advanced Computer Knowledge and Algorithms Vol 1, No 3 (2024): Journal of Advanced Computer Knowledge and Algorithms - July 2024
Publisher : Department of Informatics, Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jacka.v1i3.16870

Abstract

Forecasting is estimating future conditions by examining conditions in the past. In social life, everything is uncertain and difficult to predict precisely, so forecasting is needed. Efforts are always made to make forecasts in order to minimize the influence of this uncertainty on a problem. In other words, forecasting aims to obtain forecasts that can minimize forecast errors, which are usually measured by the mean absolute percentage error. This method is usually used for time series-based forecasting and uses data or information from the past as a reference when predicting current data. This research will compare the application of the Fuzzy Time Series Chen method and the Triple Exponential Smoothing method in forecasting drug stock determination at the Kuala Community Health Center, Blang Mangat District, Lhokseumawe City Regency, Aceh. The research results showed that the Triple Exponential Smoothing method was better in forecasting drug stock inventories compared to Chen's Fuzzy Time Series method. Chen's Fuzzy Time Series method produces a MAPE value of 17.67%, which means it has an accuracy of 82.33%, while the Triple Exponential Smoothing method produces a MAPE value of 9.842%, which means it has an accuracy of 90.158%
Implement the Analytical Hierarchy Process (AHP) and K-Nearest Neighbor (KNN) Algorithms for Sales Classification Husna, Asmaul; Retno, Sujacka; Rijal, Himmatur
Journal of Advanced Computer Knowledge and Algorithms Vol 1, No 4 (2024): Journal of Advanced Computer Knowledge and Algorithms - October 2024
Publisher : Department of Informatics, Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jacka.v1i4.17819

Abstract

The Analytical Hierarchy Process (AHP) and K-Nearest Neighbor (KNN) algorithms are two algorithms that have proven efficient in various classification and prediction applications. This research examines the application of these two algorithms in the context of selling goods in PIM supermarkets. In this research, AHP and KNN are used to classify goods sold based on various criteria such as price, number of stock items sold, total sales amount. The research results show that KNN outperforms AHP in predicting the best-selling, best-selling and least-selling items based on sales in 2022 at PIM supermarkets. Based on this research, it can be concluded that the KNN algorithm is suitable for predicting the classification of goods sales in PIM Supermarkets. This research classifies sales of goods using the Analytical Hierarchy Process (AHP) and K-Nearest Neighbor (KNN) methods. This research uses 3 criteria. By using the value K=1, the experimental results show that the highest KNN has an accuracy of 38%, while AHP has an accuracy of 32%. Differences in accuracy results can be influenced by parameter settings and characteristics of the dataset used. Therefore, further analysis of these factors is needed to understand the performance differences between the two methods.
Analysis of Clustering Results for Crime Incident Data in Indonesia Using Fuzzy C-Means Retno, Sujacka; Hakimi, Musawer
Journal of Advanced Computer Knowledge and Algorithms Vol. 2 No. 3 (2025): Journal of Advanced Computer Knowledge and Algorithms - July 2025
Publisher : Department of Informatics, Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jacka.v2i3.22565

Abstract

This study examines the clustering of crime incident data across Indonesia from 2000 to 2024 using the Fuzzy C-Means (FCM) algorithm, with a focus on the impact of data normalization. Comprehensive annual provincial crime statistics from Badan Pusat Statistik (BPS) were preprocessed to handle missing values and then standardized via the Standard Scaler. FCM clustering was performed separately on both the original and normalized datasets, with the number of clusters set to three. Cluster quality was evaluated over ten independent runs using five metrics: Davies-Bouldin Index (DBI), Silhouette Score (SS), Calinski-Harabasz Index (CH), Adjusted Rand Index (ARI), and Normalized Mutual Information (NMI). Results indicate that normalization consistently yields lower DBI values (average 0.824 vs. 0.830) and higher SS (average 0.367 vs. 0.363) and CH scores (average 55.35 vs. 54.09), while ARI and NMI remain stable across treatments. These findings demonstrate that normalization enhances cluster compactness and separation without altering underlying data structures, leading to more interpretable and reliable groupings. By uncovering regional crime patterns and highlighting the methodological importance of preprocessing, this research provides actionable insights for policymakers and law enforcement agencies to allocate resources more effectively and develop targeted crime prevention strategies.
Co-Authors Abdul Azis Alfika, Selly Angga Pratama 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 Hakimi, Musawer Haried Novriando Hidayatsyah Hidayatsyah ilham - sahputra Ilham Sahputra Irvan Na’syakban Lidya Rosnita Lubis, Syahrul Andika Maghfirah, Riezka Mahsa, Masithah Maida, Eka Maryana Maryana Maryana Maryana Maryana, Maryana Muhammad Daud Muhammad Ikhwanus Muhammad Nurfahmi Muhammad, Muhammad Munirul Ula Mutammimul Ula Mutasar Nasrul ZA, Nasrul Nasution, Wahyu Isnanda 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 Wibowo, Patmono Yafis, Balqis Yanti, Riski Yesy Afrillia Zara Yunizar Zulfadl, Zulfadl Zulfia , Anni