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All Journal J-SAKTI (Jurnal Sains Komputer dan Informatika) IJISTECH (International Journal Of Information System & Technology) Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming Building of Informatics, Technology and Science IJISTECH J-SAKTI (Jurnal Sains Komputer dan Informatika) Journal of Artificial Intelligence and Digital Business Journal of Informatics, Electrical and Electronics Engineering
Harianto, Kusno
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Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Mechanical Engineering Public Health Social Sciences

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Journal : Building of Informatics, Technology and Science

 1 
Penerapan Algoritma K-Nearest Neighbor Untuk Klasifikasi Warga Penerima Bantuan Sosial Pahrudin, Pajar; Harianto, Kusno
Building of Informatics, Technology and Science (BITS) Vol 4 No 3 (2022): December 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i3.2276

Abstract

Social Assistance (BanSos) is a government program intended for lower-middle families. Social assistance is assistance given to the community, especially the lower middle class, which is not continuous and selective. Many types of social assistance are provided by the government with the aim of prospering and helping the community's economy. However, the problem that occurs is that there are still many people who receive social assistance that are not people who deserve to receive social assistance, while the lower middle class who should receive social assistance are neglected and do not receive the social assistance. It should be for the distributor or the kelurahan to form groups for residents who are entitled to receive social assistance. The process of grouping the recipients of social assistance can be done by processing the data of residents who have the right to receive the social assistance. The data processing can be done by using data mining. One of the algorithms that can be used to solve problems in data mining is the K-Nearest Neighbor algorithm. After carrying out the overall process with a value of K = 5, it was found that the new data from residents was declared eligible to receive social assistance
Penerapan Metode MOORA pada Sistem Pendukung Keputusan Pemilihan Kepala Laboran Harianto, Kusno; Arfyanti, Ita; Yusika, Andi
Building of Informatics, Technology and Science (BITS) Vol 4 No 3 (2022): December 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i3.2288

Abstract

In the process of carrying out academic activities in every university, it is inseparable from the existence of tendik. In college, the head of the laboratory is in charge of ensuring the implementation of the use of the laboratory in supporting the ongoing learning process. The head of the laboratory is in charge of regulating work mechanisms and procedures in the laboratory unit. The importance of the role of the head of laboratory for tertiary institutions requires universities to have a head of laboratory in accordance with the implementation of the tasks and responsibilities given. The selection of the head of the laboratory is not only done based on the length of work at the tertiary institution, but also must be seen from the knowledge, abilities, expertise, decision making and competency certificates possessed. Therefore, we need a way to help solve problems, especially by using a computerized system. Decision support system is a computerized information system. Decision support systems are widely used for corporate organizations to solve problems in the process of making or supporting decisions. The results obtained from the application of the MOORA Method are that alternative A1 was chosen to be the head of the laboratory with a final score of 0.48
Student Class Grouping in Junior High Schools Based on Academic Performance Using the Fuzzy C-Means Method Bustomi, Tommy; Hasiholan, Jundro Daud; Harianto, Kusno
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8585

Abstract

Abstrak−Differences in academic abilities among junior high school students often pose a challenge for schools in conducting class groupings objectively and efficiently. Many educational institutions, including SMP Negeri Y, still rely on manual grouping methods that are subjective and do not accurately reflect the actual conditions of students. Inaccurate grouping may lead to imbalanced learning processes, where students with high and low academic abilities are placed in the same group without considering their performance variations. Therefore, a data-driven approach is needed to represent student characteristics comprehensively and flexibly. This study aims to apply the Fuzzy C-Means (FCM) method to cluster students of SMP Negeri Y based on four main attributes: Academic Average, Attitude Score, Activeness Score, and Attendance. The FCM method was chosen for its ability to handle data uncertainty and assign multiple membership degrees to each student across different clusters. Prior to clustering, the data underwent a preprocessing stage involving data cleaning, normalization using StandardScaler, and scale adjustment across attributes to improve the accuracy of Euclidean distance calculations. The analysis results revealed the formation of two main clusters representing student academic performance levels. Cluster 0 has an average academic score of 78.37 with moderate attitude and activeness levels, while Cluster 1 shows a higher academic average of 82.18 accompanied by better attitude, activeness, and attendance scores. Based on the highest membership degree, 38 students were assigned to Cluster 0 and 26 students to Cluster 1. Model evaluation using Fuzzy Partition Coefficient (FPC), Modified Partition Coefficient (MPC), and Silhouette Score indicated the optimal configuration at a fuzziness level of m = 2, yielding FPC = 0.680, MPC = 0.359, and Silhouette Score = 0.334. These findings demonstrate that FCM is effective in representing variations in student abilities more realistically, while also providing an objective foundation for schools to design adaptive learning strategies and implement data-driven academic policies.
Co-Authors Amelia Yusnita Azahari, Azahari Bustomi, Tommy Daud, Jundro Ekawati, Hanifah Harpad, Bartolomius Hendri Setiawan Ita Arfyanti Pahrudin, Pajar Pitrasacha Adytia Pratama, Dion Pratama, Jerio Putra Pratiwi, Heny Sari, Aulia Rahmita Suhariyadi, Yonatan Yusika, Andi