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All Journal IAES International Journal of Robotics and Automation (IJRA) Jurnal Informatika POSITIF Knowledge Engineering and Data Science Jusikom : Jurnal Sistem Komputer Musirawas Prosiding SAKTI (Seminar Ilmu Komputer dan Teknologi Informasi) J-Icon : Jurnal Komputer dan Informatika
Suprapty, Bedi
Politeknik Negeri Samarinda
Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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Journal : J-Icon : Jurnal Komputer dan Informatika

 1 
KLASIFIKASI MINAT SISWA UNTUK PROGRAM STUDI JURUSAN TEKNOLOGI INFORMASI - POLITEKNIK NEGERI SAMARINDA MENGGUNAKAN METODE FUZZY C-MEANS CLUSTERING Bedi Suprapty; Fariyanti Fariyanti
J-Icon : Jurnal Komputer dan Informatika Vol 8 No 1 (2020): Maret 2020
Publisher : Universitas Nusa Cendana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35508/jicon.v8i1.2184

Abstract

Students who have graduated from high school or high school will continue to a higher level such as Samarinda State Polytechnic. Samarinda State Polytechnic consists of several Departments approved by the Information Technology department. The Information Technology Department has 4 Study Programs including; D3 Informatics Engineering, D3 Computer Engineering, D4 Multimedia Information Technology and D4 Computer Technology Engineering. This research was conducted by classifying specialization of students who would continue their studies to the Department of Information Technology, Samarinda State Polytechnic. Sources of data obtained from the questionnaire. Data collection was carried out by questionnaire method, the questionnaire consisted of 15 questions and had 5 criteria. Each criterion has 3 questions. The questionnaire was distributed to 160 high school and vocational high school students in the city of Samarinda, East Kalimantan. Clusters in this study are divided into 4, namely cluster 1, an interest in the D3 Study Program in Informatics Engineering, cluster 2 an interest in the D3 Study Program in Computer Engineering, cluster 3 an interest in the D4 Study Program in Multimedia Information Technology and cluster 4 an interest in D4 in Computer Engineering Technology. Fuzzy C-means method is used in resolving these complications where the results of clustering cluster 1 consists of 41 students, cluster 2 consists of 46 students, cluster 3 consists of 21 students, cluster 4 consists of 52 students. The average MAPE percentage for the whole cluster is 27.07%.
PENGELOMPOKAN SEBARAN TRANSFORMATOR DISTRIBUSI BERDASARKAN KAPASITAS DAYA MENGGUNAKAN METODE NAÏVE BAYES Studi Kasus: PT. PLN RAYON KOTA SAMARINDA Rheo Malani; Bedi Suprapty
J-Icon : Jurnal Komputer dan Informatika Vol 8 No 1 (2020): Maret 2020
Publisher : Universitas Nusa Cendana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35508/jicon.v8i1.2185

Abstract

ABSTRACT Human needs for energy are mostly obtained from electrical energy, both for daily needs and for industrial needs. PT. PLN (Persero) is one of the state electricity companies that serves the community's need for electricity. Transformer or better known as "transformer" or "transformer" is actually an electrical device that converts AC power at one voltage level to one voltage level based on the principle of electromagnetic induction without changing its frequency. Because of the lack of distribution of transformers around the Samarinda area, it can result in electricity demand services to the community. Therefore we need a method that can facilitate the distribution of PT. PLN Rayon Kota Samarinda, one of the methods is by applying Naïve Bayes. The purpose of this study is to facilitate the distribution in each region and the type of transformer used. The results of calculations using the Naïve Bayes method, obtained the probability of grouping the training data is P (160) = 0.006441224, P (100) = 0.016304348, P (80) = 0.001610306, P (50) = 0.001610306, P (40) = 0.000402576, P P (20) = 0,000679348. From the calculation results, it appears that the probability value P (100) is more dominant, then 100 is recommended for real consumption which is used as training data. The Naïve Bayes method produces an accuracy rate of 92%.
Seleksi Benih Padi Unggul Dengan Penerapan Metode Fuzzy dan K-Means Clustering Suprapty, Bedi; Malani, Rheo; Gaffar, Achmad Fanany Onnilta
J-Icon : Jurnal Komputer dan Informatika Vol 12 No 2 (2024): Oktober 2024
Publisher : Universitas Nusa Cendana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35508/jicon.v12i2.18159

Abstract

This study aims to develop a superior rice seed selection method using a Fuzzy and K-Means Clustering approach, with a case study in Kutai Kartanegara Regency, East Kalimantan Province, one of Indonesia's major rice-producing regions. The Fuzzy method is used to handle uncertainties in assessing seed characteristics, allowing each seed attribute (such as plant height, amylose content, grain weight, and yield) to have a membership value within specific categories. This fuzzification process provides flexibility in evaluating seed quality in stages, which is then converted through defuzzification to obtain a final score determining seed quality. K-Means Clustering plays a role in grouping seeds based on characteristics that have been assigned membership values. This algorithm divides seed data into several clusters, such as low, medium, and high quality, by calculating the distance between seed characteristics and each cluster's centroid. This iterative process yields seed groups with similar characteristics, simplifying recommendations for superior varieties. The evaluation was conducted using clustering accuracy metrics and silhouette score validation to ensure cluster cohesion and separation. The study results demonstrate that this method effectively identifies high-quality rice seeds with high accuracy. Recommended varieties include standard rice seeds like Mengkongga and Ciherang, as well as superior varieties like Inpari 32, Inpari 48, Padjajaran Agritan, Inpari IR Nutri Zinc, and Pamera, which are well-suited to Kutai Kartanegara’s specific conditions. Implementing this method is expected to assist farmers in selecting high-quality seeds, thereby supporting increased crop productivity in the study area.
Co-Authors Abdul Najib Achmad Fanany Onnilita Gaffar Aldy Gustiannur Rachmat Fariyanti Fariyanti Gaffar, Achmad Fanany Onnilta Lia Dwi Utami Malani, Rheo Muhammad Taufiq Sumadi Mulyanto Mulyanto Mulyanto Mulyanto Roman Voliansky Syamsul Arifin Utami, Lia Dwi Wikarno, Wikarno