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All Journal J-SAKTI (Jurnal Sains Komputer dan Informatika)
Magdalena A. Ineke Pakereng
FTI UKSW, Salatiga, Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Energy

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Journal : J-SAKTI (Jurnal Sains Komputer dan Informatika)

 1 
Penerapan K-Means Clustering Pada Data Mahasiswa Fakultas Interdisiplin Program Studi D4 Destinasi Pariwisata Untuk Menentukan Strategi Promosi Rioldy Leonard Pattipeilohy; Magdalena A. Ineke Pakereng
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 7, No 1 (2023): EDISI MARET
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v7i1.595

Abstract

The research was conducted to see the trend of supply of students studying at the Interdisciplinary Faculty, Tourism Destination D4 Study Program. The algorithm used in the student supply process is K-means. Data processing with the K-means algorithm helps extract information and knowledge from student data in the Interdisciplinary Faculty, D4 Tourism Destination Study Program. By using data mining, stakeholders in the Interdisciplinary Faculty, D4 Tourism Destination Study Program, can take strategic steps in the screening process in provinces that are indicated to supply students. The K-means algorithm facilitates the process of data analysis and grouping of student data for 4 years. The purpose of this research is to provide an accurate and strategic picture of the provinces that can have a significant impact on the supply of students each year. The results showed that the largest supply of students came from Central Java and the smallest from Bangka Belitung, West Sulawesi, Banten, East Kalimantan, West Papua, Bengkulu and Riau, so promotion strategies need to be improved in areas with the smallest student supply.
Implementasi Algoritma Genetika Untuk Penjadwalan Sekolah (Studi Kasus: SMP Negeri 2 Wonosegoro) Pipit Puspitasari; Magdalena A. Ineke Pakereng
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 7, No 1 (2023): EDISI MARET
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v7i1.600

Abstract

The development of increasingly advanced technology can help in the work, so that it can be done more quickly and easily. One of them is scheduling subjects using genetic algorithms. Genetic algorithm is a technique to find the best solution from several solutions so that it gets the best results according to the stopping criteria if it has done 10 experiments and reached 100 generations. This study uses teacher data, subject data, and time. The results of the study provide a list of optimal schedules with the highest fitness score of 1, where teachers do not have teaching hours at the same time. The system can assist in solving existing problems, which can process data to carry out the scheduling process.
Penerapan Metode K-Means Clustering Untuk Analisis Potensi Lahan Pangan Pada Provinsi Kalimantan Selatan Rhaka Pradena Harjono; Magdalena A. Ineke Pakereng
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 7, No 1 (2023): EDISI MARET
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v7i1.596

Abstract

National food needs are increasing along with the current population of 237 million people with growth that continues to increase every year, so it needs to be balanced with the provision of sufficient agricultural land resources. The application of the K-Means in the grouping of potential agricultural land in South Kalimantan Province, with the aim of obtaining groups of potential land data. By using the K-means clustering algorithm, the data is divided into 3 clusters, namely cluster 0 with a low potential of 6 districts, cluster 1 with a medium potential of 5 districts, and cluster 2 with a high potential of 2 districts.
Penentuan Tingkat Pemahaman Mahasiswa dalam Matakuliah Kelas Daring dengan Algoritma C4.5 (Studi Kasus: Mahasiswa/i FTI Angkatan 2019) Aldy Alvharo Tarigan; Magdalena A. Ineke Pakereng
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 7, No 1 (2023): EDISI MARET
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v7i1.597

Abstract

The purpose of this study was to classify using the C4.5 algorithm to determine student understanding of online lectures at the SWCU Information Technology Faculty. In carrying out lecture activities where students must conduct online lectures, students are expected to be able to understand all the material provided. Many things affect student understanding in digesting online lecture material. The data was taken from the questionnaire results from the 2019 SWCU Information Technology Faculty student. The five attributes used were the learning atmosphere, learning tools, communication, teaching methods, and networking. The research method used is the C4.5 algorithm which builds a decision tree using RapidMiner software. Based on the results of the study, there were 27 rules  with 18 rules understood rules . In the case of students' level of understanding in online classes, the accuracy rate reaches 70%, which means that students quite understand the courses presented online.
Penerapan Data Mining Untuk Prediksi Jumlah Total Porduksi Bakpao Pada PT. Estetika Tata Tiara Menggunakan Algoritma Regresi Linier Berganda Prasetya Wicaksana; Magdalena A. Ineke Pakereng
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 7, No 1 (2023): EDISI MARET
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v7i1.598

Abstract

Production objective planning is the process of identifying the product to be produced, the required quantity, the completion deadline, and what sources will be needed. The research objective is to determine the total production of Bakpao PT. Estetika Tata Tiara uses multiple linear regression algorithm. At this stage, the data mining technique uses multiple linear regression algorithms. This research was conducted at PT. Aesthetics of Tata Tiara which is a bakpao production company. The results show that the regression equation obtained from the results of multiple linear regression analysis is for the prediction of Bakpao in April 2022 as follows: Y = 473.531 + 0.56 X1 + 0.043 X2. After the analysis, it can be concluded that the variables X1 and X1 affect the prediction of the amount of Bakpao production in 2022. The relationship between sales (X1) and stock (X2), and Bakpao production has a strong positive and unidirectional relationship.
Klasifikasi Anak Berpotensi Putus Sekolah dengan Metode Naïve Bayes Di Kabupaten Manokwari Maria Leonila Yawa Yoridi; Magdalena A. Ineke Pakereng
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 7, No 2 (2023): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v7i2.700

Abstract

National development is determined by qualified human resources. Education is a universal activity in the life of a human being. To create quality human beings must be equipped with education, both education at school and outside school. The main problem of education in Manokwari Regency, West Papua is that there are still many children who do not continue their education or stop going to school in the middle of their journey. The Naïve Bayes algorithm with Cross Validation operators was used to analyze the data and predict children who could potentially drop out of school. The results showed that the prediction accuracy rate was 70%. The Naïve Bayes method tends to provide accurate results in predicting children who are not likely to drop out of school with a class precision of 88.89%. However, this method has limitations in predicting children who are potentially or very likely to drop out of school, with class precision and class recall being low for the label of 0.00%.
Co-Authors Aldy Alvharo Tarigan Maria Leonila Yawa Yoridi Pipit Puspitasari Prasetya Wicaksana Rhaka Pradena Harjono Rioldy Leonard Pattipeilohy