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JURNAL KARYA ILMIAH MULTIDISIPLIN
Published by Universitas Lancang Kuning
ISSN : 2829307X     EISSN : 28081374     DOI : https://doi.org/10.31849/jurkim.v3i2.13892
Core Subject : Humanities, Science, Education, Engineering,
Humanities Decision Sciences, Operations Research & Management Education Engineering Other
Jurnal Ilmiah Multidisplin (JURKIM) menerbitkan artikel bidang multidisiplin termasuk : Pendidikan, Hukum, Ekonomi, Agama, Pendidikan, Kesehatan, Kebijakan Publik, Pariwisata, Sosial dan Politik, Budaya, Seni, Pertanian, Peternakan, Pendidikan, Hukum, Ekonomi, Agama, Kesehatan, Pariwisata, Sosial dan Politik, Budaya, Seni, Pertanian, Lingkungan,Kehutanan,Bahasa, Teknologi Informasi dan Komunikasi,Psikologi,Arsitektur, Teknik, Matematika, Biologi
Arjuna Subject : Umum - Umum
Articles 93 Documents
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Analisis Efektivitas dan Efisiensi Program PAMSIMAS dalam Penyediaan Air Bersih di Dusun V Kampung Baru, Kampar Utara Hamid, Ardiansyah; Yelmi, Harmi; Aga Wandana, Fajar
Jurnal Karya Ilmiah Multidisiplin (JURKIM) Vol. 5 No. 2 (2025): Jurnal Karya Ilmiah Multidisiplin (Jurkim)
Publisher : Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/69fs9j82

Abstract

The availability of clean water in the area of Dusun V Kampung Baru is very minimal. On average, the quality of well water in residents is yellowish, muddy and oily. To solve this problem, residents of Dusun V Kampung Baru received assistance from the PAMSIMAS clean water program from the government. Clean water facilities have been built and water has been distributed to homes in need. Therefore, an evaluation is needed in the implementation of this program, to determine the achievement of the objectives of this program. The evaluation indicators are seen from the aspects of the effectiveness and efficiency of the implementation of the PAMSIMAS program. This study uses descriptive qualitative data analysis, based on the results of observations and interviews with PAMSIMAS water customers. When viewed from the effectiveness indicator, the clean water program is quite effective for residents of Dusun V Kampugn Baru, because the program's objectives have been achieved, namely clean water has been obtained and has been distributed to residents' homes. Likewise, from the efficiency indicator, the implementation of the PAMSIMAS program is running efficiently. This is assessed from the construction of clean water facilities that are in accordance with the plan by utilizing existing human resources and optimizing available funds. With the evaluation of the PAMSIMAS clean water program, we can measure the achievement of the PAMSIMAS program objectives and also provide a picture of the implementation of PAMSIMAS in Dusun V Kampung Baru to date
Analisis Sentimen Terhadap Program Kampus Merdeka Menggunakan Algoritma Naïve Bayes Classifier  Dan Support Vector Machine Di Twitter Reza Sapitri; Costaner*, Loneli
Jurnal Karya Ilmiah Multidisiplin (JURKIM) Vol. 5 No. 2 (2025): Jurnal Karya Ilmiah Multidisiplin (Jurkim)
Publisher : Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/s7ayy294

Abstract

Kampus Merdeka is one of the policies initiated by the Minister of Education and Culture in 2020. Since it was first launched, this program has received many pros and cons from the public, one of which is from the social media Twitter. The aim of this research is to determine positive, negative and neutral sentiment in the dataset regarding the Merdeka Campus and to determine the optimal accuracy of the comparison between the SVM and NBC methods for the Merdeka Campus on Twitter. The data used in this research amounted to 1000 data based on the most recent comments when the data was taken, via the APIFY website from Twitter post comments through a crawling process. Support Vector Machine is the best algorithm for analyzing sentiment towards the Independent Campus program on Twitter with the highest level of accuracy at a data comparison of 90:10, namely 87%, for precision, recall and f1-score values for negative sentiment, namely 93%, 95% , and 94%, neutral sentiment was 76%, 84%, and 80%, and positive sentiment was 90%, 82%, and 86%. Meanwhile, the Naïve Bayes algorithm obtained the highest level of accuracy in the 90:10 data comparison, namely 81% and obtained precision, recall and f1-score values for negative sentiment, namely 73%, 100% and 85%, neutral sentiment was 78%, 66%, and 71%, and positive sentiment 93%, 76%, and 84%. Based on the highest accuracy value, namely SVM with a data sharing proportion of 90:10, the sentiment results can be visualized, namely that the public's response to the independent campus program tends to be positive with a percentage of 36.5%, while for negative sentiment the percentage is 32.8%. , and neutral sentiment gets a percentage of 30.7%.  
Clustering Mahasiswa Baru Menggunakan K-Means Untuk Perencanaan Teknologi Pembelajaran di Unilak Asril, Elvira; Siburian, Hardiano
Jurnal Karya Ilmiah Multidisiplin (JURKIM) Vol. 5 No. 2 (2025): Jurnal Karya Ilmiah Multidisiplin (Jurkim)
Publisher : Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/23d8ex68

Abstract

Mapping the characteristics of new students is an important first step in designing a technology-based education system, particularly at private universities, where competition to attract new students is intense. This study uses the K-means algorithm to cluster new students at Lancang Kuning University. The goal is to identify patterns that can inform the design of adaptive learning systems. The study employs a quantitative approach with data mining techniques using new student data with variables of academic and digital literacy scores. The clustering results revealed three groups: 54 students with high scores and high digital literacy, 52 students with moderate scores and moderate digital literacy, and 44 students with low scores and low digital literacy. Three main clusters emerged from the data clustering: digitally independent students, moderate students, and students requiring assistance and guidance. These results indicate differences in the learning needs of new students and support strategic recommendations for implementing educational technology tailored to their needs. These results contribute to the use of data mining technology for data-driven learning planning at Lancang Kuning University

Page 10 of 10 | Total Record : 93

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