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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Edulib JOIN (Jurnal Online Informatika) Sistemasi: Jurnal Sistem Informasi Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Penelitian Pendidikan IPA (JPPIPA) Technomedia Journal Astonjadro J-SAKTI (Jurnal Sains Komputer dan Informatika) Building of Informatics, Technology and Science Jurnal Teknik Informatika (JUTIF) J-SAKTI (Jurnal Sains Komputer dan Informatika) Journal Of World Science International Journal Of Science Education and Technology Management (IJSETM) The Indonesian Journal of Computer Science
Christina Juliane, Christina
STEI-Institut Teknologi Bandung
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Physics Social Sciences

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Journal : Journal Of World Science

 1 
Analysis and Design of Student Point Systems to Improve Student Achievement using The Clustering Method Bani Riyan, Ade; Fikri Rifai, Mochamad; Juliane, Christina
Journal of World Science Vol. 2 No. 3 (2023): Journal of World Science
Publisher : Riviera Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58344/jws.v2i3.155

Abstract

The student points system is an application for recording students' achievement and offense points. The lack of recording and dissemination of information on achievement results makes students less motivated to improve achievement, and the distribution of scholarships for outstanding students is inappropriate. To improve student achievement, an application program is needed that can record and disseminate student achievement data in real-time, accurate, and effective. So, the purpose in this study is to know and analyze the design of the student point system to improve student achievement using the clustering method. Researchers use the Clustering Method in calculating data to determine the accuracy of scholarship distribution for outstanding students. Clustering with the most achievement points is clustering 2 with 25,254 Achievement Points. The total number in the level 2 cluster is 1,797 which indicates the number is close to 2,000 or 2 which is the result of data transformation from the junior high level. The implication of clustering research on student point data is to provide useful information for the Foundation as an institution that houses schools in allocating scholarships for outstanding students. In this case, clustering 2 with the highest number of Achievement Points indicates that there is a group of students with high achievement points. By using the clustering results, the Foundation can allocate scholarships more effectively and efficiently, because it can identify outstanding students from various school levels more easily.
Application of Data Mining to Measure the Effectiveness of the Islamic Boarding School’s Independent Curriculum based on Learning Achievement using the Clustering Method Imron Rosyadi, Iim; Nurhadits, Fitri; Juliane, Christina
Journal of World Science Vol. 3 No. 5 (2024): Journal of World Science
Publisher : Riviera Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58344/jws.v3i5.595

Abstract

The evolution of educational curricula has been a focal point for institutions aiming to enhance learning outcomes and adapt to students' diverse needs. In this context, Islamic boarding schools, or pesantren, are increasingly exploring independent curricula to better serve their students. This research aims to measure the effectiveness of the independent curriculum at the Al Binaa Bekasi Islamic Boarding School, especially regarding learning achievements in general and Islamic subjects. The method used is data mining clustering to analyze student learning achievement data. In the initial stage, the data collected includes student scores in general subjects (such as Islamic Religious Education, Pancasila Education, Indonesian, English, Mathematics, Science, Social Sciences, Arts, Sports, ICT, Sundanese) and Syar'i (Quran tajwid, hadith, aqidah, fiqh, Hadassah, short). Then, data mining clustering techniques are used to group students based on their achievements in the two subjects. The results of the analysis show that the independent curriculum at Al Binaa Islamic Boarding School effectively increases student learning achievement. The groups formed from data mining clustering show patterns consistent with curriculum objectives, where students in the same group have similar levels of achievement in general and star subjects. This indicates that the independent curriculum has succeeded in leveling student learning achievement. This research contributes to understanding the effectiveness of the independent curriculum in Islamic boarding schools. It can be a basis for further development in designing Islamic boarding school education curricula that are more adaptive and responsive to student needs.
Application of Data Mining Techniques in Healthcare: Identifying Inter-Disease Relationships through Association Rule Mining Haryanto, Haryanto; Winarto, Hadi; Juliane, Christina
Journal of World Science Vol. 3 No. 5 (2024): Journal of World Science
Publisher : Riviera Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58344/jws.v3i5.597

Abstract

This research focuses on the application of data mining techniques in a healthcare environment by utilizing patient visit data from Hospital X, coded with ICD-10 diagnoses. The purpose of this study is to explore the application of data mining techniques in a healthcare environment, specifically to identify the relationship between diseases using patient visit data from X Hospital. This research utilizes the FP-Growth algorithm method followed by Association Rule Mining to find frequent occurrences of diseases in the data set. The research process involved data pre-processing, transformation into binary format, and careful parameter setting (minimum support 0.95 and confidence 0.9). The results showed a strong association between chronic conditions such as hypertension and diabetes, which are prevalent in the patient population. This association provides insight into potential comorbidities and may assist healthcare providers in improving diagnosis accuracy and treatment effectiveness. This research has implications for the application of data mining techniques, demonstrating its potential in improving predictive analytics in healthcare and strategic planning. This approach not only aids in the efficient allocation of healthcare resources, but also aligns with the broader goal of improving personalized patient care.
Analysis of the Placement of Disaster Early Warning Facilities Based on Village Data in West Java with a Classification Approach Utilizing Naive Bayes Algorithm Geo Ginantaka, Prafangasta; Sudartha, Doddi; Juliane, Christina
Journal of World Science Vol. 3 No. 6 (2024): Journal of World Science
Publisher : Riviera Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58344/jws.v3i6.598

Abstract

West Java is one of the regions in Indonesia that is prone to various natural disasters such as earthquakes, floods, and landslides. These disasters are frequent and difficult to predict, such as the tornado that hit Rancaekek, Bandung on February 2, 2024, which caused significant damage. According to data from the West Java Regional Disaster Management Agency (BPBD), this disaster resulted in many damaged buildings and injuries. An early warning system is essential to reduce the impact of disasters. This study aims to place early warning facilities based on village data in West Java using the Naive Bayes method. The method used in this study is a data mining approach to extract patterns and valuable information from data that will be used in strategic decision-making related to the placement of early warning facilities. The data used was obtained from the West Java government's open data site, which includes attributes such as codes and names of provinces, districts, sub-districts, villages/sub-districts, as well as the availability status of disaster mitigation facilities. The results of the study show that many areas in West Java still do not have adequate early warning facilities. The use of Naive Bayes' algorithm aids in data classification and provides insights into the placement of more effective early warning facilities. The implication of this study is the need for more serious and coordinated efforts from the government, non-governmental organizations, and the community to increase the availability of disaster mitigation facilities in West Java.
Optimizing Marketing Strategies Using FP-Growth and Association Rule Mining Algorithms in the Textile Industry NG, Wijaya; Sukma, Robby; Juliane, Christina
Journal of World Science Vol. 3 No. 5 (2024): Journal of World Science
Publisher : Riviera Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58344/jws.v3i5.599

Abstract

This study leverages association rule mining to analyze transaction data from PT. Labda Anugerah Tekstil, a prominent player in the textile industry, to uncover significant purchasing patterns and associations between different fabric types. Utilizing data from January 1, 2022, to December 31, 2023, which includes 7,143 transaction entries, the research applies the FP-Growth algorithm followed by Association Rule Mining to identify and evaluate frequent itemsets and strong association rules within the dataset. The analysis revealed robust associations among fabrics such as Cotton, Linen, Rayon, and Viscose, suggesting substantial opportunities for targeted marketing strategies and inventory management enhancements. The findings indicate that strategically bundling and promoting associated fabrics can drive higher sales volumes and improve customer purchasing experiences. The insights from this study provide actionable strategies for optimizing marketing efforts and inventory management, aiming to enhance sales performance and customer satisfaction in the competitive textile market.
Analysis of the Application of the K-Means Algorithm to the Clustering Method Approach for Grouping Consumer Purchasing Trends at One of the Textile Companies Kurniawan, Debby; Anwari, Hidayat; Juliane, Christina
Journal of World Science Vol. 3 No. 6 (2024): Journal of World Science
Publisher : Riviera Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58344/jws.v3i6.601

Abstract

In Indonesia, the regulation of sexual abuse crimes is a critical aspect of ensuring justice and protection for victims. However, challenges remain in the effectiveness and comprehensiveness of these regulations. This study aims to analyze and evaluate the current legal framework addressing sexual abuse in Indonesia, identifying gaps and proposing improvements to enhance legal protections for victims. The research employs a qualitative approach, utilizing legal analysis and case studies to assess the application of existing laws. Data collection involves reviewing legal documents, court cases, and expert interviews to gather comprehensive insights into the regulatory landscape. The findings indicate significant shortcomings in the legal framework, including inconsistencies in legal definitions, procedural delays, and inadequate victim support mechanisms. The study discusses the implications of these findings, emphasizing the need for a more cohesive and victim-centered approach in legal reforms. This research underscores the necessity for legislative improvements to address the identified gaps in the regulation of sexual abuse crimes. Recommendations include clearer legal definitions, expedited legal processes, and enhanced victim support services. These measures are essential for ensuring justice and effective protection for victims of sexual abuse in Indonesia.
Co-Authors AA Sudharmawan, AA Adjani, Kannisa Aji, Tri Wahyu ALAM Alana, Divi Adiffia Freza Alfian, Iqbal Amirulloh, Moch Mumin Anwari, Hidayat Azhar, Rizaldi Bani Riyan, Ade Damayunita, Aina Ernawan Fathah, Adittia Fauzia, Fathia Alisha Fikri Rifai, Mochamad Geo Ginantaka, Prafangasta Hadi Winarto, Hadi Haryanto Haryanto Hermawan, Sari Hikmawati, Nina Kurnia Imron Rosyadi, Iim Indra Gunawan, Gun Gun Irawan Irawan Karsa, Koko Kurniawan, Debby Lesmana, Agrian Dwi Luffi Septian Marlia, Sari Mutia, Acep Sandi NG, Wijaya Nugraha, Anwar Nurhadits, Fitri Nurwijayanti Pascalina, Donna Prasakti, Lukito Angga Pratama, Denni Purwandi Putra, Rendri Ridian Gusdiana Rifqi Syamsul Fuadi Saepulloh, Yusep Setiawan, Kiki SRI RAHAYU Sudartha, Doddi Sukma, Robby Surahman, Adi Syakur, Achmad Teguh Aljauza Widhiastono, Raymondhus Yayan Sopyan Yosep Bustomi