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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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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.
IMPLEMENTATION OF TEXT PROCESSING FOR SENTIMENT ANALYSIS OF TAX PAYMENT INTEREST AFTER THE "RUBICON" PHENOMENON Gusdiana, Ridian; Alfian, Iqbal; Juliane, Christina
Jurnal Teknik Informatika (Jutif) Vol. 4 No. 5 (2023): JUTIF Volume 4, Number 5, October 2023
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2023.4.5.1014

Abstract

In February 2023, an incident occurred involving the child of an official from the Indonesian Directorate General of Taxes who committed violence against a member of the GP Ansor organization. The news spread widely and brought a new issue, namely suspicious reporting of the official's wealth with an amount of up to 56 billion Indonesian Rupiahs. In order to determine public sentiment towards the "RUBICON" case, which was receiving attention, sentiment analysis of tax payment interest was conducted using text mining techniques. Data processing was done using the R language and RStudio application, taking a dataset of 23,785 tweets from the public about paying taxes on Twitter. Next, text cleaning was done to remove numbers, symbols, and URLs, as well as text processing using stemming, tokenizing, stopword removal, and TF-IDF methods. The TF-IDF method shows that the words "rafael" and "case" are the top keywords. This study used a supervised model by comparing SVM, KNN, and Naive Bayes algorithms, and evaluation was done using a confusion matrix with accuracy results in descending order of 0.8922, 0.8049, and 0.7369. The conclusion of this study is that the SVM algorithm successfully classified sentiment with the highest level of accuracy and obtained the highest negative sentiment of 5,616 sentences.
Analysis of Music Features and Song Popularity Trends on Spotify Using K-Means and CRISP-DM Marlia, Sari; Setiawan, Kiki; Juliane, Christina
Sistemasi: Jurnal Sistem Informasi Vol 13, No 2 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i2.3757

Abstract

Spotify, known as one of the best music streaming platforms, has played an important role in changing how listeners access, enjoy and interact with music. With millions of songs and extensive user data, Spotify provides an opportunity to understand listener behavior and the factors that contribute to a song's success and popularity. This research aims to examine the relationship between music features and the popularity of songs on the Spotify music platform by analyzing SSE values, Euclidean distance values, and cluster center values on the dataset attributes loudness, danceability, and energy. The framework used in this research is CRISP-DM (Cross-Industry Standard Process for Data Mining). The K-Means clustering algorithm and the Weka data mining application are used to decipher the features that influence the success and popularity of songs on Spotify. The research results show that groups/clusters 1, 2, and 3 are groups/clusters with songs that have high, medium, and low loudness, danceability, and energy respectively. Popular songs on Spotify are currently increasingly focused on loudness, danceability, and energy with a prominent trend, namely songs with high loudness, danceability, and energy are becoming more popular, while songs with low loudness, danceability, and energy are becoming less popular.
Analysis of Digital Transformation Readiness in State-Owned Construction Enterprises Based on the INDI 4.0 Measurement Framework Ernawan; Juliane, Christina
Jurnal Penelitian Pendidikan IPA Vol 11 No 11 (2025): November: In Progress
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v11i11.13138

Abstract

Digital transformation is a strategic necessity for state-owned construction companies in the face of global competition and the demands of operational efficiency. This study aims to evaluate the readiness of digital transformation of PT. PQR uses the Indonesia Industry 4.0 Readiness Index (INDI 4.0) framework which includes five pillars: Management and Organization, People and Culture, Products and Services, Technology, and Business Operations. The research method used a descriptive quantitative approach with an action research design through questionnaires of 53 respondents (executives, managers, and specialists) and in-depth interviews, analyzed using descriptive statistics and thematic analysis. The results of the study showed an average score of 3.51 from a target of 4.00, placing PT. PQR in the category of ready readiness towards full implementation. The pillar scores show: Management and Organization (3.92), People and Culture (3.75), Products and Services (3.00), Technology (3.83), and Business Operations (2.89). The biggest gap is in the Business Operations pillar with partial automation and suboptimal intelligent maintenance systems. Key challenges include limited human digital competencies, organizational cultural resistance, budget constraints, and weak external collaboration. The research recommends strengthening digital governance, increasing human resource capacity through structured programs, investment in enabler technologies (cloud, AI, IoT), and ecosystem collaboration. Academically, the research contributes to the literature on the implementation of INDI 4.0 in the SOE construction sector; Practically, it is a reference for a sustainable digital transformation strategy.
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