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Scientific Journal of Computer Science
Published by PT. Teknologi Futuristik Indonesia
ISSN : -     EISSN : 31103170     DOI : https://doi.org/10.64539/sjcs
Core Subject : Science,
Computer Science & IT
The Scientific Journal of Computer Science (SJCS) (e-ISSN: 3110-3170) is a peer-reviewed and open-access scientific journal, managed and published by PT. Teknologi Futuristik Indonesia in collaboration with Universitas Qamarul Huda Badaruddin Bagu and Peneliti Teknologi Teknik Indonesia. The SJCS dedicated to publishing high-quality research across all areas of computer science, with a particular focus on emerging technologies that are shaping the future of computing. SJCS invites original research, review papers, and studies that involve practical applications, simulations, and theoretical advancements. The journal scope includes, but is not limited to: Artificial Intelligence and Machine Learning Data Science and Big Data Cybersecurity and Cryptography Cloud Computing and Distributed Systems Software Engineering Human-Computer Interaction Computer Vision and Natural Language Processing Internet of Things (IoT) Blockchain Technologies Robotics and Automation Computational Biology and Bioinformatics All fields related to computer science SJCS aims to advance the development of innovative computing systems that contribute to technological progress across industries.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 5 Documents
 1 
Search results for , issue "Vol. 1 No. 1 (2025): June" : 5 Documents clear
Machine Learning 5.0 In-depth Analysis Trends in Classification Dianda Rifaldi; Tri Stiyo Famuji; Setiawan Ardi Wijaya; Ahmed Jaber Abougarair; Phichitphon Chotikunnan; Alfian Ma'arif; Furizal
Scientific Journal of Computer Science Vol. 1 No. 1 (2025): June
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjcs.v1i1.2025.18

Abstract

In the era of Technology 5.0 Machine Learning continues to show significant advancements across various sectors. This study aims to examine the latest trends in Machine Learning classification, focusing on four key approaches Explainable Artificial Intelligence, Federated Learning, Transfer Learning, and Generative Adversarial Networks. The methodology involves a comprehensive literature review of research in Asia and experimentation with related datasets. The findings indicate that Explainable Artificial Intelligence enhances transparency and accuracy in data classification, Federated Learning enables decentralized learning while safeguarding data privacy, Transfer Learning improves accuracy with small datasets, and Generative Adversarial Networks aids in data augmentation for better model training. In conclusion, these techniques not only enhance the efficiency and accuracy of classification but also open up new opportunities for innovation in various fields, including healthcare, transportation, and cybersecurity.
Identification of Dominant Topics in Public Discussions on IKN using Latent Dirichlet Allocation (LDA) and BERTopic Ningrum, Ariska Fitriyana; Talirongan, Florence Jean B.; Tangaro, Diana May Glaiza G.
Scientific Journal of Computer Science Vol. 1 No. 1 (2025): June
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjcs.v1i1.2025.19

Abstract

This study aims to analyze public opinion related to the relocation of Indonesia's National Capital City (IKN) through topic modeling on Twitter data. The two main approaches used are Latent Dirichlet Allocation (LDA) based on Bag of Words and BERTopic based on Transformer language model. LDA was chosen for its ability to identify topic distribution in large text collections, while BERTopic was used to overcome the limitations of LDA in capturing semantic meaning in short and informal texts such as tweets. The analysis was conducted on a collection of tweets discussing the relocation of IKN, with the aim of uncovering the main themes and public perceptions. The result of LDA showed three main topics in the public discussion, namely (1) political debate and nationalism related to the relocation, (2) policy implementation and project execution, and (3) economic justification and challenges facing Jakarta. Mean-while, BERTopic identified topics with more contextual representations, including aspects of investment, economic impact construction progress, and public perception. Dominant topics include urban relocation, investment in IKN, and socio-economic impacts. The novelty of study lies in the comparison of two topic modeling approaches in the context of social media sentiment analysis related to major public policy issues. These findings not only enrich the understanding of the narratives that develop in society, but also provide important insights for policy makers in responding to public opinion more appropriately and contextually.
Analysis of Suspected Factors in Tuberculosis Cases in Semarang City Using a Logistic Regression Model Amri, Ihsan Fathoni; Rohim, Febrian Hikmah Nur; Ardiansyah, Muhammad Ivan; Saputra, Farid Sam; Supriyanto; Ningrum, Ariska Fitriyana; Nakib, Arman Mohammad
Scientific Journal of Computer Science Vol. 1 No. 1 (2025): June
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjcs.v1i1.2025.32

Abstract

Tuberculosis (TB) is one of the world's deadliest infectious diseases, with Indonesia being among the countries with the highest TB burden. Semarang City, as an urban area with a dense population, faces significant challenges in controlling TB, particularly among vulnerable populations. This study identifies significant risk factors influencing TB incidence in Semarang City using a binary logistic regression model. Descriptive analysis reveals an imbalance in the data, with the majority of patients categorized as "not indicated for TB." Chi-Square tests show that variables such as shortness of breath, persistent fever for more than one month, diabetes mellitus, and household contact are significantly associated with TB incidence. The logistic regression model demonstrates overall significance (G statistic = 275.13; p-value = 1.23×10−55), with shortness of breath and diabetes mellitus emerging as major risk factors based on odds ratio interpretation. However, the model's performance in detecting the "indicated for TB" category is very low (Precision 36.36%; Recall 2.05%; F1-Score 3.88%), despite an overall accuracy of 87.25%. The poor performance in the "1" category and the Pseudo R2 value of 7% are likely related to data imbalance, where the number of cases in the "1" category is much smaller than in the "0" category, leading to bias toward the majority class. Additionally, the distribution of predictor variables that do not provide sufficient information to distinguish the "1" category from the "0" category further contributes to the model's limited ability to explain data variability overall.
Waiting Time Analysis of Willingness to Pay for Rice Farming Insurance Premiums Using Cox Proportional Hazard Modeling and Weibull Method Mutiah, Siti; Bisoumi, Yan Nazala; Nudyawati, Elsa; Daud, Khamidah Arsyad; Nisa, Rofiah Ainun; Sulistiani, Dwi; Amri, Ihsan Fathoni; Ningrum, Ariska Fitriyana; Mostfa, Ahmed A.
Scientific Journal of Computer Science Vol. 1 No. 1 (2025): June
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjcs.v1i1.2025.34

Abstract

Rice is a primary commodity in Indonesia's agricultural sector but is highly vulnerable to climate risks such as floods, droughts, and pest infestations. To mitigate these risks, the government, in collaboration with PT. Asuransi Jasa Indonesia (Jasindo), launched the Rice Farming Insurance Program (AUTP) in 2015. This study aims to analyze the willingness-to-pay time of farmers for AUTP premiums in Jayaraksa Village, Cimaragas Subdistrict, Ciamis Regency, using Weibull regression and Cox Proportional Hazard models. Factors such as education, secondary employment, rice production, and farming costs were examined to understand their influence on farmers' participation. Based on the analysis, the Weibull regression model, with a lower AIC value compared to Cox Proportional Hazard (270.4431 vs. 330.9111), demonstrated better performance in explaining the data. This research contributes to the development of more effective AUTP policies by identifying key factors influencing farmers' participation.
A Waterfall Model Approach to Designing and Developing a Web-Based Housing Information System for Pioneer Residence Ramadhan, Fauzan Purma
Scientific Journal of Computer Science Vol. 1 No. 1 (2025): June
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjcs.v1i1.2025.39

Abstract

PT. Askata Properti Unggul is a property development company located in Mayang Mangurai, Kota Baru District, Jambi City, with its flagship project, Pioneer Residence. The company’s marketing strategy has predominantly relied on conventional methods such as brochure distribution, billboard installation, exhibitions, and word-of-mouth promotion, which have proven to be less effective, inefficient, and costly. This study aims to develop a web-based housing information system as a digital promotional medium to improve marketing effectiveness. The system was developed using the Waterfall model, consisting of requirements analysis, system design, implementation, testing, and deployment. The results show that the Pioneer Residence website increased potential buyers by 45%, reduced promotional costs by 35%, and improved the efficiency of information delivery to consumers by 50% compared to conventional methods. Therefore, the implementation of a web-based information system is proven to be effective in expanding market reach, enhancing marketing performance, and providing consumers with convenient access to information for decision-making before visiting the housing site.

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