cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Riyan Naufal Hays
Contact Email
jsii.editor@gmail.com
Phone
-
Journal Mail Official
anhar.dean@gmail.com
Editorial Address
Universitas Serang Raya Gedung Utama Lantai 3, Fakultas Teknologi Informasi Program Studi Sistem Informasi Jl. Raya Cilegon KM. 5, Taman, Drangong, Kec. Taktakan, Kota Serang, Banten 42162
Location
Kota serang,
Banten
INDONESIA
JSiI (Jurnal Sistem Informasi)
Published by Universitas Serang Raya
ISSN : 24067768     EISSN : 25812181     DOI : https://doi.org/10.30656
Core Subject : Science,
Computer Science & IT Decision Sciences, Operations Research & Management
JSiI (Jurnal Sistem Informasi) is a scientific journal published by the Department of Information System Universitas Serang Raya (UNSERA). This journal contains scientific papers from Academics, Researchers, and Practitioners about research on information systems. JSiI (Jurnal Sistem Informasi) is published twice a year in March and September. The paper is an original script and applied research in information systems.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 365 Documents
 33   34   35   36   37 
COMPARISON OF DECISION TREE AND NAIVE BAYES METHODS FOR RAINFALL CLASSIFICATION USING A WEATHER DATASET WITH A WEB-BASED APPLICATION Yuda Samudra; Amin Hidayat; Nanang
Jurnal Sistem Informasi Vol. 13 No. 1 (2026)
Publisher : Universitas Serang Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30656/fxnw2631

Abstract

Rainfall prediction is an important component of weather analysis as it provides valuable information to support decision-making in sectors such as agriculture, transportation, and environmental management. Although various studies have compared machine learning algorithms for rainfall classification, many of them lack detailed discussion on dataset characteristics and practical system implementation. Therefore, this study aims to evaluate and compare the performance of Decision Tree and Naive Bayes algorithms for rainfall classification while considering dataset characteristics and implementing the model in a web-based application. The dataset used in this study consists of 2,500 records with meteorological parameters including temperature, humidity, wind speed, cloud cover, and atmospheric pressure. The data underwent preprocessing, including data cleaning and label encoding, where rainfall was represented as 1 and no rainfall as 0. The dataset was divided into training and testing sets, and both algorithms were applied to build classification models. Model performance was evaluated using confusion matrix, accuracy, and ROC curve analysis. The results show that the Decision Tree algorithm achieved an accuracy of 1.00 (100%), while the Naive Bayes algorithm achieved 0.972 (97.2%). Although Decision Tree shows superior performance, the perfect accuracy may indicate potential overfitting, and therefore the results should be interpreted carefully. Furthermore, the developed models were successfully implemented into a web-based application that enables users to perform rainfall prediction interactively. This study demonstrates that Decision Tree provides better performance for rainfall classification in the given dataset, while also highlighting the importance of considering dataset characteristics and evaluation methods. The integration of machine learning models into a web-based system provides a practical contribution for real-world weather prediction applications.   Keywords: Rainfall Classification, Decision Tree, Naive Bayes, Machine Learning, Weather Dataset, Web-Based Application
PERAN CRM BAGI LOYALITAS PELANGGAN DI TOKO FASYA PANE Devika Yani Nainggolan; Dewi Maharani; Abdul Karim Syahputra
Jurnal Sistem Informasi Vol. 13 No. 1 (2026)
Publisher : Universitas Serang Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30656/6604rp15

Abstract

Perkembangan persaingan usaha yang semakin kompetitif menuntut pelaku bisnis untuk menerapkan strategi yang mampu mempertahankan dan meningkatkan loyalitas pelanggan. Salah satu strategi yang dapat diterapkan adalah Customer Relationship Management (CRM), yang berfokus pada pengelolaan hubungan jangka panjang dengan pelanggan. Penelitian ini bertujuan untuk menganalisis peran CRM dalam meningkatkan loyalitas pelanggan di Toko Fasya Pane. Hasil penelitian menunjukkan bahwa penerapan CRM memberikan kemudahan bagi pelanggan dalam melakukan pembelian barang, meningkatkan kualitas pelayanan, serta menciptakan pengalaman berbelanja yang lebih efektif dan nyaman. Pelayanan yang responsif, komunikasi yang baik, serta perhatian terhadap kebutuhan pelanggan terbukti mampu meningkatkan tingkat kepuasan pelanggan. Peningkatan kepuasan tersebut berdampak positif terhadap terbentuknya loyalitas pelanggan yang berkelanjutan. Dengan demikian, penerapan CRM berperan penting dalam memperkuat hubungan antara Toko Fasya Pane dan pelanggan serta mendukung keberlangsungan usaha secara jangka panjang.
ANALYSIS OF THE ACCURACY LEVEL OF FINANCIAL DISTRESS PREDICTION MODELS USING THE NAÏVE BAYES METHOD Ridho Dwi Maulida; Arief Wibowo; Selamet Riyadi
Jurnal Sistem Informasi Vol. 13 No. 1 (2026)
Publisher : Universitas Serang Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30656/pz5ckv70

Abstract

The ability to accurately predict financial distress is crucial for State-Owned Enterprises (SOEs), given their strategic role in maintaining national economic stability. However, existing studies predominantly examine financial distress models in isolation and rely mainly on financial ratios, with limited attention to comparative evaluation under a unified machine learning framework and alternative input structures. This gap limits the understanding of how model performance may vary across different data representations. This study aims to evaluate and compare the predictive performance of four financial distress models Altman Z-Score, Springate S-Score, Zmijewski X-Score, and Grover G-Score by integrating them within a Naïve Bayes classification approach. Using a dataset of 20 Indonesian SOEs listed on the Indonesia Stock Exchange over the 2020–2023 period, this study applies a quantitative comparative method with two types of input variables, namely financial ratios and financial statement account balances. The results show that the Springate S-Score model demonstrates the highest predictive accuracy, achieving 95% when using financial ratios and 82.5% when using account balances. Overall, models based on financial ratios outperform those utilizing raw financial statement data, indicating that structured financial indicators provide more effective signals for classification. The main contribution of this study lies in providing a comprehensive and consistent comparison of multiple financial distress prediction models within a single probabilistic machine learning framework, while also highlighting the impact of different input variable structures on model performance. This study extends the financial distress literature by bridging traditional financial analysis and data mining approaches, and offers practical implications for developing more reliable early warning systems for financial distress in SOEs.   Keywords : Financial Distress Prediction, Naïve Bayes, Machine Learning
Analisis Polarisasi Opini BBM Menggunakan GCN dan PMI Chalifa Chazar; Dea Amelia Azzahra; Asep Nana Hermana; Marisa Premitasari
Jurnal Sistem Informasi Vol. 13 No. 1 (2026)
Publisher : Universitas Serang Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30656/8pz6gc44

Abstract

This study analyzes the polarization of public opinion regarding the removal of fuel subsidies in Indonesia using a graph-based text classification approch that integrates Pointwise Mutual Information (PMI) and Graph Convolution Networks (GCN). One of the main challenges in sentiment analysis of social media data lies in capturing contextual and semantic relationships within short and noisy texts, which often limits the performance of conventional classifications methods. A dataset consisting of 602 tweets was preprocessed and represented as a graph, where word-word relationships were weighted using PMI and document-word connections were weighted using TF-IDF. Two model configurations were evaluated: a baseline GCN without PMI and a PMI-enhanced GCN, both trained using a 70:15:15 train-validation-test split with indentical hyperparameter settings. Experimental results indicate that the baseline GCN achieved a maximum accuracy of 65%, while the integration of PMI improved performance, yielding the highest the accuracy of 73%. These results demonstrate that incorporating PMI enriches graph representations and enhances the effectiveness of GCN in sentiment classification of political opinion data from social media.
CLASSIFICATION OF STUDENTS' ACADEMIC STRESS LEVELS THROUGH DATA MINING TECHNIQUES BASED ON THE C4.5 ALGORITHM Raihan Apriandi Putra Pratama; Sumiati; Hendry Gunawan
Jurnal Sistem Informasi Vol. 13 No. 1 (2026)
Publisher : Universitas Serang Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30656/y35a5z47

Abstract

Academic stress is a common psychological problem experienced by students during their education at university. This condition can be influenced by various factors, including high academic load, limited time to complete assignments, difficulty understanding lecture material, and an unsupportive learning environment. If not managed effectively, academic stress has the potential to negatively impact students' academic achievement, learning motivation, and mental health. Along with the development of information technology, the application of data mining techniques can be utilized to help identify and classify students' academic stress levels more objectively and accurately. This study aims to classify the academic stress levels of using the C4.5 algorithm. Research data were obtained through distributing questionnaires to students from the Informatics Engineering, Information Systems, and Computer Engineering Study Programs. The research stages include data preprocessing, the formation of a classification model, and evaluation of model performance. Algorithm was chosen because it has the ability to build a classification model in the form of a decision tree that is easy to interpret and is able to determine the most influential attributes in the decision-making process. The resulting classification model is expected to be a tool for universities, especially lecturers and student affairs, in conducting early detection of students who have the potential to experience academic stress. Thus, appropriate mentoring and intervention steps can be provided to support the academic success and psychological well-being of students.   Keywords: Academic Stress ,C4.5 Algorithm, Classification,Data Mining, Students

Page 37 of 37 | Total Record : 365

 33   34   35   36   37 

Filter by Year

2014 2026


Reset
Filter By Issues
All Issue Vol. 13 No. 1 (2026) Vol. 12 No. 2 (2025) Vol. 12 No. 1 (2025) Vol 11 No 2 (2024) Vol 11 No 1 (2024) Vol 10 No 2 (2023): Vol. 10 No. 1 (2023) Vol. 10 No. 1 (2023) Vol 10 No 1 (2023) Vol 9 No 2 (2022) Vol 9 No 1 (2022) Vol. 8 No. 2 (2021) Vol 8 No 1 (2021) Vol 7 No 2 (2020) Vol 7 No 1 (2020) Vol 6 No 2 (2019) Vol 6, No 2 (2019) Vol 6, No 1 (2019) Vol 6 No 1 (2019) Vol 5 No 2 (2018) Vol 5, No 2 (2018) Vol 5, No 1 (2018) Vol 5 No 1 (2018) Vol 4 (2017) Vol 3 (2016) Vol 2 (2015) Vol 1 (2014) More Issue