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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 361 Documents
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DESIGN AND IMPLEMENTATION OF A WEB-BASED STUDENT SAVINGS MANAGEMENT SYSTEM USING THE AGILE METHOD Haerani, Reni; Hasanah, Shopi Nurul; Tri Wahyuni, Sefta; Maldini Rosady, Melinne
Jurnal Sistem Informasi Vol. 13 No. 1 (2026)
Publisher : Universitas Serang Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30656/688ptj31

Abstract

The implementation of a web-based student savings information system created with the Agile methodology is covered in this study. This approach is intended to assist schools in more effectively and transparently managing student savings. The purpose of this method is to assist schools in more effectively and openly managing student savings. The Agile approach method is used in the information system design; the development process is broken up into iterative sprints that enable ongoing improvement based on input from stakeholders. for kids,  parents, and school officials, this system offers features like savings contributions, withdrawals, transaction history, and user account management. Agile makes it possible to build useful, user-friendly apps in a comparatively short development cycle by improving communication between the development team and users. The final findings demonstrate how well the Agile methodology supports the development of an educational financial system that calls for adaptability, reactivity, and ongoing user involvement. A website-based student savings information system that may enhance the efficacy and efficiency of finding, generating, and storing student saving data is the result of this study.             Keywords: Student Savings; Agile Methods; Information System; Data Management; Web Application;
Penerapan Metode Weighted Product dalam Sistem Pendukung Keputusan Rekomendasi Pemilihan Jurusan Kuliah di Perguruan Tinggi Halim, Suherman; Putra Dini, Geyza Falihy; Gunawan, Hendry; Samsuni, Sunny
Jurnal Sistem Informasi Vol. 13 No. 1 (2026)
Publisher : Universitas Serang Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30656/4m084410

Abstract

Determining a college major is an important decision that students often make without objective consideration, risking a mismatch between interests and abilities, which can ultimately lead to changing majors or dropping out. This research aims to design and implement a web-based Decision Support System (DSS) capable of providing objective major recommendations by considering students' academic abilities. The Weighted Product (WP) method was chosen due to its capability in handling multi-criteria problems with proportional weighting and automatic normalization. The system was developed using a technology stack of PHP, MySQL, HTML, CSS, and JavaScript, with XAMPP as the local server. Data was collected from 30 students of grade XII Science program at SMAN 8 Kota Serang, covering scores from six subjects (Mathematics, English, Indonesian, Physics, Chemistry, Biology) and five alternative engineering majors (Electrical Engineering, Mechanical Engineering, Civil Engineering, Chemical Engineering, Informatics). Testing results show that the system can generate major recommendations in less than 2 seconds. Accuracy evaluation using blackbox testing and cross-validation between final grades and try-out scores resulted in a compatibility rate of 36.67% for recommendations based on try-out scores, which is statistically more accurate compared to final grade-based recommendations (with 63.33% incompatibility). These findings indicate that try-out data is more representative in predicting academic potential for university admission. This system has proven to be effective, efficient, and has significant potential to be adopted by educational institutions as a data-based counseling tool
MODEL ADOPSI GEMINI AI PADA MAHASISWA PERGURUAN TINGGI: ANALISIS BERBASIS VALUE-BASED ADOPTION MODEL DAN TECHNOLOGY ACCEPTANCE MODEL Sukmawati, Melisa Dwi Adinda; Prassida, Grandys Frieska
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/qp49wv97

Abstract

Peningkatan pemanfaatan kecerdasan buatan (Artificial Intelligence/AI), khususnya Gemini AI, dalam kegiatan akademik mahasiswa mendorong perlunya pemahaman terhadap faktor-faktor yang memengaruhi niat adopsinya di lingkungan pendidikan tinggi. Penelitian ini bertujuan menganalisis pengaruh perceived benefit dan perceived sacrifice terhadap perceived value, serta peran perceived value dalam membentuk attitude dan adoption intention mahasiswa terhadap penggunaan Gemini AI, melalui integrasi Value-Based Adoption Model (VAM) dan Technology Acceptance Model (TAM). Penelitian ini menggunakan pendekatan kuantitatif dengan metode survei, melibatkan 246 responden mahasiswa perguruan tinggi di Jawa Timur yang memiliki pengalaman menggunakan Gemini AI dalam kegiatan akademik. Data dikumpulkan melalui kuesioner berbasis Google Forms dan dianalisis menggunakan metode Structural Equation Modeling - Partial Least Squares (SEM-PLS). Hasil penelitian menunjukkan bahwa perceived benefit berpengaruh positif dan signifikan terhadap perceived value, sedangkan perceived sacrifice berpengaruh negatif dan signifikan terhadap perceived value. Selanjutnya, perceived value terbukti berpengaruh positif dan signifikan terhadap attitude dan adoption intention, serta attitude juga berpengaruh positif terhadap niat adopsi Gemini AI. Temuan ini menegaskan bahwa nilai yang dirasakan menjadi faktor kunci dalam proses adopsi Gemini AI, yang terbentuk dari evaluasi manfaat dan risiko yang dirasakan mahasiswa. Secara keseluruhan, penelitian ini mengonfirmasi bahwa integrasi VAM dan TAM mampu menjelaskan proses adopsi teknologi AI generatif secara komprehensif dalam konteks pendidikan tinggi. Penelitian ini juga memberikan kontribusi teoretis melalui penguatan integrasi VAM dan TAM serta memperkaya kajian adopsi AI generatif di lingkungan pendidikan tinggi.
Evaluasi Klasifikasi Hasil Catur Blitz pada Dataset Tidak Seimbang Skala Besar Menggunakan Cost-Sensitive Learning Khairuddin
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/xv0d9m54

Abstract

The rapid growth of online chess platforms has generated large-scale structured game data that can be utilized for data-driven analysis. In blitz mode games, match outcomes are categorized into win, lose, and draw; however, the distribution of these outcomes is inherently imbalanced, with draw representing a small minority of the dataset. This study aims to evaluate the effectiveness of cost-sensitive learning through balanced class weighting in improving classification performance on an imbalanced large-scale blitz chess dataset. A total of 100,000 rated blitz games were extracted from the Lichess open database and processed through preprocessing, feature extraction, and stratified data splitting. Three supervised learning algorithms - Support Vector Machine (SVM), Decision Tree, and Random Forest - were implemented. Model performance was evaluated using Macro F1-score as the primary metric, along with accuracy and 5-fold stratified cross-validation. The results indicate that without cost-sensitive learning, the recall for the minority class (draw) approaches zero despite achieving higher overall accuracy (0.54). In contrast, applying balanced class weighting significantly improves minority class detection, increasing recall for draw up to 0.73 with a Macro F1-score of approximately 0.40, although overall accuracy decreases to 0.45. This demonstrates the trade-off between global performance and minority class sensitivity. Feature importance analysis further reveals that move count is the most influential predictor of match outcomes. These findings confirm that imbalance-aware learning plays a critical role in large-scale chess outcome classification and highlight the importance of appropriate evaluation metrics in imbalanced datasets
PENGEMBANGAN SISTEM INFORMASI ADMINISTRASI KARYAWAN BERBASIS MOBILE MENGGUNAKAN METODE RAPID APPLICATION DEVELOPMENT Lisa, Lisa; Susanti, Susanti; Salsabila, Zulpa; Joosten, Joosten
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/rkgq2144

Abstract

PT Yorgo Anugerah Nusantara is a palm oil company with 510 employees whose leave, permit, overtime, and business-trip requests are handled entirely through paper forms. Each submission requires a physical handover from the employee to the Head of Department for approval, then to HRD for recording, resulting in delays of more than one working day per cycle and errors in documentation. This study develops a mobile-based employee request administration system that digitises those four workflows. The system is built using the Rapid Application Development (RAD) methodology, implemented with the .NET MAUI cross-platform framework, ASP.NET Core Web API, and PostgreSQL. System modelling used Use Case Diagrams and Activity Diagrams to capture both the current and proposed processes, and an Entity Relationship Diagram for data structure design. Functional validation applied Black Box Testing with the Equivalence Class Partitioning technique, all of which returned valid results. The system successfully replaces paper-based workflows with a digital end-to-end approval chain, enables real-time tracking of request status, and enforces automatic validation rules including a two-hour cap on permit duration, time-logic checks for overtime, and mandatory document attachment for sick leave.
A DECISION SUPPORT SYSTEM FOR DETERMINING THE BEST EMPLOYEE PERFORMANCE EVALUATION USING THE ANALYTICAL HIERARCHY PROCESS (AHP) METHOD Polinus Gulo; Sri Mujiyono
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/hsza5003

Abstract

Employee performance appraisal plays a critical role in organizational decision-making, particularly in determining promotions, bonuses, and competency development. Conventional manual evaluation methods are often subjective, inconsistent, and lack systematic validation. While prior studies have applied multi-criteria decision-making methods in decision support systems (DSS) for employee evaluation, a critical gap remains: most existing systems omit consistency testing, use incomplete weighting procedures, or lack end-to-end system implementation—undermining the reliability of their outputs. To address this gap, this study proposes a novel DSS that integrates a complete Analytical Hierarchy Process (AHP) procedure, including pairwise comparison, normalization, priority weight calculation, and consistency validation, within a fully operational web-based system developed using the SDLC Waterfall model at PT Sam Sam Jaya Garments. Data were collected through observation and interviews to define evaluation criteria and system requirements. The results reveal that Discipline holds the highest weight (0.4391), followed by Target Achievement (0.2661) and Honesty (0.1507), with a Consistency Ratio (CR) of 0.029, confirming reliable judgments. The system successfully ranked ten employees, identifying Cahya Annsyiah as the top performer with a final score of 0.39116. The key contribution of this study lies in its end-to-end integration of AHP with consistency validation into a deployable DSS, directly addressing the methodological shortcomings identified in previous research and enhancing objectivity, accuracy, and transparency in employee performance evaluation.   Keywords: Decision Support System, Analytical Hierarchy Process, Employee Performance Evaluation, AHP, SDLC Waterfall
DEVELOPMENT OF AN EXPERT SYSTEM FOR DIAGNOSING EGGPLANT DISEASES USING THE TSUKAMOTO FUZZY LOGIC METHOD Efrizal Yudhantoro; Yoannes Romando Sipayung
Jurnal Sistem Informasi Vol. 13 No. 1 (2026)
Publisher : Universitas Serang Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30656/6ddknb34

Abstract

Eggplant diseases are a major factor contributing to decreased crop quality and yield, particularly among novice farmers with limited knowledge of early disease identification. The uncertainty of symptom manifestation and limited access to agricultural experts further increase the risk of crop failure. This study aims to develop a web-based expert system for diagnosing eggplant diseases using the Tsukamoto Fuzzy Logic method. The novelty of this research lies in the integration of weighted symptom severity, fuzzy inference rules, and confidence-level outputs into a practical decision-support system specifically designed for eggplant disease diagnosis. The research adopts the Waterfall development model, including requirements analysis, system design, implementation, and testing. The knowledge base consists of five main diseases and twenty symptoms with weighted values ranging from 0.55 to 1.00. System evaluation using Black Box Testing shows that 100% of functional features operate successfully according to system requirements. Furthermore, diagnostic results demonstrate high confidence levels, reaching up to 97% for certain disease cases, indicating reliable system performance in handling uncertainty. This study contributes to the development of intelligent agricultural decision-support systems by providing an accessible, accurate, and efficient diagnostic tool. The proposed system can assist farmers in early disease detection, reduce dependency on experts, and potentially minimize crop losses while improving eggplant productivity.   Keywords: Expert System, Eggplant Disease, Tsukamoto Fuzzy Logic, Decision Support System, Smart Agriculture
DESIGN AND IMPLEMENTATION OF A WEB-BASED ELECTRONIC SUPPLY CHAIN MANAGEMENT SYSTEM FOR SEWING MATERIAL INVENTORY MANAGEMENT IN THE GARMENT INDUSTRY Kalsumi; Riki Andri Yusda; Mustika Fitri Larasati
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/xjhe3347

Abstract

The development of information technology encourages small businesses to adopt digital systems to improve operational efficiency and data management. However, many small-scale garment businesses still rely on manual inventory management processes, which often result in data inconsistencies, delays in reporting, and inefficient coordination. This study aims to develop a web-based Electronic Supply Chain Management (E-SCM) system to improve inventory control and operational performance. The research applies the Software Development Life Cycle (SDLC) using the Waterfall model, which includes requirement analysis, system design, implementation, testing, and evaluation. Data collection was conducted through observation and interviews, while system testing was performed using the Black Box Testing method. The results show that the implementation of the system significantly improves operational efficiency. The time required to generate inventory reports decreased from approximately 60 minutes to 10 minutes (an improvement of 83%), while stock data accuracy increased from 85% to 95%. In addition, stock discrepancies were reduced from 15% to 5%, indicating better data consistency and control. This study contributes by providing an integrated E-SCM model combined with measurable performance indicators for small-scale garment businesses. The findings imply that the adoption of the proposed system can enhance efficiency, accuracy, and decision-making in inventory management.   Keywords: E-SCM, Inventory System, SDLC, Web-Based System, Supply Chain Management.
DEVELOPMENT OF A WEB-BASED ELECTRONIC CUSTOMER RELATIONSHIP MANAGEMENT SYSTEM FOR IMPROVING CUSTOMER SERVICE IN RETAIL BUSINESSES Puspita Rini; Masitah Handayani; Sudarmin
Jurnal Sistem Informasi Vol. 13 No. 1 (2026)
Publisher : Universitas Serang Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30656/1zfhhv60

Abstract

The development of information technology encourages businesses to adopt digital systems to improve customer service and operational efficiency. Small retail businesses often face challenges in managing customer data and transactions due to manual processes, which can lead to inefficiencies and data inconsistencies. This study aims to design and implement a web-based Electronic Customer Relationship Management (E-CRM) system to support customer data management and enhance service quality. The research method used is the Software Development Life Cycle (SDLC) with the Waterfall model, which includes requirement analysis, system design, implementation, testing, and evaluation. Data were collected through observation, interviews, and documentation. The system was tested using Black Box Testing to ensure that all functionalities operate as expected. The results show that the developed system is able to integrate customer data, transaction records, order monitoring, and customer feedback into a unified platform, thereby improving service efficiency and reducing data recording errors. The implementation of the system also enables faster transaction processing and more structured customer data management. This study contributes to the field of information systems by providing an integrated E-CRM model tailored for small retail businesses, emphasizing the use of customer interaction features and data-driven service strategies. The findings also imply that the adoption of E-CRM can enhance service effectiveness and support better decision-making in small-scale business environments.   Keywords: E-CRM; Information System; Web-Based System; Customer Service.
IMPLEMENTATION OF XGBOOST AND SUPPORT VECTOR MACHINE FOR BREAST CANCER PREDICTION USING BAYESIAN OPTIMIZATION Jupron; Nugroho, Fajar Agung
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/rnfpgf88

Abstract

Breast cancer remains one of the most prevalent causes of cancer-related mortality among women worldwide, making early and accurate detection critically important. Machine learning techniques have been widely applied for this purpose; however, many existing studies primarily focus on predictive accuracy without providing comprehensive analysis of model optimization and interpretability. This study proposes a comparative framework integrating Support Vector Machine (SVM) and Extreme Gradient Boosting (XGBoost) with Bayesian Optimization to enhance hyperparameter tuning and model performance. The Breast Cancer Wisconsin Dataset, consisting of 569 samples with 30 numerical features, is used for evaluation. The proposed approach includes data preprocessing, dataset splitting, systematic hyperparameter optimization, model training, and performance evaluation. Experimental results show that the XGBoost model achieves superior performance compared to SVM, with an accuracy of 98.24% and an Area Under the Curve (AUC) of 0.994. Further analysis indicates that the model maintains a strong balance between precision and recall, with minimal misclassification. In addition, feature importance analysis reveals that attributes related to tumor size and structural irregularities contribute significantly to the prediction results, supporting the interpretability of the model in a medical context. The main contribution of this study lies in providing a more comprehensive evaluation that combines performance comparison, optimization effectiveness, and feature-level interpretation within a unified framework. The findings demonstrate that the integration of XGBoost and Bayesian Optimization offers a reliable and interpretable approach for breast cancer classification, with strong potential for implementation in machine learning–based clinical decision support systems. Keywords: breast cancer, machine learning, XGBoost, Support Vector Machine, Bayesian Optimization.

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