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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
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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 14 Documents
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Search results for , issue "Vol. 13 No. 1 (2026)" : 14 Documents clear
Prediksi Tingkat Kesepian Berdasarkan Profil Media Sosial Menggunakan Algoritma Random Forest Hidayat, Asep Lukman Arip; Solichin, Achmad; Arip Hidayat, Asep Lukman; Achmad Solichin
Jurnal Sistem Informasi Vol. 13 No. 1 (2026)
Publisher : Universitas Serang Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30656/10rg3t91

Abstract

Pada era digital yang semakin berkembang, media sosial telah menjadi bagian penting yang tidak terpisahkan dari kehidupan manusia modern, terutama bagi generasi muda, termasuk Generasi Z di SMK Tunas Media yang lahir pada awal abad ke-21 dan menjadi generasi pertama yang tumbuh dengan akses internet, teknologi dan media sosial. Beberapa penelitian terkini mengungkapkan bahwa meskipun Generasi Z tumbuh dalam era teknologi yang sangat terhubung, mereka ternyata memiliki gejala tingkat kesepian yang sangat tinggi bahkan melampaui seluruh generasi sebelumnya, salah satu penyebabnya adalah penggunaan media sosial yang berlebihan, sehingga mereka lebih mengutamakan komunikasi digital dibandingkan interaksi secara langsung. Sekitar 73% dari mereka mengatakan bahwa mereka merasa terisolasi. Hal ini tentunya berdampak pada kesehatan mental mereka, 91% melaporkan mengalami stres fisik atau emosional, dan 68% mengatakan bahwa mereka merasa khawatir tentang masa depan. Oleh karena itu, sangatlah penting untuk melakukan deteksi dini pada siswa-siswi SMK Tunas Media yang berisiko mengalami kesepian. Teknologi pembelajaran mesin Random Forest, dapat digunakan untuk memprediksi tingkat kesepian siswa berdasarkan data profil media sosial. Algoritma ini dapat mempelajari data profil media sosial, seperti pola aktivitas, konteks konten yang dibagikan, interaksi sosial, ekspresi emosi, dan jaringan sosial. Selain itu, penggunaan data UCLA Loneliness Scale merupakan salah satu alat psikometri yang paling umum digunakan untuk mengukur tingkat kesepian dapat diintegrasikan dengan hasil prediksi model pembelajaran mesin untuk memberikan validitas tambahan dalam menilai kesepian. Penelitian ini menunjukkan hasil yang signifikan dalam peningkatan kinerja model klasifikasi setelah diterapkan metode Random Forest yang dioptimalkan dengan Optuna. Dalam penelitian ini, model digunakan untuk memprediksi tingkat kesepian berdasarkan profil media sosial, dan berhasil mencapai akurasi 90%. Hal ini menunjukkan bahwa model Random Forest yang dioptimalkan dengan Optuna memiliki potensi besar dalam mengklasifikasikan tingkat kesepian rendah dan tinggi pada siswa generasi Z.
Prediksi Perubahan Luas Perkebunan Aren di Jawa Barat Berbasis Geospasial dengan Algoritma ARIMA dan Machine Learning Zaliluddin, Dadan; Heryadiana, Asep Dian; Pateman, Dimar
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/djx30932

Abstract

Aren palm (Arenga pinnata) plays a significant role as an economic commodity and a renewable energy source in West Java, Indonesia. However, fluctuations in plantation areas caused by land use change, climate variability, and socio-economic factors have created challenges for sustainable management. Accurate prediction of aren plantation area dynamics is required to support decision-making and policy design for renewable energy development and environmental sustainability.This study aims to predict changes in aren plantation areas in West Java using a combination of Autoregressive Integrated Moving Average (ARIMA) for time-series forecasting and Machine Learning algorithms for enhanced prediction accuracy. Historical data of aren plantation areas from 2013 to 2023 were collected from official government databases. ARIMA was applied to model temporal trends, while Machine Learning approaches such as Random Forest and Long Short-Term Memory (LSTM) were employed to capture non-linear relationships and integrate external factors such as rainfall, soil characteristics, and urbanization patterns. In addition, a geospatial approach using Geographic Information System (GIS) was adopted to visualize spatial changes in plantation areas.Preliminary results indicate that ARIMA successfully models short-term trends with relatively low forecasting errors (RMSE < 15%). Machine Learning models demonstrate the potential to improve robustness and predictive accuracy by incorporating multidimensional variables. The integration of spatial visualization enables stakeholders to identify high-risk regions for land conversion and areas with strong potential for sustainable aren cultivation. The findings of this research provide a foundation for developing a decision support system to enhance sustainable plantation management and bioethanol policy planning in West Java. The proposed predictive framework contributes not only to the field of computational forecasting but also to the strategic alignment of renewable energy development with local socio-economic priorities. Keywords: ARIMA, Machine Learning, Geospatial, Aren Plantation, Forecasting  
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.

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