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Jurnal Sistem Informasi dan Informatika (SIMIKA)
Published by Universitas Banten Jaya
ISSN : 26226901     EISSN : 26226375     DOI : 10.47080
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering
Jurnal Sistem Informasi dan Informatika aims to provide scientific literature specifically on studies of applied research in information systems (IS), information technology (IT) and public review of the development of theory, method, and applied sciences related to the subject.
Arjuna Subject : Ilmu Komputer (semua kategori) - Sistem Informasi
Articles 202 Documents
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PENERAPAN METODE SAW UNTUK REKOMENDASI PARFUM INDOOR MAUPUN OUTDOOR DI GALLERY PARFUM Emelia Emelia; Maulana Dwi Sena; Ruri Ashari Dalimunthe
Jurnal Sistem Informasi dan Informatika (Simika) Vol. 9 No. 2 (2026): Jurnal Sistem Informasi dan Informatika (Simika)
Publisher : Program Studi Sistem Informasi, Universitas Banten Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/simika.v9i2.4587

Abstract

Choosing the right perfume for the intended use is often a challenge for consumers due to the wide variety of products with different characteristics. This study aims to assist the decision-making process in determining the most suitable perfume for indoor and outdoor use by using the Simple Additive Weighting (SAW) method. The SAW method was chosen because it is capable of assessing and ranking alternatives based on several criteria objectively. This study uses five perfume alternatives that are evaluated based on four criteria, namely fragrance durability, fragrance type, fragrance intensity, and price. Each alternative is assessed based on predetermined criteria weights, followed by a normalization process and preference value calculation using the SAW method. The calculation results show that the perfume alternative with the highest preference value of 0.88 ranks first and is therefore recommended as the most suitable perfume based on the combination of all criteria used. Thus, the SAW method can be used as a decision support system approach to help consumers choose perfumes more objectively and systematically.
SISTEM PENDUKUNG KEPUTUSAN PENENTUAN PRIORITAS PENERIMA DISTRIBUSI PUPUK MENGGUNAKAN METODE SAW PADA UD. JAYA TANI Wanda Ramadhani; Muhammad Ardiansyah Sembiring; Amalia Amalia
Jurnal Sistem Informasi dan Informatika (Simika) Vol. 9 No. 2 (2026): Jurnal Sistem Informasi dan Informatika (Simika)
Publisher : Program Studi Sistem Informasi, Universitas Banten Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/simika.v9i2.4597

Abstract

This study aims to design and implement a Decision Support System (DSS) to determine the priority of fertilizer distribution recipients at UD. Jaya Tani. The main problem faced is that the fertilizer distribution process is still carried out conventionally based on estimates without accurate data analysis, so that there is often a mismatch between the availability of fertilizer stocks and the demand of farmers in the field. This condition has the potential to cause losses due to excess stock or lost sales opportunities due to stock shortages. This study uses the Simple Additive Weighting (SAW) method, which works by weighted summation of the performance values of each alternative based on several predetermined criteria. The criteria used in this study include land area, crop age, remaining fertilizer stock owned by farmers, and fertilizer demand. The system was developed web-based using the PHP programming language with a MySQL database. The results of the study show that the application of the SAW method can help determine the priority of fertilizer distribution recipients in a more objective and measurable manner. The system built is capable of ranking farmers based on the preference values generated, thereby facilitating management in making more targeted fertilizer distribution decisions. With this system in place, UD. Jaya Tani is expected to improve the efficiency of fertilizer distribution management and optimize stock availability to meet farmers' needs more effectively.
KLASTERISASI RISIKO BURNOUT AKADEMIK DAN KEAGAMAAN SISWA MTSN MENGGUNAKAN ALGORITMA K-MEANS BERDASARKAN NILAI AKADEMIK DAN AKTIVITAS BELAJAR Sapriani Lubis; Dewi Anggraeni; Sudarmin
Jurnal Sistem Informasi dan Informatika (Simika) Vol. 9 No. 2 (2026): Jurnal Sistem Informasi dan Informatika (Simika)
Publisher : Program Studi Sistem Informasi, Universitas Banten Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/simika.v9i2.4598

Abstract

Academic burnout is a condition of physical, emotional, and mental exhaustion experienced by students due to continuous academic pressure. In addition to academic burnout, students may also experience religious burnout, which can reduce their participation in religious activities at school. At MTsN Tanjungbalai, the identification of student burnout risk is still conducted manually by counseling teachers, making the process subjective and less effective. This study aims to classify students based on the risk level of academic and religious burnout using the K-Means clustering algorithm. The research method applies a data mining approach with clustering techniques using student academic scores, religious scores, and learning activity data as variables. The system is designed and implemented as a web-based application to support the clustering process. The results show that the K-Means method is able to group students into several clusters representing different burnout risk levels. The developed system assists teachers in identifying students who require early attention and provides data-based information to support decision-making in student guidance and monitoring.
PENGEMBANGAN SISTEM PENJUALAN PRODUK DIGITAL DENGAN PENGIRIMAN AKUN OTOMATIS MENGGUNAKAN METODOLOGI RAD Marshep Ollo; Hafiyyan Putra Pratama
Jurnal Sistem Informasi dan Informatika (Simika) Vol. 9 No. 2 (2026): Jurnal Sistem Informasi dan Informatika (Simika)
Publisher : Program Studi Sistem Informasi, Universitas Banten Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/simika.v9i2.4599

Abstract

The rapid growth of subscription-based digital services in Indonesia creates significant business opportunities. However, many digital business owners still rely on manual processes for QRIS payment verification and account delivery, which often result in errors such as duplicate transactions, inaccurate stock distribution, and increased order abandonment, especially during non-productive hours (22:00–05:00 WIB). This study aims to develop a web-based digital product sales system for Premku.com that ensures transaction accuracy and operational efficiency. The system was developed using the Rapid Application Development (RAD) approach, focusing on automating payment verification, ensuring real-time stock control, and streamlining digital product delivery. Accuracy is achieved through automated transaction validation using unique payment identifiers, real-time stock synchronization to prevent double-selling, and a controlled transaction process that minimizes human intervention. In addition, secure authentication mechanisms are implemented to maintain data integrity. Black-box testing was conducted to evaluate system performance under various conditions, including high transaction loads, payment validation, and process reliability. The results show that the system successfully eliminates duplicate transactions, maintains consistent stock accuracy, and significantly reduces processing time for each order. Overall, the developed system improves accuracy, efficiency, and reliability in digital product distribution, while enabling continuous 24-hour operation. This contributes to better user experience and supports business scalability in the digital service sector.
MACHINE LEARNING-BASED ASSESSMENT OF SKINCARE PRODUCT VALUE FOR MONEY USING CUSTOMER REVIEWS Suwarno; Deli; Devina Benhans
Jurnal Sistem Informasi dan Informatika (Simika) Vol. 9 No. 2 (2026): Jurnal Sistem Informasi dan Informatika (Simika)
Publisher : Program Studi Sistem Informasi, Universitas Banten Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/simika.v9i2.4620

Abstract

Skincare products play an important role in personal care and consumer satisfaction. However, the wide variety of products and price ranges often makes it difficult for consumers to assess whether a product offers good Value for Money. This study proposes a machine learning approach to evaluate the Value for Money of skincare products using customer reviews from the Sephora platform. Review texts were preprocessed using Natural Language Processing (NLP) techniques, including lowercasing, tokenization, stopword removal, and lemmatization. Text features were extracted with TF-IDF and combined with product price, helpfulness score, and sentiment score. Support Vector Machine (SVM) and Logistic Regression were compared for classification, while SMOTE was applied to address class imbalance. Both models achieved similar performance, with an accuracy of 0.76, a macro-average F1-score of 0.70, and a weighted-average F1-score of 0.78. The results indicate that combining customer reviews with product-related features can effectively assess Value for Money and support consumers in making better purchasing decisions.
ANALISIS PERBANDINGAN TEKNOLOGI BLOCKCHAIN DAN DATABASE KONVENSIONAL DALAM MENJAMIN KEAMANAN TRANSAKSI DIGITAL Nico Salim; Wilbert William Yunoto; Alvin Mak; Juliansyah Putra
Jurnal Sistem Informasi dan Informatika (Simika) Vol. 9 No. 2 (2026): Jurnal Sistem Informasi dan Informatika (Simika)
Publisher : Program Studi Sistem Informasi, Universitas Banten Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/simika.v9i2.4621

Abstract

The rapid development of information technology has significantly increased the use of digital transactions across various sectors, including banking and e-commerce, making data security a crucial factor in maintaining system integrity, trust, and sustainability. Conventional databases have long been the primary technology for managing transaction data; however, their centralized architecture poses potential security risks, such as data manipulation and cyberattacks. In contrast, blockchain technology offers a decentralized approach that utilizes consensus mechanisms and cryptography to enhance data integrity and transaction transparency. This study aims to analyze and compare the security aspects of blockchain and conventional databases in the context of digital transactions using the Systematic Literature Review (SLR) method. The literature selection process follows PRISMA guidelines, including identification, screening, and eligibility stages, resulting in 26 selected articles from an initial 402 articles indexed in Scopus. The results indicate that blockchain excels in ensuring data integrity and transparency through its distributed and immutable nature, while conventional databases demonstrate superior performance in terms of efficiency, transaction speed, and ease of implementation. Therefore, the choice of technology should be aligned with security requirements, system scale, and organizational operational characteristics, highlighting the importance of selecting appropriate data management solutions to ensure secure and reliable digital transactions.
ANALISIS SPASIO-TEMPORAL DETEKSI ANOMALI SUHU PERMUKAAN BUMI ISOLATION FOREST: STUDI KASUS INDONESIA Nicolaus Owen Marvell; Muhammad Iqbalul Khoiri; Chrisjuanito Clancy; Ken Ditha Tania; Allsela Meiriza; Ahmad Rifai
Jurnal Sistem Informasi dan Informatika (Simika) Vol. 9 No. 2 (2026): Jurnal Sistem Informasi dan Informatika (Simika)
Publisher : Program Studi Sistem Informasi, Universitas Banten Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/simika.v9i2.4626

Abstract

Land surface temperature (LST) is an important indicator of climate change because increasing surface temperatures can trigger environmental degradation, drought, and extreme weather events. In Indonesia, long-term monitoring of extreme temperature anomalies remains limited, as most studies rely on conventional statistical methods that are less effective in detecting complex and non-linear anomaly patterns. Therefore, this study analyzes the spatio-temporal dynamics of LST and identifies extreme temperature anomalies across Indonesia during 1940–2024 using a machine learning approach. Monthly LST data were examined through exploratory data analysis (EDA), including temporal trend analysis and 10-year moving averages, to characterize long-term temperature variability, while the Isolation Forest algorithm was implemented as an unsupervised anomaly detection method using n_estimators = 100 and contamination = 0.05. The results identified 51 temperature anomalies, representing approximately 5% of the 1,020 monthly observations analyzed. Most anomalies occurred during periods associated with major climate disturbances and corresponded closely with documented El Niño events, particularly in 1997–1998 and 2015. Trend analysis revealed a persistent increase in Indonesia’s surface temperature, indicating an ongoing warming pattern consistent with climate change, while anomaly score distributions showed a clear separation between normal and extreme observations, confirming the effectiveness of the Isolation Forest algorithm. These findings demonstrate that integrating spatio-temporal analysis with machine learning provides a robust framework for detecting extreme temperature events and monitoring climate variability, thereby supporting climate risk assessment and strengthening BMKG’s early warning systems for climate change adaptation and mitigation.
KOMPARASI MODEL REGRESI DALAM MEMPREDIKSI GAJI PEKERJAAN ARTIFICIAL INTELLIGENCE M Rafly Ramdhani; M Luthfi Aldi Pratama; Muhammad Emirshah Yusuf; Ken Ditha Tania; Allsela Meiriza; Ahmad Rifai
Jurnal Sistem Informasi dan Informatika (Simika) Vol. 9 No. 2 (2026): Jurnal Sistem Informasi dan Informatika (Simika)
Publisher : Program Studi Sistem Informasi, Universitas Banten Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/simika.v9i2.4627

Abstract

The increasing demand for Artificial Intelligence-related jobs has intensified global labor market dynamics, characterized by high salary variability and growing industry uncertainty. These conditions pose significant challenges for organizations and professionals in determining accurate, objective, and data-driven salary estimations. This study aims to develop and compare the performance of several regression models for predicting Artificial Intelligence job salaries, namely Linear Regression, Gradient Boosting, and Support Vector Regression. A large-scale global job postings dataset is employed, incorporating conventional job attributes such as location, experience level, and job type. In addition, this study integrates industry risk variables, including layoff risk and automation risk, to capture more realistic labor market dynamics. The research methodology consists of data preprocessing, model development using a machine learning pipeline to ensure consistent processing between training and testing data, and performance evaluation. The dataset is split into training and testing sets using an 80:20 ratio, and model performance is assessed using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and the coefficient of determination (R²). The experimental results indicate that Gradient Boosting achieves the best performance with the lowest prediction errors and the highest explanatory power, followed by Linear Regression. In contrast, Support Vector Regression exhibits relatively poor performance on high-dimensional feature representations. These findings confirm that ensemble-based approaches are more effective in modeling the heterogeneous and non-linear salary structures of Artificial Intelligence jobs and provide valuable insights for data-driven labor market analysis.
EVALUASI USABILITY DAN USER EXPERIENCE APLIKASI AGODA MENGGUNAKAN SUS DAN UEQ DI SUMATERA SELATAN RA Aliffyaa Ramadhani; Fathoni; Hardini Novianti
Jurnal Sistem Informasi dan Informatika (Simika) Vol. 9 No. 2 (2026): Jurnal Sistem Informasi dan Informatika (Simika)
Publisher : Program Studi Sistem Informasi, Universitas Banten Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/simika.v9i2.4629

Abstract

The evaluation of mobile application quality is essential in human–computer interaction studies, particularly in assessing usability and user experience (UX) which influence user satisfaction and continued usage. However, the usability level of Online Travel Agent (OTA) applications may vary, and empirical evaluations integrating multiple measurement methods remain limited, especially in regional contexts. This research is undertaken to analyze the usability and user experience of the Agoda application in South Sumatra using an integrated approach of the System Usability Scale (SUS) and User Experience Questionnaire (UEQ). A descriptive-evaluative quantitative approach was applied in this study. Research data were gathered by distributing an online questionnaire, resulting in 132 valid responses that were included in the analysis. The results show that the SUS score is 75, which falls into the good and acceptable category, indicating that the application is generally usable. Meanwhile, the UEQ results indicate that all dimensions have positive mean values ranging from 1.195 to 1.710, with most dimensions categorized as Good and one dimension categorized as Above Average. These findings indicate that the Agoda application provides a positive user experience in both pragmatic and hedonic aspects, supported by the SUS score of 75 and positive UEQ evaluations across all dimensions. However, improvements are still needed in certain dimensions, particularly perspicuity, to enhance clarity and ease of use. Overall, the integration of SUS and UEQ provides a comprehensive evaluation of usability and user experience, contributing to the improvement of OTA application quality.
PENERAPAN LEAN UX TERHADAP PENINGKATAN USER EXPERIENCE PADA MARKETPLACE DIGITAL: SYSTEMATIC LITERATURE REVIEW Yosi Sofyan Pangestu; Dwi Krisbiantoro; Darso
Jurnal Sistem Informasi dan Informatika (Simika) Vol. 9 No. 2 (2026): Jurnal Sistem Informasi dan Informatika (Simika)
Publisher : Program Studi Sistem Informasi, Universitas Banten Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/simika.v9i2.4693

Abstract

This study analyzes the implementation of Lean UX and its impact on user experience within marketplace and digital transaction platforms using a Systematic Literature Review (SLR) methodology. The PRISMA protocol was applied to identify, screen, evaluate, and synthesize relevant scientific articles. Searches were conducted via Google Scholar using Publish or Perish and the Boolean search strategy: ("Lean UX" OR "Lean User Experience" OR "LeanUX") AND ("Marketplace" OR "E-commerce" OR "Online Store"). In total, 130 articles were initially identified, with 15 final articles published between 2021 and 2026 selected following duplication removal, screening, eligibility, and quality assessment. The findings demonstrate that Lean UX is widely implemented in e-commerce, online stores, retail digital services, marketplace-like platforms, and digital transaction systems. The most prevalent Lean UX stages identified were creating minimum viable products (MVP), conducting experiments, and gathering feedback and research. The System Usability Scale (SUS) and the User Experience Questionnaire (UEQ) were the most frequently used evaluation instruments. Overall, Lean UX implementation contributed positively to usability, task completion efficiency, navigation clarity, and users’ perceptions of interface quality. However, variations in evaluation instruments and the predominance of prototype-based studies limited direct comparisons of effectiveness across platforms. This review offers a structured overview of Lean UX implementation patterns and their contributions to enhancing user experience in digital transaction platforms.

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