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No full textSelecting a major in Senior High School significantly shapes students’ academic paths and future careers. However, the current process often lacks objectivity, relying on subjective teacher consultations and academic data without standardized analysis. This study addresses this gap by developing a decision support system using the Decision Tree algorithm to assist students at Al-Istiqomah High School in choosing between science and social science majors, based on academic performance and non-academic factors like attitudes and attendance. The study follows the CRISP-DM methodology, which includes six stages: business understanding, data understanding, data preparation, modeling, evaluation, and deployment. Academic records from 123 students were used to build the model. The Decision Tree algorithm identified mathematics, biology, and physics scores as key predictors for major classification. The model demonstrated high predictive performance, with 96% accuracy, 100% precision, and 95% recall. Additionally, an Area Under the Curve (AUC) of 97% confirmed the model’s robust ability to distinguish between science and social science tracks. This system was implemented as a user-friendly web application using Streamlit, enabling students and educators to input data and receive immediate major predictions. By offering objective, data-driven recommendations, the system helps students make more informed decisions about their academic futures and provides educators with targeted, evidence-based advice. These results highlight the Decision Tree algorithm as an effective, efficient, and practical tool for enhancing the academic advising process and supporting students in selecting the major that best fits their strengths and interests.Bahasa Inggri
Design and Development of a Digital Travel Authorization System Using Extreme Programming Method
Full text linkThe process of submitting travel authorization letters at SMKN 6 Surabaya was previously carried out manually, leading to issues such as writing errors, miscalculations, and the lack of a digital archiving system. These problems highlighted the necessity for a digital solution to improve the efficiency and accuracy of the process. This study aims to design and develop a web-based travel authorization system using the Extreme Programming (XP) software development method. The XP methodology emphasizes close collaboration with users and rapid iterations throughout the development process. The system was designed with multi-role features, online approval, and digital archiving, enabling a full replacement of the manual process. Testing was conducted using Black-box Testing to verify that the system's functionality worked as expected. Additionally, User Acceptance Testing (UAT) was carried out to assess the design, usability, and efficiency from the user's perspective. The results from the UAT showed an average user satisfaction level of over 80%, indicating that the system was well-received by the users. This indicates that the system is effective in supporting the administrative process for travel authorization at SMKN 6 Surabaya. The implementation of this system is expected to enhance administrative efficiency, reduce human error, and streamline the management and digital archiving of travel documents. Furthermore, by automating the process, the system reduces the time and effort required for manual processing, ultimately improving the overall workflow within the institution.The process of submitting travel authorization letters at SMKN 6 Surabaya was previously carried out manually, leading to issues such as writing errors, miscalculations, and the lack of a digital archiving system. These problems highlighted the necessity for a digital solution to improve the efficiency and accuracy of the process. This study aims to design and develop a web-based travel authorization system using the Extreme Programming (XP) software development method. The XP methodology emphasizes close collaboration with users and rapid iterations throughout the development process. The system was designed with multi-role features, online approval, and digital archiving, enabling a full replacement of the manual process. Testing was conducted using Black-box Testing to verify that the system's functionality worked as expected. Additionally, User Acceptance Testing (UAT) was carried out to assess the design, usability, and efficiency from the user's perspective. The results from the UAT showed an average user satisfaction level of over 80%, indicating that the system was well-received by the users. This indicates that the system is effective in supporting the administrative process for travel authorization at SMKN 6 Surabaya. The implementation of this system is expected to enhance administrative efficiency, reduce human error, and streamline the management and digital archiving of travel documents. Furthermore, by automating the process, the system reduces the time and effort required for manual processing, ultimately improving the overall workflow within the institution
Evaluation of the SSW Alfa Website using Webqual 4.0 Modification and Importance Performance Analysis
Full text linkAdvancements in information technology have encouraged governments to digitize public services to enhance accessibility, transparency, and efficiency. The Surabaya Single Window (SSW) Alfa website was developed to facilitate public administrative processes. However, recurring user complaints regarding registration failures, verification delays, and system instability suggest weaknesses in service delivery. This study evaluates the quality of the SSW Alfa website using a modified WebQual 4.0 framework—expanded with reliability, trust, and citizen support—combined with the Importance Performance Analysis (IPA) method. The model consists of six variables: usability, information quality, service interaction quality, reliability, trust, and citizen support. A quantitative approach was employed by distributing a questionnaire to 385 users who had accessed the platform at least twice. Each item was assessed using a five-point Likert scale for both performance and importance. Data were analyzed using SPSS 29 and Excel. The largest gaps were found in information quality and reliability, with conformity level of 80–81%. The results showed an average conformity level of 93%, with most indicators producing negative gap values, indicating that actual performance falls short of user expectations. The IPA mapping identified four priority indicators in Quadrant I—information accuracy, information completeness, system reliability, and performance consistency—as critical areas for immediate improvement. These findings highlight the need for technical and content-related enhancements to better align digital services with user needs. This study contributes to the evaluation of e-government platforms by extending the WebQual model and offering actionable insights for improving digital service quality in the public sector.Advancements in information technology have encouraged governments to digitize public services to enhance accessibility, transparency, and efficiency. The Surabaya Single Window (SSW) Alfa website was developed to facilitate public administrative processes. However, recurring user complaints regarding registration failures, verification delays, and system instability suggest weaknesses in service delivery. This study evaluates the quality of the SSW Alfa website using a modified WebQual 4.0 framework—expanded with reliability, trust, and citizen support—combined with the Importance Performance Analysis (IPA) method. The model consists of six variables: usability, information quality, service interaction quality, reliability, trust, and citizen support. A quantitative approach was employed by distributing a questionnaire to 385 users who had accessed the platform at least twice. Each item was assessed using a five-point Likert scale for both performance and importance. Data were analyzed using SPSS 29 and Excel. The largest gaps were found in information quality and reliability, with conformity level of 80–81%. The results showed an average conformity level of 93%, with most indicators producing negative gap values, indicating that actual performance falls short of user expectations. The IPA mapping identified four priority indicators in Quadrant I—information accuracy, information completeness, system reliability, and performance consistency—as critical areas for immediate improvement. These findings highlight the need for technical and content-related enhancements to better align digital services with user needs. This study contributes to the evaluation of e-government platforms by extending the WebQual model and offering actionable insights for improving digital service quality in the public sector
Digital Transformation of Catfish Ponds with AI-based Monitoring System
Full text linkDigital transformation in the aquaculture sector, particularly in catfish farming, holds significant potential to improve operational efficiency and farm productivity. This study developed an artificial intelligence (AI)-based monitoring system called NusAIra to assist farmers in managing ponds in real-time by monitoring water quality, feed management, and harvest prediction. The system integrates physical sensors with a Decision Tree Regression machine learning algorithm, validated using an 80:20 hold-out split strategy and evaluated through accuracy and Root Mean Square Error (RMSE) metrics. NusAIra was built using Flask and Docker frameworks, employing a POST endpoint with JSON-formatted data for seamless data exchange. Implementation was carried out on three catfish ponds in Jepara Regency from February to April 2025. The predictive model achieved an accuracy of 87% with an RMSE of 0.35. One application example demonstrated that the system reduced the Feed Conversion Ratio (FCR) from 1.9 to 1.6, increased productivity by up to 22%, and lowered average operational costs by 15%. Additionally, NusAIra effectively predicted market prices with stable seasonal patterns, such as the projected catfish price in Boyolali for April reaching IDR 36,442, closely aligning with historical data. These results highlight NusAIra’s role in supporting data-driven decision-making. However, challenges remain, including infrastructure constraints and the low level of digital literacy among traditional fish farmers. Further development will focus on enhancing prediction accuracy, integrating adaptive features, and expanding system reach through cloud computing to support the sustainability and food security of Indonesia’s aquaculture sector.Digital transformation in the aquaculture sector, particularly in catfish farming, holds significant potential to improve operational efficiency and farm productivity. This study developed an artificial intelligence (AI)-based monitoring system called NusAIra to assist farmers in managing ponds in real-time by monitoring water quality, feed management, and harvest prediction. The system integrates physical sensors with a Decision Tree Regression machine learning algorithm, validated using an 80:20 hold-out split strategy and evaluated through accuracy and Root Mean Square Error (RMSE) metrics. NusAIra was built using Flask and Docker frameworks, employing a POST endpoint with JSON-formatted data for seamless data exchange. Implementation was carried out on three catfish ponds in Jepara Regency from February to April 2025. The predictive model achieved an accuracy of 87% with an RMSE of 0.35. One application example demonstrated that the system reduced the Feed Conversion Ratio (FCR) from 1.9 to 1.6, increased productivity by up to 22%, and lowered average operational costs by 15%. Additionally, NusAIra effectively predicted market prices with stable seasonal patterns, such as the projected catfish price in Boyolali for April reaching IDR 36,442, closely aligning with historical data. These results highlight NusAIra’s role in supporting data-driven decision-making. However, challenges remain, including infrastructure constraints and the low level of digital literacy among traditional fish farmers. Further development will focus on enhancing prediction accuracy, integrating adaptive features, and expanding system reach through cloud computing to support the sustainability and food security of Indonesia’s aquaculture sector
Implementation of Weighted Product Method for Teacher Selection at an Islamic Boarding School
Full text linkService teachers play a crucial role in Islamic boarding schools by imparting the knowledge and values they acquired during their studies. At Al-Kamil Islamic Boarding School, the selection of these service teachers is conducted annually to ensure only the most competent and ethically grounded candidates are chosen. Traditionally, this selection has been performed manually, which introduces several challenges, including subjectivity, inefficiency, and inconsistencies in evaluation. These limitations can compromise the fairness and transparency of the process, potentially leading to biased outcomes and reduced institutional credibility. To address these issues, this study proposes the implementation of a web-based Decision Support System (DSS) utilizing the Weighted Product (WP) method to support the teacher selection process. The WP method is particularly effective in multi-criteria decision-making, as it applies multiplicative weights to criterion scores, enabling a comprehensive and balanced evaluation of each candidate. In this application, 14 candidates are assessed based on eight criteria: morality, academic performance, craftsmanship, Quran memorization, achievements, understanding of classical Islamic texts, language skills, and computer literacy. The system calculates a preference score for each candidate using vector S and vector V normalization processes. The top three candidates identified are Arizkia Aulia (V = 0.07377), Zaskia Faliza (V = 0.07279), and Zulfa Nisa (V = 0.07231). The integration of this WP-based DSS enhances the objectivity, fairness, and efficiency of decision-making. Furthermore, it provides a replicable and scalable framework for educational institutions seeking to implement structured and data-driven staff selection processes
Decision Support System for Selecting the Best Employee Using the Simple Additive Weighting Method
Full text linkEmployee performance appraisal is a crucial aspect of human resource management, as it influences strategic decisions such as promotions, rotations, and incentives. However, manual evaluations are often prone to subjectivity and inefficiencies in terms of time and effort. This study aims to design and implement a decision support system (DSS) using the Simple Additive Weighting (SAW) method to determine the best employee objectively and measurably. The research adopts a software engineering approach with the waterfall model through stages of requirement analysis, system design, implementation, testing, and maintenance. The developed system is web-based and incorporates five key criteria: productivity, loyalty, work attitude, team contribution, and innovation. The testing results indicate that the system can process employee data, compute preference values, and display final rankings accurately and consistently with manual calculations. The system is also equipped with result export features and a user-friendly interface that facilitates the evaluation process. This study contributes a digital tool that reduces subjectivity in performance assessments and improves HR operational efficiency. In conclusion, the implementation of the SAW method in a web-based system is proven effective for supporting multi-criteria decision-making in selecting the best employee and is suitable for dynamic work environments.Employee performance appraisal is a crucial aspect of human resource management, as it influences strategic decisions such as promotions, rotations, and incentives. However, manual evaluations are often prone to subjectivity and inefficiencies in terms of time and effort. This study aims to design and implement a decision support system (DSS) using the Simple Additive Weighting (SAW) method to determine the best employee objectively and measurably. The research adopts a software engineering approach with the waterfall model through stages of requirement analysis, system design, implementation, testing, and maintenance. The developed system is web-based and incorporates five key criteria: productivity, loyalty, work attitude, team contribution, and innovation. The testing results indicate that the system can process employee data, compute preference values, and display final rankings accurately and consistently with manual calculations. The system is also equipped with result export features and a user-friendly interface that facilitates the evaluation process. This study contributes a digital tool that reduces subjectivity in performance assessments and improves HR operational efficiency. In conclusion, the implementation of the SAW method in a web-based system is proven effective for supporting multi-criteria decision-making in selecting the best employee and is suitable for dynamic work environments
Personalized Skincare Recommendation System Based on Ontology and User Preferences
Full text linkPersonalized skincare product selection remains a complex but critically important challenge, as tailoring recommendations to individual skin profiles directly enhances treatment efficacy and fosters consumer trust. Traditional systems, such as content-based and collaborative-filtering, often fail to capture semantic interactions among skin types, concerns, and ingredients. To address these limitations, we propose an innovative ontology-based skincare recommendation system that integrates structured dermatological knowledge with semantic reasoning. Leveraging the Methontology framework, we developed an ontology composed of twelve core classes such as Product, Ingredient, Skin Type, and Skin Concern and more than twenty-five object properties to model interrelated concepts. The knowledge base was populated via web scraping from three prominent platforms (Sociolla, Beautyhaul, Skinsort), yielding over 3,800 products and 28,000 ingredients. We augmented this dataset with dermatological literature to ensure clinical validity. The architecture employs Apache Jena Fuseki and SPARQL for inference, with a React-Node.js web interface. Users input skin type, concerns, and sensitivities, which are translated into RDF triples and processed through semantic rules to generate personalized recommendations. An evaluation based on the Technology Acceptance Model (TAM) assessed Perceived Usefulness and Ease of Use. Ten diverse respondents rated the system with an average score of 4.5 out of 5 (SD=0.3) and endorsed the relevance of recommendations with a score of 4.8. Our findings demonstrate that semantic technologies can significantly enhance personalization and transparency in skincare solutions. This work lays a robust foundation for future innovations in beauty technology, clinical decision support, and consumer health platforms.Personalized skincare product selection remains a complex but critically important challenge, as tailoring recommendations to individual skin profiles directly enhances treatment efficacy and fosters consumer trust. Traditional systems, such as content-based and collaborative-filtering, often fail to capture semantic interactions among skin types, concerns, and ingredients. To address these limitations, we propose an innovative ontology-based skincare recommendation system that integrates structured dermatological knowledge with semantic reasoning. Leveraging the Methontology framework, we developed an ontology composed of twelve core classes such as Product, Ingredient, Skin Type, and Skin Concern and more than twenty-five object properties to model interrelated concepts. The knowledge base was populated via web scraping from three prominent platforms (Sociolla, Beautyhaul, Skinsort), yielding over 3,800 products and 28,000 ingredients. We augmented this dataset with dermatological literature to ensure clinical validity. The architecture employs Apache Jena Fuseki and SPARQL for inference, with a React-Node.js web interface. Users input skin type, concerns, and sensitivities, which are translated into RDF triples and processed through semantic rules to generate personalized recommendations. An evaluation based on the Technology Acceptance Model (TAM) assessed Perceived Usefulness and Ease of Use. Ten diverse respondents rated the system with an average score of 4.5 out of 5 (SD=0.3) and endorsed the relevance of recommendations with a score of 4.8. Our findings demonstrate that semantic technologies can significantly enhance personalization and transparency in skincare solutions. This work lays a robust foundation for future innovations in beauty technology, clinical decision support, and consumer health platforms
Pengaruh Influencer Marketing dan Content Marketing Terhadap Purchase Intention Melalui Brand Awareness Studi Pada Instagram Pocari Sweat
Full text linkPerkembangan teknologi yang kian pesat membawa pengaruh besar bagi kehidupan masyarakat saat ini. Salah satu platform jejaring sosial yang dipergunakan dalam mempromosikan produk secara efektif yaitu instagram. Studi ini bermaksud guna mengetahui pengaruh influencer marketing dan content marketing terhadap purchase intention melalui brand awareness. Data yang terhimpun berupa kuesioner yang disebar pada responden dengan jumlah sebanyak 101 responden. Penentuan sampling pada studi ini mengaplikasikan teknik purposive sampling dengan pertimbangan tertentu untuk bisa menjadi sampel pada studi ini. Adapun pertimbangannya ialah responden yang mengoperasikan plikasi Instagram dan mengetahui influencer Fadil Jaidi. Penelitian ini mempergunakan Partial Least Square (PLS) dengan software SmartPLS. Temuan ini mengindikasikan variabel influencer marketing berpengaruh secara positif dan signifikan terhadap brand awareness, tetapi tidak signifikan terhadap purchase intention. Pada content marketing memiliki pengaruh positif dan siginifikan terhadap brand awareness dan purchase intention. Kemudian pada variabel brand awareness berpengaruh positif tetapi tidak signifikan terhadap purchase intention. Selain itu brand awareness tidak menjadi mediator yang signifikan antar variabel
Pengaruh Media Sosial dan Citra Destinasi Terhadap Kepuasan Pengunjung Melalui Keputusan Berkunjung Pada Objek Wisata Lembah Asri Serang Kabupaten Purbalingga
Full text linkTujuan dari penelitian ini untuk menelaah dampak media sosial serta reputasi destinasi pada kepuasan wisatawan dengan keputusan berkunjung sebagai variabel intervening. Studi ini menggunakan metode kuantitatif pada wisatawan Objek Wisata Lemba Asri Serang Kabupaten Purbalingga dengan sampel 100 responden lewat purposive sampling dan kuesioner online. Data penelitian dianalisis dengan berbagai uji statistik menggunakan SPSS. Analisis regresi digunakan guna mencari keterkaitan antara variabel dependen dan independen. Uji sobel dalam pengamatan ini dilakukan guna melihat dampak variabel mediasi terhadap keterkaitan variabel independen dan dependen.Hasil penelitian membuktikan jika media sosial serta reputasi destinasi berdampak baik serta signifikan terhadap keputusan berkunjung (R Square = 0,618), serta media sosial, citra destinasi, dan ketatapan berkunjung berdampak pada keputusan wisatawan (R Square = 0,719). Keputusan berkunjung terbukti memoderasi hubungan antara media sosial dan kepuasan pengunjung, serta antara citra destinasi dan kepuasan pengunjung. Secara teoritis, penelitian ini memperkuat pemahaman mengenai peran media sosial dan citra destinasi dalam meningkatkan pengalaman wisatawan. Secara praktis, hasil penelitian dapat menjadi pedoman bagi pengelola destinasi wisata dalam mengoptimalkan strategi pemasaran digital dan meningkatkan citra destinasi guna meningkatkan kepuasan serta loyalitas pengunjung
Efektivitas Media Sosial dan Word of Mouth Dalam Meningkatkan Pembelian Kuliner Bakso Raja dan Es Campur Nita di Medan Amplas
Full text linkPenelitian ini mengkaji pengaruh Word of Mouth (WOM) dan media sosial Instagram terhadap keputusan pembelian konsumen pada UMKM Bakso Raja dan Es Campur Nita di Medan Amplas. Latar belakang penelitian dilatarbelakangi oleh transformasi perilaku konsumen di era digital, di mana rekomendasi dari mulut ke mulut dan konten media sosial berperan penting dalam proses pengambilan keputusan. Namun, UMKM kuliner lokal seperti Bakso Raja dan Es Campur Nita masih mengalami kendala dalam mengoptimalkan strategi pemasaran digital untuk meningkatkan daya saing bisnis. Penelitian ini dilaksanakan dengan metode kuantitatif, serta cara pengumpulan data digunakan melalui penyebaran kuesioner kepada 75 orang yang merupakan pelanggan UMKM tersebut. Analisis data dilakukan dengan menggunakan program SPSS 27. Hasil penelitian menunjukkan bahwa WOM dan media sosial Instagram memengaruhi keputusan pembelian secara parsial dan simultan. Kedua variabel independen dapat bertanggung jawab atas 84,1% variabilitas keputusan pembelian, menurut nilai koefisien determinasi (R Square) sebesar 0,841. Temuan kunci penelitian ini menegaskan peran strategis integrasi antara rekomendasi konsumen (WOM) dan pemanfaatan media sosial Instagram dalam meningkatkan minat beli dan loyalitas pelanggan. Implikasi penelitian ini adalah bahwa kombinasi strategi WOM yang efektif dengan konten kreatif di Instagram dapat menjadi pendorong utama pertumbuhan UMKM kuliner lokal di tengah persaingan pasar yang semakin ketat. Rekomendasi praktis yang diajukan meliputi optimalisasi konten Instagram, peningkatan interaksi dengan pelanggan, serta penguatan strategi untuk mendorong ulasan positif dari konsumen