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Underwriting Technology Trends: A Systematic Literature Review Budy Santoso, Cahyono; Ghaniy, Rajib
JESII: Journal of Elektronik Sistem InformasI Vol. 2 No. 1 (2024): JournaI of Elektronik Sistem InformasI - JESII (JUNE)
Publisher : Departement Information Systems Universitas Kebangsaan Republik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31848/jesii.v2i1.3420

This study systematically reviews trends in underwriting technology to enhance the precision and personalization of insurance companies' risk assessment and decision-making processes. Using the Kitchenham method, we conducted a systematic review of scientific publications indexed by Scopus from 2011 to 2021. Our findings reveal the extent of research activity in this field, the leading contributing countries, the methodologies employed, the technologies utilized, and the specific areas investigated. The results indicate significant advancements in the application of machine learning, blockchain, and other technologies in underwriting, providing a comprehensive overview of current trends and future directions. This study offers valuable insights for researchers and practitioners aiming to improve underwriting technology, highlighting potential areas for further research and development. These insights are crucial for advancing the field and enhancing the efficiency and effectiveness of underwriting practices.

Aplikasi Pengelolaan Acara dan Informasi Kampus Budy Santoso, Cahyono; Makarena, Maria Rachel Kesya; Nuraina, Anezza; Dzahabiyyah, Firah; Bilqis, Sahla Lutfiah
REMIK: Riset dan E-Jurnal Manajemen Informatika Komputer Vol. 9 No. 1 (2025): Volume 9 Nomor 1 Januari 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/remik.v9i1.14378

Pengelolaan acara di lingkungan kampus sering kali menghadapi tantangan terkait koordinasi, penyebaran informasi, dan keterlibatan mahasiswa. Untuk menjawab permasalahan ini, Universitas Pembangunan Jaya (UPJ) mengembangkan JayaEvents, sebuah aplikasi digital yang dirancang untuk mempermudah perencanaan, pelaksanaan, dan promosi acara kampus. Aplikasi ini mengintegrasikan berbagai fitur, seperti pendaftaran acara, pengelolaan tiket, notifikasi, dan forum diskusi, yang bertujuan untuk meningkatkan keterlibatan mahasiswa serta memfasilitasi koordinasi antar pihak yang terlibat dalam acara. Penelitian ini bertujuan untuk mengevaluasi efektivitas JayaEvents dalam meningkatkan pengelolaan acara kampus, mempermudah akses informasi, serta meningkatkan partisipasi mahasiswa dalam berbagai kegiatan. Penelitian ini menggunakan metode kualitatif dengan pendekatan studi kasus yang mengkaji pengalaman pengguna aplikasi, efektivitas fitur-fitur utama, dan dampaknya terhadap keterlibatan mahasiswa. Hasil penelitian diharapkan memberikan wawasan tentang penerapan aplikasi manajemen acara dalam konteks universitas, serta memberikan rekomendasi untuk pengembangan lebih lanjut JayaEvents agar lebih efektif dan efisien dalam mendukung kegiatan kampus.

Implementasi Data Warehouse dan Bussiness Intelligence Kasus AIDS di Jawa Barat Budy Santoso, Cahyono; Muhammad Mujiburochman; Reyner Shaquille Rachim; Raihan Cikal Herlambang
JSAI (Journal Scientific and Applied Informatics) Vol 8 No 1 (2025): Januari
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v8i1.7567

This study discusses the design of a data warehouse for analyzing AIDS cases in West Java using the Nine Step Methodology. The background of this research is the high prevalence of AIDS cases in West Java during 2018–2019 and the need for an integrated data management system to support data-driven health policies. The objective of this study is to design and implement a data warehouse capable of integrating data from various dimensions, such as region, age group, gender, and year, to support epidemiological analysis of AIDS. The methodology employed includes stages such as data extraction from various sources, data transformation to enhance quality, and data loading into a PostgreSQL-based data warehouse system. The study also utilizes the ETL (Extract, Transform, Load) process to ensure the integrity of the processed data. The results indicate that the designed data warehouse successfully maps the distribution of AIDS cases based on relevant dimensions. Key findings reveal that the productive age group (25–49 years) and males have the highest number of cases, with Bandung City being the region with the most cases. The contribution of this study is the provision of a data platform that supports evidence-based decision-making while identifying high-risk regions and groups for more effective health interventions. Limitations include the scope of data limited to two years and the absence of predictive analytics features. Future research is recommended to expand the time coverage and integrate predictive analysis to enhance the effectiveness of health policy

Implementasi NLP Klasifikasi Berita Pemilu Menggunakan Algoritma LSTM Harry Vadilan Sianturi; Budy Santoso, Cahyono
JSAI (Journal Scientific and Applied Informatics) Vol 8 No 2 (2025): Juni
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v8i2.8212

This study examined the application of a Long Short-Term Memory (LSTM)-based text classification method to categorize election news according to presidential and vice-presidential candidate entities. The core problem addressed was the lack of an automated classification system capable of identifying political affiliations directly within the vast volume of digital news content. In this research, news data were collected from open-access sources and automatically labeled based on the occurrence of candidate-related keywords. A supervised learning approach was implemented using the LSTM architecture to capture sequential patterns within the news text. The evaluation results demonstrated that the model achieved a validation accuracy of 95.44% and a macro-averaged F1-score of 0.95, indicating strong classification performance across all candidate categories. Furthermore, predictions on test data revealed the model’s consistency and stability in recognizing political entities. This study confirmed the effectiveness of the LSTM-based approach for entity-based election news classification and highlighted its potential for integration into automated media analytics and political discourse monitoring systems.

Perancangan Aplikasi Sistem Presensi Guru Berbasis Web Menggunakan Geo Fencing Pada Sekolah SDN XYZPerancangan Aplikasi Sistem Presensi Guru Berbasis Web Menggunakan Geo Fencing Pada Sekolah SDN XYZ Faizul Anwar Ramdhani; Budy Santoso, Cahyono
JSAI (Journal Scientific and Applied Informatics) Vol 8 No 2 (2025): Juni
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v8i2.8231

The web-based teacher attendance system application with Geo-Fencing technology integration is designed to improve the accuracy and efficiency of the teacher attendance recording process in the school environment. The use of Geo-Fencing allows attendance to only be done when the user is in a predetermined area, thus minimizing the potential for fraud and data manipulation. This research aims to develop a web-based attendance application and evaluate its usability level using the System Usability Scale (SUS) method. The evaluation was conducted on 20 respondents consisting of teachers and school administrators. Based on the test results, an average SUS score of 75 was obtained, which is included in the Good usability category. Thus, this application is considered quite easy to use, effective, and acceptable to users. The results of this study indicate that the web-based teacher attendance system application with Geo-Fencing has the potential to be widely implemented in the school environment, with some further development recommendations to improve user experience.

Perancangan Web E-Commerce Pada Toko XYZ Dengan Fitur Sistem Rekomendasi Hafid Roihan; Budy Santoso, Cahyono
JSAI (Journal Scientific and Applied Informatics) Vol 8 No 2 (2025): Juni
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v8i2.8268

This research aims to design and implement a web-based e-commerce system for XYZ Store by providing a fresh vegetable product recommendation feature, using the Apriori algorithm and the Rapid Application Development (RAD) approach. The design process begins with collecting data through interviews to find out the needs of the system. Transaction data for one month was analyzed using the Apriori algorithm to identify patterns of association between products that are often purchased together. The results of this analysis were used as the basis for creating recommendation features. The RAD approach was used because it allows the system to be developed quickly and gradually by involving users in each phase of development. This research analyzes the usability level of the platform using the System Usability Scale (SUS) method. Testing was conducted on 15 participants who had tried the platform prototype in the designed usage scenario. The measurement results indicate that the average SUS score is 78.2, placing it in the “Good” category. The recommendation system feature received a positive response, with the majority of users finding it helpful in finding relevant products. These findings suggest that integrating the recommendation system not only enriches the platform's features but also contributes to improving the overall user experience.

Deteksi Penyakit Daun Kapas Dengan Deep Learning Berbasis Convolutional Neural Network (CNN) Bhagawanta, Bajra; Budy Santoso, Cahyono
IDEALIS : InDonEsiA journaL Information System Vol. 8 No. 2 (2025): Jurnal IDEALIS Juli 2025
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/idealis.v8i2.3517

Penelitian ini mengembangkan model kecerdasan buatan dengan algoritma Convolutional Neural Network (CNN) untuk mendeteksi penyakit pada daun kapas secara akurat dan otomatis. Metode konvensional seperti observasi visual seringkali tidak efektif dalam mengidentifikasi penyakit tanaman. Dengan menggunakan pendekatan deep learning, khususnya CNN, penyakit seperti Fusarium Wilt dan Bacterial Blight dapat diidentifikasi secara otomatis dan akurat melalui analisis citra daun kapas. Teknologi ini memungkinkan tindakan pencegahan lebih cepat untuk meminimalisir kerugian serta mendukung pengambilan keputusan berbasis data. Penelitian ini dilakukan melalui tahapan: pengumpulan data gambar daun kapas, preprocessing, modelling, analisis, dan evaluasi model menggunakan confusion matrix dan kurva ROC. Dengan dataset berisi 4.778 gambar dari enam kelas kondisi daun, model mencapai akurasi pelatihan 97% dan validasi 90% setelah 20 epoch, serta hasil evaluasi menunjukkan kinerja klasifikasi yang sangat baik dengan nilai precision, recall, f1-score yang tinggi, dengan nilai Area Under Curve (AUC) mendekati 1. Model ini mampu mendeteksi penyakit berdasarkan fitur visual dan memberikan hasil klasifikasi real-time, membuktikan bahwa CNN efektif dalam membantu identifikasi dini penyakit tanaman kapas.

KLASTERISASI DATA PENGANGGURAN DI PULAU JAWA MENGGUNAKAN ALGORITMA K-MEANS DALAM PENANGGULANGAN PENGANGGURAN TAHUN 2020-2023 Rachma, Mutia; Budy Santoso, Cahyono
IDEALIS : InDonEsiA journaL Information System Vol. 8 No. 2 (2025): Jurnal IDEALIS Juli 2025
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/idealis.v8i2.3522

Pengangguran merupakan masalah utama dalam sektor perekonomian dan turut menimbulkan berbagai permasalahan sosial. Tingkat pengangguran ini muncul akibat dari ketidaksesuaian antara jumlah individu yang siap kerja dengan jumlah lapangan pekerjaan yang tersedia sehingga menimbulkan tantangan dalam penyerapan tenaga kerja. Penelitian ini bertujuan untuk mengidentifikasi dan mengelompokkan Kabupaten/kota di Pulau Jawa berdasarkan rata-rata Tingkat Pengangguran Terbuka (TPT) dan Tingkat Partisipasi Angkatan Kerja (TPAK) selama periode 2020-2023. Metode analisis yang digunakan adalah clustering dengan algoritma K-Means, dengan memanfaatkan data sekunder yang diolah berasal dari Badan Pusat Statistik (BPS) yang mencakup 119 Kabupaten/kota di enam provinsi di Pulau Jawa. Validasi jumlah klaster optimal dilakukan menggunakan Silhouette score, yang menunjukkan nilai tertinggi 0,55 menghasilkan dua klaster optimal. Hasil penelitian menunjukkan dua kelompok wilayah yang berbeda dalam karakteristik ketenagakerjaan. Klaster pertama terdiri dari 52 wilayah yang memiliki TPAK rendah dan TPT tinggi, mengindikasikan tantangan dalam penyerapan tenaga kerja yang lebih kompleks, terutama pada area urban atau pusat industri. Sebaliknya, klaster dua meliputi 67 wilayah yang memiliki TPAK tinggi dan TPT rendah, menunjukkan kondisi ketenagakerjaan yang relatif lebih stabil, seringkali di sektor pertanian atau pekerjaan informal. Analisis ini divisualisasikan menggunakan scatter plot dan boxplot untuk memperkuat interpretasi. Hasil klasterisasi ini diharapkan dapat menjadi acuan bagi pemerintah untuk menetapkan prioritas dan merumuskan kebijakan ketenagakerjaan yang lebih tepat sasaran sesuai dengan karakteristik masing-masing klaster wilayah di Pulau Jawa.

Underwriting Technology Trends: A Systematic Literature Review Budy Santoso, Cahyono; Ghaniy, Rajib
JESII: Journal of Elektronik Sistem InformasI Vol 2 No 1 (2024): JournaI of Elektronik Sistem InformasI - JESII (JUNE)
Publisher : Departement Information Systems Universitas Kebangsaan Republik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31848/jesii.v2i1.3420

This study systematically reviews trends in underwriting technology to enhance the precision and personalization of insurance companies' risk assessment and decision-making processes. Using the Kitchenham method, we conducted a systematic review of scientific publications indexed by Scopus from 2011 to 2021. Our findings reveal the extent of research activity in this field, the leading contributing countries, the methodologies employed, the technologies utilized, and the specific areas investigated. The results indicate significant advancements in the application of machine learning, blockchain, and other technologies in underwriting, providing a comprehensive overview of current trends and future directions. This study offers valuable insights for researchers and practitioners aiming to improve underwriting technology, highlighting potential areas for further research and development. These insights are crucial for advancing the field and enhancing the efficiency and effectiveness of underwriting practices.

Implementasi Business Intelligence untuk Prediksi Produksi Perikanan Budidaya Berbasis Web Dashboard Visualisasi Vistiyawati, Vanessa; Budy Santoso, Cahyono
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2902

Aquaculture plays an essential role in supporting food security and meeting the protein needs of the population, particularly in urban areas such as Jakarta. However, data management in aquaculture production is often still performed manually, making analysis and prediction difficult. This study aims to design a web-based visualization dashboard integrated with Business Intelligence implementation to predict aquaculture production in the Jakarta region. The research employs the CRISP-DM (Cross Industry Standard Process for Data Mining) methodology, which consists of six main stages: business understanding, data understanding, data preparation, modeling, evaluation, and deployment. Aquaculture production data were processed through cleaning and integration stages, followed by the application of predictive models using Random Forest and Linear Regression algorithms, with Python as the data processing tool. The prediction and analysis results are visualized in an interactive web-based dashboard for easy access and interpretation. Evaluation results indicate that the predictive models used were able to provide an overview of production trends with a satisfactory level of accuracy. The contribution of this research lies in the integration of predictive methods with interactive web-based visualization, which has rarely been applied in the context of urban aquaculture, offering a new approach to supporting strategic decision-making. Through this dashboard, stakeholders can obtain more comprehensive information to enhance strategic decisions in aquaculture management in Jakarta.

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