Universitas Ciputra Surabaya e-Journal
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Evaluation of User Interface (UI) and User Experience (UX) in A Web-Based Entrepreneurial Student Application
No full textMeasurement of entrepreneurial orientation remains limited and is predominantly conducted using manual methods. To address this gap, this study aims to develop a web-based application for measuring entrepreneurial orientation—which has not previously been developed—and to examine its reliability, user interface (UI), and user experience (UX). The instrument used is an adaptation of the entrepreneurial orientation scale, which was converted into a website-based format and complemented with recommendation features and user constraint identification. Data were collected through a focus group discussion (FGD) involving junior and senior high school teachers and students (N = 5), as well as a survey of junior and senior high school students (N = 60) following a trial of the web-based "Entrepreneurial Student" application. The results indicate that this web-based instrument demonstrates high reliability, with Cronbach’s alpha ranging from 0.806 to 0.845, and adequate validity, with Corrected Item–Total Correlation (CITC) values ranging from 0.404 to 0.630. After revisions based on user feedback, UI scores were in the "Very Good" category (range 495–554), and UX scores were also in the "Very Good" category (range 258–274). The Entrepreneurial Student application demonstrates promising potential for measuring entrepreneurial orientation among secondary school students. With further testing involving larger samples, the application may be implemented more broadly within the adolescent population
Deep Learning-Based Diagnostic Support for Complex Autoimmune Syndromes: A Multi-label Classification Study
No full textAutoimmune conditions frequently show similar clinical signs and symptoms, making it difficult to accurately diagnose when there are coexisting conditions. This study proposes a computational framework that aims to simultaneously predict multiple autoimmune labels using routine clinical data and advanced serological tests. The study examined 13,812 patients with 79 diagnostic features, including hematologic tests, inflammatory tests, and specific autoantibodies. The study trained a multi-layer perceptron (MLP) to predict autoimmune conditions such as Systemic Lupus Erythematosus (SLE), Rheumatoid Arthritis, and Graves' Disease. The model achieved strong performance, with validation F1 scores starting at 0.4723 and near-perfect Area Under the Curve (AUC) values ranging from 0.98 to 1.00 for all categories. The 79-parameter approach was highly effective in describing the autoimmunity “mosaic” and identifying polyautoimmunity with high precision compared to contemporary models that use fewer autoantibodies. The study shows that Multi-Label Multi-Layer Perceptron (ML-MLP) are able to process different types of data from lab tests and provide high utility diagnostic support for complex and overlapping autoimmune conditions
Pelatihan Strategi Transformasi dan Siklus Hidup Organisasi bagi Lembaga Pendidikan Dasar dan Menengah dengan Kolaborasi Pemangku Kepentingan
Full text linkLembaga pendidikan formal sebagai organisasi nirlaba memiliki kerentanan terhadap penurunan kinerja saat melewati siklus hidup organisasi. Fenomena ini sering kali memaksa sejumlah sekolah untuk berhenti beroperasi tanpa sempat melakukan proses transformasi yang memadai. Padahal, transformasi strategis memungkinkan sekolah untuk bertahan dalam siklus hidupnya serta beradaptasi dengan perubahan lingkungan demi menjaga keberlangsungan misi pendidikan. Menanggapi tantangan tersebut, FEB UPH berkolaborasi dengan mitra sekolah menyelenggarakan pelatihan strategi transformasi dan siklus hidup organisasi sebagai wujud kegiatan pengabdian kepada masyarakat. Program ini ditujukan bagi pengurus sekolah, guru, staf, serta pemangku kepentingan di Purwodadi, Kabupaten Grobogan, Jawa Tengah. Tujuan utama kegiatan ini adalah membekali peserta dengan pemahaman mendalam mengenai siklus organisasi dan langkah awal transformasi sekolah melalui sinergi antarpemangku kepentingan guna menghadirkan pendidikan berkualitas yang berkelanjutan. Metode pelatihan dilaksanakan dalam bentuk pemaparan materi, sesi tanya jawab, serta diskusi interaktif. Untuk mengukur efektivitas program, dilakukan pemeriksaan pemahaman melalui evaluasi sebelum (pre-test) dan sesudah (post-test) pelatihan. Hasil evaluasi menunjukkan peningkatan pemahaman peserta yang signifikan, tecermin dari kenaikan skor rata-rata sebesar 35%. Pelatihan ini juga menghasilkan luaran konkret berupa dokumen Rencana Aksi Transformasi Organisasi Sekolah (RATOS) yang siap diimplementasikan oleh pihak sekolah
A Deep Learning Approach Focusing on Diagnostic Sensitivity to Enhance Clinical Differentiation of Brain Tumors via Sequential ADC-MRI Analysis
No full textThe differentiation of benign and malignant brain tumors using Apparent Diffusion Coefficient (ADC) MRI scans remains a challenge for clinicians, owing to the high variability of morphological features and the subtle signs of tissue densities. The present study proposes a simple yet highly effective deep learning-based framework for the classification of brain tumors as benign or malignant. The proposed framework incorporates a combination of Convolutional Neural Network and Long Short-Term Memory (CNN-LSTM). The major advantage of the proposed framework is the use of seven consecutive image slices for brain tumor classification, unlike the conventional methods where a single individual image slice is considered. The use of seven consecutive image slices by the proposed framework actually attempts to capture the volumetric features of brain tumors, thus creating a more comprehensive picture of the brain tumor. The accuracy of the proposed framework for brain tumor classification is 98.05%, with a sensitivity of 100%, thus making the framework more reliable for the identification of malignant brain tumors and their safe elimination
PENGARUH SUKU BUNGA KEBIJAKAN, MONEY SUPPLY, DAN PAJAK PERTAMBAHAN NILAI TERHADAP INFLASI DI INDONESIA
Full text linkPrice stability is commonly assessed through the behavior of inflation, making it one of the key indicators used to evaluate the overall condition of an economy. When inflation exhibits continuous fluctuations, maintaining macroeconomic stability becomes more challenging and requires policy responses that are both effective and well integrated. In this context, the interaction between monetary and fiscal policy instruments becomes particularly important in shaping inflation dynamics. This study therefore investigates how several policy variables influence inflation in Indonesia during the period 2015 to 2024, specifically focusing on the policy interest rate, the broad money supply (M2), and the Value Added Tax (VAT) policy. The analysis adopts a quantitative framework using monthly time series data sourced from Bank Indonesia, Statistics Indonesia, and the Ministry of Finance of the Republic of Indonesia. Empirically, the relationship among these variables is estimated through a multiple linear regression model, while robust standard error are applied to strengthen the reliability of the estimates in the presence of potential violations of classical regression assumptions. The results from the joint significance test demonstrate that the policy rate, money supply, and VAT policy collectively exert a statistically significant influence on inflation. However, further examination through partial tests indicates that the policy interest rate does not significantly affect inflation, whereas variations in money supply and the implementation of VAT policy contribute more strongly to changes in inflation. These findings indicate that inflation dynamics in Indonesia during the observed period are more closely associated with liquidity conditions and fiscal policy measures than with the policy interest rate alone. Consequently, inflation management cannot depend solely on a single policy instrument. Effective and sustained price stability therefore requires strong coordination between monetary and fiscal authorities in responding to increasingly complex economic developments.Inflasi merupakan indikator makroekonomi yang krusial karena mencerminkan stabilitas harga dan kondisi perekonomian secara keseluruhan. Fluktuasi inflasi yang berlangsung secara berkelanjutan menuntut penerapan kebijakan moneter dan fiskal yang efektif serta terkoordinasi dengan baik guna menjaga stabilitas ekonomi. Penelitian ini mengkaji pengaruh suku bunga kebijakan, jumlah uang beredar (M2), dan kebijakan Pajak Pertambahan Nilai (PPN) terhadap inflasi di Indonesia selama periode 2015–2024. Pendekatan kuantitatif digunakan dengan memanfaatkan data runtut waktu bulanan yang diperoleh dari Bank Indonesia, Badan Pusat Statistik, dan Kementerian Keuangan Republik Indonesia. Data dianalisis menggunakan regresi linear berganda dengan penerapan robust standard error untuk memastikan keandalan hasil estimasi meskipun terdapat potensi pelanggaran asumsi klasik. Hasil uji simultan menunjukkan bahwa suku bunga kebijakan, jumlah uang beredar, dan kebijakan PPN secara bersama-sama berpengaruh signifikan terhadap inflasi. Namun, hasil uji parsial menunjukkan bahwa suku bunga kebijakan tidak berpengaruh signifikan terhadap inflasi, sedangkan jumlah uang beredar dan kebijakan PPN memiliki peran yang lebih dominan dalam mempengaruhi dinamika inflasi. Temuan ini menunjukkan bahwa inflasi di Indonesia selama periode penelitian terutama dipengaruhi oleh kondisi likuiditas dan kebijakan fiskal, bukan semata-mata oleh instrumen suku bunga kebijakan. Oleh karena itu, pengendalian inflasi tidak dapat bergantung pada satu instrumen kebijakan saja. Diperlukan koordinasi yang kuat antara kebijakan moneter dan fiskal untuk menjamin stabilitas harga yang berkelanjutan di tengah dinamika ekonomi yang semakin kompleks
Differential Diagnosis of Infectious Respiratory Diseases via Hybrid Deep Learning: Clinical Evaluation of COVID-19, Pneumonia, and Tuberculosis on Chest Radiography
No full textThe timely and precise differentiation of infectious respiratory diseases is vital in ensuring opti-mal patient outcomes and reinforcing public health infection control measures. The present study presents a precise hybrid deep learning architecture for the classification of infectious respiratory diseases based on chest X-ray images. The model was able to classify images into COVID-19, Normal, Pneumonia, and Tuberculosis. The model was able to perform a comprehensive analysis of localized pathological features in the images through the employment of a seg-mental random patch-based sampling strategy. The model also employed a pretrained ResNet-18 model in com-bination with a Context Pooling layer and Long Short-Term Memory networks. The model was able to achieve a remarkable 96% overall accuracy on a comprehensive dataset of 7,132 images. Notably, the model achieved a remarkable level of diagnostic sensitivity for high-priority com-municable diseases, including a 99% F1-score for Tuberculosis and 95% for COVID-19 after only three training epochs. The results indicate that the inclusion of spatial-temporal sequence model-ing results in a potent and efficient Clinical Decision Support System (CDSS), which can aid in automated screening and triage in resource-constrained settings where access to radiological ex-pertise may be limited
Enhancing Clinical Governance: A Multi-Criteria Decision Support Approach for Physician Performance Evaluation
No full textHigh-quality healthcare services require an objective and measurable performance evaluation system for medical personnel. Clinic and Laboratory X in Regency X, Central Java, has thus far assessed doctors’ performance solely based on the number of patients treated, which may lead to subjectivity. This study aims to design and implement a Decision Support System (DSS) using the Simple Additive Weighting (SAW) method to determine the best-performing doctor in a more fair and transparent manner. The research methodology includes data collection through observation, interviews, and questionnaires, followed by the determination of criteria and alternatives, weighting, normalization, and ranking processes. Five evaluation criteria were employed: diagnostic accuracy, participation in training, efficiency in medication usage, clinical skills, and mentoring ability, with eight doctors serving as the alternatives. The calculation results indicate that Dr. LL and Dr. PP achieved the highest score of 63.75, followed by Dr. II, Dr. AA, and Dr. TT with a score of 57.5, while the lowest score of 45 was obtained by Dr. FF and Dr. KK. The implementation of the SAW method has proven effective in generating a more systematic, transparent, and measurable evaluation process, while also assisting management in identifying performance aspects that require improvement
Optimizing Prostate Cancer Management: A Review on the Diagnostic Accuracy and Health Disparities using Machine Learning
No full textProstate cancer requires diagnostic tools that transcend conventional clinical paradigms. This systematic literature review aggregates primary research articles from 2021 to 2025 on the application of machine learning in prostate cancer management. We specifically selected studies on diagnostic accuracy and health disparities while excluding secondary sources. Overall, the results indicate a promising trend in multimodal fusion and explainable machine learning. All models had an area under the curve ranging from 0.84 to 0.91, which was either comparable to or even surpassing the performance of human experts such as radiologists and pathologists. Notably, the in-tegrated models had a positive impact on biopsy specificity, while AI-based pathology had a kappa statistic of above 0.90 in Gleason grading. One of the issues, however, remains how to ensure the generalizability of the models to different racial and geographic populations. Overall, machine learning significantly enhances the accuracy of diagnosis and the efficiency of management. Therefore, it can be said that while machine learning significantly enhances the accuracy of diagnosis and management of prostate cancer, its effective application remains a matter of prospective validation of models and fairness in machine learning
Gender Diversity: ESG terhadap kinerja keuangan
No full textThe connection between financial outcomes and Environmental, Social, And Governance (ESG) is explored in this study, where gender diversity acts as a moderating influence within Indonesian manufacturing companies. This quantitative research employs a purposive sampling method for firms listed on the Indonesia Stock Exchange from 2020 to 2024, utilizing agency theory and stakeholder theory. Over a five-year period, 120 observation units were established from 24 companies that met the study's criteria out of 232 manufacturing firms. The data was processed through moderated regression analysis using the Eviews 12 software. The results indicate that ESG has a significant positive effect on the financial performance of manufacturing companies. Moreover, gender diversity notably enhances the connection between ESG efforts and financial outcomes, implying that having diverse leadership positively influences the impact of ESG practices on financial performance. In the Indonesian manufacturing industry, these insights incorporate gender diversity as a moderating element in the relationship between ESG and financial performance, providing a new theoretical perspective. The study's real-world implication suggests that manufacturing companies should adopt ESG practices and enhance gender diversity in leadership roles to optimize financial performance and promote business sustainability in the long run.Perubahan lanskap bisnis global yang ditandai dengan meningkatnya tekanan lingkungan, sosial, dan tata kelola mendorong perusahaan untuk mengadopsi strategi keberlanjutan secara lebih komprehensif penelitian ini berupaya menjelaskan hubungan sebab akibat antara variabel independen ESG terhadap variabel dependen kinerja keuangan, dengan gender diversity sebagai variabel moderasi penelitian ini menggunakan teori stakeholder dan Agency jenis penelitian adalah kuantitatif. Populasi dalam penelitian ini adalah seluruh perusahaan manufaktur yang tercatat di BEI. sampel dalam penelitian ini adalah perusahaan manufaktur, Dari total 232 diperoleh 24 perusahaan yang memenuhi kriteria, sehingga menghasilkan 120 unit observasi selama lima tahun pengamatan, Untuk mengukur interaksi variabel secara lebih tepat, analisis regresi moderasi Eviews 12.Temuan utama mengonfirmasi bahwa ESG, gender diverstiy secara signifikan meningkatkan kinerja keuangan, memberikan kontribusi baru pada integrasi gender diverstiy sebagai moderasi di sektor manufaktur
Peran E-Commerce dalam Menggerakkan Konsumsi Nasional dan PDRB Indonesia pada Tahun 2024
No full textThe evolution of e-commerce in Indonesia has shown quite rapid expansion in line with advances in digital technology and the growing use of the internet. E-commerce usage is found on various platforms that provide various conveniences for people to conduct transactions, thus bringing about structural changes in household consumption patterns, which are the largest component of the Gross Regional Domestic Product (GRDP) of provinces in Indonesia. This study was conducted to investigate the effect of the number of e-commerce businesses and determine the amount of household consumption on the GRDP of provinces in Indonesia. In addition, the research method used is quantitative by utilizing secondary data from the Central Statistics Agency (BPS). The data analysis used multiple linear regression supported by classical assumption tests to investigate the feasibility of this study. The analysis demonstrates that the number of e-commerce businesses has a significant positive effect on the GRDP of provinces in Indonesia, while household consumption has a positive but insignificant effect on GRDP. Individually, household consumption has a dominant effect on GRDP, and the number of e-commerce businesses also contributes to increasing trade and economic digitalization in each region. These findings confirm that the evolution of e-commerce supported by increasing public purchasing power plays a significant role in driving regional economic growth. The findings of this research are expected to offer implications for local governments in formulating digital economic development policies and strategies for increasing regional economic growth.Perkembangan e-commerce di Indonesia mengalami peningkatan yang cukup pesat dengan kemajuan teknologi digital, dan meningkatnya penggunaan internet. Penggunaan e-commerce terdapat dari berbagai platform yang memberikan berbagai kemudahan bagi masyarakat untuk melakukan transaksi, sehingga membawa perubahan besar pada konsumsi rumah tangga yang merupakan komponen terbesar dalam Produk Domestik Regional Bruto (PDRB) provinsi di Indonesia. Penelitian ini bertujuan untuk menganalisis pengaruh jumlah pelaku usaha e-commerce dan mengetahui jumlah konsumsi rumah tangga terhadap PDRB provinsi di Indonesia. Metode penelitian yang digunakan untuk menganalisis adalah kuantitatif dengan memanfaatkan data sekunder yang diperoleh dari Badan Pusat Statistika (BPS), dan Databoks. Metode analisis yang digunakan dalam meneliti adalah regresi linier berganda dan juga terdapat uji asumsi klasik yang dapat memastikan validitas model. Hasil penelitian menunjukkan bahwa jumlah pelaku usaha e-commerce dan konsumsi rumah tangga berpengaruh terhadap PDRB provinsi di Indonesia. Secara individual, konsumsi rumah tangga memiliki pengaruh yang cukup dominan terhadap PDRB, tetapi jumlah pelaku usaha e-commerce juga turut membantu dalam peningkatan perdagangan dan digitalisasi ekonomi di tiap daerah. Temuan ini menegaskan bahwa pengembangan e-commerce dengan didukung oleh peningkatan daya beli masyarakat akan sangat berperan dalam perkembangan ekonomi di berbagai daerah. Hasil penelitian ini diharapkan dapat memberikan implikasi bagi pemerintah daerah dalam melakukan perumusan kebijakan pengembangan ekonomi digital serta strategi peningkatan pertumbuhan ekonomi regional.