SHM Publisher Journals
Not a member yet
315 research outputs found
Sort by
Support vector machine on two-class classification problem to determine an otaku
No full textMachine Learning has become a popular topic among academics and practitioners in recent years. This paper describes the use of SVM for otaku classification problem. The dataset used is a dummy dataset created with a python programme. In this research, SVM will be used as a model. The model aims to predict whether someone is an otaku or not, based on several attributes. The optimal parameters are obtained after several experiments. The parameters consist of kernel=‘poly’, C=0.1, gamma=‘auto’, degree=2, and attribute class_weight=None. The performance obtained by applying the above parameters is 100% accuracy
An advanced logistic regression model for forecasting payer revenue in private hospitals: a case study in manado
Full text linkManado, the provincial capital, stands as a vital center for healthcare services, where private hospitals compete intensively to attract patients from various economic and social backgrounds. Accurate revenue forecasting for partnered payers is essential for effective management strategies. This study employs a logistic regression model, achieving a notable accuracy of 79.55% in predicting hospital revenue based on payer partnerships. The confusion matrix reveals 21 true negatives (TN), confirming the model accurately identified low-revenue customers, with zero false positives (FP), indicating no misclassification of these individuals. However, 9 false negatives (FN) highlight a critical risk, as high-revenue customers were miscategorized as low revenue, even though 14 true positives (TP) were precisely identified. Based on these insights, hospitals can strategically target 61 payers projected to exceed median revenue, presenting a significant opportunity for income growth. Conversely, the 159 payers identified as below median revenue warrant urgent attention. To enhance engagement and increase revenue from these lower-revenue groups, targeted business strategies such as intensified marketing, personalized service offerings, and promotional discounts are recommended. This research contributes a novel approach to leveraging predictive analytics in healthcare, underscoring the pressing need for hospitals to innovate their operational strategies to optimize revenue in a competitive landscape
Tourism digital innovation geographic information system based web application for spatial information of tourism destinations
Full text linkThis study aims to develop a web-based tourism application by utilizing Geographic Information System (GIS) to optimize the dissemination of tourism information in the Pati Karisidenan area. This application is designed to assist local communities and tourists in choosing tourist destinations by providing information such as location, description, route, facilities, and visitor reviews. In addition, the app is also equipped with search and filtering features to help users find tourist attractions based on category, distance, or available facilities. This application was developed using the PHP programming language and MySQL database. The development process adopts the Waterfall method, which consists of the requirements analysis stage, system design using ERD and DFD, implementation, testing, and maintenance. Application testing is carried out using the Black Box Testing method to ensure all functions run according to specifications. The test results from visitors showed satisfaction with the use of the application by 97% of 15 audiences. This application is expected to support the promotion, accessibility, and management of tourism potential digitally by local governments
Sentiment analysis of user comments on the shopeepay feature in the shopee application: Evaluation of accuracy with k-nearest neighbors (KNN) algorithm
Full text linkThis research analyzes Shopeepay user reviews on the Shopee app using the K-Nearest Neighbor (KNN) algorithm with TF-IDF weighting and a Cosine Similarity matrix. Data was collected through web scraping 500 reviews from the Google PlayStore and labelled into positive, neutral, and negative sentiments. The process includes literature study, data collection, labelling, text preprocessing, word weighting, and sentiment classification using KNN. Results show an accuracy range of 86%-91%, with Precision, Recall, and F1-Score as evaluation metrics. The findings indicate that convenience, trust, and risk significantly affect users' interest in Shopeepay, especially during the Covid-19 pandemic. A Word Cloud was also used to visualize common terms in the reviews, providing insights for Shopee to enhance Shopeepay based on user feedback
Management physiotherapy for cervical root syndrome with ultrasound, nerve mobilization and exercise: a case report
No full textCervical Root Syndrome (CRS) is an abnormal condition caused by irritation or compensation of the cervical nerve roots, which can occur due to trauma. Arthritis, or compression of the intervertebral discs in the neck area. This research is to determine the benefits of providing ultrasound (US) modalities, nerve mobilization, isometric exercises, and stretching in CRS cases. The measurements used are pain measurement using the Numerical Rating Scale (NRS), Range of Motion (ROM) measurement using a goniometer, muscle strength measurement using Manual Muscle Testing (MMT), and functional activity measurement using the Neck Disability Index (NDI). The research was carried out directly on a patient with CRS condition by administering therapy in three meetings with physiotherapy intervention. After 3 therapy meetings carried out by the physiotherapy program for Cervical Root Syndrome with physiotherapy modalities in the form of US, nerve mobilization, isometric exercises and stretching, significant changes were seen in reducing pain, increasing joint range of motion, muscle strength and functional ability
Development of a Remote Three-Phase Motor Wiring Practice Tool Using ESP32, LabVIEW, Wokwi, and Adafruit IO
Full text linkThe practical application of three-phase induction motor wiring is an essential part of mechatronic engineering education. The integration of IoT-based tools and simulation software such as Wokwi, LabVIEW, and Adafruit IO enables remote monitoring and control of motor wiring practices. This article discusses the development of a remote three-phase induction motor wiring tool utilizing the ESP32 microcontroller, LabVIEW for real-time monitoring and control, Wokwi for circuit wiring simulation, and Adafruit IO for cloud-based data communication. Testing shows that the system effectively facilitates remote motor control and wiring simulation, providing a flexible and accessible learning environment
Sound detection of gamelan musical instruments using teachable machine
Full text linkGamelan is an instrument of musical expression that has an aesthetic function related to social, moral, and spiritual values. Gamelan consists of a variety of musical instruments that have a unique sound. In this study, the sound detection of nine gamelan musical instruments was carried out using a teachable machine. The gamelan musical instruments detected included gong, kenong, saron, bonang, gambang, kendang, flute, siter, and rebab. The algorithm used is CNN. The CNN algorithm has a fairly good performance for the sound detection process. The test results of the built model show an "acc" value of 25 ranging from 0.99 to 1, which indicates that the model achieves an accuracy rate of 99% to 100% on the training dataset. At the same time, "test accuracy" refers to a measure of the model's effectiveness in predicting data it has not encountered during training. The "test accuracy" score varied from 0. 83, which shows that the validation data has an accuracy of 83%
Tourist mobility and destination loyalty: insights for hospitality and tourism management in Indonesia
No full textTourism represents one of the fastest-growing sectors globally. In Indonesia, this growth is reinforced by abundant natural resources, cultural diversity, and substantial tourism capital, which collectively strengthen the country’s position as a prominent tourist destination. The Central Bureau of Statistics (BPS) recorded tourist arrivals to Indonesia in 2024 at 13.9 million or grew by 19.05% from the previous year. Majority of tourists came from ASEAN countries (Ministry of Tourism, 2025). The purpose of this study is to analyze tourist mobility and destination loyalty in the context of hospitality and tourism management in Indonesia. This article focuses on the economic aspect and tries to explain how tourist mobility contribute the economic growth of Indonesia's local areas. This research also aims to find and investigate methods that can help increase the role of tourism sector in supporting the economy. The type of research used in this research is descriptive qualitative research with a systematic literature review approach. The results showed that local destinations in Indonesia are very influential on the Indonesian economy. The factors that influence domestic tourists' loyalty to local destinations include destination attractiveness, accessibility, diversity, information, hospitality, cleanliness, accommodation and relative prices
Identifying Coconut Maturity Levels Using CNN and YOLOv8 Deep Learning Algorithms
Full text linkTo improve the efficiency and accuracy of determining coconut maturity levels in the processing industry, this study proposes an automated detection system employing Convolutional Neural Networks (CNN) and the You Only Look Once version 8 (YOLOv8) algorithm to classify maturity levels from image data. This study introduces an automated detection system using Convolutional Neural Networks (CNN) and the You Only Look Once version 8 (YOLOv8) algorithm to identify coconut maturity levels from image data. A dataset of 230 coconut images was utilized, classified into two categories: Young Coconut and Mature Coconut. The YOLOv8 model was trained and evaluated using standard object detection metrics, including mean Average Precision (mAP), precision, recall, and F1-score. The proposed model achieved a mAP of 90.5%, precision of 99.3%, recall of 94.2%, and F1-score of 96.6%, demonstrating high accuracy in detecting coconut maturity. This approach offers a practical and efficient alternative to manual assessment, contributing to improved accuracy and operational efficiency in agricultural practices
Development of a low-cost NTP stratum 2 time synchronization system with hybrid RTC/NTP failover for remote Indonesian mosques
Full text linkConventional time synchronization solutions are often cost-prohibitive and infrastructure-dependent, making them unsuitable for remote regions. This study develops a low-cost, Raspberry Pi 3-based NTP Stratum 2 system for areas with unstable cellular networks. It integrates NTP.bmkg.go.id as its primary source and a DS3231 RTC module for backup during outages. Testing covered: (1) Time Accuracy (0.12s average offset in stable 4G; 0.85s/hour drift in RTC mode), (2) Failover & Recovery (3.1s transition to RTC), (3) Load & Stability (15 NTP clients with 12.1ms latency), and (4) Power Efficiency (2.8W online; 1.2W offline). Results confirm the system’s reliability in maintaining sub-second accuracy amid network instability while being highly energy-efficient. The study offers recommendations for active cooling, GPS-assisted RTC calibration, and solar-powered integration to improve scalability in rural applications. The novelty of this work lies in its pragmatic hybrid architecture, combining affordable software-defined NTP with hardware-based RTC failover, tailored for infrastructural constraints. Its contribution is a validated, replicable model that delivers reliable time synchronization at a fraction of commercial costs, addressing critical gaps in affordable timekeeping for infrastructure-limited regions