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Kab. aceh selatan,
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INDONESIA
Jurnal Inotera
Published by Politeknik Aceh Selatan
ISSN : 25487574     EISSN : 25811274     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering
Arjuna Subject : -
Articles 18 Documents
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Search results for , issue "Vol. 10 No. 2 (2025): July - December 2025" : 18 Documents clear
Implementation of the Forward Chaining Method in Identifying Study Programs Based on Students Interests and Talents Raudhatul Fazira; Bustami; Rizki Suwanda
Jurnal Inotera Vol. 10 No. 2 (2025): July - December 2025
Publisher : LPPM Politeknik Aceh Selatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31572/inotera.Vol10.Iss2.2025.ID481

Abstract

This study aims to develop a decision support system (DSS) based on the Forward Chaining method to assist prospective students in selecting a study program that aligns with their interests, talents, and abilities. The system is designed to analyze 34 facts related to the respondents' interests and talents using predefined Rules, generating program recommendations in the form of rankings based on suitability weights. Testing was conducted with 100 respondents, one of whom, named Najmil Ula, was recommended to choose the Chemical Engineering program with a suitability weight of 2,3 or 26.44%, based on fulfilled facts such as an interest in understand in physics and love doing an experiment. Additionally, the system provided alternative recommendations, such as Informatics Engineering and Arcitecture, with lower suitability levels. These results demonstrate that the system can provide relevant and objective recommendation, making it an effective tool to support prospective students in selecting study programs that match their potential.
english : english Mustika Sari, Ratna; Melantika Sihombing; Rizky Ahmad Yudanegara; Meraty Ramadhini
Jurnal Inotera Vol. 10 No. 2 (2025): July - December 2025
Publisher : LPPM Politeknik Aceh Selatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31572/inotera.Vol10.Iss2.2025.ID462

Abstract

Slums are densely populated settlements that are not habitable, the building density is very high in a very limited area, causing various problems such as being prone to social diseases and diseases. The distribution of slums in an area can be analyzed using geographic information systems as an innovative strategy to visualize geospatial information accurately, allowing for more in-depth and data-based analysis to support decision making. This study uses the Geographically Weighted Regression (GWR) method. Geographically Weighted Regression (GWR) is a statistical analysis that allows researchers to evaluate spatial variations in the relationship between dependent and independent variables, using the Fixed Gaussian kernel function to provide constant weighting across research locations, so that they can understand complex and different spatial relationships in each location. Based on data processing on seven indicators and 16 slum parameters stipulated in PUPR Regulation No. 14 of 2018, Panjang District, Bandar Lampung City, there are 5 sub-districts that are categorized as light slums with slum distribution points spread across various locations without clear concentration. The results of the Geographically Weighted Regression (GWR) model show that the independent variables (X) and dependent variables (Y) reflect a complex relationship with variable X 11 (non-standard wastewater management system) having the highest coefficient of 0.213830. The results of the GWR model in the ANOVA table show that the GWR model does not provide significant improvement.
Structure Analysis of Warren Truss Bridge Using 3D CAD Software Satria, Adam; Adinda Rahmah Shalihah; Fadhil Fadhlurrohman Nurhadi; Ridho Hans Gurning; Sanurya Putri Purbaning
Jurnal Inotera Vol. 10 No. 2 (2025): July - December 2025
Publisher : LPPM Politeknik Aceh Selatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31572/inotera.Vol10.Iss2.2025.ID486

Abstract

The Warren truss consists of longitudinal members joined only by angled cross-members, forming alternately inverted equilateral triangle-shaped spaces along its length, ensuring that no individual strut, beam, or tie subjected to bending or torsion straining forces, but only to tension or compression. Tension and compression resulting from the reaction force from the load received. Every bridge has a load limit that can be received. These limits can be seen from the bridge safety factor. The purpose of the analysis of safety factor that occurs on a Warren truss bridge, i.e. to know the load limit that can be received for a bridge structure design, Warren truss types. On the bridge with a length of 57.38 m, 9.25 m wide bridge, with a thickness of 27.5 m of concrete, and asphalt thickness of 4.5 m, will be given a load containing 28 Toyota avanza 4 adults with 75 kg per person, which to determine load limits and safety factors of this bridge design. Before the static analysis, safety of factor and stress calculations performed with the determination of area of road A = 530.765 m2, the determination of the height of asphalt t = 0.045 m, and the determination of the height of concrete t = 0.275 m, this calculation aims to facilitate the determination of volume of asphalt and volume of concrete, and the results of maximum load obtained from the determination of volume, and the determination of gravitation to gain maximum load. From maximum load, it can be determined of reaction force that to be used for analysis. Stress analysis and safety factors analysis were performed using solidworks software.
Application of Triple Exponential Smoothing Method for Predicting the Number of Patients at RSUD dr.Fauziah Bireuen Maysura; Nurdin; Nunsina
Jurnal Inotera Vol. 10 No. 2 (2025): July - December 2025
Publisher : LPPM Politeknik Aceh Selatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31572/inotera.Vol10.Iss2.2025.ID487

Abstract

RSUD dr. Fauziah Bireuen is the main referral hospital in Bireuen Regency which has an important role in public health services. Fluctuations in the number of patients per polyclinic are a challenge in managing resources such as medical personnel, medicines, and other supporting facilities. This study aims to apply the triple exponential smoothing method in predicting the number of patients per polyclinic. The results showed that the triple exponential smoothing method has a high level of accuracy with a MAPE value of 1.456% (98.544% accuracy). Predictions using triple exponential smoothing predict 211,460 patients in January 2025, 211,454 in February 2025, and 211,455 for March 2025 to December 2026. Based on these results, triple exponential smoothing is recommended as it provides accurate results and supports the hospital's operational efficiency.
Comparison of Convmixer Method and Resnet Method in Classification and Detection of Gastrointestinal Diseases Using Kvasir Dataset Yuliana; Ivan Septa A P; Riska Veny F
Jurnal Inotera Vol. 10 No. 2 (2025): July - December 2025
Publisher : LPPM Politeknik Aceh Selatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31572/inotera.Vol10.Iss2.2025.ID493

Abstract

This research discusses the comparison of ConvMixer and ResNet methods in the classification and detection of gastrointestinal diseases using the Kvasir dataset. Gastrointestinal diseases are often difficult to detect early due to the similarity of visual patterns in endoscopy images, requiring an efficient deep learning-based solution. The purpose of this research is to compare and evaluate the models used. The research used a quantitative approach with an experimental method. Endoscopy image data was processed through augmentation, normalization, and division of the dataset into train, validation, and test. ConvMixer and ResNet were trained with customized hyperparameters, and evaluated using accuracy, precision, recall, and F1-score metrics. The results showed that ResNet excelled with 86% accuracy, slightly higher than ConvMixer which recorded 84% accuracy. ResNet's residual structure overcomes the problem of vanishing gradients, while ConvMixer offers better training speed. Both models showed high performance, although the challenge of similar patterns between classes was still an obstacle. As a result, ResNet provides better results in detecting gastrointestinal diseases, but ConvMixer is also a promising alternative. Further development with more diverse datasets is needed to improve model performance.
Determining the Main Sizes of Gears to Drive Rubber Sheet Roll Grinding Machines on Speed Roll Rotation on the Roll Machine Unit Sheeter Sitorus, Fransnazoan; Dejoi Irfian Situngkir; Aji Mirwarna; Nuzuli Fitriadi
Jurnal Inotera Vol. 10 No. 2 (2025): July - December 2025
Publisher : LPPM Politeknik Aceh Selatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31572/inotera.Vol10.Iss2.2025.ID496

Abstract

This research aims to determine the main dimensions of the roll drive gear on the roll rotation speed in the roll sheeter machine. The roll rotation speed influences the quality and efficiency of the milling process to produce Ribbed Smoke Sheet. The results of the research show that the main sizes of gears on the roll sheeter machine are the gear module used is 8.34 mm, the diameter of the stitch is (150.12 mm), the inside diameter is (129.27 mm), the outside diameter is (166.80 mm), tooth height (18.76 mm), circle distance (26.18 mm), tooth width (125.10 mm), and tooth thickness (14.40 mm) and roll rotation speed on the roll sheeter machine (roll-1 =0.45 m/s), (roll-2 =0.49 m/s), (roll-3 =0.53 m/s), (roll-4 =0.57 m/s), (roll-5 =0.68 m/s), and (roll-6 =0.56 m/s).
Evaluating the Impact of Data Balancing Techniques on the k-Nearest Neighbors Algorithm for Microarray Data Classification Febi Nur Salisah; Inggih Permana; Sanusi; Shir Li Wang
Jurnal Inotera Vol. 10 No. 2 (2025): July - December 2025
Publisher : LPPM Politeknik Aceh Selatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31572/inotera.Vol10.Iss2.2025.ID497

Abstract

Microarray data classification poses significant challenges in bioinformatics due to the nature of the data, which has a very high number of features but a limited number of samples, and an unbalanced class distribution. This condition can cause a decrease in the performance of classification models, including k-Nearest Neighbor (kNN). This study aims to evaluate the performance of the kNN algorithm in classifying unbalanced and balanced data. The balancing techniques used are Random Undersampling (RUS), Random Oversampling (ROS), and Synthetic Minority Over-sampling Technique (SMOTE). The datasets used in this study are three leukemia datasets with different class structures, namely two, three, and four classes. The experimental results show that the ROS and SMOTE techniques consistently improve the performance of kNN, with the best accuracy reaching more than 97%. In the two-class dataset, ROS gave the best performance (99.4%), while in the three-class dataset, SMOTE showed the most optimal results (98.5%). In the four-class dataset, the performance improvement due to balancing was very significant; SMOTE and ROS were able to improve the accuracy from 89.7% (without balancing) to 99.0% and 98.8%, respectively. Although RUS recorded perfect accuracy of 100%, the results were anomalous and inconsistent. RUS showed less stable performance and was often lower than the condition without balancing, especially on datasets with four classes. Overall, the SMOTE technique proved to be the most stable and effective for various class structures. This study shows the importance of balancing strategies in the classification of complex and imbalanced microarray data.
Klasifikasi Berbasis Pembelajaran Mendalam pada Rontgen Dada TB dan Normal Menggunakan CNN Kustom dengan Pelatihan Minimal Epoch Muthi'ah, Zharifah; Oktalia Triananda Lovita; Mohd Iqbal Muttaqin
Jurnal Inotera Vol. 10 No. 2 (2025): July - December 2025
Publisher : LPPM Politeknik Aceh Selatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31572/inotera.Vol10.Iss2.2025.ID503

Abstract

Tuberculosis (TB) is a major global health concern and remains one of the deadliest infectious diseases, particularly in developing countries. Early and accurate diagnosis is crucial to initiate timely treatment, prevent complications, and reduce transmission rates. Conventional diagnostic methods, such as sputum tests and laboratory cultures, are often time-consuming and require specialized resources. Therefore, there is a growing need for automated, efficient, and accurate computer-aided diagnosis (CAD) systems. This study proposes a lightweight Convolutional Neural Network (CNN) architecture to classify chest X-ray images into TB and normal categories. The model is trained using the publicly available Shenzhen chest X-ray dataset, with three training durations: 10, 25, and 50 epochs. Although the model trained for 25 epochs achieved a slightly higher accuracy (86.36%) compared to the 10 epochs model (85.61%), the latter is considered more optimal due to its better balance between performance and efficiency. Specifically, the 10 epochs model produced high precision (92.86%) and a competitive F1-score (84.27%) while requiring significantly less training time and computational resources. Moreover, it maintained stable validation performance without signs of overfitting. In contrast, models trained for longer durations showed diminishing returns or performance degradation, particularly at 50 epochs. These results indicate that a shorter training cycle, when coupled with appropriate architectural design and regularization, can yield a robust and efficient classification model. This approach is particularly beneficial for deployment in resource-constrained environments, where rapid and reliable TB screening using chest X-ray images is critically needed.
Pemetaan Masyarakat Pendonor Darah Berbasis WebGIS di Kota Metro Miristika Alliyu; Ilyas; Nurhayati, Misfallah; Ramadhini, Meraty; Aulia, Rizqi
Jurnal Inotera Vol. 10 No. 2 (2025): July - December 2025
Publisher : LPPM Politeknik Aceh Selatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31572/inotera.Vol10.Iss2.2025.ID420

Abstract

The availability of sufficient blood supplies is essential for fulfilling medical requirements, particularly in emergency scenarios. In Indonesia, the Indonesian Red Cross (Palang Merah Indonesia, PMI) is tasked with managing blood supplies across various regions, including Metro City. However, the current donor data management system at PMI Metro City is not yet fully optimized, potentially undermining efforts to meet local blood demands. Inconsistent donor data management poses significant challenges to blood transfusion processes, especially during emergencies. To address these inefficiencies, this study aims to develop a database system integrated with a Web-based Geographic Information System (WebGIS). The proposed WebGIS platform provides an interactive and informative visualization of blood donor distribution across Metro City. The system development follows a structured methodology comprising several phases: planning, data identification, WebGIS development using PHP, HTML, CSS, JavaScript, and Leaflet, and web hosting. The key outcomes of this research include a well-structured database and an interactive web-based map for identifying donor locations. The WebGIS system enhances PMI’s capacity to optimize blood supply strategies, particularly for urgent medical needs, by improving real-time visibility and management of donor data.
Apriori Algorithm in Electrical Equipment Sales for Inventory Control Optimization Jupron; Okta Irawati; Muhammad Bahrein
Jurnal Inotera Vol. 10 No. 2 (2025): July - December 2025
Publisher : LPPM Politeknik Aceh Selatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31572/inotera.Vol10.Iss2.2025.ID465

Abstract

XYZ Electrical Store is one of the small and medium enterprises that sells various types of electrical equipment for household needs. The management of sales data in the store is still done by recording sales transactions in a book, making the sales report generation process take a long time. With such a sales data management system, control and planning of stock items become less than optimal. Customers often find that the items they want to buy are out of stock. Moreover, the accumulation of less popular items also poses a challenge to the store's cash flow, resulting in subopti mal business processes. This research was conducted to address the issues of controlling and planning stock of goods using data mining techniques with the Apriori algorithm on sales transactions of electrical tools. Through a series of threshold tests, a minsup of 10% and minconf of 60% were obtained. This study results in information on the types of electrical tools that need to be monitored as they can lead to stock shortages when buyers need them, namely, the categories of LED lights, electrical cables, plugs, electrical insulation, and out bow. By knowing this information, it can provide solutions to Sinar Mulya Electric Store in stocking the most frequen tly sold items in the store.

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