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Kab. aceh selatan,
Aceh
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 309 Documents
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Sistem Informasi Persediaan Barang Berbasis Web pada Usaha Elektronik Mitra Com Aryan Fauzi; Khairuman; Mukhroji
Jurnal Inotera Vol. 11 No. 1 (2026): January-June 2026
Publisher : LPPM Politeknik Aceh Selatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31572/inotera.Vol11.Iss1.2026.ID646

Abstract

An inventory information system is important for helping Micro, Small, and Medium Enterprises (MSMEs) manage stock data accurately and efficiently. MSME Mitra Com still uses manual stock recording, which often causes data errors, delays in reporting, and difficulties in monitoring inventory conditions. This study aims to develop a web-based inventory information system to support stock management activities at MSME Mitra Com. The research method used is software engineering with a qualitative descriptive approach. The research stages include system requirement analysis, system design using Unified Modeling Language (UML), system implementation using PHP and MySQL, and system testing using the Black Box Testing method. The results show that the developed system can manage item data, record incoming and outgoing goods, and generate inventory reports automatically. The system helps improve work efficiency, reduce recording errors, and support decision-making at MSME Mitra Com.
Reduksi Cacat Sink Mark pada Proses Injection Molding Polioksimetilena (POM) Melalui Pemodelan Termal dan Optimasi Parameter Proses Topandi, Abdussalam; Khadijah S. Nisa; Herlin Arina; Subhan Rizki Fadilah; Diva Pahlevi Putra Aumee; Pranata
Jurnal Inotera Vol. 11 No. 1 (2026): January-June 2026
Publisher : LPPM Politeknik Aceh Selatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31572/inotera.Vol11.Iss1.2026.ID579

Abstract

This study aims to minimize sink-mark defects in polyoxymethylene (POM) injection-moulded products through thermal modelling and process parameter optimization. The end-of-packing temperature (TEOP) was estimated using a one-dimensional transient cooling model. At the same time, the specific volume at the end of packing (vEOP) was calculated using the Two-Domain Tait Equation of State. Volumetric (SV) and linear shrinkage (SL) were derived following Chen’s shrinkage framework. Results showed that vEOP ranged from 0.1640 to 0.1764 m³/kg, SV ranged from 13.30 to 19.40%, and SL ranged from 4.64 to 6.94%. Higher TEOP correlated with increased vEOP and higher shrinkage, indicating ineffective packing. Optimization revealed that a melt temperature of 203.41 °C, combined with TEOP of 145.02 °C and a cooling temperature of 16 °C, produced zero shrinkage in the model. These findings provide a quantitative basis for defining process control limits for melt temperature, coolant stability, and packing conditions to reduce sink marks and improve dimensional consistency of POM products.
Development of an Interactive Website as an Information and Product Ordering Medium for Raja Konveksi Engineering Aceh MSME Farabi, Fadlal Ramadhan; Mik Salmina; Ully Muzakir
Jurnal Inotera Vol. 11 No. 1 (2026): January-June 2026
Publisher : LPPM Politeknik Aceh Selatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31572/inotera.Vol11.Iss1.2026.ID636

Abstract

This study focuses on the development of an interactive website designed to function as an information and product ordering medium for Raja Konveksi Engineering Aceh MSME. This research was conducted in response to the absence of an official online platform, where promotional activities and product orders were previously handled manually through photo sharing and direct communication, resulting in limited efficiency and reach. The system was developed using the Waterfall method, encompassing requirement analysis, system design, implementation, testing, and maintenance stages. The website was built using PHP with the CodeIgniter framework, supported by a MySQL database, and integrated with HTML, CSS, JavaScript, and Bootstrap to enhance interactivity and responsiveness. System evaluation was carried out through a user satisfaction questionnaire involving 25 respondents. The findings indicate a satisfaction rate of 95.52%, categorized as very high. This result demonstrates that the developed website effectively supports information delivery and online product ordering. The implementation of this system is expected to strengthen digital promotion, broaden market access, and improve the efficiency of the ordering process for Raja Konveksi Engineering Aceh.
Comparison of LSTM and Naïve Bayes in Google Play Store App Review Sentiment Analysis Endar Nirmala; Andri Fahmi
Jurnal Inotera Vol. 11 No. 1 (2026): January-June 2026
Publisher : LPPM Politeknik Aceh Selatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31572/inotera.Vol11.Iss1.2026.ID653

Abstract

The development of mobile application technology has driven increased user interaction through digital reviews on the Google Play Store platform. The review contains opinions that reflect the user's level of satisfaction, experience, and complaints about the app. However, the large number of reviews and variations in language expressions make manual analysis inefficient and potentially subjective. The main problem in this study is how to determine the most effective sentiment classification model to accurately identify users emotional tendencies. This study aims to compare the performance of the Naive Bayes method as a conventional machine learning model with Long Short Term Memory (LSTM) as a deep learning model based on word order in analyzing the sentiment of user reviews of Google Play Store applications. The dataset used comes from Google Play Store Reviews and goes through a pre-process process that includes text cleanup, tokenization, stopword removal, and sentiment labeling based on rating scales. The Naive Bayes model is trained using the TF-IDF representation, while the LSTM model uses an embedding sequence with standardized input padding. Evaluation uses accuracy metrics and F1-score with a ratio of 80 : 20 to train and test data distribution. The test results showed that the Naïve Bayes model achieved an accuracy of 65.78% with an F1 score of 0.5589, while the LSTM only achieved an accuracy of 45.26% with an F1-score of 0.2077. Thus, Naive Bayes was established as the best model in this study
Application of Six Sigma to Reduce Defect in Tofu Products at UD Tahu Dua Saudara Akmal, Abdiel Khaleil; Jumelia Ardika; Dahnil Ikhwan; Zulia Ananda
Jurnal Inotera Vol. 11 No. 1 (2026): January-June 2026
Publisher : LPPM Politeknik Aceh Selatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31572/inotera.Vol11.Iss1.2026.ID558

Abstract

Every company must experience problems related to the transportation sector. One of these problems is the TSP (Traveling Salesman Problem) where the problem is used in determining the shortest route with the requirement that the vehicles used must start and end at the same point. The above conditions were found in one of the companies engaged in the distribution of bottled water, especially the Kuades brand, located in Padang Panyang, Kuala Pesisir sub- district, Nagan Raya district, Aceh. The distribution system carried out by this AMDK company is not optimal because the distribution carried out only tries to meet the demand of each outlet or shop without taking into account the distance and travel time of the distribution. In this study using the Genetic Algorithm approach in solving problems in research. With this method, two results will be found, namely the shortest distribution path and the optimal cost. The use of the Genetic Algorithm method in solving the problem discussed provides results to achieve the optimal position, namely with the results of the shortest route is found in chromosome 2 in generation 1 being the best chromosome with a fitness value of 0.0016 and the length of the route traveled is 626 km, the optimal cost based on the distribution route that has been determined, the optimal cost result is Rp 1.38 Km Unit.
Analysis of Unemployment Patterns in Indonesia Using K-Means Clustering and Identification of Dominant Factors Using Random Forest Yuliana; Rima Fazri Ramadhania; Asep Abdul Latip; Muhammad Farhan Harahapa
Jurnal Inotera Vol. 11 No. 1 (2026): January-June 2026
Publisher : LPPM Politeknik Aceh Selatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31572/inotera.Vol11.Iss1.2026.ID574

Abstract

The disparity in the unemployment rate between provinces in Indonesia, exacerbated by the COVID-19 pandemic, is the main focus of this study. This study aims to (1) map the grouping of unemployment patterns in 34 provinces based on the Open Unemployment Rate (TPT) time series data for the 2020-2024 period, and (2) analyze the most significant socio-economic determinants of the formation of these patterns. Applying a two-stage methodology, cluster analysis using K-Means—validated through the Elbow Method and Silhouette Score of 0.456—succeeded in classifying the provinces into four different groups. The two prominent clusters identified were "Cluster 2: Pandemic Shock Pattern" (e.g., DKI Jakarta, West Java) which showed a surge in TPT above 10%, and "Cluster 3: Resilient Pattern" (e.g., Bali, DIY) which showed the lowest TPT rate and fastest recovery. Furthermore, the Random Forest Classifier analysis identified a hierarchy of determining factors, with the 2024 Average School Length (RLS) as the strongest predictor, followed by the 2024 Provincial Minimum Wage (UMP) and the 2024 GDP. These findings underline that the quality of human capital (education) is a more crucial factor than economic output (GDP) in shaping the resilience of the labor market. The study concludes the need for differentiated and cluster-specific unemployment policy interventions, rejecting a nationally uniform approach.
Design and Construction of an Automatic Door System Based on Iris Detection Using Esp32-Cam and OpenCV Using the Rapid Application Development (RAD) Method Angelika, Nanda; Nurhasanah
Jurnal Inotera Vol. 11 No. 1 (2026): January-June 2026
Publisher : LPPM Politeknik Aceh Selatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31572/inotera.Vol11.Iss1.2026.ID581

Abstract

Conventional home security systems typically depend on mechanical keys and passive CCTV cameras, which do not offer active access control or real-time response. This study presents the design and implementation of an automatic door system that uses iris recognition for authentication, developed with an ESP32-S3 camera and OpenCV, and integrated into a Flutter-based mobile application. The system was created using the Rapid Application Development (RAD) method to ensure flexibility and fast iteration. The prototype includes biometric iris authentication, solenoid-based door locking, live video monitoring, alarm activation, and real-time notifications. Through black-box testing, the system achieved high recognition accuracy, maintained stable operation in low-light environments, and successfully prevented unauthorized access using photo or video spoofing. Additionally, users receive instant alerts for failed access attempts or suspicious motion. The results show that integrating iris biometrics with IoT technology significantly enhances both home security and user convenience. Compared to traditional locks and CCTV systems, this approach provides a more intelligent, responsive, and secure solution for modern smart homes, offering users greater safety, automation, and peace of mind.
Smart Mangrove Monitoring for Resilience and Blue Carbon Governance Kano Mohamad, Abdurahman; Rustam Anwar; Romi Djafar
Jurnal Inotera Vol. 11 No. 1 (2026): January-June 2026
Publisher : LPPM Politeknik Aceh Selatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31572/inotera.Vol11.Iss1.2026.ID615

Abstract

Mangrove ecosystems provide essential coastal protection, nursery habitat for fisheries, and blue-carbon services; however, many Indonesian coastal areas continue to experience degradation due to land conversion, pollution, and limited monitoring capacity. This article develops a technology-oriented, marine–fisheries governance approach for mangrove rehabilitation by integrating multi-source observation and decision support. The proposed framework combines multi-temporal satellite analysis (e.g., NDVI/NDWI and mangrove-specific vegetation indices), UAV shoreline mapping, and low-power Internet of Things (IoT) sensing to capture both spatial and in situ dynamics of habitat quality. IoT nodes are configured to continuously record salinity, temperature, turbidity, dissolved oxygen, and water level as operational indicators for fisheries-relevant ecosystem conditions, while remote sensing quantifies canopy cover recovery, fragmentation, and coastline change. A data pipeline is designed for near-real-time ingestion, quality control, and anomaly detection to enable early warnings and support evidence-based enforcement. To translate measurements into management actions, the study introduces measurable performance indicators aligned with rehabilitation targets, including seedling survival, canopy recovery rate, shoreline stabilization, and compliance with water-quality thresholds, complemented by community participation and institutional coordination metrics. Scenario analysis demonstrates that the integrated approach improves prioritization of restoration zones, reduces uncertainty compared with single-source assessments, and strengthens monitoring, reporting, and verification (MRV) for blue-carbon initiatives. The framework offers a scalable model for adaptive coastal management that connects ecological monitoring, fisheries sustainability, and governance accountability.
Development of a Web-Based Tourism and Culinary Promotion Information System Using the Waterfall Method: A Case Study of Aceh Ilham; Rita Novita; Khairuman
Jurnal Inotera Vol. 11 No. 1 (2026): January-June 2026
Publisher : LPPM Politeknik Aceh Selatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31572/inotera.Vol11.Iss1.2026.ID637

Abstract

The rapid advancement of information technology has significantly influenced various sectors, including tourism and culinary industries, which are key drivers of regional economic growth. However, the promotion of Aceh’s tourism and culinary potential remains less effective due to the limited use of digital platforms. This study aims to design and develop a web-based information system for promoting Aceh’s tourism and culinary sectors as part of a digital investment strategy for the Aceh Investment and One-Stop Service Office (DPMPTSP). The system was developed using the Waterfall model through five stages: requirements analysis, system design, implementation, testing, and maintenance. The prototype was built using React.js for the front end and MySQL as the database; however, not all components are dynamic some website contents remain static to suit the needs of the institution and the scope of this research. The system provides integrated information about tourist destinations, traditional culinary products, and local events. Based on Blackbox testing, all features functioned properly according to specifications, and usability evaluation showed that the website is easy to use, efficient, and visually appealing. This research demonstrates that a semi-dynamic web-based information system can be an effective digital solution for promoting regional tourism and culinary potential while enhancing Aceh’s digital investment attractiveness.
Classification of Heart Disease Based on Clinical Data Using the K-Nearest Neighbor Method Muhajir, Abdullah; Cendra Harmon
Jurnal Inotera Vol. 11 No. 1 (2026): January-June 2026
Publisher : LPPM Politeknik Aceh Selatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31572/inotera.Vol11.Iss1.2026.ID655

Abstract

Heart disease is one of the leading causes of death worldwide; therefore, methods that can support early and accurate diagnosis are urgently needed. This study aims to classify heart disease based on patients’ clinical data using the K-Nearest Neighbor (KNN) method. The dataset used consists of patients’ clinical data, including attributes such as age, gender, blood pressure, cholesterol levels, maximum heart rate, and other medical attributes.The research stages include data preprocessing, transformation of categorical data into numerical form, data normalization using StandardScaler, and data splitting into training and testing sets with a ratio of 80% and 20%, respectively. The classification process is carried out using the K-Nearest Neighbor algorithm with a K value of 7. Model performance evaluation is conducted using a confusion matrix and evaluation metrics including precision, recall, f1-score, and accuracy.The results show that the KNN method is able to classify heart disease with an accuracy rate of 57%. The model demonstrates good performance on the majority class; however, its performance on the minority class remains low due to data imbalance and similarities in characteristics between classes. Therefore, the KNN method can be used as an initial approach for classifying heart disease based on clinical data, although further development is still required to improve model performance

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