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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 294 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

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