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Methods in Science and Technology Studies
Published by PT. Teknologi Futuristik Indonesia
ISSN : -     EISSN : 31234232     DOI : https://doi.org/10.64539/msts
Core Subject : Engineering,
Engineering
The Methods in Science and Technology Studies (MSTS) (e-ISSN: 3123-4232) is a peer-reviewed and open-access scientific journal, managed and published by PT. Teknologi Futuristik Indonesia in collaboration with Universitas Qamarul Huda Badaruddin Bagu and Peneliti Teknologi Teknik Indonesia. The journal publishes research that focuses on methods, models, analytical approaches, and systematic studies in science, technology, and science- and technology-based education. It aims to support the development and application of scientific and technological methods in addressing research problems and practical challenges. The journal accepts original research articles and review papers that present methodological frameworks, experimental and analytical methods, computational models, and applied studies in science, technology, and education, including interdisciplinary and applied perspectives. Scope includes: Natural and applied sciences Engineering and technology studies Computational, mathematical, and data-driven methods Machine learning, artificial intelligence, and information technology Decision-making, optimization, and forecasting methods Science and technology–based education studies Legal and regulatory studies related to science and technology The journal provides a focused platform for methodological and applied studies in science, technology, education, and related regulatory contexts.
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 5 Documents
 1 
Search results for , issue "Vol. 1 No. 1 (2025): June" : 5 Documents clear
Looking at Legal Regulation in Handling the Issues of Online Gender-Based Violence Fadhilah Aini; Zulfa Ajda Khoiriyah; Yogi Yoga Swara
Methods in Science and Technology Studies Vol. 1 No. 1 (2025): June
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/msts.v1i1.2025.16

Abstract

The development of information and communication technology has changed the way of social interaction, especially among Generation Z. The increasing use of the internet in Indonesia, which will reach 79.5% in 2024, brings new challenges in the form of Online Gender-Based Violence (OGBV). This study aims to explore the condition of legal education related to OGBV and efforts to prevent and handle it. This study uses a qualitative approach with a descriptive study method. Data were collected through random sampling among Generation Z, by combining primary and secondary data from various sources such as books, articles, and reports. The analysis was conducted to describe the actual condition of legal education in the context of OGBV. The findings show that despite increased legal awareness and regulations in the law related to OGBV, cases of this violence are still increasing. Data from the National Commission on Violence Against Women shows a spike in reports of OGBV cases from 16 complaints in 2017 to 1,272 in 2023. The most common type of violence is the threat of spreading sexual content. Although regulations have been in place, their effectiveness in preventing OGBV is still questionable. This study emphasizes the importance of socialization and increasing legal understanding among individuals to address the issue of OGBV more effectively. Legal culture reform and community engagement are needed to create a safer environment for internet users.
Profile of Student Achievement Through PBL-Based Sound Wave Worksheets Azrul Hamidi; Danang Habib Pratama; Jumadi; Sabar Nurohman
Methods in Science and Technology Studies Vol. 1 No. 1 (2025): June
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/msts.v1i1.2025.17

Abstract

The purpose of this project is to create sound wave worksheets based on PBL that will enhance class VIII students' learning results. The development of the LKPD that was carried out obtained the result that the PBL-based sound wave LKPD was feasible to use to improve student learning outcomes. This study has a one-group posttest-only design and is a quasi-experimental investigation. The field test was carried out at SMP Negeri 24 Padang class VIII obtained the result that LKPD could improve student learning outcomes. Data analysis using SPSS 25 One Grub Posttest-Only Design with Sig. (2-tailed) 0.006. This learning outcome profile can provide an overview of students' understanding of the concept of noise pollution and their ability to identify, analyze, and find solutions to noise pollution problems after going through PBL learning using worksheets.
Medical Product Sales Forecasting for Business Optimization Using Double Exponential Smoothing Salsa Fitiansyah; Sumijan; Devia Kartika
Methods in Science and Technology Studies Vol. 1 No. 1 (2025): June
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/msts.v1i1.2025.24

Abstract

Accurate sales forecasting plays a critical role in inventory management, particularly for medical equipment companies where stock availability directly affects operational efficiency and customer service. However, many small and medium-scale distributors still lack reliable forecasting systems, resulting in overstocking, high storage costs, or stockouts that lead to missed sales opportunities. Addressing this gap, this study aims to develop a web-based sales prediction system for PT Etiqa Prima Utama—a medical equipment distributor in Padang, West Sumatra—by applying the Double Exponential Smoothing method. The system was designed using PHP and MySQL to generate monthly sales forecasts for various medical products based on historical data. Key findings show diverse forecast accuracy across 20 product categories. The Glucose HK product achieved the lowest MAPE value at 10%, indicating excellent predictive performance, while the Clean Chem product showed the highest MAPE at 54%. Several other products, such as Total Bilirubin (12%), Urea (10%), and Diluent 20L (14%), demonstrated favorable accuracy with MAPE values below 60%. These results imply that Double Exponential Smoothing can support inventory optimization by providing reasonably accurate forecasts for most products, enabling better stock planning and more informed decision-making within the company.
Machine Learning-Based Early Prediction of Stunting Risk: A Comparative Study Abdul Fadlil; Dikky Praseptian M; Muhammad Ma’ruf; Furizal
Methods in Science and Technology Studies Vol. 1 No. 1 (2025): June
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/msts.v1i1.2025.351

Abstract

Stunting remains a critical nutritional issue among children, affecting growth and long-term human resource quality. Despite national programs and global targets for stunting reduction, early prediction of stunted children using data-driven methods remains limited. This study aims to evaluate and compare the performance of four supervised machine learning algorithms—Naïve Bayes, Multilayer Perceptron (MLP), Decision Tree (J48), and Support Vector Machine (SVM)—in predicting stunting using a dataset of 97 child records from three villages in East Kalimantan, Indonesia. Data were tested in both unnormalized and normalized forms and split into training and testing sets at 70%–30%, 80%–20%, and 90%–10% ratios. The results indicate that MLP and Decision Tree consistently achieved 100% accuracy across all splits and preprocessing conditions, while Naïve Bayes and SVM showed lower and more variable accuracy in certain cases. These findings suggest that MLP and Decision Tree are the most reliable methods for stunting prediction in small datasets, providing a practical approach for early identification and intervention. The study highlights the importance of algorithm selection and preprocessing in achieving optimal predictive performance in health-related datasets.
A SAW-Based Multi-Criteria Approach for Selecting Strategic Café Branch Locations Asih Anggina; Shary Armonitha Lusinia; Devia Kartika
Methods in Science and Technology Studies Vol. 1 No. 1 (2025): June
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/msts.v1i1.2025.352

Abstract

The rapid development of information technology and artificial intelligence has increased the importance of data-driven decision-making, particularly in competitive industries such as cafés, where branch location significantly affects business success. However, selecting the optimal location remains a challenge due to the variability of local market conditions and the subjectivity of manual assessments, representing a gap in practical, objective evaluation methods. This study aims to determine the most suitable location for Anomali Café’s new branch in Padang City using the Simple Additive Weighting (SAW) method, a transparent and effective multi-criteria decision-making approach. The analysis of ten candidate sites reveals that Pantai Air Manis Street achieves the highest overall score, followed closely by Sitebal, Gajah Mada, Raya Lubuk Buaya, and Dr. Sutomo Streets, while the remaining locations are less competitive. These findings provide actionable, data-driven guidance for strategic branch expansion and demonstrate the applicability of SAW in tailoring location decisions to the café industry’s specific context.

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