Article Per Year (5 Year)
p-Index From 2021 - 2026
1.978
P-Index
This Author published in this journals
All Journal Jurnal Akuntansi & Auditing Indonesia Al-Ulum Jurnal Mirai Management ADMINISTRASI PUBLIK SEIKO : Journal of Management & Business AkMen JURNAL ILMIAH YUME : Journal of Management Jurnal Agritechno Jurnal Sistem Informasi, Manajemen, dan Akuntansi (SIMAK) Journal of Language Education and Educational Technology (JLEET) Akrab Juara : Jurnal Ilmu-ilmu Sosial Jurnal Bisnis dan Kewirausahaan Algoritma: Jurnal Matematika, Ilmu Pengetahuan Alam, Kebumian dan Angkasa International Journal of Mechanical, Industrial and Control Systems Engineering JIMEK
Mursalim Mursalim
Unknown Affiliation
Religion Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Mathematics Medicine & Pharmacology Social Sciences Other

Published : 26 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 26 Documents
Search

 1   2   3 
Enhancing Speaking Performance and Reducing Speaking Anxiety Using Ted Talks Nurfitriani Arifin; Mursalim Mursalim; Sahlan Sahlan
Journal of Language Education and Educational Technology (JLEET) Vol 5, No 1 (2020): Journal of Language Education and Educational Technology
Publisher : Halu Oleo University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33772/jleet.v5i1.6953

Abstract

This study aims at (1) investigating the effect of TED Talks on students speaking performance; (2) investigating whether or not gender affects speaking performance; (3) investigating the effect of TED Talks on reducing speaking anxiety; and (4) identifying whether or not gender affects anxiety. The study involved 42 students as the sample from 217 tenth graders of SMA Negeri 8 Kendari. The students were grouped into 2 cohorts, experimental and control class. Under quasi-experimental design, the students were assessed on their speaking performance and language anxiety. The data were analyzed using independent sample t-test and paired sample t-test using SPSS 20.0. The result of the study revealed that (1) TED Talks had a significant effect on improving student speaking performance; (2) prior to any treatment, there was no significant difference between male and female students on their speaking performance; (3) TED Talks was proven to have a significant effect on reducing student speaking anxiety; and (4) gender was not proven to have a significant effect on language anxiety. Implications of the study will be critically examined in this paperKeywords: TED Talks; Speaking Performance; and Anxiety
Implementasi Program Makanan Sehat dan Halal di SMK Ibnu Khaldun Balikpapan sebagai Upaya Peningkatan Kesadaran Gizi Siswa Zeny Lilla Mewaty; Mursalim Mursalim; Muhammad Birusman N
Jurnal Ilmu Manajemen, Ekonomi dan Kewirausahaan Vol. 5 No. 2 (2025): Juli : Jurnal Ilmu Manajemen, Ekonomi dan Kewirausahaan
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jimek.v5i2.6330

Abstract

This study aims to analyze the implementation of healthy and halal food in schools, particularly at SMK Ibnu Khaldun Balikpapan. The background of this research is driven by the importance of consuming food that meets health and halal standards in forming a healthy lifestyle in accordance with Islamic law. The research method used is a descriptive quantitative approach with a survey technique through questionnaires administered to 100 students in grades X and XI. The results of the study indicate that students' awareness of the importance of healthy and halal food is relatively high, although there are still challenges in its comprehensive implementation, particularly in terms of school canteen supervision and regular education. These findings serve as an important basis for school authorities and policymakers to design more intensive programs to promote the adoption of healthy and halal eating habits.
Edge Computing Enabled Real Time Anomaly Detection Framework for Secure Industrial Cyber Physical Systems Using Lightweight Deep Neural Networks Deny Prasetyo; Suyahman Suyahman; Rosalina Yani Widiastuti; Mursalim Mursalim; Antoni Pribadi
International Journal of Mechanical, Industrial and Control Systems Engineering Vol. 1 No. 1 (2024): March: IJMICSE: International Journal of Mechanical, Industrial and Control Sys
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/ijmicse.v1i1.399

Abstract

Cyber Physical Systems (CPS) are vital for managing and controlling critical infrastructures, such as industrial control systems, power grids, and transportation networks. These systems integrate digital and physical components, offering numerous benefits for industrial automation. However, the increasing interconnectivity of these systems has introduced new security vulnerabilities, particularly in anomaly detection and system reliability. This research aims to address these challenges by proposing an edge based anomaly detection framework that leverages lightweight deep learning models, specifically designed to operate efficiently on resource constrained edge devices. Literature Review: Previous studies have shown the effectiveness of anomaly detection in CPS, with traditional methods struggling to keep up with the complexity and scale of modern industrial environments. Machine learning and deep learning approaches, particularly hybrid models combining rule based systems and AI, have emerged as effective solutions for real time anomaly detection. Techniques such as model compression, quantization, and pruning are essential for adapting these models to resource limited edge devices while maintaining high detection accuracy and low latency. Materials and Method: The proposed framework integrates deep learning models such as Convolutional Neural Networks (CNNs) and Long Short Term Memory (LSTM) networks, optimized for edge computing environments. The datasets used for training and testing include industrial network traffic and sensor anomaly datasets. Model optimization techniques like pruning and quantization were applied to reduce computational overhead and energy consumption on edge devices. Results and Discussion: The framework demonstrated high detection accuracy (AUC of 0.9720) with ultra low latency (0.0019 seconds training time), making it highly suitable for real time anomaly detection in CPS. Resource efficiency was achieved by optimizing the models for edge devices, reducing energy consumption while maintaining performance. The framework also significantly improved security by identifying anomalies early, preventing potential threats to critical infrastructures. Future directions include exploring federated learning to enhance privacy and data sharing across distributed devices.
Multi Objective Evolutionary Optimization of Additive Manufacturing Process Parameters for Enhanced Mechanical Performance and Surface Integrity Yulaikha Maratullatifah; Dwi Utari Iswavigra; Very Dwi Setiawan; Mursalim Mursalim; Budi Wibowo
International Journal of Mechanical, Industrial and Control Systems Engineering Vol. 1 No. 1 (2024): March: IJMICSE: International Journal of Mechanical, Industrial and Control Sys
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/ijmicse.v2i1.400

Abstract

Introduction: Additive Manufacturing (AM) has revolutionized the production of complex geometries, offering flexibility, customization, and precision across various industries. However, optimizing multiple process parameters simultaneously to enhance AM performance remains a significant challenge. This study focuses on improving both mechanical properties and surface quality by utilizing multi-objective optimization techniques. Literature Review: The research reviews existing approaches in AM optimization, highlighting the limitations of single-objective optimization and the potential of multi-objective evolutionary algorithms (MOEAs). Previous studies demonstrate the difficulty of balancing competing objectives, such as tensile strength and surface roughness, within AM processes. Materials and Method: This study employs NSGA-II, MOEA/D, and SPEA2 algorithms to optimize AM parameters like layer thickness, build orientation, and infill density. The optimization aims to improve mechanical performance, including tensile strength and impact resistance, while reducing build time and surface roughness. The methodology integrates experimental validation with computational predictions to evaluate the effectiveness of these algorithms. Results and Discussion: The optimization process yielded Pareto-optimal solutions that balanced mechanical strength and surface quality. The results demonstrated improvements in tensile strength and surface finish without significantly increasing build time. Trade-off analysis highlighted the inherent conflicts between mechanical performance and surface quality, allowing for better decision-making in industrial applications. The study contributes to the AM industry by offering a comprehensive optimization framework for improving both efficiency and product quality.
Integrated Digital Twin and Physics Informed Machine Learning Model for Real Time Performance Prediction of Industrial Mechanical Systems Irlon Irlon; Siti Shofiah; Helmi Wibowo; Erick Fernando; Genrawan Hoendarto; Mursalim Mursalim
International Journal of Mechanical, Industrial and Control Systems Engineering Vol. 2 No. 2 (2025): June :IJMICSE: International Journal of Mechanical, Industrial and Control Syst
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/ijmicse.v2i2.404

Abstract

Background: The rapid advancement of digital technologies in the Industry 4.0 era has transformed industrial mechanical systems into highly interconnected and data driven environments through the integration of sensors, the Internet of Things (IoT), data analytics, and cyber physical systems. This increasing complexity requires more adaptive and accurate monitoring and prediction methods than conventional simulation approaches, which often face limitations in capturing real time dynamic system behavior. Objective: This study aims to develop a predictive performance model for industrial mechanical systems by integrating Digital Twin technology with Physics Informed Machine Learning in order to improve monitoring accuracy and support predictive maintenance strategies. Methods: This research adopts a data driven modeling and simulation approach by developing a digital representation of an industrial mechanical system that is connected to real time sensor data. The prediction model is constructed using a Physics Informed Neural Network (PINN), which integrates operational data with physical principles governing system dynamics. The research process includes the development of a Digital Twin model, integration of sensor data, training of the PINN model, model validation using experimental data, and evaluation of prediction performance using statistical metrics. Results: The results indicate that the integration of Digital Twin technology and PINN significantly improves the prediction accuracy of industrial mechanical system performance compared with conventional simulation methods and purely data driven machine learning models. The proposed model is capable of representing system dynamics more consistently, accurately following sensor data patterns, and providing strong potential for supporting machine condition monitoring and predictive maintenance strategies in modern industrial environments.
Hubungan Keterlibatan Orang Tua dengan Prestasi Belajar Matematika Siswa Kelas IV SD Inpres 18 Kabupaten Sorong Glory Gracia Christadella; Mursalim Mursalim; Dwi Pamungkas
Algoritma : Jurnal Matematika, Ilmu pengetahuan Alam, Kebumian dan Angkasa Vol. 4 No. 3 (2026): Algoritma : Jurnal Matematika, Ilmu pengetahuan Alam, Kebumian dan Angkasa
Publisher : Asosiasi Riset Ilmu Matematika dan Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62383/algoritma.v4i3.975

Abstract

This study aimed to determine the relationship between parental involvement and mathematics learning achievement of fourth-grade students at SD Inpres 18 Sorong Regency. The research employed a quantitative approach with a correlational design. The research subjects consisted of all fourth-grade students from classes IV A and IV B, totaling 66 students. Data on parental involvement were collected using a questionnaire, while students’ mathematics achievement data were obtained from documentation of end-of-semester promotion examination scores. Data analysis was conducted using descriptive and inferential statistics. The prerequisite tests included the Kolmogorov–Smirnov normality test with a Monte Carlo approach and a linearity test. Hypothesis testing was carried out using Pearson Product Moment correlation with the assistance of SPSS software. The results showed a positive and significant relationship between parental involvement and students’ mathematics achievement, with a correlation coefficient of r = 0.637 and a significance value of p = 0.000 (p < 0.05). This correlation is categorized as strong. The findings indicate that higher levels of parental involvement in guiding, supervising, and providing emotional support are associated with higher mathematics learning achievement among students. Therefore, parental involvement plays an important role in supporting the mathematics learning success of elementary school students.
Co-Authors Abdul Waris Amiruddin H Antoni Pribadi Apriyati Hutami Aris Badara Basri Modding BUDI WIBOWO Busman Busman Deddy Amrand Deny Prasetyo Diah Ayu Gustiningsih Dwi Pamungkas Dwi Utari Iswavigra Edy Susanto Erick Fernando Fadliah Nasaruddin Genrawan Hoendarto Glory Gracia Christadella Hastang Hastang Helmi Wibowo Irlon Irlon Junaedi Muhidong La Ode Muhammad Nur Rahim Lisa Indrajaya Lukytta Gusti Acfira M. Su'un Masrullah, Masrullah Muhammad Birusman N Nur Alamzah Nurfitriani Arifin Nurhawa Nurhawa Rezki Yunita Apriani R Sahlan Sahlan Siti Shofiah Sulastri Sulastri Suyahman Suyahman Ummu Kalsum Very Dwi Setiawan Widiastuti, Rosalina Yani Yogi Hady Afrizal Yulaikha Maratullatifah Zainuddin Zainuddin Zeny Lilla Mewaty