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Rancang Bangun Electronic Control Unit Berbasis Arduino pada Mesin Motor Dua Langkah Fahmy Ferdian Dalimarta; Muzakki Mahdi; Jaelani Jaelani; Randi Dwi Wibisono
Jurnal Dinamika Vokasional Teknik Mesin Vol. 7 No. 2 (2022)
Publisher : Department of Mechanical Engineering Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/dinamika.v7i2.53301

Pengguna sepeda motor dua-langkah dihadapkan pada realitas bahwa konsumsi bahan bakar kendaraan mereka relatif boros, disamping itu terdapat kendala yang sering terjadi yakni tidak berfungsinya karburator kendaraan secara optimal. Hal ini menjadi permasalahan karena sistem karburator yang tidak stabil mengakibatkan debit bahan bakar yang keluar tidak sesuai dengan yang semestinya. Tujuan penelitian ini adalah perancangan dan pembangunan sebuah rangkaian Electronic Control Unit (ECU) yang terhubung dengan pompa injeksi bahan bakar guna menggantikan karburator yang dapat mengatur bukaan bahan bakar pada mesin dua-langkah dan menyimpan pengaturan tersebut secara permanen. Perancangan ECU dikembangkan dengan metode riset dan pengembangan purwarupa mesin kendaraan Engine Module Simulator yang merepresentasikan Throttle Position Sensor dan Pulser. Setelah purwarupa menunjukkan hasil uji yang sesuai, maka ECU diterapkan pada kendaraan yang sebenarnya. Hasil pengujian perangkat ECU ini didapatkan peningkatan rasio konsumsi bahan bakar berbanding jarak tempuh dari 1:14.4 menjadi 1:27.4 kilometer per liter bahan bakar. Persentase penghematan yang didapatkan adalah sebesar 90.3 persen.

Application of the Problem Based Learning Model Combined with Think Pair Share to Improve Conceptual Understanding and Applied Physics Learning Outcomes for Electrical Engineering Students Setiawan, Doni; Faoziyah, Nina; Dalimarta, Fahmy Ferdian; Kusaeri, Didi
JISIP: Jurnal Ilmu Sosial dan Pendidikan Vol 8, No 2 (2024): JISIP (Jurnal Ilmu Sosial dan Pendidikan) (Maret)
Publisher : Lembaga Penelitian dan Pendidikan (LPP) Mandala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58258/jisip.v8i2.6756

This research aims to improve understanding of physics concepts and learning outcomes of Electrical Engineering students at the Politeknik Muhammadiyah Tegal by using the Problem Based Learning model combined with Think Pair Share on dynamic electrical material. The research instruments used were lecturer and student observation sheets, worksheets and test question sheets. In the analysis of students' conceptual understanding, an increase of 8.58% was obtained from cycle I to cycle II. Meanwhile, in the analysis of student learning outcomes in cycle I, the number of students who passed was 61.76%, and in cycle II the number of students who passed was 82.35%. The results of this research show that the application of the Problem Based Learning model combined with Think Pair Share can improve students' understanding of concepts and learning outcomes.

Mengenalkan Matematika Yang Menyenangkan Bagi Anak Usia Dini Faoziyah, Nina; Dalimarta, Fahmy Ferdian
Jurnal Pengabdian Masyarakat Bangsa Vol. 2 No. 3 (2024): Mei
Publisher : Amirul Bangun Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59837/jpmba.v2i3.858

Matematika merupakan bagian integral dari kehidupan sehari-hari yang penting untuk dipahami sejak dini. Tujuan pengabdian masyarakat ini adalah mengenalkan konsep matematika kepada anak usia dini secara menyenangkan. Hal ini diharapkan tidak hanya akan membantu mereka membangun dasar yang kuat dalam matematika, tetapi juga meningkatkan minat dan kepercayaan mereka terhadap matematika untuk masa depan yang lebih cerah. Metode yang digunakan pertama adalah dengan observasi dan wawancara langsung dengan guru, kedua dengan ceramah dalam memaparkan materi, ketiga dengan pelatihan dan ke empat pemberian angket untuk mengukur kepuasan pelanggan. Hasil pengabdian kepada masyarakat menunjukan bahwa sebagaian besar peserta pelatihan antusias mengenal pembelajaran matematika yang menyenangkan untuk anak usia dini dan mayoritas peserta puas dengan adanya pelaksanaan kegiatan pengabdian masyarakat dengan judul Mengenalkan Matematika yang Menyenangkan bagi Anak Usia Dini.

A Novel Privacy-Preserving Algorithm for Secure Data Sharing in Federated Learning Frameworks Dalimarta, Fahmy Ferdian; Faoziyah, Nina; Setiawan, Doni
Journal of Computer Networks, Architecture and High Performance Computing Vol. 7 No. 1 (2025): Article Research January 2025
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v7i1.5385

Federated Learning (FL) has emerged as a promising paradigm for the collaborative training of machine learning models across decentralized devices while preserving data privacy. However, ensuring data security and privacy during model updates remains a critical challenge, particularly in scenarios that involve sensitive data. This study proposes a novel Privacy-Preserving Algorithm (PPA-FL) designed to enhance data security and mitigate privacy leakage risks in FL frameworks. The algorithm integrates advanced encryption techniques, such as homomorphic encryption, with differential privacy to secure model updates without compromising the utility. Furthermore, it incorporates a dynamic noise-adjustment mechanism to adaptively balance privacy and model accuracy. Extensive experiments on benchmark datasets demonstrate that PPA-FL achieves a competitive trade-off between privacy protection and model performance compared to existing methods. The proposed approach is computationally efficient and scalable, making it suitable for real-world applications in healthcare, finance, and the IoT environment. This research contributes to advancing secure data-sharing practices in federated learning, fostering the broader adoption of privacy-preserving machine learning solutions.

Design and Construction of IoT-Based Smart Wardrobe with RGB Sensor and Sound Sensor Susanto, Nugroho; Setiawan, Doni; Sulistyo, Adi; Pratama, Aziz Yulianto; Faoziah, Nina; Dalimarta, Fahmy Ferdian
Jurnal Improsci Vol 2 No 4 (2025): Vol 2 No 4 February 2025
Publisher : Ann Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62885/improsci.v2i4.595

This research aims to design and build a smart wardrobe based on the Internet of Things (IoT) equipped with color sensors and sound sensors. This cabinet is designed to make it easier for users to organize and find the clothes they want with the help of advanced technology. The color sensor will detect the color of the garment and allow the system to suggest a combination of garments that suit the user's preferences. Meanwhile, the voice sensor allows for control of the wardrobe through voice commands, providing convenience for users to access clothes without having to open the closet door manually. This smart cabinet uses microcontroller-based hardware connected to a mobile app to manage and monitor the status of cabinets. The integrated IoT system allows the cabinet to function automatically and respond quickly to the input received from the sensors. The results of this research are expected to produce more efficient devices for organizing and searching for clothes, as well as provide a more interactive and practical user experience. In addition, by optimizing IoT technology, this research aims to introduce a more sophisticated smart home concept, with a wardrobe as one of the elements that support the ease of daily life. Keywords: Arduino, Sound sensor, Color sensor.

Application of Small Group Discussion Method for Improving Students' Critical Thinking Ability in Integral Material Faoziyah, Nina; Dalimarta, Fahmy Ferdian; Kusaeri, Didi
JUPE : Jurnal Pendidikan Mandala Vol, 9 No 4 (2024) : JUPE : Jurnal Pendidikan Mandala (Desember)
Publisher : Lembaga Penelitian dan Pendidikan Mandala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58258/jupe.v9i4.7799

The purpose of this study is to analyze the implementation of the Small Group Discussion learning method in improving students' critical thinking skills on the topic of integrals. This study employs a Classroom Action Research (CAR) design, conducted through four iterative stages: planning, action, observation, and reflection. The research subjects are students of Politeknik Muhammadiyah Tegal, focusing on enhancing their critical thinking skills using the Small Group Discussion method. Data were collected using various instruments, including pre-tests, learning outcome tests, interviews, and observations. The results indicate that the average learning score in Cycle 1 fell into the "moderate" category, with an average score of 60.26%. In Cycle 2, the results improved to the "high" category, with an average score of 78.21%. In Cycle 3, the average score significantly increased to 85.90%, which is classified as "very high." Therefore, the implementation of the learning process in Cycle 3 was deemed highly successful.

Enhancing Motoric Impulsivity Detection in Children through Deep Learning and Body Keypoint Recognition Dalimarta, Fahmy F.; Andono, Pulung N.; Soeleman, Moch. A.; Hasibuan, Zainal A.
JOIV : International Journal on Informatics Visualization Vol 9, No 1 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.1.2779

Quantifying motoric impulsivity in pediatric settings is crucial for safeguarding children and for devising effective intervention strategies. Existing quantitative techniques, such as accelerometry, have been utilized to assess it, but they often prove insufficient for accurately differentiating impulsive movements from regular ones. Conventional assessment methods are frequently used and rely on subjective assessments, which hinders the accurate characterization of impulsive behavior. To address this research gap, our study introduced an innovative objective approach using computer vision and deep learning techniques. We utilized MediaPipe to track precise body movement data from a child. The data were then analyzed using a Bidirectional Long Short-Term Memory (Bi-LSTM) network to process sequential information and recognize patterns indicative of impulsivity. Our approach successfully distinguished impulsive movements, marked by rapid changes in position and inconsistent movement velocities, from typical behavioral patterns with an accuracy rate of 98.21%. This research demonstrates the effectiveness of combining computer vision and deep learning to measure motoric impulsivity more precisely and impartially than prevailing qualitative techniques. Our model quantifies behaviors, enabling the development of improved safety protocols and targeted interventions in educational and recreational settings. This research has broader implications, suggesting a framework for future studies on pediatric motion analysis and behavioral assessment.

Towards Automated Motor Impulsivity Monitoring in Real-world Scenarios: A Multiple Object Tracking Approach Dalimarta, Fahmy; Andono, Pulung Nurtantio; Soeleman, Moch. Arief; Hasibuan, Zainal Arifin
Data Science: Journal of Computing and Applied Informatics Vol. 9 No. 1 (2025): Data Science: Journal of Computing and Applied Informatics (JoCAI)
Publisher : Talenta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32734/jocai.v9.i1-16686

Assessment of motor impulsivity often faces several challenges. Conventional assessments that rely on controlled settings often fail to capture impulsive behaviors in real-world contexts. This study proposes an automated approach using Multiple Object Tracking (MOT) technology to assess motor impulsivity. The aim was to develop a system for detecting and quantifying motor impulsivity in naturalistic, multi-person environments. By employing cutting-edge MOT algorithms, the solution tracks multiple individuals concurrently, enabling movement and interaction analyses. This methodology integrates MOT with behavioral models to identify motor impulsivity patterns such as abrupt trajectory changes or impulsive gesturing. Trained on real-world annotated datasets, the system ensures adaptability across settings. Our approach successfully distinguished impulsive movements from typical behavioral patterns, with an accuracy of 95.43%. This approach could revolutionize assessments by providing objective and quantitative measurements and facilitating enhanced diagnostics and personalized interventions. Extensive evaluations are required to assess real-time capabilities, robustness in occluded environments, and accurate impulsive pattern identification. These findings could enable broader clinical, research, and behavioral monitoring applications, advancing our understanding of the implications of motor impulsivity.

Analysis of Student Perceptions of the Use of ChatGPT as a Learning Media: A Case Study in Higher Education in the Era of AI-Based Education Adiyono, Adiyono; Said Al Matari, Ali; Ferdian Dalimarta, Fahmy
Journal of Education and Teaching (JET) Vol 6 No 2 (2025): Mei 2025
Publisher : Fakultas Keguruan dan Ilmu Pendidikan, Universitas Muhammadiyah kendari

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51454/jet.v6i2.538

This study aims to identify how students from STIT Ibnu Rusyd Tanah Grogot respond to the use of ChatGPT in the academic world, in the context of the role of artificial intelligence (AI) that can support the learning process in Islamic education. This research utilized a mixed-method design comprising quantitative surveys and qualitative interviews with 120 students to produce comprehensive data. The results of the investigation show how students view ChatGPT as an effective means of increasing the efficiency of certain tasks and significantly improving their understanding of academic material. However, issues were flagged on over-reliance on AI as well as the accuracy of generated information. According to the study, students' attitudes toward ChatGPT are significantly influenced by their level of digital literacy and adherence to Islamic values. Religious values focusing on academic integrity and critical assessment of value and talent, as thoroughly taught in educational institutions contribute to students' shying off from the AI-generated content unless it aligns with Islamic teaching. Qualitative interviews further showed that students view ChatGPT as a tool to support their studies but still desire human supervision to ensure compliance with ethical and religious standards.

A Novel Privacy-Preserving Algorithm for Secure Data Sharing in Federated Learning Frameworks Dalimarta, Fahmy Ferdian; Faoziyah, Nina; Setiawan, Doni
Journal of Computer Networks, Architecture and High Performance Computing Vol. 7 No. 1 (2025): Article Research January 2025
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v7i1.5385

Federated Learning (FL) has emerged as a promising paradigm for the collaborative training of machine learning models across decentralized devices while preserving data privacy. However, ensuring data security and privacy during model updates remains a critical challenge, particularly in scenarios that involve sensitive data. This study proposes a novel Privacy-Preserving Algorithm (PPA-FL) designed to enhance data security and mitigate privacy leakage risks in FL frameworks. The algorithm integrates advanced encryption techniques, such as homomorphic encryption, with differential privacy to secure model updates without compromising the utility. Furthermore, it incorporates a dynamic noise-adjustment mechanism to adaptively balance privacy and model accuracy. Extensive experiments on benchmark datasets demonstrate that PPA-FL achieves a competitive trade-off between privacy protection and model performance compared to existing methods. The proposed approach is computationally efficient and scalable, making it suitable for real-world applications in healthcare, finance, and the IoT environment. This research contributes to advancing secure data-sharing practices in federated learning, fostering the broader adoption of privacy-preserving machine learning solutions.

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