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Yunidar
Universitas Tadulako Jalan Soekarno Hatta, Tondo, Mantikulore, Kota Palu, Sulawesi Tengah
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Studi Empiris tentang Hubungan antara Kepuasan Kerja, Efikasi Diri, dan Kinerja Pegawai di Kementerian Agama Banda Aceh Masyithah, Syarifah Mauli; Jumeil, T Muhammad; Yunidar, Syafira; Nurrahmad, Nurrahmad
Jurnal Humaniora : Jurnal Ilmu Sosial, Ekonomi dan Hukum Vol 9, No 1 (2025): April 2025
Publisher : Center for Research and Community Service (LPPM) University of Abulyatama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30601/humaniora.v9i1.6389

Abstract

The objective of this study is to determine the influence of job satisfaction and self-efficacy, both partially and simultaneously, on the performance of employees at the Ministry of Religious Affairs Office in Banda Aceh City. The sample size for this study consists of 98 respondents. The research method employed is quantitative. Data were collected through questionnaires and analyzed using a multiple linear regression model. Hypothesis testing was conducted using multiple linear regression analysis, F-test, and t-test to determine the partial and simultaneous effects of the independent variables on the dependent variable. The results of the study indicate that job satisfaction has a significant partial effect on employee performance at the Ministry of Religious Affairs Office in Banda Aceh City. Self-efficacy, on the other hand, does not have a significant partial effect on employee performance at the same office. However, job satisfaction and self-efficacy together have a very significant simultaneous effect on employee performance at the Ministry of Religious Affairs Office in Banda Aceh City. The conclusion based on the simultaneous test shows that job satisfaction and self-efficacy together have a very significant effect on employee performance at the Ministry of Religious Affairs Office in Banda Aceh City.
Classification of Arrhythmia Electrocardiogram Signals Using Kernel Principal Component Analysis and Naive Bayes Melinda, Melinda; Farhan; Irhamsyah, Muhammad; Miftahujjannah, Rizka; D Acula, Donata; Yunidar, Yunidar
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 10, No. 3, August 2025
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v10i3.2219

Abstract

Arrhythmia is a cardiovascular disorder commonly detected through electrocardiogram (ECG) signal analysis. However, classifying arrhythmias based on ECG signals remains challenging due to signal complexity and individual variability. This study aims to develop a more accurate and efficient method for arrhythmia classification. The proposed method utilizes Kernel Principal Component Analysis (KPCA) and the naïve Bayes algorithm to classify arrhythmic ECG signals. KPCA is chosen for its ability to reduce data dimensionality, facilitating the processing of complex ECG signal and improving classification accuracy by minimizing noise. The naïve Bayes algorithm is chosen for its simplicity and computational speed, as well as its effective performance, even with limited data. ECG signals are processed using KPCA to reduce data dimensionality and extract relevant features. Subsequently, the naïve Bayes algorithm is then applied to classify the ECG signals into four categories: Premature Atrial Contraction (PAC), Premature Ventricular Contraction (PVC), Left Bundle Branch Block (LBBB), and Right Bundle Branch Block (RBBB).  The model's performance is evaluated using metrics such as accuracy, sensitivity, specificity, precision, and F1-score. The naïve Bayes model achieves an overall accuracy of 97.67%, with the highest performance observed in the RBBB class at 99.33%. Additionally, the F1-scores across all classes range from 96.62% to 98.57%, demonstrating the model's capability in detecting arrhythmias effectively. These results indicate that the combination of KPCA and naïve Bayes is effective for arrhythmic ECG signals classification.
Implementation of Discrete Wavelet Transform and Xception for ECG Image Classification of Arrhythmic Heart Disease Patients Irhamsyah, Muhammad; Melinda, Melinda; Yunidar, Yunidar; Muttaqin, Ikram; Zakaria, Lailatul Qadri
JURNAL INFOTEL Vol 17 No 2 (2025): May
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v17i2.1341

Abstract

The electrocardiogram (ECG) is one of the most important methods in the process of diagnosing heart disease. Visualizes the voltage and time relationship of the electrical activity of the heart. Cardiovascular or heart disease can be classified into several types, one of which is arrhythmia, a condition that involves changes in heartbeat rhythm, either too fast or too slow at rest. This study aims to develop a cardiac arrhythmia classification model using Deep Wavelet Transform (DWT) and Xception. It was evaluated on 2,200 spectrogram samples from the MIT-BIH dataset, containing normal and arrhythmia classes. The process compared epochs 30, 50, and 100 with learning rates of 0.001 and 0.0001 using cross-validation. Data were converted into spectrogram images for classification with Xception. The highest accuracy, 99.79%, was achieved at epoch 100 with a 0.0001 learning rate. Then, the highest precision occurs when the epoch is 50 with a learning rate of 0.001 and 0.0001, which is 100%. Lastly, Xception performed very well in the ECG image classification. This advantage demonstrates the ability of the model to recognize complex patterns in ECG data more effectively, increasing the reliability of arrhythmia detection. In addition, using DWT as a feature extraction technique allows better signal processing,which contributes to optimal results.
Psikologi Komunikasi dalam Meningkatkan Dakwah Da’i di Masjid Fajar Ikhlas Kelurahan Sumberejo Kecamatan Kemiling Mutia Yanti, Yunidar Cut
AL-ADYAN Vol 12 No 2 (2017): Al-Adyan: Jurnal Studi Lintas Agama
Publisher : Universitas Islam Negeri Raden Intan Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/ajsla.v12i2.2112

Abstract

Communication psychology explains about how to communicate well by taking a psychiatric approach to his mad'u. Because by mastering the psychology of communication, a preacher can adjust the material and the way the delivery of preaching to his madam. So that an effective da'wah can occur. For this, a preacher must use the science of communication psychology in order to be able to understand the state or psychology of his mad'u. Because basically a person's psyche is different, therefore it takes a way to communicate or convey messages in different ways. So an effective da'wah will occur. The research method used is field research (research that is conducted directly in the field or the respondent. The population and sample in this study are the preachers and members of the Ikhlas Fajar Council. From the results of this study it can be seen that the Da'i at the Fajar Ikhlas Mosque, the Da'i have not fully used the science of Communication Psychology, therefore the preaching it delivered was less effective.
The Role of Indonesian Language Learning in Facilitating a Multicultural Classroom at Junior High School Ramadani, Nurhaliza; Ulfah; Yunidar; Nur'aeni, Ida
IDEAS: Journal on English Language Teaching and Learning, Linguistics and Literature Vol. 13 No. 1 (2025): IDEAS: Journal on English Language Teaching and Learning, Linguistics and Lite
Publisher : Institut Agama Islam Negeri Palopo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24256/ideas.v13i1.7204

Abstract

This study aims to analyze how the role of Indonesian language learning as the main communication tool and unifier in multicultural classes at Junior High School. The type of research used is qualitative research. The method used in this research is descriptive method. The data collection techniques used in this research are: 1) Observation, 2) Interview, and 3) documentation. Data analysis techniques used in this research are data reduction, data presentation and conclusion drawing. Based on the results of research on the role of Indonesian language in Junior High School, researchers see the role of Indonesian language learning in multicultural classes can be seen from how to facilitate cultural interaction through the short story "Dia Berbeda" which has a multicultural context that raises multicultural themes, opening space for students to get to know other cultures through stories and characters. Through short stories that contain conflicts and resolutions related to cultural, religious, or social differences, learners are invited to recognize the importance of accepting and understanding each other which strengthens the sense of tolerance between people. Researchers can conclude that the role of Indonesian language learning in multicultural classes can facilitate intercultural interaction through inclusive expression, foster a sense of tolerance through emotional and reflective experiences of the stories read and increase understanding of multiculturalism by presenting the reality of diversity in a form that is close to the lives of students. Not only by learning works of fiction but all Indonesian language learning plays an important role in the multicultural classroom.
EEG Performance Signal Analysis for Diagnosing Autism Spectrum Disorder using Butterworth and Empirical Mode Decomposition Fathur Rahman, Imam; Melinda, Melinda; Irhamsyah, Muhammad; Yunidar, Yunidar; Nurdin, Yudha; Wong, W.K.; Zakaria, Lailatul Qadri
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 3 (2025): July
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i3.788

Abstract

Electroencephalography (EEG) is a technique used to measure electrical activity in the brain by placing electrodes on the scalp. EEG plays an essential role in analyzing a variety of neurological conditions, including autism spectrum disorder (ASD). However, in the recording process, EEG signals are often contaminated by noise, hindering further analysis. Therefore, an effective signal processing method is needed to improve the data quality before feature extraction is performed. This study applied the Butterworth Band-Pass Filter (BPF) as a preprocessing method to reduce noise in EEG signals and then used the Empirical Mode Decomposition (EMD) method to extract relevant features. The performance of this method was evaluated using three main parameters, namely Mean Square Error (MSE), Mean Absolute Error (MAE), and Signal-to-Noise Ratio (SNR). The results showed that EMD was able to retain important information in EEG signals better than signals that only passed through the BPF filtration stage. EMD produces lower MAE and MSE values than Butterworth, suggesting that this method is more accurate in maintaining the original shape of the signal. In subject 3, EMD recorded the lowest MAE of 0.622 compared to Butterworth, which reached 20.0, and the MSE value of 0.655 compared to 771.5 for Butterworth. In addition, EMD also produced a higher SNR, with the highest value of 23,208 in subject 5, compared to Butterworth, which reached only 1,568. These results prove that the combination of BPF as a preprocessing method and EMD as a feature extraction method is more effective in maintaining EEG signal quality and improving analysis accuracy compared to the use of the Butterworth Band-Pass Filter alone.
Community And Family-Based Intervention Strategies To Prevent Stunting: A Systematic Review Puspitasari, Yunidar Dwi; Indarwati, Retno; Wahyuni, Silvya Dwi; Suraya, Andi Safutra
Care : Jurnal Ilmiah Ilmu Kesehatan Vol 13, No 2 (2025): EDITION JULY 2025
Publisher : Universitas Tribhuwana Tunggadewi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33366/jc.v13i2.6613

Abstract

Stunting is a state of chronic malnutrition associated with nutritional insufficiency.  Stunting can increase the risk of morbidity and mortality, hindering children's growth and development.  Malnutrition in toddlers can also arise due to the cultural practices, eating habits, and social norms related to food intake. The role of the family and the community is very important in ensuring the success of stunting prevention programs. This study aimed to undertake a systematic review of the international literature on community-based interventions to prevent stunting in children under 5 years. We reviewed original quantitative research from Scopus, Science Direct, PubMed, and ProQuest using the keywords “community intervention” OR “family intervention” AND “stunting” AND “toddlers”. We conducted a critical appraisal using PRISMA guidelines on evidence documenting the community-based intervention or program as a strategy to reduce stunting in toddlers, between 1 January 2013 and 31 January 2023 with the inclusion criteria of English-language, full-text, open-access, quantitative studies. Thirteen peer-reviewed papers met the inclusion criteria. This review found that family-based intervention could improve child growth and community-based intervention could strengthen the community health systems. The existing evidence showed a positive impact of community and family-based intervention to prevent stunting.  Family-strengthening interventions may have an impact on reducing stunting among children under 5 years. 
Performance Comparison of Variational Mode Decomposition and Butterworth in Processing EEG Signals of Autism Patients Wardana, Surya; Melinda, Melinda; Ramdhana, Rizka; Yunidar, Yunidar; Away, Yuwaldi; Basir, Nurlida
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 3 (2025): August
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v7i3.105

Abstract

Electroencephalography (EEG) is a non-invasive technique for monitoring and recording the brain's electrical activity with electrodes applied to the scalp. The method is important in neurological studies, like that of Autism Spectrum Disorder (ASD), because it measures patterns of brain waves that can identify developmental abnormalities. However, EEG signals are often contaminated by multiple noise sources, including eye movements, muscle activity, and extraneous interference. This interference can significantly reduce the quality and intelligibility of signals. Therefore, preprocessing is required to enhance the reliability and precision of the data obtained. In this study, a Butterworth Band-Pass Filter (BPF) was used during preprocessing to filter out undesirable frequency components and to mitigate noise. After filtering, EEG signals were handled using the Variational Mode Decomposition (VMD) technique. VMD is an adaptive method for decomposing multidimensional signals into intrinsic mode functions while preserving critical details of the original data. For performance comparison, four quantitative metrics were used: Mean Squared Error (MSE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Signal-to-Noise Ratio (SNR). Results showed that VMD performed better than BPF alone. As an example, for Subject 1, VMD achieved an MAE of 0.26 and MSE of 0.42, which was far superior to the MAE of 13.72 and MSE of 674.96 of BPF. Subject 3 had the least RMSE (0.40) when using VMD, whereas BPF scored 25.90. VMD also reported a highest SNR of 28.56, compared to BPF's 2.43. Overall, integrating VMD with BPF significantly improves EEG signal quality and enables more accurate analysis, particularly in ASD-related studies.
Autism Face Detection System using Single Shot Detector and ResNet50 Melinda, Melinda; Alfariz, Muhammad Fauzan; Yunidar, Yunidar; Ghimri, Agung Hilm; Oktiana, Maulisa; Miftahujjannah, Rizka; Basir, Nurlida; Acula, Donata D.
JURNAL INFOTEL Vol 17 No 2 (2025): May
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v17i2.1331

Abstract

The facial features of children can provide important visual cues for the early detection of autism spectrum disorder (ASD). This research focuses on developing an image-based detection system to identify children with ASD. The main problem addressed is the lack of practical methods to assist healthcare professionals in the early identification of ASD through facial visual characteristics. This study aims to design a prototype facial image acquisition and detection system for children with ASD using Raspberry Pi and a deep learning-based single shot detector (SSD) algorithm. In this method, the face detection model uses a modified ResNet50 architecture, which can be used for advanced analysis for classification between autistic and normal children, achieving 95% recognition accuracy on a dataset consisting of facial images of children with and without ASD. The system is able to recognize the visual characteristics of the faces of children with ASD and consistently distinguish them from those of normal children. Real-time testing shows a detection accuracy ranging from 86% to 90%, with an average accuracy of 90%, despite fluctuations caused by variations in movement and viewing angle. These results show that the developed system offers high accuracy and has the potential to function as a reliable diagnostic tool for the early detection of ASD, which ultimately facilitates timely intervention by healthcare professionals to support the optimal development of children with ASD.
Improving the Classification Performance of SVM, KNN, and Random Forest for Detecting Stress Conditions in Autistic Children Melinda, Melinda; Yunidar, Yunidar; Miftahujjannah, Rizka; Rusdiana, Siti; Amalia, Amalia; Qadri Zakaria, Lailatul
International Journal of Engineering, Science and Information Technology Vol 5, No 4 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i4.1206

Abstract

This paper addresses the critical challenges of managing stress in autistic children by introducing an innovative deployable system designed to detect signs of stress through continuous monitoring of physiological and environmental indicators. The system, implemented as a convenient portable detection system, measures key parameters such as heart rate, body temperature and skin conductance. The data is accessed in real-time and displayed on the Blynk application with an IoT system and viewed remotely via an Android device, allowing caregivers to receive instant notifications upon detection of potential stress symptoms. This timely alert system enables rapid intervention, potentially reducing stress intensity and providing peace of mind to caregivers. The study further compares three powerful data analysis methods namely Support Vector Machine (SVM), K-nearest neighbors (KNN) and Random Forest (RF) in interpreting the collected sensor data. The SVM-based system achieved a fairly good detection accuracy of 90%, KNN also showed excellent results of 92% while the Random Forest-based system showed superior performance with an impressive accuracy of 95%. These findings suggest that the Random Forest method exhibits a superior level of effectiveness in accurately predicting the onset of stress conditions., providing the importance for technological advancements that can be applied in supporting better management of autism-related behavioral defenses.
Co-Authors . Roslidar Abdul Kamaruddin Acula, Donata D Acula, Donata D. Ade Nurul Izatti G. Yotolembah Akbar Akbar Akbar, Muhazir Albahri, Albahri Alfariz, Muhammad Fauzan Ali Karim Ali Karim Ali Karim Amalia Amalia Aman Aman Amrie Firmansyah Andi Safutra Suraya Anizar, Lis Arini Nurazizah Arum Pujining Tyas Arum Pujiningtyas Asniar Asniar Asrianti, Asrianti Azhari, Rizki Aziz, Zulfadli Abdul Azra, Ery Bashir, Nurlida Basir, Nurlida Christi L., Rita Cindy Afitasari Cut Dewi, Cut D Acula, Donata Darmawan Darmawan Daud, Bukhari Dian Safitri Dwi Yunita Efendi Elfalini Warnelis Elizar Elizar, Elizar Fahmi Fahmi Farhan Fathur Rahman, Imam Fathurrahman Fathurrahman Fauzan, Arfan Fauziah Gusvita Syarah Femmy Jacoba Ferdi Nazirun Sijabat, Ferdi Nazirun Ferdinand, Frans Fitri Arnia Gazali Lembah Ghimri, Agung Hilm Golar Golar Gopal Sakarkar Gusti Alit Saputra Gusti Alit Suputra Gusti Ketut Alit Suputra Harisa, Sitti Hasan, Hafidh Hasan, Vania Pratama Hasriani Muis Heltha, Fahri Herlina Dimiati, Herlina Hidayat Hidayat I Gusti Ketut Alit Saputra I Ketut Agung Enriko I Made Sukanata Ida Nuraeni Indarwati , Retno Indra Indra Irdawati Irdawati Islamy, Fajrul Jayanti Puspita Dewi Joko Pitoyo Jumeil, T Muhammad Juniati Juniati Karlisa Priandana Khairah, Alfita Khairia, Syaidatul Khairunnisa Bakari Khairunnisa Bakari Laguliga, Syapril A. Lailatul Qadri Zakaria Lantuba, Yanis Men Leo, Hendrik Luluk Khusnul Dwihestie M Asri B M. Asri B Malahayati, M. Masyithah, Syarifah Mauli Maulida, Zenitha Maulisa, Oktiana Melinda Melinda Miftahujjannah, Rizka Mina Rizky, Muharratul Misbahuddin Misbahuddin Moh. Tahir Moh. Tahir Mohd. Syaryadhi Mohd. Syaryadhi Muh Tahir Muhammad Irhamsyah Muhammad Muhammad Muhammad Ridwan Muna, Lia Aulial Mursidin . Muthia Aryuni Muttaqin, Ikram Nabila, Nissa Hasna Nasaruddin Nasaruddin Nazilla, Izza NFN Nursyamsi NFN TAMRIN Nirmayanti, Nirmayanti Nizam Salihin Nur Ahyani Nur Fadilah Nur Halifah Nur Halifah, Nur Nur'aeni, Ida Nuraedah Nurbadriani, Cut Nanda Nurbaya Nurbaya, Nurbaya Nurbismi, Nurbismi Nurlida Basir Nurrahmad, Nurrahmad Nursyamsi Nursyamsi Oktiana, Maulisa Pandaleke, Alex Y. Pertiwi, Rizqina Wahyu Laras Putri Mauliza, Putri Qadri Zakaria, Lailatul Rafiqi, Ashaf Rahmatika, Laily Raihan, Siti Ramadani, Nurhaliza Ramadhan, Irsyan Ramadhani, Hanum Aulia Ramdhana, Rizka Rhamdhani, Rhamdhani Ridara, Rina Rini Safitri Roslawa, Roslawa Sabiran, Sabiran Sadia, Fachrudin Saharudin Barasandji Sahrul Saehana Sakarkar, Gopal Salsabila, Unik Hanifah Samad, Muhammad Ahsan Santi Santi Sarmin Sarmin Sarmin Sarmin Satria Satria, Satria Setiawan, Verdy Siti Fatinah Siti Rusdiana Sitti Harisah Sri Jelis Suci Rahayu Suharja, Anggi Auliyani Sukma, Sukma Suyanda, Arya Syahyadi, Rizal Syakir, Fakhrus Syamsuddin Syamsuddin Tamrin Tamrin Tamrin Tamrin Tanjung, Wilda Nurafdila Tiara Artamefia Ulfah Ulinsa, Ulinsa Ulinsa, Ulinsa Ulul Azmi Vilzati, Vilzati Wachidi, Achmad Wahyuni, Silvya Dwi Wardana, Surya Wong, W.K Wong, W.K. Yazid Yaskur Yudha Nurdin Yusni, Y Yuwaldi Away Zainab Zainal, Zulfan Zulfikar Taqiuddin Zulhelmi, Zulhelmi Zulianto, Sugit