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Dahlan Abdullah

Universitas Malikussaleh

Author-ID : 217988

Religion Agriculture, Biological Sciences & Forestry Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Nursing Physics Social Sciences Transportation Other

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Prediction Of Unemployment Rate Using The Fuzzy Time Series Chen Model Method Annisa Karima; Dahlan Abdullah; Muchlis ABD Muthalib; Nurdin Nurdin; Muhammad Daud
Brilliance: Research of Artificial Intelligence Vol. 5 No. 2 (2025): Brilliance: Research of Artificial Intelligence, Article Research November 2025
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v5i2.7310

Unemployment is a significant socio-economic problem in Lhokseumawe City that requires serious attention from policymakers. The unemployment rate fluctuates from year to year, making accurate forecasting an important aspect in formulating effective strategies and policies to reduce unemployment. One method that can be used to analyze and forecast time series data with uncertainty is the Fuzzy Time Series (FTS) method, which applies fuzzy logic concepts to handle vague and imprecise data patterns. In this study, the Fuzzy Time Series method is applied to predict the number of unemployed people in Lhokseumawe City. The data used are historical unemployment data over a period of 10 years, from 2013 to 2022. The research process begins with defining the universe of discourse (U), determining the number and length of interval classes, defining fuzzy sets on U, and fuzzifying the unemployment data. Furthermore, Fuzzy Logical Relationships (FLR) are identified and grouped into Fuzzy Logical Relationship Groups (FLRG). The defuzzification process is then carried out to obtain crisp values, followed by forecasting calculations.The analysis was conducted using the RStudio application. The forecasting results show that the predicted number of unemployed people in 2023 is 10,514.125, which is rounded to 10,514 people. The accuracy of the forecasting model is evaluated using Mean Absolute Percentage Error (MAPE) and Average Forecasting Error Rate (AFER), both of which yield values of 6.70%. Since the MAPE and AFER values are less than 10%, the forecasting results can be categorized as very good and reliable for decision-making purposes.

ETHICAL DILEMMAS IN NEUROMARKETING: NAVIGATING PERSUASION, CONSUMER AUTONOMY, AND EMERGING TECHNOLOGIES Muhamad Stiadi; Luana Sasabone; Deisye Supit; Cut Ita Erliana; Dahlan Abdullah
International Journal Of Humanities, Social Sciences And Business (INJOSS) Vol. 3 No. 1 (2024): INTERNATIONAL JOURNAL OF HUMANITIES, SOCIAL SCIENCES AND BUSINESS (INJOSS)
Publisher : ADISAM Publisher

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Neuromarketing is a rapidly growing field that uses neuroscience to understand consumer behavior and improve marketing effectiveness. This article explores the complex ethical implications of neuromarketing practices, especially regarding persuasive influence on consumer autonomy and the use of emerging technologies. First, we investigate advances in neurotechnology, such as fMRI, EEG, and brain-computer interfaces (BCIs), that enable a deeper understanding of consumer preferences. The challenge is enforcing this technology's ethical use and protecting individual privacy. Second, we discuss "neuro hacking," exploiting neurological vulnerabilities to influence consumer decisions. This raises ethical questions about mental safety and individual autonomy. Third, algorithms and artificial intelligence in Neuromarketing create highly personalized marketing experiences. This raises questions about how to protect privacy, obtain consent, and maintain individual agency. Fourth, we explore cultural issues in Neuromarketing, exploring how cultural values must be respected in marketing practices. Fifth, we discuss security and data protection issues in using neuromarketing technology. Sixth, we investigate the role of government and regulatory agencies in regulating neuromarketing practices and protecting consumer rights. Finally, we highlight the importance of education about neuroethics in educating marketers, researchers, and consumers about the ethical aspects of Neuromarketing. This article encourages critical reflection on the ethical dilemmas businesses, researchers, and policymakers face in the rapidly evolving world of Neuromarketing.

CULTIVATING SUSTAINABLE EMPLOYEE ENGAGEMENT AND WELL-BEING INITIATIVES IN INDONESIAN ORGANIZATIONS: A MULTIFACETED EXAMINATIONOF STRATEGIES, CHALLENGES, AND IMPACT ON ORGANIZATIONAL PERFORMANCE Kosasih Kosasih; Yuarini Wahyu Pertiwi; Cut Ita Erliana; Defi Irwansyah; Dahlan Abdullah
International Journal Of Humanities, Social Sciences And Business (INJOSS) Vol. 3 No. 1 (2024): INTERNATIONAL JOURNAL OF HUMANITIES, SOCIAL SCIENCES AND BUSINESS (INJOSS)
Publisher : ADISAM Publisher

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We provide an overview of the research on cultivating sustainable employee engagement and well-being initiatives in Indonesian organizations. The study aimed to comprehensively examine the strategies employed, the challenges faced, and the impact of these initiatives on organizational performance in the Indonesian context. The research was rooted in a mixed-methods approach, combining qualitative and quantitative methods to understand the subject matter better. Data was collected from surveys, interviews, and organizational records. Stratified random sampling was employed for surveys, while purposive sampling was used for interviews, ensuring a diverse and representative dataset. Our analysis incorporated descriptive statistics and inferential statistical methods, such as regression analysis, for quantitative data. Qualitative data underwent thematic analysis, allowing for the identification of patterns and themes. Integrating both data types enabled triangulation and a more profound insight into the research questions. Ethical considerations were paramount throughout the research, with data treated confidentially and participant identities protected. Informed consent was obtained from all participants, and potential biases or conflicts of interest were transparently addressed. While the mixed- methods approach enriched our understanding, it did pose resource and time constraints. While valuable within the Indonesian context, the findings may not be universally applicable. Additionally, challenges related to data collection, such as participant availability and the completeness of organizational records, were acknowledged. This research contributes to the existing body of knowledge by offering insights into the strategies and impact of sustainable employee engagement and well-being initiatives in Indonesian organizations while shedding light on the challenges faced in implementing such programs.

EXPLORING THE INTEGRATION OF QUANTUM MACHINE LEARNING ALGORITHMS IN HIGHER EDUCATION TO ENHANCE CURRICULUM DEVELOPMENT ANDCYBERSECURITY PROGRAMS Muhammad Ihsan Dacholfany; Miswar; Cut Ita Erliana; Dahlan Abdullah; Indrawati
International Journal of Teaching and Learning Vol. 1 No. 1 (2023): International Journal of Teaching and Learning (INJOTEL)
Publisher : Adisam Publisher

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This research delved into a dynamic landscape in exploring the integration of Quantum Machine Learning (QML) algorithms in higher education for curriculum development and cybersecurity programs. The study aimed to investigate the potential impact of QML on higher education and the security domain, addressing the evolving educational needs and the ever-pressing cybersecurity challenges. Through comprehensive analysis, this research unveiled the transformative capacity of QML technology and its implications for the academic and security sectors. Research findings disclosed significant gaps in current curricula and the need for a comprehensive approach to QML integration. Faculty and student perceptions illustrated the challenges and opportunities surrounding QML, with the former emphasizing the necessity for professional development and the latter expressing enthusiasm and the desire for more hands-on experiences. Insights from cybersecurity experts highlighted QML's potential in fortifying security measures, underlining the importance of collaboration between quantum computing and cybersecurity communities. This research contributes by providing a multifaceted understanding of QML integration in higher education and its ability to reshape learning and security paradigms. However, it acknowledges certain limitations, such as sample diversity and the evolving nature of quantum technology. Despite these limitations, this exploration lays the groundwork for future adaptations and advancements in education and cybersecurity in the quantum age

EDUCATIONAL ADVANCEMENTS IN THE DIGITAL EPOCH: A MULTIFACETED EXPLORATION OF THE STRATEGIC INCORPORATION OF TECHNOLOGY TO AMPLIFYEDUCATIONAL QUALITY AND LEARNING OUTCOMES IN PRIMARY EDUCATION ACROSS INDONESIA Iswan Riyadi; Sitti Nur Alam; Cut Ita Erliana; Dahlan Abdullah; Al-Amin
International Journal of Teaching and Learning Vol. 1 No. 1 (2023): International Journal of Teaching and Learning (INJOTEL)
Publisher : Adisam Publisher

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Amid the digital era, this research embarked on a comprehensive exploration of the strategic Integration of technology in primary education within the diverse educational landscape of Indonesia. The study delved into the multifaceted dynamics of technology's role in amplifying the quality of education and enhancing learning outcomes. The research unveiled a rich tapestry of findings, elucidating the promises and challenges associated with technology integration. It unearthed a positive correlation between technology integration and improved learning outcomes, where students in schools with higher technology integration consistently achieved better results in standardized assessments. However, the study also illuminated the striking disparities in technology integration, with urban schools benefiting from more advanced technological infrastructure than their rural counterparts. The implications of this research reverberate throughout the Indonesian educational landscape, emphasizing the imperative to bridge the digital divide and ensure equitable access to technology. As the digital epoch continues to reshape educational paradigms, this study stands as a testament to the transformative potential of technology when harnessed strategically and responsibly, with an unwavering focus on enhancing educational quality and empowering the next generation of learners.

SOCIOECONOMIC DISPARITIES IN EDUCATIONAL OUTCOMES ARISING FROM THE IMPLEMENTATION OF TECHNOLOGY-ENABLED LEARNING IN EARLY CHILDHOOD EDUCATION IN INDONESIA Al-Amin; Ika Rahayu Satyaninrum; Cut Ita Erliana; Dahlan Abdullah; Mohd Syahrin
International Journal of Teaching and Learning Vol. 1 No. 1 (2023): International Journal of Teaching and Learning (INJOTEL)
Publisher : Adisam Publisher

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The integration of technology in early childhood education has garnered significant attention worldwide. In 2021, Indonesia embarked on an ambitious journey to incorporate technology-enabled learning into its educational framework. This study retrospectively examines the socioeconomic disparities that emerged from this endeavor. Through extensive data analysis and assessments, it was observed that the introduction of technology in early childhood education had a mixed impact on the educational outcomes of children from various socioeconomic backgrounds. While children from affluent families exhibited enhanced digital literacy and cognitive development, those from economically disadvantaged households encountered disparities in access to technology and resources. The digital divide became more pronounced, leading to disparities in academic achievement. Furthermore, it was found that educators and policymakers encountered significant challenges in adapting the curriculum to address these disparities. The study also highlights the importance of addressing issues related to infrastructure and equity in technology access in early childhood education, ensuring that all children have an equal opportunity to thrive. These findings emphasize the necessity for a more inclusive and equitable approach to integrating technology in early childhood education, aiming to bridge the socioeconomic gaps and provide a level playing field for all children, thereby fostering a more equitable and promising educational landscape in Indonesia.

Traffic Accident Prediction Using Machine Learning Based on PT Jasa Raharja Data Utomo, Muhammad Fikri; Fikry, Muhammad; Hamdhana, Defry; Abdullah, Dahlan; Nurdin, Nurdin
Informatika Mulawarman : Jurnal Ilmiah Ilmu Komputer Vol 21, No 1 (2026): Informatika Mulawarman : Jurnal Ilmiah Ilmu Komputer
Publisher : Mulawarman University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/jim.v21i1.25392

Traffic accidents represent a critical issue that significantly affects public safety and generates substantial social and economic impacts, particularly within the operational area of PT. Jasa Raharja Lhokseumawe Branch. The lack of predictive information regarding accident occurrences often results in reactive policy making. This study aims to develop a machine learning–based forecasting model for traffic accident rates using a combination of K-Means Clustering and Recurrent Neural Network (RNN). The dataset consists of historical traffic accident records from 2022 to 2024, which were preprocessed and aggregated on a weekly basis at the district level. K-Means Clustering was employed to group districts according to weekly accident patterns, resulting in two optimal clusters based on silhouette score evaluation. Subsequently, separate RNN models were developed for each cluster to forecast weekly accident occurrences. Model performance was evaluated using Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). The results indicate that the RNN model achieved higher prediction accuracy for clusters with more stable accident patterns compared to clusters exhibiting higher fluctuation. Overall, the proposed combination of clustering and RNN demonstrates strong potential in producing accurate traffic accident forecasts

ANALYZING STRATEGIES FOR STRENGTHENING LITERACY COMPETENCE AT THE JUNIOR HIGH SCHOOL LEVEL AMONG SCHOOL TEAMS AT THE DISTRICT AND CITY LEVELS IN INDONESIA Deslana Roidja Hapsarini; Al-Amin; Cut Ita Erliana; Defi Irwansyah; Dahlan Abdullah
Indonesian Journal of Education (INJOE) Vol. 2 No. 3 (2023): Indonesian Journal of Education (INJOE)
Publisher : CV. ADIBA AISHA AMIRA

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This study delved into the intricate realm of enhancing literacy competence at the junior high school level among school teams operating at the district and city levels in Indonesia. With a focus on collaborative efforts, this research analyzed various strategies to bolster literacy skills, comprehensively exploring the multifaceted challenges and potential solutions. The study examined the historical background and significance of literacy competence in Indonesia's junior high schools to grasp the context. It addressed the pressing problem of inadequate literacy skills among students and identified vital contributing factors, such as limited access to resources, linguistic diversity, and disparities in socioeconomic conditions. The study set forth four primary research objectives: (1) to assess the current literacy competence levels in junior high schools, (2) to uncover the internal and external factors influencing literacy development, (3) to scrutinize effective strategies and best practices for strengthening literacy skills within school teams, and (4) to formulate targeted recommendations for district and city-level policymakers, educators, and stakeholders, through a comprehensive investigation encompassing public and private schools across diverse regions, the research aimed to shed light on the complex interplay of factors shaping literacy outcomes. This study, despite its limitations in terms of time and resource constraints and personal influences, seeks to provide valuable insights to guide collaborative efforts to boost among junior high school students in Indonesia.

Analysis of Informatics Engineering Students’ Dependency Level on the Use of ChatGPT Using the Support Vector Machine Method Aswita Indah Luthfiana Hasibuan; Dahlan Abdullah; Kurniawati Kurniawati
International Journal of Engineering, Science and Information Technology Vol 6, No 2 (2026)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v6i2.1813

This study aims to analyze the level of dependency among Informatics Engineering students on the use of ChatGPT in academic activities using the Support Vector Machine (SVM) method. The research data were collected through questionnaires distributed to students of the Informatics Engineering Study Program at Universitas Malikussaleh from the 2022–2025 cohorts, involving a total of 400 respondents. The research indicators included usage intensity, duration of use, purpose of use, perceived effectiveness, and dependency behavior toward ChatGPT. The collected data underwent several preprocessing stages. including missing value checking. label transformation. data normalization using Min-Max Scaling. and dataset splitting into 80% training data and 20% testing data. The classification process was performed using the Support Vector Machine (SVM) algorithm with a linear kernel. The experimental results showed that the proposed SVM model successfully classified student dependency levels into three categories, namely low, medium, and high, achieving an accuracy of 95%, which indicates excellent classification performance. The findings also revealed that usage frequency, duration of use, and the utilization of ChatGPT for academic assignments and programming activities were the most influential factors affecting student dependency. Furthermore, a web-based system was developed using Python Flask and SQLite to facilitate data processing, model training, and visualization of classification results. The system testing results demonstrated that all implemented features functioned properly according to the specified requirements.

Prediction Of Industrial Waste Using The Autoregressive Integrated Moving Average Method Roslaini Roslaini; Dahlan Abdullah; Rizki Suwanda
Jurnal Informasi dan Teknologi 2025, Vol. 7, No. 2
Publisher : SEULANGA SYSTEM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60083/jidt.vi0.624

This study presents the development of a web-based industrial waste prediction system using the Autoregressive Integrated Moving Average (ARIMA) method to forecast the volume of liquid and solid waste generated by PT Pupuk Iskandar Muda (PIM). The predictive model is built upon historical waste data collected between 2020 and 2023, serving as the foundation for the statistical analysis. The system is developed using the Flask web framework, offering an interactive and user-friendly interface, while SQLite3 is employed as a lightweight local database solution for efficient data handling. The ARIMA (1,1,1) model was selected based on stationarity testing and examining ACF and PACF patterns. The results suggest that the model can moderately capture prediction trends, although limitations in accuracy are evident. For 2024, liquid waste is projected to decrease from 30,600 tons in January to 29,400 tons in December. In contrast, solid waste displays a more stable trend, with an average monthly generation of approximately 23.2 tons. Model performance was evaluated using the Mean Absolute Percentage Error (MAPE) method, yielding high error rates—166.11% for liquid waste and 100% for solid waste, highlighting the significant impact of data quality and completeness on prediction accuracy. The system generates visual outputs through interactive graphs and tables accessible via a web browser, supporting data-driven decision-making. This research is a predictive tool for PT PIM and a reference for future development of technology-driven waste management systems to promote environmental sustainability.

Co-Authors - Hartono . Zulfan Abdul Wahab Abdullah A. Z Achmad Harristhana Mauldfi Sastraatmadja Aditia, Donny Adli Zain, Razlan Afisman, Heri Juni Afriza, Muhammad Ridho Agil, Helvina Agus Sukoco Akbar, Abdul Hanif Al Kautsar Aidilof, Hafizh Al- Amin Al-Amin Ali Nasith Alimul Haq, Nur Ambas, Jamin Amny Yasira Amrina, Amrina Ananda Faridhatul Ulva Andik Bintoro ani, Muli Anni Zulfia Annisa Karima Ansyari, Taufik Habib Anuar Bahri, Khairil Ar Razi Ar Razi, Ar Razi Arifah, Mutia Arifin, Arifin Arnawan Hasibuan Aslam Aslam, Aslam Aswita Indah Luthfiana Hasibuan Aulia Barus, M Farhan Ayu Anggreni, Made Berkah Nadi, Muhammad Abi Chaliza Nur, Wan Amalia Cindy Rahayu Cut Agusniar Cut Ita Erliana Cut Yusra Novita Daniel Akhyar Darmawanta Sembiring Dedi Fariadi Defi Irwansyah Defi Irwansyah Deisye Supit Deslana Roidja Hapsarini Devin Mahendika Dita Amelia, Dita Eko Prastyo Elviwani Elviwani Elviwani Elviwani Elviwani, Elviwani Emy Yunita Rahma Pratiwi, Emy Yunita Rahma ER. UMMI KALSUM Erianto Ongko Erlina Erlina Erlina Erlina Fachry Abda El Rahman Fadlisyah Fadlisyah Fadlisyah Fajriana, Fajriana Fakhruddin Ahmad Nasution Farida Juliarta, Evie Fazil, M Fidyatun Nisa Fikri, Khusnul Firman Aziz Fuzna Febriani Gamar Al Haddar Gio, Prana Ugiana H. Hartono Habib Muharry Yusdartono Haedir, Haedir Halim, Paisal Hamdhana, Defry Hapsarini, Deslana Roidja Haris Danial Hartono Hartono Hartono, Natalia Rumanti Hasanun Hasibuan, Abdurrozzaq Hasibuan, Fadilah Suryani Hasibuan, Muammar Faturrahman Heri Juni Afisman Herry Rachmat Widjaja Hery Budiyanto HS, Nurmadina I Ketut Sutapa I MADE MULIARTA . I P G. Adiatmika I Putu Agus Dharma Hita Ika Rahayu Satyaninrum IKhwanda Putra, Al Malikul Imanullah, Imanullah Indah Sulistiani Indra Pilianti D Indra Tjahyadi Indrawati Indrawati Irdanil Kamal, Irdanil Irianto, Sugeng Irma Oktari Irwansyah, Defi Islam, Khoirul Iswan Riyadi Iswandi Iswandi Iswandi Iswandi Iylia Azlan, Rabiatul Juarni Siregar Judijanto, Loso Kamilah, Muna Kartika Kartika Kartini Rahayu Khairul Fuadi Khairullah Yusuf Komang Ayu Krisna Dewi Kosasih Kosasih , Kosasih Kurniawansyah, Kurniawansyah Kurniawati Kurniawati La Ode Muhammad Idrus Hamid B Lahap, Johanudin Lamsir, Seno Laros Tuhuteru Latif, Sarifudin Andi Lestari, Nana Citrawati Lestari, Veronika Nugraheni Sri Lidya Rosnita Limbong, Hendra Putranta Lisa Mulia Al Ikhlas Luana Sasabone Lubis, Fauzan Arbi Luh Yusni Wiarti M Farhan Aulia Barus M Fauzan Maharani Asnur, Sardian Mahesa Reglisalo Manik, Aktina Marganda Simarmata Marlina Sari Maryana Maryana Maryana Maryana, Maryana Masriadi Maulana, Fatur Rahman Maulinda, Rerin Maya Savira Meilyana Meilyana Meutia Rahmi Mirna Dewi Misbahul Jannah Miswar Miswar Miswar Mochamad Gilang A Mubarok Mohammad, Wily Mohd Said, Noraslinda Mohd Syahrin Muchlis Abdul Muthalib Muhamad Stiadi Muhammad Chaizir Muhammad Daud Muhammad Fikry Muhammad Hanafi Sahar Lubis Muhammad Ichsan Muhammad Ihsan Dacholfany Muhammad Ikhsan Setiawan Muhammad Ikhsan Setiawan Muhammad Ikhwani Muhammad Iqbal Muhammad Khahfi Zuhanda Muhammad Khalis, Muhammad Muhammad Riansyah Muhammad Zarlis Muhammad Zarlis, Muhammad Muharam, Suhari muli ani Munirul Ula Muslem Muslem Muslem Muthmainnah Muthmainnah Muzaffar Rigayatsyah Muzaffar Rigayatsyah N. Nazaruddin Nadia Karunia, Meutia Nadya Raudathul Sofa Nanda Imanda Nashihin Nasution, Zainannur Nefo Preyandre Ni Ketut Dewi Irwanti Noviany, Henny Nunsina, Nunsina Nurdin Nurdin Nurhasanah Nursyamsi SY Nuruddin Nuruddin Oksfriani Jufri Sumampouw Pasaribu, Hafni Maya Sari Pertiwi, Anggun Pikri, Faizal Poetri AL-Viany Maqfirah Puji, Ari Andriyas Putra, Arwin Putu Eka Wirawan Rafi’i, Rafi’i Rahma Fitria, Rahma Rahmat, Rezqiqah Aulia Ramlan, Rifqi Ramli, Rahmat Rasna, Rasna Razi, Ar Reskiawan, Bimas - Reza Pratama Rezzy Eko Caraka Riansyah, Muhammad Richki Hardi Rifkial Iqwal Rini Meiyanti Risawandi, Risawandi Rizka Salsabila Nasution Rizki Suwanda Rizki Wahyuri Rizky Putra Fhonna Roslaini Roslaini S, Syarifuddin Sabriana, Riska Safwandi Safwandi Safwandi Safwandi, Safwandi Said Anshari Said Fadlan Anshari salamah salamah Salat, Junaidi Samsul A Rahman Sidik Hasibuan Sandya, Deasy Saputra, Nanda Saputra, Rizwan Sasabone, Luana Satria Pati Alam Sayed Fachrurrazi Selamat Meliala Setiawan, Muhammad Ikhsan Silvia Nanda Sima, Yenny Simarmata, Marganda Sitti Nur Alam Subhan, Roni Sulistyandari, Sulistyandari Surnihayati Surnihayati Susanti, Putu Herny Sutarna, Agus Syamsiah Badruddin Syarifuddin Syarifuddin Tahulending, Anneke A Tarigan, Tasya Amelia Taufiq Taufiq Teuku Mudi Hafli Touwe, Yohana S Touwe, Yohana S. Tri Suryowidiyanti Ulfa, Nur Saufani Ultra Prayogi Ulumul Haq, Bahrul Utomo, Muhammad Fikri Veronika Nugraheni Sri Lestari Victor E. D Palapessy Wa Ode Riniati Wahyu Pertiwi, Yuarini wandi, risa Wawan Syahputra Wenny J, Syilvia Widiyani, Maya Yeni Risyani Yesy Afrillia Yuarini Wahyu Pertiwi Yuliah, Yuliah Yulisda, Desvina Yuniningsih Yuniningsih Yusra, Muhammad Zafera Adam, Jeanne d'Arc Zahratul Fitri, Zahratul Zainannur Nasution Zalfie Ardian Zara Yunizar Zega, Subhansah Zulfahmi Zulfahmi

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