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All Journal IQTISHODUNA Jurnal Ekonomi Modernisasi The Asian Journal of Technology Management (AJTM) JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Studi Komunikasi Seminar Nasional Variansi (Venue Artikulasi-Riset, Inovasi, Resonansi-Teori, dan Aplikasi Statistika) Jurnal Varian Abdi Dosen : Jurnal Pengabdian Pada Masyarakat Journal of Applied Science, Engineering, Technology, and Education Variansi : Journal of Statistics and Its Application on Teaching and Research Mattawang: Jurnal Pengabdian Masyarakat JINAV: Journal of Information and Visualization Quantitative Economics and Management Studies LOSARI: Jurnal Pengabdian Kepada Masyarakat Jurnal Riset dan Inovasi Pembelajaran Panrannuangku Jurnal Pengabdian Masyarakat EduLine: Journal of Education and Learning Innovation Daengku: Journal of Humanities and Social Sciences Innovation ARRUS Journal of Social Sciences and Humanities ARRUS Journal of Mathematics and Applied Science al-fikrah: Jurnal Manajemen Pendidikan Seminar Nasional Pengabdian Kepada Masyarakat VARIANSI: Journal of Statistics and Its Application on Teaching and Research Seminar Nasional Hasil Penelitian LP2M UNM ARRUS Jurnal Pengabdian Kepada Masyarakat

Ahmar, Ansari Saleh

Department Of Statistics, Universitas Negeri Makassar, 90223, Makassar, Indonesia

Author-ID : 592968

Religion Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Physics Public Health Social Sciences Transportation Other

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Title

The date predicted 200.000 cases of Covid-19 in Spain Ahmar, Ansari Saleh; Boj, Eva
Journal of Applied Science, Engineering, Technology, and Education Vol. 2 No. 2 (2020)
Publisher : Yayasan Ahmar Cendekia Indonesia

The aim of this study is to predict 200.000 cases of Covid-19 in Spain. Covid-19 Spanish confirmed data obtained from Worldometer from 01 March 2020 â€“ 17 April 2020. The data from 01 March 2020 â€“ 10 April 2020 using to fitting with data from 11 April â€“ 17 April 2020. For the evaluation of the forecasting accuracy measures, we use the mean absolute percentage error (MAPE). Based on the results of SutteARIMA fitting data, the accuracy of SutteARIMA for the period 11 April 2020 - 17 April 2020 is 0.61% and we forecast 20.000 confirmed cases of Spain by the WHO situation report day 90/91 which is 19 April 2020 / 20 April 2020.

The date predicted 200.000 cases of Covid-19 in Spain Ahmar, Ansari Saleh; Boj del Val, Eva
Journal of Applied Science, Engineering, Technology, and Education Vol. 2 No. 2 (2020)
Publisher : Yayasan Ahmar Cendekia Indonesia

The date predicted 200.000 cases of Covid-19 in Spain Ansari Saleh Ahmar; Eva Boj
Journal of Applied Science, Engineering, Technology, and Education Vol. 2 No. 2 (2020)
Publisher : Yayasan Ahmar Cendekia Indonesia

The aim of this study is to predict 200.000 cases of Covid-19 in Spain. Covid-19 Spanish confirmed data obtained from Worldometer from 01 March 2020 – 17 April 2020. The data from 01 March 2020 – 10 April 2020 using to fitting with data from 11 April – 17 April 2020. For the evaluation of the forecasting accuracy measures, we use the mean absolute percentage error (MAPE). Based on the results of SutteARIMA fitting data, the accuracy of SutteARIMA for the period 11 April 2020 - 17 April 2020 is 0.61% and we forecast 20.000 confirmed cases of Spain by the WHO situation report day 90/91 which is 19 April 2020 / 20 April 2020.

Bibliometric Analysis of “Statistics: A Journal of Theoretical and Applied Statistics” on 1985-2021 Period Ansari Saleh Ahmar; Miguel Botto-Tobar; Abdul Rahman; Angela Diaz Cadena; R. Rusli; Rahmat Hidayat
Journal of Applied Science, Engineering, Technology, and Education Vol. 4 No. 1 (2022)
Publisher : PT Mattawang Mediatama Solution

This study is a quantitative research using bibliometric analysis. This study aimed to find out more detail about the “Statistics: A Journal of Theoretical and Applied Statistics” or SJTAS which was published during 1985-2021. This was seen from the topic of study, country productivity, author contributions, and analysis of their citation. The data in this study were taken from the Scopus database using keywords: (ISSN(0233-1888) OR ISSN(1029-4910)). The results obtained from the Scopus database are 1.798 documents. The average article citation fluctuates annually and the highest article citation is in 2018. Keywords from articles published in the SJTAS are dominated by topics: order statistics (55 articles), asymptotic normality (43 articles), bootstrap (33 articles), exponential distribution (32 articles), and consistency (31 articles).

Machine Learning Algorithms with Parameter Tuning to Predict Students’ Graduation-on-time: A Case Study in Higher Education Rizal Bakri; Niken Probondani Astuti; Ansari Saleh Ahmar
Journal of Applied Science, Engineering, Technology, and Education Vol. 4 No. 2 (2022)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.asci1581

This study aims to predict a student’s graduation on time (GOT) using machine learning algorithms. We applied five different machine learning algorithms, namely Random Forest, Support Vector Machine (Linear Kernel), Support Vector Machine (Polynomial Kernel), K-Nearest Neighbors, and Naïve Bayes. These algorithms were tested using 10-fold cross validation and simulated various parameter tuning values. The results show that the Random Forest algorithm produces the best accuracy and kappa statistics values, so this algorithm is suitable for modeling predictive data of students graduating on time. This predictive model is expected to be useful for higher education management in designing their strategies to assist and improve student academic performance weaknesses in order to achieve graduation on time.

Android-Based E-Learning Application Design in Schools Akbar Iskandar; Mansyur Mansyur; Ansari Saleh Ahmar; Muliadi Muliadi; Abdul Rahman
Journal of Applied Science, Engineering, Technology, and Education Vol. 5 No. 1 (2023)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.asci1643

The concept of E-Learning itself is a guide between learning processes that are packaged in the form of information technology. During the pandemic, schools in Indonesia began to make changes to offline learning to online by utilizing various applications such as the Google Classroom and WhatsApp applications. Based on these conditions, the researcher tried to develop a system for use in schools in the form of Android-based e-learning using the waterfall method. Based on the results of system design and testing it appears that this system can be used at research locations because the system can run well based on the results of black box testing, while the expert judgment of 10 people said it was very good, while the user response was 150 people with a very good response of 70 percent (105 people), good 30 percent (45 people). So that by using an Android-based E-Learning application, of course learning done in schools is better than those that only use conventional models or only use Whatsapp or Google Classroom. In addition, with E-Learning the learning process that is usually carried out in schools can be done without having to go to school again, students and teachers can carry out the learning process from their respective homes using smartphones and the internet.

Design of Quantized Deep Neural Network Hardware Inference Accelerator Using Systolic Architecture Rifqie, Dary Mochamad; Djawad, Yasser Abd.; Samman, Faizal Arya; Ahmar, Ansari Saleh; Fakhri, M. Miftach
Journal of Applied Science, Engineering, Technology, and Education Vol. 6 No. 1 (2024)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.asci2689

This paper presents a hardware inference accelerator architecture of quantized deep neural networks (DNN). The proposed accelerator implements all computation in a quantize version of DNN including linear transformations like matrix multiplications, nonlinear activation functions such as ReLU, quantization and dequantization operation. The hardware accelerator of quantized DNN consists of matrix multiplication core which is implemented in systolic array architecture, and the QDR core for computing the operation of quantization, dequantization, and ReLU. This proposed hardware architecture is implemented in Verilog Hardware Description Language (HDL) code using modelsim. To validate, we simulated the quantized DNN using Python programming language and compared the results with our proposed hardware accelerator. The result of this comparison shows a very slight difference, confirming the validity of our quantized DNN hardware accelerator.

Barriers to Effective Learning: Examining the Influence of Delayed Feedback on Student Engagement and Problem Solving Skills in Ubiquitous Learning Programming Fakhri, M. Miftach; Ansari Saleh Ahmar; Rosidah, Rosidah; Fadhilatunisa, Della; Tabash, Mosab
Journal of Applied Science, Engineering, Technology, and Education Vol. 6 No. 1 (2024)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.asci2825

The rapid evolution of technology has reshaped the educational landscape, ushering in ubiquitous learning environments that provide learners with unparalleled access to educational resources at any time and location. This study aimed to investigate the impact of delayed feedback on student engagement and problem-solving skills in ubiquitous learning programming environments. The purpose was to understand how different forms of student engagement—behavioral, emotional, and cognitive—influence problem-solving abilities and how students perceive and handle delayed feedback. A quantitative method was employed using a cross-sectional survey design. Data were collected from 293 students enrolled in the Department of Informatics and Computer Engineering, Faculty of Engineering, Makassar State University, who had studied web and mobile programming courses. Standardized questionnaires were administered to measure variables. Quantitative data analysis involved descriptive statistical analysis and structural equation modeling (SEM) using SmartPLS 4.0. The research results revealed that behavioral engagement (BE) significantly improves problem-solving skills and helps students better handle delayed feedback. Emotional engagement (EE) has the strongest influence on problem-solving abilities and responses to delayed feedback. Cognitive engagement (CE), while not directly enhancing problem-solving skills, significantly aids in the management of delayed feedback. These findings underscore the importance of fostering behavioral and emotional engagement to enhance problem-solving skills and mitigate the adverse effects of delayed feedback. Strategies such as gamification, real-time collaboration, and immediate feedback mechanisms are essential to improve learning outcomes in ubiquitous learning programming environments.

BetaSutte: Applying Novelty in Data Forecasting with the Modified Trend-Augmented α-Sutte Indicator, A Case Study on Bank Mandiri (BMRI) Stock Prices Ahmar, Ansari Saleh
Journal of Applied Science, Engineering, Technology, and Education Vol. 6 No. 2 (2024)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.asci3826

This study assesses the effectiveness of the BetaSutte forecasting model, an enhanced version of the α-Sutte Indicator, dubbed the Modified Trend-Augmented α-Sutte Indicator, in forecasting the stock prices of Bank Mandiri (BMRI). Data from investing.com, spanning January 2018 to December 2023, was divided into training and testing subsets to both develop and validate the forecasting model, ensuring it performs well across unseen data. BetaSutte builds on the foundational α-Sutte by integrating advanced trend analysis, mitigating the influence of outliers, and utilizing automatic parameter optimization to boost forecasting precision. The efficacy of BetaSutte is evaluated against well-established models such as SVR, XGBoost, and ARIMA. ARIMA was chosen for its detailed management of time-series data via autoregressive, differencing, and moving average components. In contrast, SVR and XGBoost are recognized for their strong predictive performance. The performance of these models was gauged using Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). These metrics shed light on the extent of forecasting errors and the percentage of relative errors, respectively, providing a comprehensive view of each model’s predictive accuracy for BMRI stock prices. The results demonstrated that BetaSutte outstripped the other models in terms of RMSE and MAPE, highlighting its enhanced ability to accurately reflect the dynamics of BMRI’s stock prices with greater precision and dependability. This establishes BetaSutte as a formidable tool in financial forecasting, particularly valuable in environments characterized by volatile market conditions and unpredictable data patterns.

Time Series Innovation: Leveraging BetaSutte Models to Enhance Indonesia's Export Price Forecasting Ahmar, Ansari Saleh; Boj, Eva
Journal of Applied Science, Engineering, Technology, and Education Vol. 7 No. 1 (2025)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.asci3831

This study introduces a novel application of the Modified Trend-Augmented α-Sutte Indicator (BetaSutte) model for forecasting Indonesia's export prices and compares its performance with the traditional ARIMA approach. Accurate export price forecasting is crucial for economic planning, trade policy formulation, and business strategy development in Indonesia's dynamic and globally connected economy. Using monthly export value data from January 2022 to September 2024 obtained from Indonesia's Central Bureau of Statistics (BPS), we examined whether the BetaSutte model's decomposition of trend and residual components offers enhanced predictive accuracy over the conventional ARIMA methodology. Results show that while the ARIMA(0,1,0) model demonstrated superior in-sample performance (Training MAPE: 7.71% vs. 80.78%), the BetaSutte model achieved better out-of-sample forecasting accuracy (Testing MAPE: 11.22% vs. 11.61%). The BetaSutte model's linear trend component identified a negative slope (coefficient: -158.4), indicating a systematic decline in Indonesia's export values over the study period, which has important implications for trade policy. Furthermore, the model successfully captured the volatility in export prices through its residual forecasting component. These findings suggest that the BetaSutte model's explicit modeling of trend components provides meaningful advantages for export price forecasting, despite its more complex implementation. This research contributes to the growing literature on hybrid forecasting methodologies and offers practical guidance for stakeholders interested in Indonesia's international trade dynamics. For policymakers, the results highlight potential challenges for Indonesia's export competitiveness and suggest the need for targeted interventions to address the identified downward trend in export values.

Co-Authors Abdul Rahman Abdul Rahman Abdussakir Abdussakir Absussakir Abdussakir Achmad Sani Supriyanto Agus Nasir Ahmad Rifad Riadhi Ahmad Talib Akbar Iskandar Akbar Iskandar Alfairus, Muh. Qodri Ali Mokhtar Alief Imron Juliodinata Alok Kumar Panday Alsa, Yudhistira Ananda Andika Isma ANDIKA SAPUTRA Angela Diaz Cadena Asfar Asfar Asmar Asmar, Asmar Astuti, Niken Probondani Aswi, Aswi Ayu Rahayu Azzajjad, Muhammad Fath Boj del Val, Eva Boj, Eva Bustan, M Nadjib Dary Mochamad Rifqie Della Fadhilatunisa Dewi Fatmarani Surianto Dewi Satria Ahmar Djawad, Yasser Abd. Ersa Karwingsi Eva Boj Faizal Arya Samman Fathahillah Fathahillah Hamzah Upu Hardianti Hafid Hastuty Hastuty Hastuty Hastuty Hastuty Musa Herman Herman Hidayat M., Wahyu Ifriana Ifriana Ilimu, Edi Irwan Irwan Irwan Irwan Isma Muthahharah Jamaluddin Jamaluddin Kamaluddin Kamaluddin Kasmudin Mustapa Khadijah Khaeruddin Khaeruddin Lince, Ranak M. Miftach Fakhri Maemunah Magfirah Manalu, Yessi Febianti Mansyur Mansyur Marni Marni, Marni Meliyana R, Sitti Masyitah Miguel Botto-Tobar Misriani Suardin Mohd. Rizal Mohd. Isa Muhammad Abdy Muhammad Arif Tiro Muhammad Arif Tiro Muhammad Farhan Muhammad Kasim Aidid Muhammad Kasim Aidid Muhammad Nadjib Bustan Muhammad Nadjib Bustan Muhammad Nusrang Muliadi Muliadi N. Nurahdawati Nachnoer Arss Nasrul Ihsan Niken Probondani Astuti Niken Probondani Astuti Novi Afryanthi S. Nur Anisa Nurdin Arsyad, Nurdin Nurhikmawati, Nurhikmawati Nurul Khofifah Salsabila Parkhimenko Vladimir Anatolievich Patmasari, Andi Poerwanto, Bobby R. Ruliana R. Rusli R. Rusli R. Rusli Rahman, Abdul Rahman, Muhammad Fatur Rahmat Hidayat Rahmat Hidayat Rais, Zulkifli Rajesh Kumar Ramli Umar Riny Jefri Rizal Bakri Robbi Rahim Rosidah Rosidah Rosidah Rosidah Ruliana Ruliana Ruliana, Ruliana Rusli Rusli Rusli Rusli Rusli Rusli Rusli Rusli Rustam, Sitti Nailah Sahid Sahid Salim Al Idrus Salim Al Idrus Sapto Haryoko Sarinah Emilia Tonio Shofiyah Al Idrus Singh, Pawan Kumar Siti Nurazizah Auliah Sitti Masyitah Meliyana R. Sitti Rahmawati Sobirov, Bobur Sri Hastuti Virgianti Pulukadang Sri Muliani Sriwahyuni, Andi Ayu Suci Lestari Sutamrin, Sutamrin Suwardi Annas Suwardi Annas Suwardi Annas Syafruddin Side Tabash, Mosab Tri Santoso Triutomo, Agung wahyuni wahyuni Yunus, Asmar Zakiyah Mar'ah Zakiyah Mar'ah Zamil Wahab Zulkifli Rais

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