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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 ARRUS Journal of Engineering and Technology 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
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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Geographically Weighted Regression (GWR) Modeling in Identifying Factors Affecting the Gender Empowerment Index in Indonesia Meliyana, Sitti Masyitah; Ahmar, Ansari Saleh; Rahman, Abdul
Daengku: Journal of Humanities and Social Sciences Innovation Vol. 5 No. 4 (2025)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.daengku4449

Abstract

This study aims to analyze the factors influencing the Gender Empowerment Index (GEI) in Indonesia using the Geographically Weighted Regression (GWR) method. The variables used in this study include the proportion of women in managerial positions, women’s income contribution, the proportion of professional workers, reported health complaints, and the proportion of women in parliament. The findings indicate that, among the five independent variables examined, only two variables significantly affect the dependent variable: the proportion of women in managerial positions (X1) and the percentage of women reporting health complaints (X5). This is evidenced by their respective probability values (Pr(>F)) of 0.0045 and 0.0128, which are below the 0.05 significance threshold. This implies that X1 and X5 have a statistically significant influence in the model. The GWR model was found to be the most suitable compared to other models, with an AIC value of 186.72 and an R² of 92.03%, indicating superior model performance in capturing spatial and non-spatial effects across regions.
Application of Neural Network Time Series (NNAR) and ARIMA to Forecast Infection Fatality Rate (IFR) of COVID-19 in Brazil Ahmar, Ansari Saleh; Boj, Eva
JOIV : International Journal on Informatics Visualization Vol 5, No 1 (2021)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.5.1.372

Abstract

Forecasting is a method that is often used to view future events using past time data. Past time data have useful information to use in obtaining the future. The aim of this study was to forecast infection fatality rate (IFR) of COVID-19 in Brazil using NNAR and ARIMA. ARIMA and NNAR are used because (1) ARIMA is a simple stochastic time series method that can be used to train and predict future time points and ARIMA also capable of capturing dynamic interactions when it uses error terms and observations of lagged terms; (2) the Artificial Neural Network (ANN) is a technique capable of analyzing certain non-linear interactions between input regressor and responses, and Neural Network Time Series (NNAR) is one method of ANN in which lagged time series values were used as inputs to a neural network. Data included in this study were derived from the total data of confirmed cases and the total data of death of COVID-19. The data of COVID-19 in Brazil from February 15, 2020 to April 30, 2020 were collected from the Worldometer (https://www.worldometers.info/coronavirus/) and Microsoft Excel 2013 was used to build a time-series table. Forecasting was accomplished by means of a time series package (forecast package) in R Software.  Neural Network Time Series and ARIMA models were applied to a dataset consisting of 76 days. The accuracy of forecasting was examined by means of an MSE. The forecast of IFR of COVID-19 in Brazil from May 01, 2020 to May 10, 2020 with NNAR (1,1) model was around in 6,85% and ARIMA (0,2,1) was around in 7.11%.
Evaluating Random Forest Regression for Air Quality Prediction Izabi, Muh. Basyar; Annas, Suwardi; Ahmar, Ansari Saleh
Jurnal Varian Vol. 9 No. 1 (2026)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v9i1.6046

Abstract

Air pollution is a growing environmental issue in Makassar due to rapid urban development and increasing transportation activity. This study aims to model and predict air pollutant concentrations using the Random Forest (RF) regression method. The data consist of daily PM2.5, PM10, CO, NO2, SO2, and O3 measurements from September 2024 to September 2025, totaling 395 observations. Missing values (14.05%) were addressed using a hybrid approach combining linear interpolation and multiple linear regression. The RF model was trained under two data-split scenarios (70:30 and 80:20) and evaluated using SMAPE, RMSE, MAE, and R2. The results show that the 80:20 configuration provides the best predictive accuracy. CO and O3 yield the most accurate predictions with SMAPE values of 9.75% and 10.87%, and R2 of 0.973 and 0.964, respectively. PM2.5 and PM10 also show strong performance, with R2 values above 0.84. These results indicate that the RF model effectively captures pollutant variability and provides reliable forecasts. Overall, Random Forest has been shown to be a robust and accurate method for predicting air quality in Makassar, supporting environmental monitoring and early warning systems. Despite its strong performance, this study is limited to two data-partition schemes and does not incorporate temporal deep-learning architectures. Future studies may investigate hybrid ensembles or deep learning approaches to determine whether incorporating sequential modeling further enhances predictive stability.
Statistical Dashboards and Business Intelligence in Campus Information Systems: A Bibliometric Review of Implementation Trends Ahmar, Ansari Saleh; Triutomo, Agung
Journal of Applied Science, Engineering, Technology, and Education Vol. 7 No. 3 (2025)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.asci2969

Abstract

Campus academic information systems have become mission-critical infrastructure in higher education, yet a significant paradox persists. Many universities operate monolithic architectures that consolidate student data and administrative functions within unified platforms—offering inherent security advantages including centralized authentication, unified access control, and simplified vulnerability monitoring. However, scholarly discourse examining how these secure integrated systems can simultaneously achieve advanced business intelligence capabilities remains remarkably thin. This bibliometric study analyzes 749 publications from Scopus (2010-2025) to map the intellectual landscape of campus information systems research, with particular attention to security frameworks and statistical dashboard implementations. The methodology combines linear regression trend analysis (? = 2.54, p = 0.00135, R² = 0.5317), Bradford's Law, Lotka's Law, and k-means clustering (k = 9). Results reveal statistically significant publication growth (CAGR = 1.46%), accumulating 6,039 citations (mean = 8.06) across 1,945 authors from 86 countries. Indonesia dominates contributions (26.1%), followed by China (10.3%) and the United States (8.1%). Thematic analysis identifies nine research clusters, with security-focused studies employing PTES and OWASP methodologies achieving 83% intrusion detection accuracy, while governance evaluations using COBIT and ISO 27001 reveal system maturity gaps. Critically, fewer than 10% of publications address real-time analytics or decision support visualization within secure monolithic architectures. The collaboration index (3.03 authors/document) and degree of collaboration (83.2%) indicate robust interdisciplinary practices. Findings suggest that while security research has matured, significant gaps persist in integrating business intelligence dashboards with secure monolithic systems—highlighting urgent need for research bridging data protection frameworks with analytical capabilities.
Performance Evaluation of the K-Means Clustering Method in Grouping Indonesian Provinces Based on Potential Disaster Impact Meliyana, Sitti Masyitah; Dunggio, Anugra S. S.; Ahmar, Ansari Saleh
JINAV: Journal of Information and Visualization Vol. 6 No. 2 (2025)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

This study aims to cluster the provinces in Indonesia based on their level of potential disaster impact, which consists of hazard area, exposed population, physical losses, economic losses, and environmental damage, using the K-Means clustering algorithm and to evaluate the performance of the resulting model. The optimal number of clusters was determined using the Silhouette Coefficient and the Elbow Method with the Within-Cluster Sum of Squares (WSS) approach. The performance evaluation of the K-Means clustering was conducted using the Davies–Bouldin Index (DBI). Based on the selection of the optimal number of clusters, the Silhouette Coefficient produced the highest value at K=3, with a score of 0.699. Similarly, the Elbow Method showed a significant decrease in the mean WSS at K=3, indicating that three clusters were optimal. The performance evaluation using DBI for K=3 resulted in a score of 0.30. According to the principle of DBI evaluation, the closer the DBI value is to zero (without being negative), the better the clustering quality. Therefore, it can be concluded that the K-Means clustering algorithm successfully produced a very good clustering structure in grouping Indonesian provinces based on their potential disaster impact.
Workshop on Student Graduation Decisions Using Statistical Methods at Takalar State Senior High School 7 Suwardi Annas; Ansari Saleh Ahmar; Zulkifli Rais; Rahmat H.S; Agung Tri Utomo
ARRUS Jurnal Pengabdian Kepada Masyarakat Vol. 4 No. 2 (2025)
Publisher : PT ARRUS Intelektual Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.abdiku4458

Abstract

This community service program was conducted at SMA Negeri 7 Takalar to enhance teachers’ ability to utilize statistical methods specifically logistic regression to support data-driven graduation decisions. The training addressed challenges related to manual graduation assessment processes that often lack objective analytical support. Participants were introduced to the basic concepts of logistic regression, followed by hands-on practice using an interactive R Shiny dashboard to analyze student data and estimate graduation probabilities. The results indicate that teachers were able to understand and apply statistical analysis procedures, interpret logistic regression outputs, and recognize the importance of evidence-based decision-making. This activity not only improved teachers’ data literacy but also supported digital transformation efforts in education and strengthened collaboration between Universitas Negeri Makassar and SMA Negeri 7 Takalar. The program is expected to contribute to more accurate, transparent, and data-informed graduation assessments in the future.
Pelatihan Pembuatan Flipbook Interaktif Berbasis Deep Learning bagi Guru di Kabupaten Maros Hastuty Musa; Rusli; Abdul Rahman; Andika Saputra; Ansari Saleh Ahmar
ARRUS Jurnal Pengabdian Kepada Masyarakat Vol. 5 No. 1 (2026)
Publisher : PT ARRUS Intelektual Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.abdiku4791

Abstract

Transformasi pendidikan di era digital menuntut guru untuk mampu mengembangkan pembelajaran yang tidak hanya berbasis teknologi, tetapi juga mendorong pemahaman konseptual yang mendalam. Kegiatan pengabdian ini bertujuan untuk meningkatkan kompetensi guru di Kabupaten Maros dalam mengembangkan flipbook interaktif berbasis deep learning (pembelajaran mendalam). Permasalahan yang dihadapi guru meliputi keterbatasan literasi digital, minimnya pengalaman dalam merancang media interaktif, serta pembelajaran yang masih berorientasi pada hafalan. Kegiatan dilaksanakan melalui sosialisasi, pelatihan teknis, praktik mandiri, serta pendampingan dan evaluasi. Guru diperkenalkan pada konsep flipbook interaktif yang dirancang untuk mendukung pembelajaran bermakna, yaitu pembelajaran yang menekankan pemahaman konsep, keterkaitan antar materi, serta kemampuan berpikir kritis. Hasil kegiatan menunjukkan adanya peningkatan pemahaman guru dalam mengembangkan media pembelajaran interaktif serta kemampuan awal dalam merancang aktivitas pembelajaran yang mengarah pada pembelajaran mendalam.
Forecasting Farmer Exchange Rates as a Welfare Proxy: BetaSutte's Role in Predicting Agricultural Income Stability in Indonesia Ansari Saleh Ahmar
Daengku: Journal of Humanities and Social Sciences Innovation Vol. 6 No. 1 (2026)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.daengku1036

Abstract

This study presents BetaSutte — a novel hybrid forecasting model applying the four-lag ?-Sutte Indicator to OLS-detrended residuals rather than to the raw level series — and evaluates it on Indonesia’s monthly Nilai Tukar Petani (NTP, Farmer Exchange Rate), a composite agricultural welfare index for over 40 million farming households. Using 84 monthly observations from January 2019 to December 2025 (Badan Pusat Statistik), the model separates NTP into a linear trend component and a residual , applies the ?-Sutte formula to the stationary residual domain, and generates forecasts as with ? optimised by grid search. Calibrating on the first 60 observations (January 2019–December 2023), the OLS trend explains 82.75% of NTP variance , and the optimal ? = 0.30 yields in-sample RMSE = 1.6887, MAE = 1.3695, and MAPE = 1.2881% — an 11.5% RMSE reduction versus the trend-only baseline. Crucially, two full years of genuinely out-of-sample validation (January 2024–December 2025, n = 24) confirm BetaSutte’s operational superiority: RMSE = 5.4841 versus 6.0782 for trend-only, a 9.8% improvement representing 112 months of independently collected data never seen during calibration. Residuals are normally distributed (Shapiro-Wilk p = 0.130), confirming well-conditioned model inputs. The full-sample retrained model (n = 84) estimates , forecasting January 2026 NTP at 123.91. This study constitutes the first BetaSutte application to a composite agricultural welfare index with two-year prospective out-of-sample validation.
A Hybrid Soft Computing Approach to Inflation Forecasting: HybridSutte Versus Exponential Smoothing Benchmarks in an Emerging Economy Ansari Saleh Ahmar; Abdul Rahman
Daengku: Journal of Humanities and Social Sciences Innovation Vol. 6 No. 2 (2026)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.daengku4841

Abstract

Central banks in commodity-dependent emerging economies face a structural forecasting challenge: exponential smoothing methods calibrated on supply-shock training windows systematically overproject the downward trend into post-shock stabilisation phases, producing compounding errors that undermine monetary policy communication. This paper proposes HybridSutte, a soft computing model that fuses four-point alpha-Sutte recurrence with exponential smoothing correction, as an alternative to conventional exponential smoothing benchmarks. Monthly year-on-year Consumer Price Index data published by Bank Indonesia cover January 2021 through December 2025 (n = 60 observations), capturing Indonesia's complete monetary policy cycle: COVID-19 demand recovery, Russia-Ukraine commodity supply shock (peak: 5.95%, September 2022), Bank Indonesia's 250 basis-point rate-hike disinflation campaign, and the subsequent 2025 post-shock stabilisation within the 2.5% ± 1% target band. The 51/9 in-sample/out-of-sample partition places the evaluation window (April–December 2025) entirely within the structurally distinct post-shock stabilised regime. HybridSutte achieves out-of-sample RMSE of 0.606% and MAPE of 21.25%, compared with Holt's double exponential smoothing (ETS) RMSE of 3.069% and MAPE of 121.60%, yielding reductions of 80.2% and 82.5%, respectively. The performance advantage grows monotonically with forecast horizon h, reaching a 451.1% cumulative absolute error differential by  = 9. This is the first application of HybridSutte to central bank inflation data in an emerging market and the first to evaluate a soft computing hybrid model across a complete five-year monetary policy cycle. Findings support regime-aware model selection for central bank forecasting departments.
Earth System Variables as Drivers of Environmental Commodity Price Dynamics: A Systematic Review of Physics-Informed and Data-Driven Modelling Approaches (2010–2026) Muhammad Nusrang; Ansari Saleh Ahmar; Abdul Rahman; Agung Tri Utomo; Muh. Qodri Alfairus
ARRUS Journal of Social Sciences and Humanities Vol. 6 No. 3 (2026)
Publisher : PT ARRUS Intelektual Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/soshum4973

Abstract

Environmental commodity markets (carbon allowances, electricity, natural gas, and renewable energy instruments) are, at their root, earth system markets: their price-generating processes are as much a product of atmospheric circulation, hydrological regimes, and climate teleconnections as of supply-demand fundamentals or regulatory signals. Despite this physical reality, the machine learning forecasting literature has treated these markets primarily as benchmarking arenas, decoupling predictive architectures from the physical processes that drive price-relevant forcing. This systematic review follows PRISMA 2020 guidelines on a corpus of 413 peer-reviewed studies drawn from 652 Scopus records (2010–2026) and examines how earth system variables, including temperature anomalies, precipitation regimes, wind resource indices, atmospheric pollution metrics, and climate teleconnections such as ENSO and NAO, have been integrated into ML-based environmental commodity price models, and evaluates the evidence for whether physics-informed feature engineering confers measurable accuracy advantages over purely data-driven approaches. Bibliometric analysis reveals rapid field expansion (71.6% of publications in 2021–2026), geographic concentration in Chinese ETS research (?68% of high-impact output), and methodological dominance of hybrid decomposition-deep learning architectures. Models incorporating earth system variables consistently outperform endogenous-only ML architectures by 12–35% on MAPE, yet fewer than 30% of corpus studies include any physical predictor and climate teleconnection indices appear in under 4% of studies despite their relevance at energy market planning horizons. Six research priorities are identified, centred on numerical weather prediction ensemble integration, cross-climate-regime validation, and probabilistic forecasting grounded in physical uncertainty quantification.
Co-Authors - Asmar Abdul Rahman Abdul Rahman Abdul Rahman Abdul Rahman Abdussakir Abdussakir Absussakir Abdussakir Achmad Sani Supriyanto Agung Tri Utomo Agung Tri Utomo Agus Nasir Ahmad Rifad Riadhi Ahmad Talib Aidid, Muhammad Kasim Akbar Iskandar Alfairus, Muh. Qodri Ali Mokhtar Alief Imron Juliodinata Alok Kumar Panday Alsa, Yudhistira Ananda Andika Isma Andika Saputra ANDIKA SAPUTRA Andika Saputra Anggreni, Afrillia Annas, Suwardi Asfar Asmar Asmar, Asmar Astuti, Niken Probondani Aswi, Aswi Ayu Rahayu Azzajjad, Muhammad Fath Boj del Val, Eva Boj, Eva Botto-Tobar, Miguel Bustan, M Nadjib Cadena, Angela Diaz Dary Mochamad Rifqie Della Fadhilatunisa Dewi Fatmarani Surianto Dewi Satria Ahmar Djawad, Yasser Abd. Dunggio, Anugra S. S. Ersa Karwingsi Eva Boj Faizal Arya Samman Fathahillah Fathahillah Halim, Patmawati 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 Izabi, Muh. Basyar Jamaluddin Jamaluddin Kamaluddin Kamaluddin Kasmudin Mustapa Khadijah Khadijah Khaeruddin Khaeruddin Kusmaladewi Kusmaladewi Lince, Ranak M. Miftach Fakhri 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 Abdy Muhammad Arif Tiro Muhammad Arif Tiro Muhammad Farhan Muhammad Kasim Aidid Muhammad Kasim Aidid Muhammad Nadjib Bustan Muhammad Nadjib Bustan Muhammad Nusrang Muhammad Nusrang Muliadi N. Nurahdawati Nachnoer Arss Nasrul Ihsan Niken Probondani Astuti Novi Afryanthi S. Nur Anisa Nurdin Arsyad, Nurdin Nurhikmawati, Nurhikmawati Parkhimenko Vladimir Anatolievich Patmasari, Andi Poerwanto, Bobby Purnama Ningsih R. Ruliana R. Rusli R. Rusli Raden Mohamad Herdian Bhakti Rahman, Abdul Rahman, Muhammad Fatur Rahmat H.S Rahmat Hidayat 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 Rusli Rusli Rusli Rustam, R. Rustam, Sitti Nailah Sahid Salim Al Idrus Salim Al Idrus Salsabila, Nurul Khofifah Sapto Haryoko Shofiyah Al Idrus Singh, Pawan Kumar Siswahyudianto Siti Nurazizah Auliah Sitti Masyitah Meliyana R. Sitti Rahmawati Sobirov, Bobur Sri Hastuti Virgianti Pulukadang Sri Muliani, Sri Sriwahyuni, Andi Ayu Suci Lestari Sutamrin, Sutamrin Suwardi Annas Suwardi Annas Syafruddin Side Tabash, Mosab Tonio, Sarinah Emilia Tri Santoso Triutomo, Agung Utomo, Agung Tri Wahab, Zamil wahyuni wahyuni Yunus, Asmar Zakiyah Mar'ah Zakiyah Mar'ah Zulkifli Rais