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All Journal EIGEN MATHEMATICS JOURNAL Mattawang: Jurnal Pengabdian Masyarakat JINAV: Journal of Information and Visualization Quantitative Economics and Management Studies Panrannuangku Jurnal Pengabdian Masyarakat Daengku: Journal of Humanities and Social Sciences Innovation ARRUS Journal of Social Sciences and Humanities ARRUS Journal of Mathematics and Applied Science Ininnawa: Jurnal Pengabdian Masyarakat ARRUS Jurnal Pengabdian Kepada Masyarakat Jurnal Hasil-Hasil Pengabdian dan Pemberdayaan Masyarakat (JHP2M) Jurnal MediaTIK Journal of Mathematics, Computation and Statistics (JMATHCOS)
Sitti Masyitah Meliyana R.
Universitas Negeri Makassar
Religion Humanities Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Physics Public Health Social Sciences Other

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Comparative Performance Analysis of Two Clustering Methods for Grouping Indonesian Provinces Based on Forest Area Size Meliyana, Sitti Masyitah; S.A. Dunggio, Anugra; Muhammad, Subhan; 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.daengku4439

Abstract

This study aims to compare the performance of two clustering algorithms, K-Means Clustering and K-Medoids Clustering in grouping Indonesian provinces based on forest area by type. The optimal number of clusters was determined using the minimum Davies–Bouldin Index (DBI), while cluster performance was evaluated using the Silhouette Coefficient. Clustering, as one of the key techniques in data mining, automatically classifies data into several groups with similar characteristics. The results reveal differences in the number of clusters produced by the two algorithms. The K-Means method generated four clusters, indicated by its lowest DBI value of 0.515, whereas the K-Medoids method produced three clusters, with a minimum DBI value of 0.559. The clustering performance of K-Means resulted in a Silhouette Coefficient of 0.610, while K-Medoids achieved a higher value of 0.644. Based on these results, the K-Medoids Clustering method with three clusters, demonstrates superior performance in analyzing the grouping of Indonesian provinces by forest area type.
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.
PKM Workshop Pembuatan Micromodul Digital untuk Meningkatkan Keterampilan IT dalam Pengajaran Para Guru SMA Negeri 7 Takalar Meliyana, Sitti Masyitah; Ruliana; Sudarmin; Hidayat, Rahmat; Alfairus, Muh. Qodri
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.abdiku4283

Abstract

Perkembangan teknologi digital menuntut guru untuk memiliki keterampilan dalam memanfaatkan media pembelajaran berbasis teknologi. Namun, guru-guru di SMA Negeri 7 Takalar masih menghadapi kendala dalam pembuatan dan penggunaan micromodul digital yang interaktif. Kegiatan Pengabdian kepada Masyarakat (PKM) ini bertujuan meningkatkan keterampilan teknologi informasi para guru melalui workshop pembuatan micromodul digital menggunakan aplikasi Canva dan Heyzine Flipbook. Metode pelaksanaan meliputi sosialisasi, pelatihan, penerapan teknologi, pendampingan, evaluasi, dan keberlanjutan program. Kegiatan diikuti oleh 25 guru dengan latar belakang mata pelajaran yang beragam. Hasil menunjukkan bahwa 85% peserta mampu membuat micromodul digital sesuai standar, 75% berhasil mengintegrasikannya ke dalam Rencana Pelaksanaan Pembelajaran (RPP), dan 80% merasakan peningkatan interaktivitas pembelajaran di kelas. Kesimpulannya, pelatihan ini efektif meningkatkan keterampilan IT guru dan berdampak pada peningkatan kualitas pembelajaran di SMA Negeri 7 Takalar.
Implementation of Support Vector Regression (SVR) and Double Exponential Smoothing (DES) for Forecasting BRI Stock Prices Meliyana, Sitti Masyitah; Aidid, Muhammad Kasim; Rahmadhani, Amaliyah
ARRUS Journal of Mathematics and Applied Science Vol. 5 No. 2 (2025)
Publisher : PT ARRUS Intelektual Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/mathscience4282

Abstract

This study aims to forecast the closing stock prices of BRI using Support Vector Regression (SVR) and Double Exponential Smoothing (DES) methods. The data used in this research is secondary data obtained from the Yahoo Finance website, covering the period from January 2020 to November 2023. The analytical steps using the SVR method involve selecting the optimal model by applying Grid Search Optimization to various kernels (linear, polynomial, radial, and sigmoid). The best-performing model was found to be the radial kernel with parameters ε = 0.1, C = 100, and γ = 10, yielding a Mean Absolute Percentage Error (MAPE) of 0.2431%, which was then used for forecasting. For the DES method, the steps involved parameter determination and minimizing the MAPE value, followed by smoothing calculations and forecasting. The optimal parameters obtained were α = 0.89 and β = 0.01, resulting in a MAPE value of 1.4832%. Based on the comparison of MAPE values, it can be concluded that the SVR method with a radial kernel (ε = 0.1, C = 100, γ = 10) provides the most accurate forecasts for BRI closing stock prices, with the lowest MAPE of 0.2431%.
Co-Authors Abdul Rahman Ahmad Yasir Aras Ahmar, Ansari Saleh Aidid, Muhammad Kasim Alfairus, Muh. Qodri Alok Kumar Panday Angri Lismayani Anisa Nurfadilah Nurdin Annisa, Nurawalia Baso Intang Sappaile Elisa, Nur Erman Syarif Hardianti Hafid Hastuty Musa Isma Muthahharah Isma Muthahharah Jamaluddin Jamaluddin Lince, Ranak Mar'ah, Zakiyah Mariani Mar’ah, Zakiyah Miguel Botto-Tobar Muhammad Ammar Naufal Muhammad Ansarullah S. Tabbu Muhammad Isbar Pratama Muhammad, Subhan Muthahharah, Isma Nachnoer Arss Parkhimenko Vladimir Anatolievich R. Rusli Rahmadhani, Amaliyah Rahman, Abdul Rahmat Hidayat Rahmat Hidayat Rais, Zulkifli Rezky Amalia Hamka Ruliana Ruliana Ruliana, Ruliana Ruliana, R. Rusli Rusli Rusli Rusli Rustam, Sitti Nail Rustam, Sitti Nailah S. Sudarmin, S. S.A. Dunggio, Anugra Siti Nurazizah Auliah Sitti Nailah Rustam Sitti Nailah Rustam Sudarmin Sudarmin Sudarmin Wahyuni, Maya Sari Yunus, Asmar Zakiyah Mar'ah Zakiyah Mar'ah Zakiyah Mar’ah Zulkifli Rais