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All Journal JURNAL ILMIAH MATEMATIKA DAN TERAPAN BAREKENG: Jurnal Ilmu Matematika dan Terapan JOURNAL OF APPLIED INFORMATICS AND COMPUTING DedikasiMU: Journal of Community Service Leibniz: Jurnal Matematika Priviet Social Sciences Journal
Harismahyanti A., Andi
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Religion Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Medicine & Pharmacology Physics Social Sciences Transportation

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Comparison of Random Survival Forest and Fuzzy Random Survival Forest Models in Telecommunications Industry Customer Data Nurhaliza, Sitti; Harismahyanti, Andi; Najiha, Alimatun
JURNAL ILMIAH MATEMATIKA DAN TERAPAN Vol. 21 No. 2 (2024)
Publisher : Program Studi Matematika, Universitas Tadulako

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22487/2540766X.2024.v21.i2.17498

Abstract

The telecommunications sector is facing increasing competition, and customer churn is still a majorchallenge despite the implementation of advanced promotions and high-quality services. Churn refers tothe discontinuation of services by customers, influenced by several factors that can be found through datamodeling. This study compares two predictive models, Random Survival Forest (RSF) and Fuzzy RandomSurvival Forest (FRSF), for predicting customer churn time in the telecommunications industry. Bothmodels are evaluated using the median C-index value obtained from 20 repetitions, ensuring moreconsistent and reliable results. RSF, a widely used survival analysis method, has shown strong predictivepower, with studies reporting up to 99% accuracy in churn prediction. However, FRSF, a modified versionthat incorporates fuzzy logic, has proved superior performance, particularly in handling imprecise oruncertain data. The results show that FRSF achieves a lower error rate of 0.1739, compared to RSF's errorrate of 0.1906. These findings suggest that FRSF outperforms RSF in churn prediction, making it a morereliable and righter model for finding at-risk customers. The study concludes that the FRSF model is thepreferred choice for predicting churn in the telecommunications industry, offering better predictive qualityand consistency in handling uncertain data.
PENINGKATAN KREATIVITAS DAN INOVASI DIGITAL PEGAWAI MELALUI PELATIHAN DESAIN GRAFIS PADA INSTANSI BADAN PUSAT STATISTIK KABUPATEN SIGI Nureni, Nureni; Jamidun, Jamidun; Gamayanti, Nurul Fiskia; Harismahyanti A., Andi; I. Djuru, Moh Syafwan
DedikasiMU : Journal of Community Service Vol 7 No 1 (2025): Maret
Publisher : Universitas Muhammadiyah Gresik

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30587/dedikasimu.v7i1.8581

Abstract

Pengabdian ini bertujuan meningkatkan kreativitas dan inovasi digital pegawai Badan Pusat Statistik (BPS) Kabupaten Sigi melalui pelatihan desain grafis menggunakan aplikasi Pixellab berbasis Android. Pelatihan ini dirancang untuk membantu pegawai menguasai keterampilan mendesain grafis yang lebih informatif dan menarik guna mempublikasikan data statistik. Pelaksanaan kegiatan meliputi penyampaian materi secara teori dan praktik langsung yang dibantu oleh tim pendamping. Setelah pelatihan, dilakukan evaluasi untuk mengukur pemahaman dan kemampuan peserta dalam memanfaatkan aplikasi Pixellab untuk membuat desain visual yang relevan dengan pekerjaan mereka. Hasil pelatihan menunjukkan peningkatan signifikan dalam pemahaman konsep desain grafis dan keterampilan teknis pegawai dalam membuat infografis yang dapat dipublikasikan. Pegawai BPS kini lebih mampu menyampaikan informasi statistik dengan lebih efektif dan menarik bagi masyarakat luas. Pelatihan ini diharapkan berkontribusi pada peningkatan kualitas layanan publikasi data BPS Kabupaten Sigi dan dapat dikembangkan lebih lanjut melalui pelatihan lanjutan yang mencakup aspek desain tingkat lanjut.
Perbandingan Ukuran Jarak pada Analisis Kluster Hirarki Yahya, Muh. Zarkawi; Sitti Nurhaliza; Morina A Fathan; Muhammad Edy Rizal; Andi Harismahyanti A
Leibniz: Jurnal Matematika Vol. 5 No. 02 (2025): Leibniz: Jurnal Matematika
Publisher : Program Studi Matematika - Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas San Pedro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59632/leibniz.v5i02.538

Abstract

Analisis klaster merupakan salah satu metode statistik untuk mengelompokkan objek berdasarkan kemiripan. Pada data kategorik, pemilihan ukuran jarak menjadi aspek penting karena memengaruhi struktur dan interpretasi klaster yang terbentuk. Penelitian ini bertujuan untuk membandingkan performa enam ukuran jarak Gower, Goodall1, Goodall2, Goodall3, Goodall4, dan Anderberg dalam analisis klaster hierarki menggunakan data kategorik dari Indonesian Family Life Survey (IFLS-5). Metode yang digunakan adalah hierarchical agglomerative clustering, dengan tahap awal pembersihan data dan konversi ke tipe faktor agar sesuai dengan karakteristik pengukuran jarak kategorik. Evaluasi hasil klaster dilakukan dengan dua indeks validasi internal, yaitu Silhouette dan Dunn, serta metrik eksternal Adjusted Rand Index (ARI) untuk menilai stabilitas klaster melalui proses bootstrapping. Ketiga metrik tersebut digunakan secara komplementer: Silhouette mengevaluasi konsistensi lokal anggota klaster (dengan nilai ? 0.5 umumnya dianggap baik), Dunn mengukur pemisahan antar-klaster secara global (semakin tinggi semakin baik), sementara ARI menunjukkan konsistensi struktur klaster terhadap variasi data (nilai mendekati 1 menunjukkan stabilitas tinggi). Hasil menunjukkan bahwa setiap ukuran jarak menghasilkan struktur klaster yang berbeda. Di antara semua ukuran yang diuji, Goodall4 memberikan hasil terbaik karena membentuk klaster yang mudah diinterpretasikan, memiliki nilai indeks Silhouette dan Dunn yang relatif tinggi, serta skor ARI mendekati sempurna. Hal ini mengindikasikan bahwa Goodall4 merupakan alternatif yang layak direkomendasikan dalam kasus serupa.
OUTLIER DETECTION ON HIGH DIMENSIONAL DATA USING MINIMUM VECTOR VARIANCE (MVV) A., Andi Harismahyanti; Indahwati, Indahwati; Fitrianto, Anwar; Erfiani, Erfiani
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 16 No 3 (2022): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (473.955 KB) | DOI: 10.30598/barekengvol16iss3pp797-804

Abstract

High-dimensional data can occur in actual cases where the variable p is larger than the number of observations n. The problem that often occurs when adding data dimensions indicates that the data points will approach an outlier. Outliers are part of observations that do not follow the data distribution pattern and are located far from the data center. The existence of outliers needs to be detected because it can lead to deviations from the analysis results. One of the methods used to detect outliers is the Mahalanobis distance. To obtain a robust Mahalanobis distance, the Minimum Vector Variance (MVV) method is used. This study will compare the MVV method with the classical Mahalanobis distance method in detecting outliers in non-invasive blood glucose level data, both at p>n and n>p. The test results show that the MVV method is better for n>p. MVV shows more effective results in identifying the minimum data group and outlier data points than the classical method.
Analyzing the impact of inflation, exports and unemployment on economic growth in indonesia: A fixed effects least squares dummy variable panel regression approach Harismahyanti A., Andi; Nur’eni, Nur’eni
Priviet Social Sciences Journal Vol. 5 No. 8 (2025): August 2025
Publisher : Privietlab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55942/pssj.v5i8.541

Abstract

This study explores the impact of inflation, exports, and unemployment on economic growth in Indonesia from 2019 to 2023 using a Fixed Effects Model Least Squares Dummy Variable (FEM LSDV) panel regression approach. The analysis incorporates both province-specific and time-specific effects to provide a comprehensive understanding of the dynamic relationships between these variables and their collective influence on Indonesia's economic growth. The results indicate that exports have a significant positive effect on economic growth, consistent with existing literature highlighting the importance of exports in driving economic performance. In contrast, inflation and unemployment were not found to have statistically significant impacts, possibly due to the model’s focus on regional and temporal variations. The study furthermore reveals significant regional disparities in economic growth, with provinces like DKI Jakarta, Banten, and Kalimantan Timur showing negative growth, while others, such as Sulawesi Selatan and Gorontalo, experienced above-average growth. The FEM LSDV model demonstrates strong explanatory power, with an R-squared value of 0.9162, indicating that it effectively captures the variability in economic growth across regions and over time. The findings suggest that promoting export-driven growth and addressing regional imbalances are key strategies for fostering sustainable economic development in Indonesia.
Comparison of FEM-LSDV Panel Regression with Classical Panel Regression Models in Analyzing Economic Growth in Indonesia Andi, Harismahyanti A; Alimatun, Najiha; Yunita, Andi Isna; Ratmila, Ratmila; Nur'eni, Nur'eni
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10318

Abstract

This study evaluates the performance of multiple panel regression approaches in modeling the determinants of regional economic growth in Indonesia. It specifically compares three classical panel models: the Common Effect Model (CEM), the Random Effect Model (REM), and the Fixed Effect Model (FEM), alongside the Fixed Effect Model with the Least Squares Dummy Variable (FEM LSDV) approach. The analysis is based on panel data covering 34 provinces from 2019 to 2023, using key macroeconomic indicators such as inflation, investment, exports, money supply, open unemployment rate, and participation in the national health insurance program (JKN). The models are assessed using formal statistical tests, including the Chow and Hausman tests, and evaluated through performance metrics such as RMSE, AIC, and R-squared. The results show that the FEM LSDV model offers the best performance, with an R-squared value of 0.7039, RMSE of 0.5442, and an AIC of 365.55. Notably, the model identifies North Maluku Province as contributing positively and significantly to economic growth, while the year 2020 shows a significant negative impact, likely due to the economic disruptions caused by the COVID-19 pandemic. These findings demonstrate the effectiveness of the FEM LSDV approach in capturing both spatial and temporal heterogeneity in regional economic analysis and support its application in policy-oriented research.
Co-Authors Alimatun Najiha Erfiani Erfiani Fathan, Morina A. Fitrianto, Anwar Gamayanti, Nurul Fiskia I. Djuru, Moh Syafwan Indahwati Jamidun, Jamidun Muhammad Edy Rizal Nur'eni, Nur'eni Nureni, Nureni Nur’eni, Nur’eni Ratmila, Ratmila Sitti Nurhaliza Sitti Nurhaliza Yahya, Muh. Zarkawi Yunita, Andi Isna