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All Journal Desimal: Jurnal Matematika Al-Khwarizmi: Jurnal Pendidikan Matematika dan Ilmu Pengetahuan Alam Indonesian Journal of Education and Mathematical Science ALACRITY : Journal Of Education BATIK: Jurnal Pengembangan dan Pengabdian Masyarakat Multikultural INTAN CENDEKIA: JURNAL PENGABDIAN MASYARAKAT Journal of Mathematics, Computation and Statistics (JMATHCOS)
Sarmada, Sarmada
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Religion Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Education Environmental Science Languange, Linguistic, Communication & Media Library & Information Science Mathematics Social Sciences Other

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Sosialisasi Pemilihan Prioritas Penerima Bantuan Dana Sosial Menggunakan Algoritma Clustering di Kelurahan Tengah Kecamatan Pelayangan Yurinanda, Sherli; Sarmada, Sarmada; Sormin, Corry; Rozi, Syamsyida; Multhadah, Cut
Jurnal Pengembangan dan Pengabdian Masyarakat Multikultural Vol 2 No 3: BATIK Desember 2024
Publisher : Institut Riset dan Publikasi Indonesia (IRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/batik.v2i3.1724

Abstract

Poverty is one of the problems that must be eradicated. The government's policies for poverty alleviation is the provision of social assistance funds. But the government must consider the eligibility of the recipients of the social assistance. Since 2021-2023, the poverty rate in Jambi Province has been recorded at over 250 thousand people each year. Of the several districts/cities in Jambi Province, Jambi City contributes the largest number of poor people. Tengah Village is one of the villages in Pelayangan District, Jambi City. As an effort to support the provision of targeted social assistance, a clustering algorithm is used group objects based on certain characteristics. One of the analysis in the clustering is the K-Means algorithm. This community service activity is the socialization of the use of clustering algorithms to help group data on social assistance recipients. The activity was attended by staff and RT heads in Tengah Village. Based on the evaluation, as many as 72,7% of the participants who attended stated that they were very satisfied, in addition they stated that this activity was very useful in supporting the decision to select priority recipients of social fund assistance in the Tengah Village.
Comparison of quantile regression and censored quantile regression methods in the case of chicken consumption Sarmada, Sarmada; Yanuar, Ferra; Devianto, Dodi
Desimal: Jurnal Matematika Vol. 6 No. 2 (2023): Desimal: Jurnal Matematika
Publisher : Universitas Islam Negeri Raden Intan Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/djm.v6i2.18949

Abstract

The censored quantile regression method is a parameter estimation method that can be used to overcome censored data and BLUE (Best Linear Unbiased Estimator) assumptions that are not met. This research aims to compare the quantile regression method and the censored quantile regression method on data on chicken consumption cases in West Sumatra. The smallest RMSE (Root Mean Square Error) is an indicator of the goodness of the model. This research proves that the censored quantile regression method tends to produce smaller RMSE values than the quantile regression method. So it is concluded that the censored quantile regression method is the appropriate method for estimating parameters with censored data.
Pelatihan Software Statistik Bagi Mahasiswa UKK KSR PMI UIN Sulthan Thaha Saifuddin Jambi Multahadah, Cut; Sormin, Corry; Mardhotillah, Bunga; Kholijah, Gusmi; Z, Gusmanely; Alim, Khairul; Rarasati, Niken; Safitri, Yuliana; Sarmada, Sarmada; Yurinanda, Sherly; Rozi, Syamsyida
INTAN CENDEKIA (Jurnal Pengabdian Masyarakat) Vol 6, No 1 (2025): INTAN CENDEKIA: JURNAL PENGABDIAN MASYARAKAT
Publisher : Yayasan Pendidikan Intan Cendekia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47165/intancendekia.v6i1.681

Abstract

Keterbatasan kemampuan dalam pengolahan dan analisis data menjadi salah satu kendala utama yang dihadapi mahasiswa dalam penyusunan tugas akhir. Di lingkungan mahasiswa UKK KSR PMI UIN Sulthan Thaha Saifuddin Jambi, kebutuhan akan pelatihan penggunaan software statistik semakin meningkat seiring dengan tuntutan akademik yang tinggi. Kegiatan pengabdian masyarakat ini bertujuan untuk meningkatkan pemahaman dan keterampilan mahasiswa dalam menganalisis data menggunakan software statistic JASP melalui pelatihan yang terstuktur. Metode pelaksanaan kegiatan terdiri dari tiga tahapan, yaitu pendekatan kepada mitra, prosedur kerja yang mencakup perizinan, persiapan, pelaksanaan, dan evaluasi serta pelaksanaan pelatihan. Kegiatan ini dilaksanakan secara dari pada tanggal 18 juli 2024 melalui platform Zoom Meeting. Hasil evaluasi menunjukkan bahwa pelatihan ini mendapat tanggapan sangat baik dari peserta dengan lebih dari 78% peserta menyatakan kepuasan tinggi terhadap materi, penyajian, relevansi serta efektivitas waktu pelatihan. Diharapkan kegiatan ini dapat memberikan dampak positif dalam peningkatan kualitas tugas akhir mahasiswa serta mendorong kemandirian mahasiswa dalam melakukan analisis data penelitian.
Forecasting PT Pertamina Geothermal Energy TBK (PGEO) Share Prices using the Arch-Garch Model Ramadhan, Ramadhan; Yurinanda, Sherli; Sarmada, Sarmada
Journal of Mathematics, Computations and Statistics Vol. 8 No. 2 (2025): Volume 08 Nomor 02 (Oktober 2025)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v8i2.9493

Abstract

This study focuses on forecasting the daily closing price of PT Pertamina Geothermal Energy Tbk (PGEO) stocks, recognizing the non-stationary and volatile nature of financial time series data. Traditional forecasting methods, such as the ARIMA (Autoregressive Integrated Moving Average) model, are often insufficient for such data because they rely on the assumption of homoscedasticity, or constant variance in the residuals. An analysis of PGEO's daily stock prices from November 2023 to July 2024 revealed significant fluctuations, indicating the presence of heteroscedasticity, where the variance of the residuals is not constant. In tackling this problem, the study utilized the ARCH (Autoregressive Conditional Heteroskedasticity) and GARCH (Generalized Autoregressive Conditional Heteroskedasticity) frameworks, purpose-built to identify and model the phenomenon of volatility clustering within financial datasets. By integrating the ARIMA model with GARCH, the study aimed to create a more robust forecasting tool. After testing various combinations, the MA(1)–GARCH(1,1) model was identified as the most suitable for predicting PGEO's stock prices. This model successfully captured the fluctuating volatility and produced a highly accurate forecast, as evidenced by a Mean Absolute Percentage Error (MAPE) of just 2.97%. A MAPE value below 10% is generally considered to represent a very high level of forecasting accuracy, confirming the effectiveness of the chosen model in providing reliable short-term predictions for stock market movements. Keywords: ARCH-GARCH, Stock price forecasting, ARIMA
Analysis of Factors Influencing Learning Motivation of Students in the Faculty of Science and Technology at Universitas Jambi Using Structural Equation Modeling (SEM) Sarmada, Sarmada; Yurinanda, Sherli; Rozi, Syamsyida; Irawan, Randi
Al-Khwarizmi : Jurnal Pendidikan Matematika dan Ilmu Pengetahuan Alam Vol. 13 No. 1 (2025): Al-Khwarizmi : Jurnal Pendidikan Matematika dan Ilmu Pengetahuan Alam
Publisher : Prodi Pendidikan Matematika FTIK IAIN Palopo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24256/jpmipa.v13i1.5696

Abstract

Abstract: Learning motivation is the driving force that ensures the continuous learning process to achieve educational goals. This study analyzes the factors influencing students' learning motivation. The sample consists of active students from the Faculty of Science and Technology at Jambi University. The research employs a quantitative survey method, with data collected through a Likert-scale questionnaire. Data analysis is conducted descriptively using Structural Equation Modeling (SEM). Three exogenous variables are examined: learning strategies, learning facilities, and learning environment, while learning motivation serves as the endogenous variable. The results indicate that learning strategies contribute the most to learning motivation, followed by the learning environment and learning facilities. Overall, these latent variables have a positive impact on learning motivation. Abstrak: Motivasi belajar adalah dorongan yang memastikan berlangsungnya proses pembelajaran secara berkesinambungan agar tujuan pembelajaran dapat tercapai. Penelitian ini melakukan analisis terhadap faktor-faktor yang memengaruhi motivasi belajar mahasiswa. Sampel yang digunakan adalah mahasiswa aktif di Fakultas Sains dan Teknologi Universitas Jambi. Metode digunakan adalah survei kuantitatif dengan pengambilan data melalui kuesioner berskala Likert. Analisis data dilakukan secara deskriptif menggunakan Structural Equation Modeling (SEM). Ada tiga variabel eksogen yang digunakan yaitu cara belajar, fasilitas belajar, dan lingkungan belajar. Sedangkan motivasi belajar digunakan sebagai variabel endogen. Hasil penelitian menunjukkan bahwa cara belajar memberikan kontribusi paling besar terhadap motivasi belajar, diikuti oleh lingkungan belajar dan fasilitas belajar. Secara keseluruhan, variabel-variabel laten tersebut memiliki pengaruh positif terhadap motivasi belajar.  
Application of Fuzzy Time Series Chen and Cheng Methods to Forecast Profit in a State-Owned Insurance Company Fajrin, Dirani Amaris; Yurinanda, Sherli; Sarmada, Sarmada
Indonesian Journal of Education and Mathematical Science Vol 6, No 3 (2025)
Publisher : Universitas Muhammadiyah Sumatera Utara (UMSU)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30596/ijems.v6i3.26750

Abstract

PT. Taspen (Persero) as a state-owned enterprise in the services sector needs to analyze financial performance to understand the fluctuations in quarterly profit in 2022. This study uses the Fuzzy Time Series (FTS) forecasting method with two approaches, namely Fuzzy Time Series Chen and Fuzz Time Series Cheng, to predict profit and dividend prospects. The analysis stage of Chen's Fuzzy Time Series method includes determining the set of the universe (U), forming intervals, defining fuzzy sets, determining the membership value of each data, fuzzification of data, formation of Fuzzy Logic Relationships (FLR) and Fuzzy Logic Relationship Groups (FLRG), forecasting and defuzzification. Meanwhile, Cheng's Fuzzy Times Series method has similar stages but is equipped with FLRG weighting into the W matrix and standardization of the W matrix*. The results of the calculation of forecasting accuracy through MAPE, MSE, and MAE show that Cheng's Fuzzy Time Series method is more accurate than Chen's Fuzzy Time Series, with a smaller error value. This confirms that Cheng's Fuzzy Times Series method is more reliable in projecting PT. Taspen (Persero). 
The Use of Elbow K-Means And K-Medoids in the Grouping of Provinces in Indonesia Based on the Indicators of the Effectiveness of the Authentication Taspen Application With DBI and Silhoutte Coefficients Tasya, Anisya; Kholijah, Gusmi; Sarmada, Sarmada
ALACRITY : Journal of Education Volume 5 Nomor 3 Oktober 2025 | In Press
Publisher : LPPPI Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52121/alacrity.v5i3.891

Abstract

This study aims to classify Indonesian provinces based on the effectiveness of using the Taspen Authentication Application and to compare the performance of the K-Means and K-Medoids clustering algorithms. The research employed a quantitative approach using secondary data derived from the Taspen Authentication metrics, which include ten variables such as user, session, retention, login effectiveness, churn rate, and conversion rate. The data from 38 provinces were analyzed using cluster analysis. The optimal number of clusters was determined using the Elbow Method, and validation was performed with the Davies-Bouldin Index (DBI). The results indicate that the K-Means algorithm provides better clustering performance, with a DBI value of 1.752 and a Silhouette Coefficient of 0.2850. The findings reveal that 50% (19 provinces) demonstrate high effectiveness, 13.16% (5 provinces) moderate effectiveness, and 36.84% (14 provinces) low effectiveness in using the application. These results can serve as a basis for PT TASPEN and policymakers to develop region-specific strategies, including enhanced socialization, training, and infrastructure support to improve the overall effectiveness of the Taspen Authentication Application across Indonesia.
Co-Authors Alim, Khairul Devianto, Dodi Fajrin, Dirani Amaris Irawan, Randi kholijah, gusmi Mardhotillah, Bunga Multahadah, Cut Multhadah, Cut Ramadhan Ramadhan rarasati, niken Safitri, Yuliana Sormin, Corry Tasya, Anisya Yanuar, Ferra Yurinanda, Sherli Yurinanda, Sherly Z, Gusmanely