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All Journal ComEngApp : Computer Engineering and Applications Journal Jurnal Lahan Suboptimal JPSriwijaya Jurnal Penelitian Sains Indonesian Journal of Applied Informatics Diseminasi : Jurnal Pengabdian kepada Masyarakat SRIWIJAYA JOURNAL OF ENVIRONMENT Albayan : Journal of Islam and Muslim Societies
Herlina Hanum
Jurusan Matematika, Universitas Sriwijaya
Religion Agriculture, Biological Sciences & Forestry Humanities Chemistry Computer Science & IT Economics, Econometrics & Finance Education Energy Engineering Environmental Science Law, Crime, Criminology & Criminal Justice Mathematics Neuroscience Physics Public Health Social Sciences Other

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The Application of Modeling Gamma-Pareto Distributed Data Using GLM Gamma in Estimation of Monthly Rainfall with TRMM Data Herlina Hanum; Aji Hamim Wigena; Anik Djuraidah; I Wayan Mangku
Sriwijaya Journal of Environment Vol 2, No 2 (2017): Water As A Vital Resource for Life
Publisher : Program Pascasarjana Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (911.747 KB) | DOI: 10.22135/sje.2017.2.2.40-45

Abstract

As a recently developed distribution, the application of Gamma-Pareto is limited to single variable modeling.  A specific transformation of Gamma-Pareto (G-P) yields gamma distribution. Therefore, it is possible to use analysis based on gamma distribution (e.g. GLM) for modeling G-P distributed data.  In this paper we study the application of modeling G-P distributed data using GLM gamma for monthly rainfall which observed in Sukadana Station.  The modeling aims to analyze whether Tropical Rainfall Measuring Mission (TRMM) satellite data is a good estimator for unobserved station’s data.  The transformed of station’s data were considered as response variable in GLM gamma.  The explanatory variable is TRMM data in 9 grids around the station. There are two kinds of modeling i.e. model for whole data and extreme data. The results show that for both data the station’s data are G-P distributed and the transformed data are gamma distributed.  TRMM rainfall data at each grid around the station can be used to estimate the observed data of monthly rainfall. The best model for both data contains dummy variables which correspond to inter quantile data.  The coefficients of dummy variables in the best model may substitute the grouping or the correction in the previous studies.
Penerapan Metode Support Vector Machine Dalam Klasifikasi Bunga Iris Anita Desiani; Irmeilyana Irmeilyana; Herlina Hanum; Yuli Andriani; Sri Indra Maiyanti; Clarita Margo Uteh; Ira Rayyani
IJAI (Indonesian Journal of Applied Informatics) Vol 7, No 1 (2022)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/ijai.v7i1.61486

Abstract

Abstrak Data mining adalah proses melatih komputer untuk mengenali suatu pola menggunakan teknik statistika mapun matematika. Salah satu teknik data mining yang sering digunakan adalah klasifikasi, yakni mengelompokkan data ke dalam suatu label menggunakan atribut. Pada klasifikasi, Support Vector Machine (SVM) merupakan salah satu metode yang paling banyak digunakan. Penelitian ini akan memanfaatkan metode SVM dalam melakukan klasifikasi bunga Iris. Data yang diteliti menggunakan sebanyak 150 data dengan menggunakan dua metode data latih, yakni percentage split dan k-fold cross validation. Data diolah melalui tahap pre-processing, lalu diklasifikasi menggunakan metode SVM melalui 2 metode data latih, percentage split sebesar 80% dan k-fold corss validation dengan k=10, perhitungan hasil prediksi menggunakan confusion matrix. Pada metode percentage split diperoleh nilai akurasi sebesar 96,7%, presisi 97,6%, recall sebesar 95,3%, dan F1-score sebesar 96,3%. Pada metode k-fold cross validation diperoleh nilai akurasi sebesar 92,6%, presisi 92,6%, recall sebesar 92,6%, dan F1-score sebesar 92,3%. Dengan demikian metode SVM menggunakan kernel polynomial dengan metode data latih percentage split dapat diimplementasikan ke dalam sistem klasifikasi bunga Iris.AbstractData mining is the process of training a computer to recognize a pattern using statistical and mathematical techniques. One of the data mining techniques that are often used is classification, which is to group data into the label using attributes. In classification, the Support Vector Machine (SVM) is one of the most widely used methods. This research will utilize the SVM method in classifying Iris flowers. The data studied used 150 data using two training data methods, percentage split and k-fold cross validation. The data is processed through the pre-processing stage, then classified using the SVM method through 2 training data methods, percentage split of 80% and k-fold cross validation with k = 10, and calculation of prediction results using a confusion matrix. In the percentage split method, the accuracy is 96.7%, precision is 97.6%, recall is 95.3%, and F1-score is 96.3%. In the k-fold cross validation method, the accuracy is 92.6%, precision is 92.6%, recall is 92.6%, and F1-score is 92.3%. So that the SVM method using a polynomial kernel with the percentage split training data method can be implemented into the iris classification system.
Efektivitas Peraturan Pemerintah Republik Indonesia Nomor 2 Tahun 2003 dalam Penegakan Kode Etik Profesi Polri di Satuan Brimob Polda Sumatera Utara Universitas Muslim Nusantara Al Washliyah, Echo Agung Wichaksono; Herlina Hanum
Albayan Journal of Islam and Muslim Societies Vol. 1 No. 01 (2024)
Publisher : Albayan Journal of Islam and Muslim Societies

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Abstract

This study aims to analyze the effectiveness of the implementation of Government Regulation of the Republic of Indonesia Number 2 of 2003 on the Disciplinary Regulations of Police Officers in the enforcement of the Indonesian National Police Professional Code of Ethics (KEPP) within the Brimob Unit of the North Sumatra Regional Police. Employing a qualitative approach with normative-empirical design, data were collected through interviews, observations, and document analysis. The findings demonstrate the regulation’s effective enforcement, indicated by a yearly decrease in disciplinary violations. Nonetheless, implementation is challenged by the limited number of accredited personnel and the procedural requirement that disciplinary hearings be conducted at the provincial police headquarters. These findings highlight the necessity for capacity-building and structural reforms within the police institution to ensure just and efficient discipline enforcement
Co-Authors Aji Hamim Wigena Anik Djuraidah Anita Desiani Bambang Suprihatin Bambang Suprihatin Clarita Margo Uteh Dian Cahyawati Dwi Setyawan Edward Saleh Eka Susanti Filda Efriliyanti I Wayan Mangku Ira Rayyani Irmeilyana Nabila Aprianti Novi Rustiana Dewi Ratih Wijayanti Sri Indra Maiyanti Teddi Pranata Universitas Muslim Nusantara Al Washliyah, Echo Agung Wichaksono Yuli Andriani