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All Journal Jambura Journal of Mathematics Jurnal Varian Variansi : Journal of Statistics and Its Application on Teaching and Research Mattawang: Jurnal Pengabdian Masyarakat JINAV: Journal of Information and Visualization Panrannuangku Jurnal Pengabdian Masyarakat ARRUS Journal of Mathematics and Applied Science ARRUS Journal of Engineering and Technology SAINSMAT: Journal of Applied Sciences, Mathematics, and Its Education 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 Abdi Kimia : Jurnal Pengabdian Masyarakat SMART: Jurnal Pengabdian Kepada Masyarakat
Zulkifli Rais
Department of Statistics, Universitas Negeri Makassar
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology 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 Other

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Hybrid Hierarchical Clustering dalam Pengelompokan Daerah Rawan Bencana Tanah Longsor di Sulawesi Selatan Fithriyah Azzahrah; Suwardi Annas; Zulkifli Rais
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 4 No. 3 (2022)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (407.161 KB) | DOI: 10.35580/variansiunm38

Abstract

This study aims to describe and classify areas prone to landslides in South Sulawesi. The method used is Hybrid Hierarchical Clustering. The data used is landslide disaster data sourced from the National Disaster Management Agency (BNPB) for 2018-2020 in South Sulawesi. The variables used are the number of landslides, deaths, damaged houses, injured victims, and damaged public facilities. Grouping using the Hybrid Hierarchical Clustering method with mutual clusters using bottom-up and top-down methods. Grouping with bottom-up method produces 2 groups, top-down method produces 2 groups and 1 best mutual cluster. The ratio results in the bottom-up method is 0.84, the top-down method is 1.07 and the mutual cluster is 0.84. The grouping results obtained were 2 groups.
Text Classification on Sentiment Analysis of Marketplace SHOPEE Reviews On Twitter Using K-Nearest Neighbor (KNN) Method Zulkifli Rais; Rezky Novita Said; Ruliana Ruliana
JINAV: Journal of Information and Visualization Vol. 3 No. 1 (2022)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.jinav1389

Abstract

This research aims to know the description and result of the classification sentiment analysis by Twitter users about Shopee. The method used in this research is K-Nearest Neighbor. K-Nearest Neighbor is a method that identifies groups or classifications based on the closest k of test data (training data). The most relative distance is calculated using the Euclidean distance. The data in this research were obtained from the Twitter API which used data on July 13, 2021, and the data according to the study were 150 tweets. Based on the results of the preprocessing text, there are 10 words that appear most often conveyed by Twitter users, and these opinions are related to the features provided by Shopee. The results obtained from this research are the highest level of text classification accuracy is 90% in training data and testing data comparison 80%: 20%
Convolutional Neural Network (CNN) Method for Classification of Images by Age Nurtiwi Nurtiwi; Ruliana Ruliana; Zulkifli Rais
JINAV: Journal of Information and Visualization Vol. 3 No. 2 (2022)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.jinav1481

Abstract

Image classification is one of the studies that is currently being developed. The details of the characteristics that must be captured make researchers compete to find the most suitable method for classifying. The Convolutional Neural Network (CNN) algorithm is one of the most superior algorithms in the field of object classification and identification today. With the help of several packages contained in Google Colab for classification, this algorithm is easier to use. In this study, the target case is the age of a person who will be classified using photos or images taken from the internet which are then stored in the form of Google Drive. The research data used is divided into 2 parts, namely for training data as many as 23.440 images, and 10,046 for testing data. Then to facilitate the extraction of features from the features to be identified, the researchers carried out the preprocessing stage, namely grayscale images, and data augmentation. The purpose of this study is to implement the concept of Deep Learning with Convolutional Neural Networks (CNN) in image classification and to determine the level of accuracy of the CNN model in classifying images. After the algorithm is run and the model has been formed, an accuracy of 78.5% is obtained. It can be concluded that the Convolutional Neural Network (CNN) method is good at classifying images
Analisis Pengaruh Profitabilitas, Ukuran Perusahaan, dan Reputasi Auditor terhadap Audit Delay pada Perusahaan Otomotif yang Terdaftar di Bursa Efek Indonesia Tahun 2015-2020 Menggunakan Regresi Logistik Hardianti Hafid; Ansari Saleh Ahmar; Zulkifli Rais
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 5 No. 01 (2023)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm71

Abstract

This research aims to determine whether profitability, company size, and auditor reputation significantly influence audit delay using binary logistic regression analysis. The research results indicate that profitability has a significant individual (partial) effect on audit delay, while company size and auditor reputation do not have a significant individual (partial) effect on audit delay
Pemodelan Regresi Data Panel pada IPM di Sulawesi Selatan Zakiyah Mar'ah; Ruliana Ruliana; Ansari Saleh Ahmar; Zulkifli Rais
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 5 No. 01 (2023)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm72

Abstract

HDI is an important indicator to measure success in efforts to build the quality of human life (community/population). HDI can determine the rank or level of development of a region/country. For Indonesia, HDI is strategic data because apart from being a measure of government performance, HDI is also used as an allocator for determining the General Allocation Fund (DAU). The development of HDI in Indonesia has always increased from year to year. In South Sulawesi, the HDI has increased significantly in the last 10 years. Where in 2012 the HDI of South Sulawesi was at 67.26 to 72.82 in 2022. This is measured based on three essential aspects, namely longevity and healthy living, knowledge, and a decent standard of living. Along with HDI, other indicators also show an increase from year to year. To find out how much these variables affect the increase in HDI during the 2018-2022 period, the panel data regression method is used which is a combination of time series data and cross section data. The regression model that is suitable for South Sulawesi HDI data from 2018-2022 is a panel data regression model with one-way random effects, namely individual effects. The model is written as follows IPM=(-1.9360e+01) + (1.0734e+00) UHH + (1.4014e-03) PPK + e
Perbandingan Model Bayesian Spasial Conditional Autoregressive (CAR): Kasus Covid-19 di Kota Makassar, Indonesia Muhammad Arif Tiro; Aswi Aswi; Zulkifli Rais
Seminar Nasional LP2M UNM SEMINAR NASIONAL 2021 : PROSIDING EDISI 5
Publisher : Seminar Nasional LP2M UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (848.202 KB)

Abstract

Abstrak. Jumlah pasien positif penyakit Coronavirus-2019 (Covid-19) meningkat secara tajam mengikuti sebaran eksponensial. Salah satu Provinsi di Indonesia di luar Pulau Jawa yang memiliki jumlah kasus Covid-19 tertinggi adalah Provinsi Sulawesi Selatan. Diantara 24 Kabupaten/Kota di Provinsi Sulawesi Selatan, Kota Makassar sebagai ibukota provinsi Sulawesi Selatan memiliki kasus terkonfirmasi positif Covid-19 tertinggi. Penelitian ini bertujuan untuk membandingkan model Bayesian spasial Conditional Autoregressive (CAR) dalam mengestimasi risiko relative (RR) kasus Covid-19 di Makassar. Beberapa model model Bayesian spasial CAR yang digunakan adalah CAR BYM, CAR Leroux, CAR localised dan model Independent. Data yang digunakan pada penelitiaan ini adalah data jumlah kasus terkonfirmasi positif Covid-19 (20 Maret 2020 - 30 Agustus 2021) dan data jumlah penduduk pada 15 kecamatan di Kota Makassar. Pemilihan model terbaik didasarkan pada beberapa ukuran kecocokan model yaitu Deviance Information Criteria (DIC), Watanabe Akaike Information Criteria (WAIC). Berdasarkan nilai DIC dan WAIC yang terkecil, dapat disimpulkan bahwa Bayesian spasial CAR localised merupakan model yang terbaik dalam memodelkan kasus terkonfirmasi Covid-19 di kota Makassar. Berdasarkan Bayesian spasial CAR localised tersebut, diperoleh bahwa Ujung Pandang memiliki RR Covid-19 tertinggi (RR=1,70) sedangkan Kabupaten Sangkarrang memiliki RR Covid-19 terendah (RR=0,09). Hasil ini dapat membantu para pembuat kebijakan dalam pengambilan keputusan.Kata Kunci: Bayesian, Conditional Autoregressive priors, Leroux, BYM, Localised
Application of K-Medoids Algorithm in Provincial Grouping in Indonesia Based On Case of Environmental Pollution Muh. Hizbul Zainul Muttaqim; Ruliana Ruliana; Zulkifli Rais
SAINSMAT: Journal of Applied Sciences, Mathematics, and Its Education Vol. 12 No. 1 (2023)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/sainsmat1775

Abstract

Cluster analysis is a method for grouping objects that have the same characteristics. One of the methods in cluster analysis used to group data is the K-Medoids method. In this study the K-Medoids method was applied to classify provinces in Indonesia based on environmental pollution. The variables used are: the number of sub-districts/villages that experience water pollution from factory waste, the number of sub-districts/villages that experience water pollution from household waste, the number of sub-districts/villages that experience soil pollution from factory waste, the number of sub-districts/villages that experience soil pollution from household waste, the number of sub-districts/villages that experience air pollution from factory waste and the number of sub-districts/villages that experience air pollution from household waste. Based on the Davies Bouldin Index, the 2 best clusters were obtained where the first cluster consisted of 31 provinces which had low environmental pollution and the second cluster consisted of 3 provinces which had high environmental pollution.
Peningkatan Pengetahuan Penyusunan Artikel Ilmiah Bagi Guru SMAN 4 Kabupaten Pinrang Melalui Pelatihan Penyusunan Karya Tulis Ilmiah A. Aswi; Zulkifli Rais; Muhammad Fahmuddin
SMART: Jurnal Pengabdian Kepada Masyarakat Vol 1, No 1 (2021): Oktober
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/smart.v1i1.26120

Abstract

Mitra Program Kemitraan Masyarakat (PKM) ini adalah guru di SMAN 4 Kabupaten Pinrang, yang memiliki masalah kurangnya artikel ilmiah yang diterbitkan oleh guru. Salah satu penyebab guru tidak menerbitkan artikel ilmiah yaitu sebagian besar guru belum memahami tata cara penulisan artikel yang baik. Metode yang digunakan adalah: memberikan workshop pemahaman penulisan artikel ilmiah. Hasil yang dicapai adalah guru memahami teknik-teknik penulisan karya tulis ilmiah pada bidang pendidikan dan mendapat gambaran mengenai teknik pengolahan data dengan menggunakan metode statisika yang baik dan benar.
Penerapan Radial Basis Function Neural Network dalam Mengklasifikasikan Kab/Kota di Provinsi Sulawesi Selatan Berdasarkan Indeks Kesejahteraan Rakyat Jiran Julita; Sudarmin Sudarmin; Zulkifli Rais
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 5 No. 02 (2023)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm95

Abstract

Jiran Julita, 2023. Penerapan Radial Basis Function Neural Network dalam Mengklasifikasikan Kab/Kota di Provinsi Sulawesi Selatan Berdasarkan Indeks Kesejahteraan Rakyat. Program Studi Statistika, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Negeri Makassar. (Dibimbing oleh Sudarmin dan Zulkifli Rais). Klasifikasi merupakan cara pengelompokan benda berdasarkan ciri-ciri yang dimiliki oleh objek klasifikasi. Metode yang digunakan dalam penelitian ini yaitu radial basis function neural network (RBFNN) yang merupakan salah satu arsitektur ANN yang popular digunakan dalam klasifikasi. Penelitian ini bertujuan untuk melihat klasifikasi dari Kab/Kota di Provinsi Sulawesi Selatan berdasarkan indeks kesejahteraan rakyat menggunakan RBFNN. Adapun data yang digunakan berjumlah 24 data dengan 10 variabel. Pada penelitian ini metode K-Means diaplikasikan untuk mengelompokan Kab/Kota di Provinsi Sulawesi Selatan berdasarkan indeks kesejahteraan rakyat dengan validasi cluster menggunakan Davies Boulding Index, hasil klasifikasi dari penelitian ini diperoleh 4 cluster terbaik berdasarkan indeks kesejahteraan rakyat Kab/Kota di Provinsi Sulawesi Selatan dengan perfoma klasifikasi dengan hasil accuracy 90%, precision 75%, recall 100% dan F-Measure 85%.
Analisis Regresi Data Panel pada Angka Partisipasi Murni Jenjang Pendidikan SMP Sederajat di Provinsi Jawa Barat pada Tahun 2018-2021 Karunia Rahayu Ayu; Muhammad Kasim Aidid; Zulkifli Rais
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 5 No. 02 (2023)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm113

Abstract

Pure Enrollment Rate (APM) is the ratio of school-age children to the corresponding age population and is expressed as a percentage. Regression analysis is a statistical analysis method that aims to see the relationship between a dependent variable and one or more independent variables. Regression using panel data is called the panel data regression model. Panel data is a combination of time series and cross-section data. This study aims to determine the modeling of regression analysis with panel data regarding the Pure Participation Rate (APM) at the junior high school education level in West Java Province in 2018-2021 and to determine the factors that affect the level of Pure Participation Rate (APM) at the junior high school education level in West Java Province in 2018-2021. Based on the model selection carried out by conducting the Chow Test, Hausman Test, and Breucsh-Pagan Test, the best model is the Random Effect Model. From the random effect model, it is known that the factor or variable that has a very significant effect on the Pure Participation Rate (APM) of equivalent junior high schools in West Java province is the student-to-school ratio variable (X1).
Co-Authors A. Bida Purnamasari Ahmar, Ansari Saleh Annas, Suwardi Asmar Asmar, Asmar Asmi, Nia Nurul Aswi, Aswi Fahmuddin, Muh. Ferigo Taufani Tri Hakiki Fithriyah Azzahrah Gunawan, Andi Restu Hardianti Hafid Irwan Irwan Irwan Irwan Irwan Irwan Isnaeni R Jiran Julita Karunia Rahayu Ayu Lili Maghfirah Rahma Sudirman Marni Marni, Marni Mar’ah, Zakiyah Misveria Villa Waru Muh. Hizbul Zainul Muttaqim Muhammad Arif Tiro Muhammad Fahmuddin Muhammad Fahmuddin Muhammad Kasim Aidid Muhammad Kasim Aidid Muhammad Refaldy Nurdin Nurdin Nurhamidah Mursyidin Nurtiwi Nurtiwi Nurul Madany Nusrang, Muhammad Rahmat Hesha Safei Rezky Novita Said Riska Aprianti Rizal Bakri Ruliana Ruliana Ruliana Ruliana, Ruliana Ruliana, R. Rusdianto S. Sudarmin, S. Sitti Masyitah Meliyana R. Sudarmin Sudarmin Sudarmin Sudarmin Surahman Nur, Surahman Suwardi Annas Uca Sideng Yusal, Muh Sri Yusniar Rasjid Yusniar Rasjid, Yusniar Zakiyah Mar'ah