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All Journal EKSAKTA: Journal of Sciences and Data Analysis Statistika JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI Kreano, Jurnal Matematika Kreatif-Inovatif Jurnal Riset Pendidikan Matematika AJIE (Asian Journal of Innovation and Entrepreneurship) JURNAL PENGABDIAN KEPADA MASYARAKAT E-Dimas: Jurnal Pengabdian kepada Masyarakat JOIV : International Journal on Informatics Visualization Jurnal Aplikasi Statistika & Komputasi Statistik Jurnal Abdimas Madani dan Lestari (JAMALI) Refleksi Pembelajaran Inovatif Jurnal Statistika dan Aplikasinya JAMBURA JOURNAL OF PROBABILITY AND STATISTICS JNPM (Jurnal Nasional Pendidikan Matematika) STATISTIKA
ACHMAD FAUZAN
Universitas Islam Indonesia
Agriculture, Biological Sciences & Forestry Arts Humanities Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Social Sciences Other

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Sinergi Metode Student Team Achievement Division (STAD) dan Tutorial pada Mata Kuliah Kalkulus I Fauzan, Achmad; Dini, Sekti Kartika; Fajriyah, Rohmatul
Kreano, Jurnal Matematika Kreatif-Inovatif Vol 10, No 1 (2019): Kreano, Jurnal Matematika Kreatif-Inovatif
Publisher : Jurusan Matematika, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Negeri Sema

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/kreano.v10i1.18214

Abstract

Penelitian ini bertujuan untuk mengetahui proses dan hasil dari penggunaan metode Student Team Achievement Division (STAD) dan tutorial berdasarkan hasil belajar serta tingkat kepuasan mahasiswa pada mata kuliah Kalkulus 1. Pengambilan data diperoleh dengan metode tes melalui ujian kompetensi (UK), UTS, dan UAS. Hasil belajar tersebut dianalisis menggunakan uji Kruskall Wallis dan post hoc test untuk mengetahui perbedaan rata-rata yang signifikan diantara sampel yang diambil. Sementara tingkat kepuasan dianalisis menggunakan text mining dari kuesioner yang diberikan berdasarkan barplot dan wordcloud yang diasosiasikan dengan kata yang dominan muncul serta evaluasi keseluruhan dari tingkat kepuasan dan saran yang diberikan. Berdasarkan hasil belajar, diperoleh nilai rata-rata kelas eksperimen lebih tinggi dibanding kelas kontrol. Sementara dari tingkat kepuasan masuk dalam kategori puas hingga sangat puas. Lebih jauh metode ini dapat digunakan sebagai alternatif metode pembelajaran untuk matakuliah lain yang khususnya berkaitan dengan konseptual.The purpose of this study is finding out how the processes and results of using the Student Team Achievement Division (STAD) and tutorial methods based on the learning outcomes and the student’s satisfaction level at Calculus 1 class. Data are provided through the competency tests (UK), mid term exam (UTS), and final exam (UAS). The learning outcomes is analyzed using the Kruskall Wallis test and the post hoc test to find out the significant differences in the samples average. The satisfaction level is analyzed by a text mining technique: barplot and wordcloud associated to other dominant words as well as the overall evaluation of the students’s satisfaction level and their suggestion. The learning outcomes analysis shows that the average exam scores of the experimental class is higher than the control class. Mean while the students’ satisfaction level is categorized as satisfied at both of the STAD and tutorial methods. Furthermore this method can be used as an alternative learning method in other subjects which are considered as conceptually heavy.
Perbandingan Metode Peramalan Jumlah Produksi Palm Kernel Oil (PKO) Menggunakan Metode Double Moving Average, Double Exponential Smothing dan Box Jenkins IKA MEIZA MAHARANI; ACHMAD FAUZAN
Jurnal Matematika, Statistika dan Komputasi Vol. 16 No. 2 (2020): JMSK, JANUARY, 2020
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (619.121 KB) | DOI: 10.20956/jmsk.v16i2.7795

Abstract

One of Indonesia's significant results is oil palm. The reality of this plantation is not only owned by the government (BUMN) but also the private sector. Every period, the company does forecasting in terms of production, especially for the next period. Among them is to set production targets, company operations, and financial planning. Based on this, a study was conducted with the aim to predict the amount of palm kernel oil (PKO) production at PT. Mitra Mendawai Sejati for the next six (6) months. The method used is Double Moving Average, Double Exponential Smoothing and Box Jenkins. While the data used is historical data from the amount of palm kernel oil production for five (5) years obtained from the company. Based on the results of the study, received the forecast value of the Suayap output in 2019 with the best method, namely the Double Exponential Smoothing method. Based on the forecast we got in January at 949181.5 Kg, February at 963505.8 Kg, March at 977830.1 Kg, April at 992154.4 Kg, May at 1006478.6 Kg and June 1020802.9 Kg with MSE value of 47031163817, and RMSE of 216866.7 and parameter values (optimum weighting) for α = 0.616667 and β = 0.1548939
COMPARING NAIVE BAYES, K-NEAREST NEIGHBOR, AND NEURAL NETWORK CLASSIFICATION METHODS OF SEAT LOAD FACTOR IN LOMBOK OUTBOUND FLIGHTS Mega Luna Suliztia; Achmad Fauzan
Jurnal Matematika, Statistika dan Komputasi Vol. 16 No. 2 (2020): JMSK, JANUARY, 2020
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (372.512 KB) | DOI: 10.20956/jmsk.v16i2.7864

Abstract

Classification is the process of grouping data based on observed variables to predict new data whose class is unknown. There are some classification methods, such as Naïve Bayes, K-Nearest Neighbor and Neural Network. Naïve Bayes classifies based on the probability value of the existing properties. K-Nearest Neighbor classifies based on the character of its nearest neighbor, where the number of neighbors=k, while Neural Network classifies based on human neural networks. This study will compare three classification methods for Seat Load Factor, which is the percentage of aircraft load, and also a measure in determining the profit of airline.. Affecting factors are the number of passengers, ticket prices, flight routes, and flight times. Based on the analysis with 47 data, it is known that the system of Naïve Bayes method has misclassifies in 14 data, so the accuracy rate is 70%. The system of K-Nearest Neighbor method with k=5 has misclassifies in 5 data, so the accuracy rate is 89%, and the Neural Network system has misclassifies in 10 data with accuracy rate 78%. The method with highest accuracy rate is the best method that will be used, which in this case is K-Nearest Neighbor method with success of classification system is 42 data, including 14 low, 10 medium, and 18 high value. Based on the best method, predictions can be made using new data, for example the new data consists of Bali flight routes (2), flight times in afternoon (2), estimate of passenger numbers is 140 people, and ticket prices is Rp.700,000. By using the K-Nearest Neighbor method, Seat Load Factor prediction is high or at intervals of 80% -100%.
Perbandingan Estimasi M, Estimasi S, dengan Estimasi MM untuk Mendapatkan Estimasi Robust Regression Terbaik dalam Perkara Pidana di Indonesia: Perbandingan Estimasi M, Estimasi S, dengan Estimasi MM Malecita Nur Atala Singgih; Achmad Fauzan
Jurnal Matematika, Statistika dan Komputasi Vol. 18 No. 2 (2022): JANUARY 2022
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v18i2.18630

Abstract

Crime incidents that occurred in Indonesia in 2019 based on Survey Based Data on criminal data sourced from the National Socio-Economic Survey and Village Potential Data Collection produced by the Central Statistics Agency recorded 269,324 cases. The high crime rate is caused by several factors, including poverty and population density. Determination of the most influential factors in criminal acts in Indonesia can be done with Regression Analysis. One method of Regression Analysis that is very commonly used is the Least Square Method. However, Regression Analysis can be used if the assumption test is met. If outliers are found, then the assumption test is not completed. The outlier problem can be overcome by using a robust estimation method. This study aims to determine the best estimation method between Maximum Likelihood Type (M) estimation, Scale (S) estimation, and Method of Moment (MM) estimation on Robust Regression. The best estimate of Robust Regression is the smallest Residual Standard Error (RSE) value and the largest Adjusted R-square. The analysis of case studies of criminal acts in Indonesia in 2019 showed that the best estimate was the S estimate with an RSE value of 4226 and an Adjusted R-square of 0.98  
Study of Student Satisfaction Level in the Faculty Based on Performance Assessment and Interest Level Achmad Fauzan; Muhammad Hasan Sidiq Kurniawan; Jaka Nugraha
EKSAKTA: Journal of Sciences and Data Analysis VOLUME 19, ISSUE 1, February 2019
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/eksakta.vol19.iss1.art8

Abstract

One way to evaluate various services at the university is seen from the level of student satisfaction. The purpose of this study is to measure how much the level of student satisfaction in the university environment, especially in the Faculty of Mathematics and Natural Sciences, Universitas Islam Indonesia (FMIPA UII) and future expectations of students focusing on their respective study programs. 6 aspects were used to find out how much satisfaction the students had, namely: (1) Tangible, (2) Reliability, (3) Responsiveness, (4) Assurance, (5) Empathy, and (6) Information. The research method used is descriptive analysis method related to satisfaction represented by the Cartesian diagram. The study was conducted in a period of 3 months with the sample used being active students in the 2016 and 2017 FMIPA classes proportionally in each study program (study program). The data used are primary data consisting of 2 main assessments, namely performance assessment and importance assessment. The results of the level of satisfaction / suitability are classified into the Cartesian diagram which consists of 4 priorities, namely: top priority, achievement priority, low priority, and excessive. The results of the study obtained overall levels of satisfaction in Mathematics as much as 90% of students were satisfied with the level of performance provided. However, there are still 2 indicators that are included in the priority, namely problems in the key-in process and ease of communication for parents of students to consult. In addition to the contents of each indicator, an analysis of suggestions for improvement in the FMIPA environment using text mining based on barplot and wordcloud is associated with the dominant words appearing to describe the general expectations of students
Analysis of Factors Influencing The Decision to Choose The Department in The Natural Science Campus Muhammad Hasan Sidiq Kurniawan; Achmad Fauzan; Jaka Nugraha
EKSAKTA: Journal of Sciences and Data Analysis VOLUME 1, ISSUE 1, February 2020
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/EKSAKTA.vol1.iss1.art12

Abstract

Education is very important thing for everyone. Parents tend to choose high-quality school or campus, to ensure their children’s education. One thing which determined parents to choose the campus for their children is the prospect for work. Faculty of Natural Science UII have high-quality department. Some of them already had highest accreditation level and even Internationally accredited. But some peoples in Indonesian often asked about what become of their children after graduated from the faculty of natural science or what is job that suit for their chlidren. The department of that faculty often not become the first choice when choosing campus. Therefore, the research to study about factors which determine people to choose their college department is needed. In this paper, the study is focused on factors which influence people’s decision score to choose the department in the faculty of natural science. We are using correlation and regression analysis. The result show that factors which influence the people’s decision are different between one department with another. Those factor consist of: product, promotion, and the price/cost.
Clustering Provinces in Indonesia based on Community Welfare Indicators Sekti Kartika Dini; Achmad Fauzan
EKSAKTA: Journal of Sciences and Data Analysis VOLUME 1, ISSUE 1, February 2020
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/EKSAKTA.vol1.iss1.art9

Abstract

The Preamble of the 1945 Constitution of the Republic of Indonesia explicitly states that the main task of the government of the Republic of Indonesia is to advance general prosperity, to develop the nation's intellectual life, and to realize social justice for all Indonesian people. Social inequality is a problem that is still faced by Indonesian people today. To solve the problem required supporting data analysis as a basis for policy formulation. This research was conducted with the aim of clustering provinces in Indonesia based on community welfare indicators using K-Means cluster analysis. K-Means cluster analysis is chosen based on the variance value (0.101), which is smaller than the variance value in the average linkage cluster analysis (0.152). Based on data analysis, provinces in Indonesia are clustered into three where the first cluster consists of 21 provinces, the second cluster consists of 3 provinces, and the third cluster consists of 10 provinces. Each cluster has different characteristics that can be of concern to the parties concerned to overcome the social welfare gap. Besides, in order cluster results are more easily understood, visualization of results is added with a Geographic Information System (GIS) using Indonesian maps accompanied by differences in color gradations for each cluster
Impacts of Human Development Index and Percentage of Total Population on Poverty using OLS and GWR models in Central Java, Indonesia Duhania Oktasya Mahara; Achmad Fauzan
EKSAKTA: Journal of Sciences and Data Analysis VOLUME 2, ISSUE 2, August 2021
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/EKSAKTA.vol2.iss2.art8

Abstract

Central Java province is one of the provinces with the highest number of poor people on the island of Java, with the number of poor people in 2020 increasing by 0.44 million people from the previous year. Poverty is caused by several factors, one of which is the Human Development Index (HDI) and the Total Population level. Each region has different characteristics from other regions. These differences in characteristics cause more specific spatial effects, namely spatial heterogeneity. Geographically Weighted Regression (GWR) is a statistical method that can analyze spatial heterogeneity by assigning different weights and models to each observation location. This study aims to determine whether the HDI variable and percentage of total population significantly impact the number of poor people in Central Java Province in 2020 without eliminating the spatial effect. There are three groupings of variables that affect the Number of Poor People for GWR with the Adaptive Kernel Bisquare weighting function and four groups for the Adaptive Kernel Tricube weighting function. The Key Performance Indicators (KPI) used are Mean , Akaike Information Criterion (AIC), Absolute Error (MAE), Mean Square Error (MSE), and Mean Absolute Percentage Error (MAPE). Based on these KPIs, the GWR model with the Adaptive Kernel Bisquare weighting function provides better results when compared to the OLS model.
Analysis of Hotels Spatial Clustering in Bali: Density-Based Spatial Clustering of Application Noise (DBSCAN) Algorithm Approach Achmad Fauzan; Afdelia Novianti; Raden Rara Mentari Ayu Ramadhani; Marcelinus Alfafisurya Setya Adhiwibawa
EKSAKTA: Journal of Sciences and Data Analysis VOLUME 3, ISSUE 1, February 2022
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/EKSAKTA.vol3.iss1.art4

Abstract

Bali is one of the hearts of tourism in Indonesia. The existence of the Covid-19 pandemic has made this tourist paradise also affected the wheels of the economy. Based on this, this study aims to determine the density clustering of one of the economic supporters in Bali, namely hospitality. The study began with the quadrant method and Ripley's K-Function to measure the distribution pattern of hospitality. From the results of the two methods, the distribution pattern of hotels in Bali is more towards clusters than random or regular distribution. If the point distribution pattern is more towards the cluster, it is continued with the Density-Based Spatial Clustering of Application Noise (DBSCAN) algorithm to form spatial clustering. In the DBSCAN algorithm, a combination of parameters, namely minimum points (MinPts) and epsilon (Eps), is carried out with evaluation using the silhouette average width value. From the results of the DBSCAN algorithm, the clustering results show that the distribution of hotels in Bali forms clusters and tends to approach the surrounding tourist attractions, such as near the beach, city market, and mountainous areas. It can help policymakers if they want to prioritize economic recovery after the Covid-19 pandemic.
OPTIMALISASI KECERDASAN SISWA DENGAN INTENSITAS PEMBINAAN OLIMPIADE MATEMATIKA Achmad Fauzan
Asian Journal of Innovation and Entrepreneurship Volume 03, Issue 03, September 2018
Publisher : UII

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Beragam cara yang dilakukan pemerintah dalam meningkatkan kualitas pendidikan supaya mampu bersaing dengan bangsa lain, salah satunya dengan diadakan kompetisi atau yang lebih dikenal dengan istilah olimpiade. Kendati demikian, tidak setiap institusi pendidikan misalnya sekolah mampu untuk memulai atau rutin untuk memberikan pembinaan guna persiapan mengikuti olimpiade tersebut. Berdasarkan hal tersebut, dilakukan pembinaan olimpiade dengan tujuan meningkatkan kompetensi siswa dalam menyelesaikan soal-soal olimpiade dalam hal ini khususnya bidang matematika. Metode kegiatan ini menggunakan sistem pendampingan secara berkala.  Kegitan pembinaan dilaksanakan pada setiap hari kamis selama bulan Januari hingga April 2018 di Madrasah Tsanawiyah (MTs) YAPI Pakem Kabupaten Sleman,  Yogyakarta. Setelah dilaksanakan pembinaan, dilakukan uji perbandingan menggunakan uji Wilcoxon untuk melihat perkembangan siswa serta klasterisasi K-Means guna melihat karakteristik dari siswa. Berdasarkan Uji Wilcoxon didapat perbedaan yang signifikan antara sebelum dan sesudah adanya pembinaan olimpiade matematika. Kendati demikian, nilai rata-rata tersebut masih dibawah target yang diharapkan oleh tim pengabdian yakni 65. Hal ini dimungkinkan karena berdasarkan analisis klaster, sebanyak hanya 27% siswa yang antusias dan fokus dalam mengikuti pembinaan sementara 73% siswa masih kurang fokus dalam pembinaan olimpiade. Selain itu juga pembinaan masih merupakan hal baru bagi siswa tersebut. Berdasarkan hasil kuesioner dan wawancara kegiatan pembinaan ini merupakan kegiatan yang positif dan membantu pihak sekolah tidak hanya dari pihak murid namun juga pihak sekolah itu sendiri. Sebagai indikator minimal adalah membuka wawasan baru siswa terkait olimpiade matematika. Banyak hal yang masih perlu diperbaiki, hal ini menjadi evaluasi dan tantangan bahwasannya diperlukan waktu yang lebih lama dan pertemuan yang lebih intensif dalam proses pembinaan lebih kurang 1 hingga 1,5 tahun dengan pertemuan setiap pekan 1 hingga 2 pertemuan. Kegiatan ini dapat diusahakan oleh tim guru matematika sekolah tersebut yang nantinya masih tetap bisa berkomunikasi dengan tim peneliti.
Co-Authors Abdullah Ahmad Dzikrullah Adriana, Hannura Afdelia Novianti Afdelia Novianti Ameilea Chealsea Ekaputrie Annisa Fitria Asyharul Muala Bariklana, Muhammad Daniar Wahyu Laraswati Dini, Sekti Kartika Duhania Oktasya Mahara Duhania Oktasya Mahara Edy Widodo Elita Nurfitriyani Sulistyo Farhatun Nissa Fitri Amalia Ganjar Fadillah, Ganjar IKA MEIZA MAHARANI Irwan Sutisna Jaka Nugraha Malecita Nur Atala Singgih Marcelinus Alfafisurya Setya Adhiwibawa Marcelinus Alfafisurya Setya Adhiwibawa Mega Luna Suliztia Muhammad Hasan Sidiq Kurniawan Muthia Citra Safira Muthia Citra Safira Muthia Dishanur Izzati Nabila Azzahra Haris Putri Nanda Lailatul Humairoh Novendri Isra Asriny Novendri Isra Asriny Novendri Isra Asriny Raden Rara Mentari Ayu Ramadhani Rahmi Novika Harahap Riefyal Arshyzha Mustain Rohmatul Fajriyah Sekti Kartika Dini Shazia Ayesha Azzahra Sheilta Alphenia Sheilta Alphenia Syifa Ayu Anjani Wiranti Nugrah Andini