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All Journal JURNAL EKONOMI DAN KEBIJAKAN PEMBANGUNAN Jurnal Ilmu Komputer JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Sistemasi: Jurnal Sistem Informasi Jurnal Teknologi dan Sistem Komputer Knowledge Engineering and Data Science Inovasi : Jurnal Ekonomi, Keuangan, dan Manajemen Indonesian Journal of Artificial Intelligence and Data Mining JITK (Jurnal Ilmu Pengetahuan dan Komputer) JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Informatika Universitas Pamulang Zero : Jurnal Sains, Matematika, dan Terapan Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) KOMPUTIKA - Jurnal Sistem Komputer JURNAL METEOROLOGI DAN GEOFISIKA Jurnal Aplikasi Statistika & Komputasi Statistik EIGEN MATHEMATICS JOURNAL International Journal of Advances in Data and Information Systems Journal of System and Computer Engineering JUSTIN (Jurnal Sistem dan Teknologi Informasi) Prosiding Seminar Nasional Official Statistics Jurnal Sains, Nalar, dan Aplikasi Teknologi Informasi PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON DATA SCIENCE AND OFFICIAL STATISTICS Jurnal Meteorologi dan Geofisika Jurnal Statistika Industri dan Komputasi
Arie Wahyu Wijayanto
Politeknik Statistika STIS
Agriculture, Biological Sciences & Forestry Humanities Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Physics Social Sciences Other

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TINJAUAN BIG DATA MOBILITAS PENDUDUK PADA MASA SOCIAL DISTANCING DAN NEW NORMAL SERTA KETERKAITANNYA DENGAN JUMLAH KASUS COVID-19 Zanial Fahmi Firdaus; Arie Wahyu Wijayanto
Seminar Nasional Official Statistics Vol 2020 No 1 (2020): Seminar Nasional Official Statistics 2020
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (195.025 KB) | DOI: 10.34123/semnasoffstat.v2020i1.502

Abstract

Berbagai kebijakan Pemerintah Indonesia telah digulirkan untuk mengatasi dampak penyebaran Pandemi Coronavirus Disease (COVID-19). Di antara kebijakan tersebut meliputi social distancing atau Pembatasan Sosial Berskala Besar (PSBB), work from home, school from home, dst. Dengan tujuan untuk menekan mobilitas penduduk, berbagai kebijakan tersebut diharapkan dapat meminimalkan pertambahan jumlah kasus harian COVID-19. Penelitian ini berfokus pada identifikasi dan perbandingan pola mobilitas penduduk selama masa social distancing dan new normal serta mencoba menganalisis keterkaitan antara mobilitas penduduk dengan kasus COVID- 19, khususnya di wilayah Provinsi DKI Jakarta. Periode penelitian dimulai sejak mulai diberlakukannya social distancing pada pertengahan bulan Maret hingga akhir Juli 2020. Dengan menggunakan independent two-sample t-test, terdapat perbedaan yang signifikan antara rata- rata mobilitas penduduk maupun peningkatan kasus COVID-19 pada masa social distancing dan pada masa new normal di Jakarta. Penerapan social distancing (PSBB) di DKI Jakarta secara umum mampu menurunkan mobilitas penduduk dan menekan penambahan kasus COVID-19.
Comparative Analysis of Hierarchical, K-Means, and K-Medoids Clustering and Methods in Grouping Indonesia's Human Development Index in 2019 Emir Luthfi; Arie Wahyu Wijayanto
INOVASI Vol 17, No 4 (2021): November
Publisher : Faculty of Economics and Business Mulawarman University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/jinv.v17i4.10106

Abstract

Dalam cabang ilmu data mining sudah banyak dilakukan analisis pengelompokan (clustering analysis) yang berguna untuk dapat mengelompokkan suatu data observasi tertentu. Pada penelitian digunakan dataset terkait Indeks Pembangunan Manusia (IPM) di Indonesia tahun 2019 dan dilakukan pengelompokkan variabel pembangun Indeks Pembangunan Manusia (IPM) yang terdiri dari angka harapan hidup, angka melek huruf, rata-rata lamanya sekolah, dan pengeluaran perkapita yang disesuaikan menggunakan metode Hirearchical, K-Means, dan K-Medoids Clustering. Metode Hirearchical yang digunakan yaitu dengan metode Algomerative menggunakan kemiripan jarak dengan Ward Method. Dari hasil ketiga metode tersebut akan dibandingkan untuk memperoleh metode yang terbaik dengan melihat ukuran validitas dari nilai Dunn Index (DN), Davies Bouldin Index (DB), Calinski-Harabasz Index (CH) serta untuk menentukan jumlah klaster/kelompok yang optimum dan yang terpenting dalam membandingkan untuk mendapatkan metode algoritma yang terbaik yaitu dengan memperoleh nilai rasio simpangan baku yang bertujuan untuk memperoleh nilai simpangan baku dalam kelompok (SW) yang minimum dan nilai simpangan baku antar kelompok (SB) yang maksimum. Model terbaik yang diperoleh yaitu menggunakan K-Medoids lebih baik dilihat dari perbandingan rasio simpangan baku kemudian diaplikasikan dalam analisis sentiment wilayah kabupaten/kota di Indonesia berdasarkan angka IPM masing-masing wilayahnya sehingga didapatkan wilayah dengan angka IPM tertinggi dan wilayah dengan IPM terendah pada tahun 2019.  
Comparison of Ensemble Learning Method: Random Forest, Support Vector Machine, AdaBoost for Classification Human Development Index (HDI) Ressa Isnaini Arumnisaa; Arie Wahyu Wijayanto
Sistemasi: Jurnal Sistem Informasi Vol 12, No 1 (2023): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v12i1.2501

Abstract

Classification in supervised learning is a way to find patterns in database that the classes are already known. In the classification of machine learning, there is a term called ensemble classifier. The workings of the ensemble classifier aimed to improve model accuracy and optimize classification performance. This study aims to analyze the comparison of algorithms that work with ensemble learning, including Random Forest, Support Vector Machine (SVM), and AdaBoost. The data used is the Human Development Index (HDI) of districts/cities in Indonesia. Other variables that are strongly related to human development are GRDP per capita, gross enrollment rate, net enrollment rate, labor force participation rate, unemployment rate, poverty rate, poverty depth, poverty severity, and average consumption per capita. The reason for using HDI is that apart from being an important macroeconomic variable in describing the condition of human resources in Indonesia, HDI already has an obvious classification according to the Badan Pusat Statistik (BPS) so that supervised learning can be applied. Comparison of model evaluation using accuracy, specificity, sensitivity, and kappa statistics. The analysis flow starts with data preprocessing, resampling and cross-validation, then modeling using the Random Forest, Support Vector Machine (SVM), and AdaBoost algorithm. The final stage is the model evaluation by comparing the best models in the classifications of districts/ cities according to HDI. The results showed that the Random Forest model had the best performance compared to the Support Vector Machine (SVM) and AdaBoost models with an accuracy value of 85,23%, specificity of 71,63%, sensitivity of 95,05%, and kappa coefficient of 0,7698. From this research, the an ensemble classifier can be developed to help classify scores on the Human Development Index in Indonesia.Keywords: AdaBoost, Random Forest, Support Vector Machine, Ensemble Learning, Human Development Index
Perbandingan Metode Data Mining dalam Pengklasifikasian Status Desa Kabupaten Purwakarta dan Bandung Barat (Podes 2021) Munifah Zuhra Almasah; Arie Wahyu Wijayanto
Eigen Mathematics Journal Vol. 6 No. 1 Juni 2023
Publisher : University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/emj.v6i1.156

Abstract

Each village has different characteristics and is constantly changing along with the level of development in a village. These changes in conditions are used as indicators to classify villages into urban or rural village status. In this study, researchers will compare or evaluate of several data mining methods, namely decision trees, support vector machines, naïve bayes, and random forests to find the best algorithm in classifying urban villages and rural villages in Purwakarta and West Bandung Regencies. The data used in this study were 357 records and 8 attributes sourced from village potential data (Podes 2021). Furthermore, it was obtained that the best method in classifying urban villages and rural villages is to use random forests with accuracy value and F- score of 0,9.
Extracting Consumer Opinion on Indonesian E-Commerce: A Rating Evaluation and Lexicon-Based Sentiment Analysis Arbi Setiyawan; Arie Wahyu Wijayanto; He Youshi
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2021 No. 1 (2021): Proceedings of 2021 International Conference on Data Science and Official St
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/icdsos.v2021i1.22

Abstract

E-commerce as a business platform offers abundant advantages in modern life all over the world. Sellers and buyers at online marketplaces may get benefits and advantages from e-commerce. One of the advantages is that e-commerce can be accessed anywhere and anytime. Despite providing advantages, e-commerce also has disadvantages including product quality fraud and data theft. Online marketplaces provide facilities for consumer evaluation, through star rating and consumer reviews. In this paper, we focus on the Business-to-Consumer (B2C) e-commerce type and extract consumer opinion data from a leading online marketplace in Indonesia and use text mining approaches to compare the rating evaluation and sentiment analysis on consumer reviews. With 2,937 records, we investigate the relationship between star rating and lexicon-based sentiment analysis. From the results, we found that most consumers do not hesitantly provide a good evaluation indicated by a 5-star rating and positive sentiment of reviews. A quite polarized rating distribution is found and indicates a straightforward consumer opinion. However, a further examination of the relation between rating and review, we discover inconsistencies in consumer opinion where the good rating may also contain negative reviews. Our result findings provide an insight to build a more integrated consumer opinion indicator in e-commerce and that online marketplace sellers need to look deeper at the detailed reviews rating.
Knowledge Management System in Official Statistics: An Empirical Investigation on Indonesia Population Census Achmad Muchlis Abdi Putra; Arie Wahyu Wijayanto
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2021 No. 1 (2021): Proceedings of 2021 International Conference on Data Science and Official St
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/icdsos.v2021i1.25

Abstract

National statistical offices around the world show a strong interest in producing reliable, objective, and accurate information in compliance with a high level of professional and scientific standards. Such a set of information provided by government agencies is known as the official statistics. To support the potential of knowledge-based business processes and deliver high-quality public services, knowledge management systems (KMS) are undoubtedly required. In this work, we study the impact of embracing KMS in one of the most massive scale statistical census in South East Asia, the 2020 Indonesia Population Census (IPC2020). The regression analysis is utilized in this study where the perceived usefulness is the dependent variable and the perceived ease of use become the independent variable. Our findings reveal that KMS utilization gains a positive influence on the perceived ease of use and usefulness among the stakeholders and organizing personnel. This provides an incentive to enlarge the range of implementation and improve the system and infrastructure capability to better support the knowledge-driven collaboration among stakeholders of the statistical office.
Optimization of Waste Transportation Routes using Multi-objective Non-dominated Sorting Genetic Algorithm II (MNSGA-II) in the Eastern and Southern Regions of Bandung City, Indonesia Natasya Afira; Arie Wahyu Wijayanto
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2021 No. 1 (2021): Proceedings of 2021 International Conference on Data Science and Official St
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/icdsos.v2021i1.27

Abstract

Ensuring high-quality and effective urban waste management has been an important priority to achieve sustainable and environmental-friendly cities and communities mandated by Sustainable Development Goals (SDGs). The massively growing population in urban regions of developing countries, such as Bandung City, Indonesia, leads to the increasing volume of daily goods consumption and households waste production. The waste transportation route is one of the main determining factors for the cost of waste management. In this paper, we introduce the Multi-objective Non-dominated Sorting Genetic Algorithm II (MNSGA-II) to solve the waste transportation route optimization problem in the Eastern and Southern Regions of Bandung City, Indonesia. Compared to the existing traditional evolutionary algorithms, MNSGA-II offers three major important benefits: efficient computational complexity, no requirement of sharing parameters, and a non-elitism mechanism. Algorithm parameters include the number of generations, mutation rate, and crossover rate. Our extensive experiments suggest the best solution resulted in 14 routes with a total distance of 152,63 km. Further, our proposed route optimization is potentially beneficial to support the improvement of the sustainable waste management service system at Bandung City.
Preserving Women Public Restroom Privacy using Convolutional Neural Networks-Based Automatic Gender Detection Desi Kristiyani; Arie Wahyu Wijayanto
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2021 No. 1 (2021): Proceedings of 2021 International Conference on Data Science and Official St
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/icdsos.v2021i1.29

Abstract

Personal safety and privacy have been the significant concerns among women to use and access public restrooms/toilets, especially in developing countries such as Indonesia. Privacy-enhancing designs are unquestionably expected to ensure no men entering the rooms neither intentionally nor accidentally without prior notice. In this paper, we propose a facial recognition approach to ensure women's safety and privacy in public restroom areas using Convolutional Neural Networks (CNN) model as a gender classifier. Our main contributions are as follows: (1) a webcam feed automatic gender detection model using CNN which may further be connected to a security alarm (2) a publicly available gender-annotated image dataset that embraces Indonesian facial recognition samples. Supplementary Indonesian facial examples are taken from a government-affiliated college, Politeknik Statistika STIS students' photo datasets. The experimental results show a promising accuracy of our proposed model up to 95.84%. This study could be beneficial and useful for wider implementation in supporting the safety system of public universities, offices, and government buildings.
Bayesian Network Model to Distinguish COVID-19 for Illness with Similar Symptoms Emir Luthfi; Arie Wahyu Wijayanto
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2021 No. 1 (2021): Proceedings of 2021 International Conference on Data Science and Official St
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/icdsos.v2021i1.36

Abstract

Numerous diseases and illnesses exhibit similar physical and medical symptoms, such as COVID-19 and its similar disguised illness (common cold, flu, and seasonal allergies). In this study, we construct a Bayesian Network model to distinguish such symptom variables in a classification task. The Bayesian Network model has been widely used as a classifier comparable to machine learning models. We develop the model with a scoring-based method and implement it using a hill-climbing algorithm with the Bayesian information criterion (BIC) score approach. Experimental evaluations using publicly available Mayo Clinic based data using this Bayesian Network model that present Directed Acyclic Graph (DAG) which can show the relationship between the similar symptoms and the type of disease with Conditional Probability Table (CPT). This model shows a promising accuracy performance up to 93.14% which is better than the performance of other machine learning classifiers, including the Support Vector Machine (SVM) and the ensemble approaches such as Random Forest (RF), while slightly smaller than that of the neural networks (NN).
Learning Bayesian Network for Rainfall Prediction Modeling in Urban Area using Remote Sensing Satellite Data (Case Study: Jakarta, Indonesia) Salwa Rizqina Putri; Arie Wahyu Wijayanto
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2021 No. 1 (2021): Proceedings of 2021 International Conference on Data Science and Official St
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/icdsos.v2021i1.37

Abstract

Rainfall modeling is one of the most critical factors in agricultural monitoring and statistics, transportation schedules, and urban flood prevention. Weather anomaly during the dry season in urban coastal areas of tropical countries such as Jakarta, Indonesia has become a challenging issue that causes unexpected changes in rain patterns. In this paper, we propose the Bayesian Network (BN) approach to model the probabilistic nature of rain patterns in urban areas and causal relationships among its predictor variables. Rain occurrences are predicted using temperature, relative humidity, mean-sea level (MSL) pressure, cloud cover, and precipitation variables. Data are obtained from the remote sensing sources of the National Oceanic and Atmospheric Administration (NOAA) satellite in Jakarta 2020-2021. We compare both of the score-based, i.e., Hill Climbing (HC), and hybrid structure learning algorithms of Bayesian Network including the techniques of Max-Min Hill Climbing (MMHC), General 2-Phase Restricted Maximization (RSMAX2), and Hybrid-Hybrid Parents & Children (H2PC). Further, we also compare the performance of score-based model (Hill Climbing) under five different popular scorings: Bayesian Information Criterion (BIC), K2, Log-Likelihood, Bayesian Dirichlet Equivalent (BDE), and Akaike Information Criterion (AIC) methods. The main contributions of this study are as follows: (1) insights that the hybrid structure learning algorithms of Bayesian Network models are either superior in performance or at least comparable to its score-based counterparts (2) our proposed best performed Bayesian Network model that is able to predict the rain occurrences in Jakarta with a promising overall accuracy of more than 81 percent.
Co-Authors A.A. Ngurah Gede, Wasudewa Achmad Muchlis Abdi Putra Akhmad Fatikhurrizqi Alfina Nurpiana Alvia Rossa Damayanti Alya Azzahra Andriansyah Muqiit Wardoyo Saputra Annisa Firnanda Arbi Setiyawan Arif Handoyo Marsuhandi Arina Mana Sikana Ariyani, Marwah Erni Atut Pindarwati Ayu Aina Nurkhaliza Az-Zahra, Afifah Bagus Almahenzar Bony Parulian Josaphat Chisan, Innas Khoirun Daulay, Nur Ainun Desi Kristiyani Dewi, Ni Kadek Ayu Purnami Sari Dwi Karunia Syaputri Dwi Wahyu Triscowati Emir Luthfi Fauzan Faldy Anggita Fauzan, Fardhi Dzakwan Febrian, M. Yandre Feriyanto, Muhamad Ghina Rofifa Suraya He Youshi Hutahaean, Yohana Madame Ika Yuni Wulansari Ikhsanudin, Muhammad Rafi Iman, Qonita Intan Kemala Iskanda, Doddy Aditya Iskanda, Watekhi Izzuddin, Kautsar Hilmi Kurniawan, Bayu Dwi Luthfi, Emir Maghfiroh, Meilinda F N Maghfiroh, Meilinda F. N. Margareth Dwiyanti Simatupang Maria Angelika H Siallagan Maria Shawna Cinnamon Claire Marsisno, Waris Marsisno, Waris Maulana, Farhan Maulidya, Luthfi Muhammad Rezza Ferdiansyah Munifah Zuhra Almasah Nabila Bianca Putri Nasiya Alifah Utami Natasya Afira Natasya Afira Ningrum, Icha Wahyu Kusuma Ningsih, I Kadek Mira Merta Nissa Shahadah Qur'ani Nora Dzulvawan Nurafiza Thamrin Nursiyono, Joko Ade Parwanto, Novia Budi Pasaribu, Ernawati Perani Rosyani Permatasari, Noverlina Putri Pindarwati, Atut Pramana, Setia Prasetyo, Rindang Bangun Pratama, Ahmad R. Prayoga, Suhendra Widi Putri, Salwa Rizqina Putri, Salwa Rizqina Rahmawati, Delvina Nur Raisa Rizky Amelia Rahman Raisa Rizky Amelia Rahman Regita Iswari Puri, Ida Ayu Wayan Renata De La Rosa Manik Ressa Isnaini Arumnisaa Ridho, Farid Rifqi Ramadhan Rifqi Ramadhan Robert Kurniawan, Robert Rudianto, Regita Dewanti Salwa Rizqina Putri Suadaa, Lya Hulliyyatus Sugiarto, Sugiarto Wahidya Nurkarim Wahyuni, Krismanti Tri Watekhi watin, Rahma Wilantika, Nori Windy Rahmatul Azizah Wulansari, Ika Yuni Yulia Aryani Yuniarto, Budi Zalukhu, Bill Van Ricardo Zanial Fahmi Firdaus