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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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Analisis Perbandingan K-Means dan K-Medoids dalam Pengelompokan Provinsi Berdasarkan Indeks Demokrasi Indonesia 2021 Rudianto, Regita Dewanti; Wijayanto, Arie Wahyu
Komputika : Jurnal Sistem Komputer Vol. 13 No. 1 (2024): Komputika: Jurnal Sistem Komputer
Publisher : Computer Engineering Departement, Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputika.v13i1.10812

Abstract

The clustering method is one method in data mining and is useful in grouping observations that do not have a target / class. One of the analyses that can be done from this clustering is the grouping of 34 provinces in Indonesia based on aspects in the 2021 Indonesian Democracy Index (IDI). The aspects of the IDI include the Freedom Aspect, Equality Aspect, and the Capacity Aspect of Democratic Institutions. Clustering analysis needs to be done to determine the grouping of IDI aspects and their characteristics. The clustering methods used in this study are K-Means and K-Medoids. For the selection of the optimal number of clusters used Dunn Index, Silhouette Index, Calinski-Harabasz Index and Davies-Bouldin Index. To obtain the best model, a comparison is made using the ratio between average within (Sw) and average between (Sb). The results obtained are that there are 5 clusters in the IDI grouping using the K-Medoids algorithm because the ratio of Sw/Sb is smaller than K-Means. With this grouping, it is hoped that the government and related parties can utilize the results of this analysis in formulating policies and maintaining political stability in Indonesia.
Perbandingan Algoritma Partitioning dan Hierarchical Clustering untuk Pengelompokan Wilayah Menurut Karakteristik Pengangguran di Pulau Jawa Tahun 2021 Rahmawati, Delvina Nur; Wijayanto, Arie Wahyu
KLIK- KUMPULAN JURNAL ILMU KOMPUTER Vol 10, No 3 (2023)
Publisher : Lambung Mangkurat University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/klik.v10i3.564

Abstract

Pengangguran merupakan masalah yang kompleks karena dipengaruhi sekaligus memengaruhi berbagai sektor kehidupan. Tingkat Pengangguran Terbuka (TPT) di Indonesia pada tahun 2021 berada pada angka 6,49 persen di mana Pulau Jawa merupakan pulau dengan TPT tertinggi dibandingkan pulau lainnya dengan jumlah pengangguran sebanyak 5.948.406 jiwa atau sebanyak 65,35 persen dari total jumlah pengangguran di seluruh Indonesia berada di Pulau Jawa. Oleh karena itu, penting untuk mengelompokkan daerah-daerah di Pulau Jawa berdasarkan karakteristik pengangguran sehingga pemerintah dapat dengan tepat merumuskan kebijakan untuk menekan angka pengangguran. Data yang digunakan meliputi 7 variabel terkait pengangguran pada 119 kabupaten/kota di Pulau Jawa tahun 2021. Penelitian ini menggunakan dua metode clustering, yaitu partitioning dan hierarki untuk mengelompokkan kabupaten/kota di Pulau Jawa berdasarkan karakteristik pengangguran. Metode partitioning yang dipilih adalah K-Means. Penentuan jumlah cluster menggunakan validasi internal dan validasi stabilitas menunjukkan bahwa metode hierarki dengan jumlah cluster 2 merupakan cluster yang paling optimal di mana metode Ward mampu memberikan hasil pengelompokan terbaik berdasarkan nilai agglomerative coefficient.Kata kunci: Pengangguran, Cluster, Partitioning, Hierarki
Tinjauan Kesejahteraan di Daerah Perbatasan Republik Indonesia Tahun 2021: Penerapan Analisis Klaster K-Means dan Hierarki Az-Zahra, Afifah; Wijayanto, Arie Wahyu
JUSTIN (Jurnal Sistem dan Teknologi Informasi) Vol 12, No 1 (2024)
Publisher : Jurusan Informatika Universitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/justin.v12i1.69040

Abstract

Kesejahteraan menjadi salah satu tujuan utama pemerintahan yang perlu ditinjau secara multidimensi. Di Indonesia, program-program pembangunan banyak menyasar Daerah Terdepan, Terluar, dan Tertinggal (3T). Daerah terdepan dan terluar merupakan daerah yang berada di garis perbatasan negara dengan banyak ancaman terhadap kesejahteraan. Oleh karena itu, diperlukan analisis klaster sebagai gambaran kesejahteraan di daerah perbatasan, yang diharapkan dapat membantu proses monitoring dan evaluasi program pembangunan. Indikator-indikator kesejahteraan yang digunakan bersumber dari publikasi Statistik Kesejahteraan Rakyat 2021, tabel Badan Pusat Statistik, Buku Saku Hasil Suvei Status Gizi Indonesia 2021, dan tabel FSVA Nasional 2021 di 204 kabupaten/kota di 13 provinsi perbatasan. Penelitian ini membandingkan dua metode analisis klaster, yaitu partitioning dengan K-Means dan hierarki dengan Ward"™s Method berdasarkan kriteria validitas internal dan stabilitas klaster. Hasilnya diperoleh bahwa ukuran sampel 2 memberikan klaster paling yang optimal dan metode K-Means menghasilkan kinerja yang lebih baik. Secara umum, kabupaten/kota yang tergabung ke dalam klaster kedua memiliki indikator kesejahteraan yang lebih tinggi dibandingkan klaster pertama.  
Perbandingan Pengelompokkan Provinsi di Indonesia Menurut Kualitas Lingkungan Hidup Menggunakan Metode Hierarki dan Partisi Ikhsanudin, Muhammad Rafi; Wijayanto, Arie Wahyu
JUSTIN (Jurnal Sistem dan Teknologi Informasi) Vol 12, No 1 (2024)
Publisher : Jurusan Informatika Universitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/justin.v12i1.71495

Abstract

Lingkungan hidup merupakan kesatuan ruang antara manusia, hewan, tumbuhan, air, dan komponen lainnya yang berdampak pada kehidupan dan kesejahteraan makhluk hidup. Kementerian Lingkungan Hidup dan Kehutanan mencatat bahwa pada tahun 2020 nilai IKLH Indonesia telah melampaui target RPJMN yang ditetapkan. Akan tetapi, pada tahun tersebut masih terdapat 8 provinsi yang belum memenuhi target yang ditetapkan. Maka dari itu, tujuan dari penelitian ini ialah untuk melakukan pengelompokkan tingkat kualitas lingkungan hidup sebagai dasar pengambilan keputusan sesuai dengan kondisi lingkungan hidup di setiap daerah. Berdasarkan hasil validasi cluster, metode clustering terbaik yang diterapkan ialah metode K-Means dengan dua cluster. Hasil akhir diperoleh bahwa cluster 1 beranggotakan sebanyak 12 provinsi dengan tingkat kualitas lingkungan hidup tinggi dan cluster 2 beranggotakan sebanyak 22 provinsi dengan tingkat kualitas lingkungan hidup rendah. Karakteristik hasil pengelompokkan menunjukkan bahwa indikator lingkungan hidup terkait sampah, air, dan air laut perlu diperhatikan secara lebih intensif oleh pemerintah daerah karena nilai rata-rata yang diperoleh cukup rendah.
Predicting Startup Success Using Machine Learning Approach Ningrum, Icha Wahyu Kusuma; Ridho, Farid; Wijayanto, Arie Wahyu
Journal of Applied Informatics and Computing Vol. 8 No. 2 (2024): December 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i2.8338

Abstract

Predicting startup success is important because it helps investors, entrepreneurs, and stakeholders allocate resources more efficiently, minimize risks, and enhance decision-making in an uncertain and competitive environment. Therefore, investors need to predict whether a startup will succeed or fail. Investors conduct this assessment to determine if a startup is worthy of funding. The company's founders mark success here by receiving a sum of money through the Initial Public Offering (IPO) or Merger and Acquisition (M&A) process. If the startup closes, we will consider it a failure. The data used consists of 923 startup companies in the United States. We carried out the classification using four methods: Random Forest, Support Vector Machines (SVM), Gradient Boosting, and K-Nearest Neighbor (KNN). We then compare the results from the four methods with and without feature selection. We determine the feature selection based on the relative importance of each method. The results of this study indicate that the Random Forest method with feature selection has the best accuracy, precision, recall, and F1 score than the other methods, respectively 81.85%, 80.19%, 87.09%, and 83.44%.
Perbandingan Kinerja Clustering Non-Hierarchical pada Indeks Daya Saing Daerah di Provinsi Jawa Tengah Tahun 2022 Ariyani, Marwah Erni; Wijayanto, Arie Wahyu
Jurnal Ilmu Komputer Vol 17 No 2 (2024): Jurnal Ilmu Komputer
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

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

Abstract

Regional Competitiveness Index (RCI) is a benchmark to measure the ability of a region to compete in a market published by the National Research & Innovation Agency (BRIN). RCI includes several pillars or indicators including infrastructure, quality human resources, innovation, and government policies that support economic growth. This study aims to compare the performance of several non-hierarchical clustering techniques. The data used are the RC) from 35 Regencies/Municipalities in Central Java,2022 which was published by the National Research and Innovation Agency. The clustering methods used are Fuzzy c-means, K-means, and K-medoid. Each method gets a different optimal number of clusters. After evaluating the best model using the Silhouette Coefficient, Dunn Index, Davies Bouldin Index, Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC), the best model was obtained using k-medoid with three clusters. Based on the clusters formed, the first cluster has three regencies/municipalities, the second cluster has regencies/cities, and the third cluster has 25 regencies/ municipalities.
Small Area Estimation Approaches Using Satellite Imageries Auxiliary Data for Estimating Per Capita Expenditure in West Java, Indonesia Feriyanto, Muhamad; Arie Wahyu Wijayanto; Ika Yuni Wulansari; Parwanto, Novia Budi
Jurnal Aplikasi Statistika & Komputasi Statistik Vol 16 No 2 (2024): Jurnal Aplikasi Statistika & Komputasi Statistik
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/jurnalasks.v16i2.799

Abstract

Introduction/Main Objectives: The economy of a country can determine the welfare of its people. One of the economic indicators in Indonesia is per capita expenditure, which has the lowest estimation at the district level. Background Problems: Sub-district level estimates provide detailed information on inequality that cannot be explained at the district level. Unfortunately, sub-district level estimates of per capita expenditure in Indonesia have poor Relative Standard Error (RSE) values. Research Method: The Small Area Estimation (SAE) method can improve estimator accuracy on small samples by using auxiliary variable information. Novelty: The existence of big geospatial data such as remote sensing provides an advantage in the efficient use of auxiliary variables. Finding Result: The Empirical Best Linear Unbiased Prediction (EBLUP) model using Nighttime Light Intensity (NTL) as an auxiliary variable provides the best results of the five proposed models. Remote sensing data can potentially be used in SAE auxiliary variables. 
Algorithm Comparison of Hierarchical and Non-Hierarchical Clustering Method in Grouping Regional Poverty Variables Maulana, Farhan; Wijayanto, Arie Wahyu
Indonesian Journal of Artificial Intelligence and Data Mining Vol 8, No 1 (2025): March 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v8i1.29393

Abstract

One of the objectives of the main Sustainable Development Goals (SDGs) is to end poverty in all forms. Although West Sumatera Province occupies ranking seventh lowest national in poverty, there is an increase amounting to 0.11 percent in September 2022 compared to March 2022. This shows the complexity of the poverty problem in the region. The Provincial Government needs to understand the poverty situation by grouping it based on characteristics in each region. This is a strategic step so that poverty reduction policies can be developed on target and efficiently according to the conditions of each region. This study aims to investigate Clustering methods, namely a non-hierarchical method represented by K-means, Fuzzy C-means, and K-medoids also the hierarchical method, represented by Divisive Analysis (DIANA) and Agglomerative Nesting (AGNES) with complete linkage, average linkage, single linkage, and Ward’s method, to group regencies/cities and compare the performance of the Clustering methods used, to get the best method using Davies Bouldin Index and Dunn index. The results of this research indicate that the divisive analysis method and agglomerative nesting, especially in complete linkage, single linkage, and Ward’s method is the best Clustering method. This method works optimally when the number of clusters is equal to 3. It is hoped that our findings can support policies that are right on target and efficient in efforts to overcome poverty in West Sumatera.
Optimizing Malaria Control: Granular and Cost-Effective Mosquito Habitat Index in Endemic Areas Through Satellite Imagery Daulay, Nur Ainun; Putri, Salwa Rizqina; Wijayanto, Arie Wahyu; Wulansari, Ika Yuni
Knowledge Engineering and Data Science Vol 7, No 1 (2024)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um018v7i12024p40-57

Abstract

Malaria, classified as a tropical disease under the Sustainable Development Goals (SDGs) indicator 3.3, remains a significant global health challenge. In this study, by taking advantage of multiple spectral composite indexes of multisource satellite imagery to capture various geospatial features relevant to the suitability of marsh mosquito habitat, we introduced the Mosquito Habitat Suitability Index (MHSI) to assess potential Anopheles mosquito breeding sites in terms of the vegetation density, water bodies, environment temperature, and humidity in any particular areas. The MHSI integrates the publicly accessible granular level of the normalized difference vegetation index, water index, land surface temperature, and moisture index from cost-effective low and medium-resolution optical satellite data. We focus on West Papua Province, Indonesia, known for diverse ecological conditions and varying malaria prevalence, as a case study area. From the built index, the risk zone map is then formed with the K-Means algorithm. One key finding is the elevated risk in Fakfak Regency, demanding particular attention, as its high-risk area represents 45% of its total. This research aids localized decision-making to combat malaria's unique challenges in West Papua Province which are relevant for implementation in other regions, contributing to SDG-aligned interventions for malaria eradication by 2030.
Optimasi Prediksi Jumlah Wisatawan Nusantara ke Provinsi Bali Melalui Big Data Analytics dengan Integrasi Google Trends dan Tingkat Penghunian Kamar Hotel Prayoga, Suhendra Widi; Wijayanto, Arie Wahyu
Seminar Nasional Official Statistics Vol 2024 No 1 (2024): Seminar Nasional Official Statistics 2024
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/semnasoffstat.v2024i1.2114

Abstract

The tourism sector plays an important role in the Indonesian economy. Bali, as a major tourist destination, attracts a large number of domestic tourists, which has a significant impact on the local economy. However, providing accurate and real time data remains a challenge. This data limitation makes it difficult to effectively monitor tourism conditions. Therefore, this research optimises the prediction of the number of domestic tourists to Bali using hotel room occupancy rate and Google Trends index. Real-time hotel availability and search interest play an important role in this prediction. The application of big data analytics allows the analysis of large amounts of data quickly and accurately. The results show that the best model is Support Vector Regression with Mean Absolute Percentage Error, Root Mean Square Error, and Mean Absolute Error of 14.8366, 94.5575, and 77.1152, respectively. This prediction is expected to help stakeholders monitor the condition of Bali tourism.
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 Karmawan, I Putu Agus 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 Sakka, Asriadi Salwa Rizqina Putri Suadaa, Lya Hulliyyatus Sugiarto, Sugiarto Wahidya Nurkarim Wahyuni, Krismanti Tri Watekhi watin, Rahma Wilantika, Nori Windy Rahmatul Azizah Wulansari, Ika Yuni Yeza, Ardhan Yulia Aryani Yuniarto, Budi Zalukhu, Bill Van Ricardo Zanial Fahmi Firdaus