Article Per Year (5 Year)
p-Index From 2021 - 2026
8.858
P-Index
This Author published in this journals
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

Published : 70 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 70 Documents
Search

 3   4   5   6   7 
Sentiment Analysis and Topic Modelling on Crowdsourced Data Maria Angelika H Siallagan; Arie Wahyu Wijayanto
Indonesian Journal of Artificial Intelligence and Data Mining Vol 7, No 1 (2024): March 2024
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v7i1.24777

Abstract

Data analysis plays a crucial role in enhancing the decision-making process by uncovering concealed patterns within the data. One valuable form of crowdsourced data is user reviews on applications, which can effectively capture the satisfaction levels of application users. Application developers can utilize these reviews to identify and assess areas of the application that require evaluation or improvement. This study focuses on the classification of application reviews by utilizing sentiment analysis and employs various classification algorithms, including logistic regression, Support Vector Machines, and Random Forest. Additionally, to address negative sentiment labels, topic modeling is conducted using Latent Dirichlet Allocation (LDA). This study demonstrates that the best sentiment classification model is logistic regression, achieving an average accuracy of 0.925 and an average F1-score of 0.763. Furthermore, the LDA analysis successfully generates topic models for negative reviews, revealing three key topics: price-related issues, accessibility concerns, and application accuracy, all of which demand reevaluation and potential improvement
Grouping of Regencies/Municipalities in Eastern Indonesia in 2021 Based on Socio-Economic Indicators Annisa Firnanda; Arie Wahyu Wijayanto
Sistemasi: Jurnal Sistem Informasi Vol 12, No 2 (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.v12i2.2499

Abstract

Differences in social, economic, demographic and resource conditions in each region can cause inequality, so it is necessary to encourage economic development according to the capabilities of the region. The importance of looking at socio-economic indicators that are development targets, especially in Eastern Indonesia (KTI). This study uses two non-hierarchical methods, namely K-Means and K-Medoids. In this study, Principal Component Analysis (PCA) was carried out to produce 3 factors. Determining the number of clusters using internal validity and stability shows that the K-Means method with a number of clusters of 2 produces the most optimal clusters. Cluster 1 consists of 152 regencies/municipalities, while cluster 2 consists of 80 regencies/municipalities. Cluster 1 has above average infrastructure and economic characteristics, while its human quality is still below average.
Implementation of K-Means and Hierarchical Clustering in Determining Levels of Smart City 2022 Based on Motion Index Nissa Shahadah Qur'ani; Arie Wahyu Wijayanto
Sistemasi: Jurnal Sistem Informasi Vol 12, No 2 (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.v12i2.2457

Abstract

Smart City is a city with an innovative development concept. However, not all Smart Cities in countries have the same standard because they are quite heterogeneous. Thus, a cluster analysis was carried out to classify Smart City. The result shows that Smart City is divided into two levels, those are high and low. k-means and hierarchical clustering is used for the method of this research. The grouping is based on the motion index, which consists of economic, environmental, mobilization and transportation indicators, and also international profiles represented by various variables. This research expects that Smart City at a certain level can be compared with other levels, in order to there are improvements and mutual learning about Smart City at a high level. This can also encourage other cities in the process towards Smart City.
PENGELOMPOKAN DATA GEMPA BUMI MENGGUNAKAN ALGORITMA DBSCAN Raisa Rizky Amelia Rahman; Arie Wahyu Wijayanto
Jurnal Meteorologi dan Geofisika Vol. 22 No. 1 (2021)
Publisher : Pusat Penelitian dan Pengembangan BMKG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31172/jmg.v22i1.738

Abstract

Gempa bumi merupakan bencana alam yang tidak dapat dicegah maupun dihindari. Oleh sebab itu, perlu dilakukan pemetaan dan pengelompokan wilayah gempa untuk mendukung upaya minimalisasi dampak yang ditimbulkan. Data yang digunakan dalam penelitian ini adalah data gempa bumi di Indonesia yang bersumber dari Badan Meteorologi, Klimatologi, dan Geofisika (BMKG). Penelitian ini menggunakan algoritma DBSCAN dalam mengelompokkan data ke dalam beberapa cluster. Metode untuk menguji validitas hasil cluster adalah dengan menggunakan Silhouette Coefficient dan Gamma Index. Hasil clustering pada penelitian ini memberikan kesimpulan bahwa dengan menggunakan algoritma DBSCAN diperoleh 3 cluster wilayah beresiko terjadi gempa bumi berdasarkan karakteristik parameter gempa bumi yang dihasilkan. Kombinasi nilai ε dan MinPts yaitu 0,28 dan 3 menghasilkan nilai Silhouette Coefficient sebesar 0,81091 dan Indeks Gamma sebesar 0,98104 yang menggambarkan bahwa DBSCAN mampu mengelompokan wilayah berpesiko terjadi gempa bumi dengan cukup baik. Hasil penelitian ini dapat digunakan sebagai bahan pertimbangan suatu instansi dalam pengambilan keputusan terkait penanganan (mitigasi) bencana gempa bumi.
The estimation of sea-breeze front velocity over coastal urban using Himawari-8 images: A case study in Jakarta Muhammad Rezza Ferdiansyah; Arie Wahyu Wijayanto
Jurnal Meteorologi dan Geofisika Vol. 23 No. 3 (2022): Special Issue
Publisher : Pusat Penelitian dan Pengembangan BMKG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31172/jmg.v23i3.810

Abstract

The sea breeze is a meteorological phenomenon that occurs due to the contrast temperature between land and oceans. The propagation velocity of sea breeze are influenced strongly by e.g., synoptic wind and geographical conditions. Therefore, it is important to understand the relationship between the spatial distribution of sea breeze velocity and the surface characteristic, for instance over urbanized and less-urbanized coastal areas. When the sea breeze propagates inland, a cumulus cloudline will form in the vicinity of the sea breeze front (SBF). Previous studies have successfully detected the cloudline automatically using the morphological-snake algorithm. In this paper, we estimate the SBF velocity using Himawari-8 satellite images. The proposed method segmented the cloudline data points using a clustering approach, named machine learning-based k-means++, on the level-set obtained from snake algorithm. We then estimate the SBF velocity by calculating the haversine distance of the segmented cloudline points that propagate over time. The comparison of estimated cloudline speed with SBF speed measured at two observation sites, namely KKP and BPL, reveals the root mean square errors 1.39 m/s and 1.41 m/s, respectively. And the propagation direction was mainly southward.
ANALISIS KLASTER BERDASARKAN TINDAKAN KRIMINALITAS DI INDONESIA TAHUN 2019 Margareth Dwiyanti Simatupang; Arie Wahyu Wijayanto
Jurnal Statistika Industri dan Komputasi Vol. 6 No. 01 (2021): Jurnal Statistika Industri dan Komputasi
Publisher : Program Studi Statistika, Fakultas Sains dan Teknologi Informasi, Universitas AKPRIND Indonesia

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

Abstract

Kriminalitas atau kejahatan merupakan masalah yang sering terjadi dalam suatu masyarakat. Saat ini indeks kejahatan di Indonesia sebesar 46.26 dari skala 100 sehingga Indonesia berada pada urutan ke-empat dengan indeks kejahatan tertinggi di negara Asean. Meskipun jumlah kejahatan di Indonesia mengalami penurunan dari tahun 2017 – 2019, namun penurunan jumlah kejahatan di Indonesia melambat dalam satu tahun terakhir. Sehingga perlu dilakukan pengelompokkan daerah rawan kriminalitas yang ada di Indonesia agar dapat memberikan informasi kepada pemerintah dan kepolisian untuk meningkatkan keamanan di Indonesia. Penelitian ini menggunakan variabel jenis-jenis kejahatan. Metode yang digunakan dalam penelitian ini adalah dengan analisis klaster K-Means dan Fuzzy C-Means. Sebelum dilakukan pengelompokkan, dilakukan penentuan jumlah klaster optimum. Setelah itu, dilakukan validasi metode yang hendak digunakan diantara K-Means dan Fuzzy C-Means untuk memperoleh metode yang terbaik. Validasi digunakan dengan melihat connectivity, dunn index, dan silhoutte masing-masing metode. Hasil yang diperoleh yakni tidak ada algoritma klastering yang bisa digunakan secara universal untuk menyelesaikan seluruh permasalahan mengenai pengelompokkan daerah kriminalitas di Indonesia. Sehingga, baik k-means dan fuzzy c-means tetap dapat melakukan pengelompokkan daerah kriminalitas di Indonesia.
Perbandingan Metode Ensemble Machine Learning untuk Klasifikasi Tenaga Kerja di Indonesia dengan Random Forest, XGBoost, dan CatBoost Kurniawan, Bayu Dwi; Wijayanto, Arie Wahyu
Jurnal Teknologi dan Sistem Komputer [IN PRESS] Volume 10, Issue 4, Year 2022 (October 2022)
Publisher : Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jtsiskom.2022.14031

Abstract

Survei Angkatan Kerja Nasional (Sakernas) adalah survei periodik yang besar sehingga membutuhkan pengolahan data  kompleks serta validasi benar untuk menjaga kualitas data. Salah satu pertanyaan Sakernas yang pengisian dan validasinya secara manual yaitu lapangan pekerjaan utama. Untuk memberikan validasi, Machine Learning dapat diterapkan dengan memanfaatkan informasi pada isian lain. Penelitian ini menggunakan metode Random Forest, XGBoost, dan CatBoost untuk klasifikasi lapangan pekerjaan utama pada Sakernas Agustus 2019. Berdasarkan hasil, ketiga model memiliki performa yang hampir sama baik dari presisi, recall, dan f1 yaitu untuk sektor primer dan tersier diatas 90 % dan sektor sekunder sebesar 80%. Model dari Random Forest, XGBoost, dan CatBoost memiliki akurasi sebesar 91,80%; 90,88%; dan 91,84%. Nilai Area Under Curve (AUC) dari ketiga model relatif tinggi dengan CatBoost memiliki nilai tertinggi pada klasifikasi sektor primer, sekunder, dan tersier masing-masing sebesar 1,00; 0,97; dan 0,98.
Analisis Cluster Provinsi di Indonesia Berdasarkan Pertumbuhan Ekonomi Tahun 2022 Ningsih, I Kadek Mira Merta; 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.v13i1.10520

Abstract

Economic development is a central agenda that aims to develop a country's economy in a sustainable manner. Indonesia's economy in 2022 grew by 5,31 percent, higher than the achievements in 2021. Therefore, considering that the economy is a very crucial sector, equitable distribution of economic growth is an important thing to pay attention to for the equal welfare of the Indonesian people. Researchers conducted an analysis related to the grouping of economic growth conditions of provinces in Indonesia in 2022 using the K-Means, K-Medoids, Hierarchical and Fuzzy C-Means Clustering. The data used are 9 variables of economic growth in 34 provinces in Indonesia in 2022. The final result was obtained by the Hierarchical Ward method with 2 cluster as the best method based on the results of internal validation and stability validation. In this method, cluster 1 is obtained totaling 28 provinces while cluster 2 totaling 6 provinces. The characteristics of cluster 1 are high economic growth seen from the variable value of factors forming high HDI but still have a high open unemployment rate, while the characteristics of cluster 2, namely low economic growth, are known from the value of the gini ratio and a high percentage of poor people.
Analisis Cluster Kondisi Keterampilan, Akses dan Fasilitas Teknologi Informasi dan Komunikasi di Indonesia watin, Rahma; Permatasari, Noverlina Putri; Wijayanto, Arie Wahyu; Marsisno, Waris
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.v13i1.10796

Abstract

In facing the digital transformation era, there are still imbalances in terms of skills, access, and information and communication technology facilities in Indonesia. It is necessary to group areas to identify areas that are still lagging, as evaluation material for equitable development. The clustering of regions is done by comparing the Partitioning and Hierarchical Clustering Methods. The Partitioning Clustering algorithm used is K-Means Clustering, with an optimum number of clusters of 4. The Hierarchical Clustering algorithm used is Agglomerative Ward, with a coefficient value of 0.864. Grouping using the Agglomerative Ward method produces an optimum number of clusters of 3. The Hierarchical Clustering method is better than the Partitioning method, with a Silhouette Value of 0.37.
Pemodelan Clustering Ward, K-Means, Diana, dan PAM dengan PCA untuk Karakterisasi Kemiskinan Indonesia Tahun 2021 Izzuddin, Kautsar Hilmi; 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.v13i1.10803

Abstract

Poverty is a serious and quite complex problem. Poverty is influenced across sectors from various factors. Poverty grouping can be done for planning and evaluating poverty programs. Cluster analysis using the ward, k-means, diana, and PAM methods can be used to group provinces in Indonesia based on six poverty indicators, namely the percentage of poor people (P0), poverty depth index (P1), poverty severity index (P2), Open Unemployment Rate (TPT), Literacy Rate (AMH), and Average Years of Schooling (RLS). Based on the evaluation of the model, the best cluster model was obtained using the ward approach with Principal Component Analysis (PCA) analysis. PCA is proven to be able to maximize the performance of clustering models. The cluster ward model forms five optimal clusters with provinces with very low to very high poverty rates.
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