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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

Author-ID : 5785214

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 PEMANFAATAN BIG DATA PENGINDERAAN JAUH DAN PEMBELAJARAN MESIN UNTUK OFFICIAL STATISTICS DI WILAYAH PERKOTAAN Arif Handoyo Marsuhandi; Dwi Wahyu Triscowati; Arie Wahyu Wijayanto
Jurnal Aplikasi Statistika & Komputasi Statistik Vol 12 No 2 (2020): Journal of Statistical Application and Computational Statistics
Publisher : Pusat Penelitian dan Pengabdian Masyarakat Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/jurnalasks.v12i2.282

Kemajuan teknologi big data tidak hanya menawarkan potensi pemanfaatan namun juga tantangan bagi penyelenggaraan official statistics. Sebagai salah satu sumber big data yang potensial, data spektral penginderaan jauh yang tersedia secara terbuka dan gratis menjadi modal berharga untuk penyempurnaan kualitas official statistics. Makalah ini meninjau peluang dan tantangan pemanfaatan penginderaan jauh di wilayah perkotaan dan menyajikan studi kasus awal pada monitoring pertumbuhan lanskap perkotaan di Indonesia. Studi kasus awal ini menggunakan metode pembelajaran mesin ansambel sebagai model untuk klasifikasi, yaitu random forest yang merupakan pendekatan statistik nonparametrik dengan penerapan agregasi dan bootstrapping pada pohon keputusan. Penelitian ini mengambil fokus pada Kabupaten Banyuwangi, Provinsi Jawa Timur sebagai studi kasus. Hasil eksperimen dengan citra satelit Landsat-8 menunjukkan keberhasilan model dalam mendeteksi perubahan area bangunan selama 6 tahun pertumbuhan lanskap perkotaan pada 2015-2020. Terhitung pada tahun 2015 dan 2020, model yang dibangun dapat mendeteksi bangunan/konstruksi dengan akurasi masing-masing 93 dan 91 persen. Kesimpulan sementara ini membuka kemungkinan penerapan penginderaan jauh untuk menunjang survei dan sensus statistik pada wilayah perkotaan, khususnya sebagai salah satu indikator penting untuk penghitungan nilai tambah bruto (NTB) lapangan usaha konstruksi yang menjadi komponen dari Produk Domestik Regional Bruto (PDRB).

Comparison of Hierarchical and Non-Hierarchical Methods in Clustering Cities in Java Island using the Human Development Index Indicators year 2018 Alvia Rossa Damayanti; Arie Wahyu Wijayanto
Eigen Mathematics Journal Vol. 4 No. 1 Juni 2021
Publisher : University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/emj.v4i1.89

The Human Development Index (HDI) is a composite index to assess the developmental level of life quality in a particular region. In 2018, Java Island, which geographically has the most regencies/ municipalities in Indonesia, achieved human development with “high” status and was followed by all its regencies which have also achieved human development with “high” status. Therefore, research was carried out on how the characteristics inherent in the high HDI have been achieved in regencies on Java Island and grouping them so that it is easy to interpret regencies/ municipalities with homogeneous characteristics. This study used the hierarchical cluster method (single linkage, average linkage, and ward) and non-hierarchical cluster methods (K-Means and FCM). The results show that the best hierarchical cluster method is the average linkage method which forms four clusters where the regencies/ municipalities with the best characteristics (dimensions of education, health, and high purchasing power) are Kepulauan Seribu, Bogor, and 78 other regencies/ municipalities. Then, the best non-hierarchical method is the FCM method which forms two clusters, with a prominent characteristic is those city areas have better characteristics than district areas.

IMPLEMENTATION OF ENSEMBLE TECHNIQUES FOR DIARRHEA CASES CLASSIFICATION OF UNDER-FIVE CHILDREN IN INDONESIA Andriansyah Muqiit Wardoyo Saputra; Arie Wahyu Wijayanto
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol 6 No 2 (2021): JITK Issue February 2021
Publisher : LPPM Nusa Mandiri

Diarrhea is an endemic disease in Indonesia with symptoms of three or more defecations with the consistency of liquid stool. According to WHO, diarrhea is the second largest contributor to the death of under-five children. Data and cases of children under five years who have diarrhea are very difficult to find, so the data analysis process becomes difficult due to the lack of information obtained. Difficulties in the data analysis process can be overcome by rebalancing, so the category ratios are balanced. The method that is popularly used is SMOTE. To solve imbalanced data and improve classification performance, this study implements the combination of SMOTE with several ensemble techniques in diarrhea cases of under-five children in Indonesia. Ensemble models that are used in this study are Random Forest, Adaptive Boosting, and XGBoost with Decision Tree as a baseline method. The results show that all SMOTE-based methods demonstrate a competitive performance whereas SMOTE-XGB gains a slightly higher accuracy (0.88), precision (0.96), and f1-score (0.86). The implementation of the SMOTE strategy improved the recall, precision, and f1-score metrics and give higher AUC of all methods (DT, RF, ADA, and XGB). This study is useful to solve the imbalanced problems in official statistics data provided by BPS Statistics Indonesia

COMPARISON OF REGIONAL CLUSTER ANALYSIS ACCORDING TO INCLUSIVE DEVELOPMENT INDICATORS IN JAVA ISLAND 2018 BETWEEN HIERARCHICAL AND PARTITIONING CLUSTERING STRATEGIES Akhmad Fatikhurrizqi; Arie Wahyu Wijayanto
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol 6 No 2 (2021): JITK Issue February 2021
Publisher : LPPM Nusa Mandiri

Gross Domestic Product (GDP) is one of the most common indicators to reflect a nation’s development. Indonesia's GDP has an average growth rate of 5 percent over the 2015-2019 period with the highest growth rate occurred in 2018. Furthermore, the provinces in Java Island contributed the most out of any province to Indonesia’s GDP in that year. However, the development in Java Island still has several issues, such as high poverty, unequal income distribution, and high unemployment. This problem indicates that the economic growth in Java Island has not been inclusive concerning development. This study aims to group regencies/municipalities in Java Island based on indicators of inclusive growth. These indicators refer to McKinley (2010) in a journal published by the Asian Development Bank (ADB). The cluster methods used to represent each hierarchical and partitioning are the Agglomerative Nesting (AGNES) and K-Means methods. The results of this study show that there are 3 clusters based on the AGNES method and 4 clusters based on the K-Means method. Clusters with good inclusive growth characteristics are dominated by municipality areas based on the K-Means method. Meanwhile, the clusters with low inclusive growth characteristics are dominated by regencies/municipalities on Madura Island based on the K-Means and AGNES methods. The comparison of the appropriate methods in this study based on the silhouette value is the AGNES method.

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

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

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.

Perbandingan Algoritma Klasifikasi Support Vector Machine dan Random Forest pada Prediksi Status Indeks Mitigasi dan Kesiapsiagaan Bencana (IMKB) Satuan Kerja BPS di Indonesia Tahun 2020 Ayu Aina Nurkhaliza; Arie Wahyu Wijayanto
Jurnal Informatika Universitas Pamulang Vol 7, No 1 (2022): JURNAL INFORMATIKA UNIVERSITAS PAMULANG
Publisher : Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/informatika.v7i1.16117

Natural and non-natural disasters are closely related to material and non-material losses. Government agencies are one of the important elements in disaster mitigation and preparedness efforts in order to reduce the number of victims and losses that will be caused. Disaster preparedness in the work unit is influenced by several factors, including regional characteristics, experience in disasters, education level, and employee conditions. This study aims to obtain a classification method that is able to predict the status of the Disaster Mitigation and Preparedness Index of work units based on several factors that affect disaster preparedness. Data processing uses the R Studio application with the Support Vector Machine (SVM) and Random Forest classification methods. Several studies have shown that the accuracy of the SVM and Random Forest classification methods tends to be better when compared to other classification methods. In addition, SVM is able to classify non-linear data and with Random Forest there will be no overfit as the number of trees increases. The results showed that the Random Forest classification method had higher accuracy, precision, and recall values than SVM with an accuracy value of 78.22%, precision of 75.54%, and recall of 76%.

Analisis Intensitas Hujan Provinsi Jawa Barat Tahun 2020 Menggunakan Association Rule Apriori dan FP-Growth Bagus Almahenzar; Arie Wahyu Wijayanto
Journal of System and Computer Engineering (JSCE) Vol 3 No 2 (2022): JSCE: Juli 2022
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47650/jsce.v3i2.397

West Java is one of the areas in Indonesia with high rainfall. One of the causes of frequent flooding in the DKI Jakarta area is due to receiving water from the West Java area. The method used in this study is the Association Rule using the Apriori algorithm and FP-Growth. Association rule is a rule in data mining in determining all association rules that meet the minimum support (minsup) and minimum confidence (minconf) requirements in a dataset. In this study, the minimum support used is 5 and the minimum confidence is 0.9. The purpose of this study was to obtain a pattern of rainfall intensity that often occurs every month in the measurement station areas. The measurement stations in the West Java region are the Citeko Meteorological Station, the Penggung Meteorological Post, the Kertajati Meteorological Station, the Bogor Climatology Station, and the Bandung Geophysics Station. The results of this study indicate that in April, May, June, October, and November there is no pattern of rain intensity that occurs between the measurement station areas. Heavy, very heavy, and extreme rains are very rare. In July, August, and September, most areas do not experience rain.

Comparison of Agglomerative Hierarchical and K-Means in Grouping Provinces Based on Maternal Health Services Alya Azzahra; Arie Wahyu Wijayanto
Sistemasi: Jurnal Sistem Informasi Vol 11, No 2 (2022): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

During the Covid-19 period, there were barriers to access for pregnant women to health services that could interfere with maternal health. Therefore, it is necessary to know the achievement of maternal health service coverage in Indonesia during the Covid-19 period in 2020, especially at the provincial level so that it can help the government to determine regional priorities for the fulfillment of more adequate maternal health services. Determination of provincial priorities for the fulfillment of maternal health services can be achieved by grouping the regions according to the characteristics of maternal health services in the local province. Cluster analysis is able to group objects in the form of provinces into one cluster. The clustering methods that will be used are agglomerative hierarchical clustering and k-means clustering. The results of the clustering of the two methods will be compared with internal validation in the form of dunn index, connectivity index, ang silhouette index. The best clustering resuls are obtained by using agglomerative hierarchical clustering alghoritm using the complete linkage similarity function with the resulting five clusters. The results of the identification of cluster characteristics show that cluster 1 with 14 members is categorized as provinces with good coverage of maternal services. Cluster 2 which consists of 15 provinces is categorized as best coverage. Cluster 3 which member are NTT and Maluku is categorized as bad. Cluster 4 which member is East Kalimantan is categorized as sufficient coverage. Meanwhile cluster 5 which member are Papua and West Papua is still on concern because its categorized as worst coverage

PELUANG DAN TANTANGAN DALAM PEMANFAATAN TEKNOLOGI PENGINDERAAN JAUH DAN MACHINE LEARNING UNTUK PREDIKSI DATA TANAMAN PANGAN YANG LEBIH AKURAT Dwi Wahyu Triscowati; Arie Wahyu Wijayanto
Seminar Nasional Official Statistics Vol 2019 No 1 (2019): Seminar Nasional Official Statistics 2019
Publisher : Politeknik Statistika STIS

Perubahan teknologi informasi menyebabkan terjadinya produksi data yang besar, cepat, dan berbagai macam. Pada era teknologi juga terjadi perkembangan pesat metode pengolahan data besar dan berdimensi tinggi seperti machine learning. Hal ini membuka peluang baru dalam penyediaan data statistik untuk berbagai permasalahan, seperti ketahanan pangan. Salah satu sumber big data gratis yang dapat dimanfaatkan untuk upaya ketahanan pangan adalah hasil penginderaan jauh. Penginderan jauh mengalami banyak kemajuan, yang tercermin dalam ketersediaan citra satelit dengan ketajaman pixel tinggi dan kaya informasi spasial temporal. Berbagai informasi vegetasi untuk prediksi tanaman pangan dapat diturunkan dari citra satelit ini. Namun, dibalik peluang yang ada, masih terdapat tantangan dalam pemanfaatan sepenuhnya citra satelit. Tujuan dari ulasan makalah ini adalah untuk mempelajari peluang dan tantangan dalam pemanfaatan teknologi penginderaan jauh dan machine learning untuk prediksi data tanaman pangan yang lebih akurat. Kami melihat ada banyak upaya penggunaan citra satelit untuk prediksi tanaman. Kami mengelompokkan metode yang digunakan untuk klasifikasi menjadi tiga kelompok, yaitu metode statistika konvensional, machine learning populer, dan deep learning. Sementara tantangan dalam pemanfaatan citra satelit yang teridentifikasi meliputi tantangan dari karakteristik citra satelit serta kondisi geografis dan pertanian di Indonesia. Lebih lanjut, hasil eksperimen kami sendiri menggunakan supervised random forest berdasarkan data multitemporal landsat 8, diperoleh bahwa pengambilan sampel, identifikasi kemungkinan seluruh kelas klasifikasi, serta rekayasa fitur berperan penting dalam peningkatan akurasi model klasifikasi. Dapat kami simpulkan bahwa ada peluang besar untuk pemantauan tanaman pangan menggunakan penginderaan jauh ini.

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

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