Self-Organizing Map Menggunakan Davies-Bouldin Index dalam Pengelompokan Wilayah Indonesia Berdasarkan Konsumsi Pangan

ABSTRAKKecukupan konsumsi pangan merupakan salah satu penunjang terbentuknya sumber daya manusia unggul yang menjadi fokus kebijakan pembangunan di Indonesia. Agar konsumsi pangan terpenuhi, salah satu cara yang dapat dilakukan adalah melakukan pengelompokan wilayah berdasarkan konsumsi pangan. Penelitian ini bertujuan untuk mengelompokkan wilayah Indonesia berdasarkan konsumsi pangan berdasarkan data konsumsi kalori per kapita sehari dari berbagai komoditas pangan. Pengelompokan wilayah dilakukan dengan metode self-organizing map (SOM) dengan terlebih dahulu ditentukan jumlah cluster optimum menggunakan nilai Davies-Bouldin Index (DBI) terkecil. Hasil penelitian menunjukkan bahwa hasil cluster optimum yang terbentuk sejumlah 4 cluster dengan jumlah anggota untuk cluster 1 sebanyak 22 provinsi, cluster 2 sebanyak 10 provinsi, cluster 3 sebanyak 1 provinsi, dan cluster 4 sebanyak 1 provinsi.ABSTRACTAdequate food consumption is one of the supports for forming superior human resources, which is the focus of development policies in Indonesia. To fulfill food consumption, one way to be done is to group regions based on food consumption. This study aims to classify regions of Indonesia based on food consumption based on average daily per capita calorie consumption data from various food commodities. Regional grouping is done using the self-organizing map (SOM) method by first determining the optimum number of clusters using the smallest Davies-Bouldin Index (DBI) value. The results showed that the optimum cluster results were 4 clusters with the number of members for cluster 1 as many as 22 provinces, cluster 2 as many as 10 provinces, cluster 3 as many as 1 province, and cluster 4 as many as 1 province.

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Job applicants clustering using self-organizing map

Bulletin of Social Informatics Theory and Application ◽

10.31763/businta.v1i2.28 ◽

2017 ◽

Vol 1 (2) ◽

pp. 60-71

Author(s):

Arum Handini Primandari ◽

Nur Aini Ikasakti

Keyword(s):

Educational Background ◽

Job Applicants ◽

Educational Quality ◽

Self Organizing Map ◽

Job Seekers ◽

Clustering Method ◽

Self Organizing Maps ◽

Grouping Method ◽

Cluster 2 ◽

Self Organizing

Yogyakarta Government through Directorate of Manpower and Transmigration (Disnakertrans) have been canvassing people looking for job. An employment program was provided by Disnakertrans to allow job applicants meet companies. This research was carried out to identify educational background of applicants, in order to obtain the suitable worker. One of the ways to identify educational background is by district clustering in Yogyakarta. Clustering method is employed to reveal the characteristic of educational quality in every district in Yogyakarta. Clustering is a grouping method which is done by minimalize the characteristic among class members and minimalize the characteristic among clusters. This research used Self Organizing Maps to grouping districts in Yogyakarta according to educational background of its job seekers. The clustering results 3 clusters: 6 districts belong to cluster 1, 4 districts belong to cluster 2, and 4 districts belong to cluster 3. Then, Yogyakarta map is used to visualize the result of district clustering.

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Analisis Cluster Data Interkomparasi Anak Timbangan dengan Algoritma Self Organizing Maps

Jurnal Teknik Informatika dan Sistem Informasi ◽

10.28932/jutisi.v7i2.3698 ◽

2021 ◽

Vol 7 (2) ◽

Author(s):

Arif Fajar Solikin ◽

Kusrini Kusrini ◽

Ferry Wahyu Wibowo

Keyword(s):

Data Mining ◽

Statistical Test ◽

Optimum Number ◽

Data Normalization ◽

Self Organizing Map ◽

Self Organizing Maps ◽

Number Of Clusters ◽

Map Algorithm ◽

Cluster Data ◽

Self Organizing

Intercomparison was conducted to determine the ability and the performance of the laboratory. Intercomparison results are usually expressed in the range of En ratio values (En ?|1|) which express the equivalence of one laboratory with other laboratories. If the laboratory is declared unequal, then it needs to identify the source of the problem by itself. To make it easier, it can be done by Clustering which is one of the data mining techniques. Clustering is done by applying a self organizing map algorithm on the KNIME (Konstanz Information Miner) analytic tools. Several experiments were carried out with different layer size and data normalization status from one experiment to another experiment. The results were analyzed through pseudo F statistical test and icdrate test. The largest pseudo F statistic value was obtained from the 8th experiment (setting the layer size 2x2 without data normalization) with a pseudo F statistic value of 167.53 for 1kg artifacts and a Pseudo F statistic value of 104.86 for 200 g artifacts where the optimum number of clusters are 4. The smallest icdrate value was obtained from the 5th experiment (setting the 2x3 layer size without data normalization) with an icdrate value of 0.0713 for 1kg artifacts and icdrate value of 0.2889 for 200g artifacts with the best number of clusters being 6. From 12 laboratories can be grouped into 6 groups where each group has the same identification. There are groups 1, 3 and 6 have 1 member, while groups 2, 4 and 5 have 3 members.

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Pengembangan Sistem Informasi Pemetaan Infrastruktur Sistem Informasi Di Kota Probolinggo

Jurnal Informatika Polinema ◽

10.33795/jip.v6i3.318 ◽

2020 ◽

Vol 6 (3) ◽

pp. 65-70

Author(s):

R. FARAH DINI QOYYIMAH ◽

Erfan Rohadi, ST., M. Eng., Ph.D ◽

Rizky Ardiansyah, S.Kom, MT

Keyword(s):

Neural Network ◽

Unsupervised Learning ◽

Self Organizing Map ◽

Cluster 2 ◽

Self Organizing

Infrastruktur dan sistem informasi merupakan sumber daya manusia yang membantu pemerintah dalam mewujudkan dan pemberdayaan masyarakat baik secara ekonomi maupun kepuasan publik. Tidak terkecuali yang dilakukan pada Dinas Komunikasi dan Informatika Pemerintah Kota Probolinggo. Dalam meningkatkan kualitas pengembangangan infrastruktur secara lebih terkoordinir maka dibuatlah sistem informasi berbasis pemetaan infrastruktur dan sistem informasi dengan menggunakan algoritma clustering SOM. Self Organizing Map (SOM) merupakan salah satu metode dalam Jaringan Syaraf Tiruan (Neural Network) yang menggunakan pembelajaran tanpa pengarahan (Unsupervised Learning). Penelitian ini menghasilkan sebuah website yang memberikan informasi kepada user atau pengguna yang merupakan pihak pemerintahan Dinas Kominfo Kota Probolinggo dalam mengevaluasi perkembangan dan pemerataan infrastruktur dan sistem informasi. Dari hasil perhitungan menggunakan metode Self -Organizing Map dapat diterapkan dalam clustering untuk pemerataan infrastruktur IT yang menghasilkan 3 cluster yang terdiri dari cluster 1 yang memiliki persebaran infrastruktur yang baik berjumlah 1 wilayah, cluster 2 yang memiliki persebaran infrastruktur yang cukup baik berjumlah 23 wilayah dan cluster 3 yang memiliki persebaran infrastrukttur yang kurang baik berjumlah 5 wilayah. Sehingga dapat diketahui pemerataan IT di Kota Probolingo dapat dinilai cukup baik. 4. Berdasarkan pengujian diperoleh hasil akurasi hasil cluster yang baik dengan menggunakan Self-Organizing Map sebanyak 62.06897%. Kata kunci : Clustering, Self Organizing Map (SOM)

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Exploring the multiscale changeability of precipitation using the entropy concept and self-organizing maps

Journal of Water and Climate Change ◽

10.2166/wcc.2019.097 ◽

2019 ◽

Vol 11 (3) ◽

pp. 655-676

Author(s):

Kiyoumars Roushangar ◽

Farhad Alizadeh ◽

Jan Adamowski ◽

Seyed Mehdi Saghebian

Keyword(s):

Time Series ◽

Annual Precipitation ◽

Optimum Number ◽

Self Organizing Map ◽

Self Organizing Maps ◽

Time Frequency ◽

Modes Of Variability ◽

Rain Gauges ◽

Spatio Temporal ◽

Self Organizing

Abstract This study utilized a spatio-temporal framework to assess the dispersion and uncertainty of precipitation in Iran. Thirty-one rain gauges with data from 1960 to 2010 were selected in order to apply the entropy concept and study spatio-temporal variability of precipitation. The variability of monthly, seasonal and annual precipitation series was studied using the marginal disorder index (MDI). To investigate the intra-annual and decadal distribution of monthly and annual precipitation values, the apportionment disorder index (ADI) and decadal ADI (DADI) were applied to the time series. The continuous wavelet transform was used to decompose the ADI time series into time-frequency domains. The decomposition of the ADI series into different zones helped to identify the dominant modes of variability and the variation of those modes over time. The results revealed the high disorderliness in the amount of precipitation for different temporal scales based on disorder indices. Based on the DI outcome for all rain gauges, a self-organizing map (SOM) was trained to find the optimum number of clusters (seven) of rain gauges. It was observed from the clustering that there was hydrologic similarity in the clusters apart from the geographic neighborhood.

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Implementasi Self Organizing Map dalam Pengelompokkan Kabupaten di Jawa Barat Berdasarkan Kasus Covid-19

Jurnal Statistika dan Aplikasinya ◽

10.21009/jsa.05206 ◽

2021 ◽

Vol 5 (2) ◽

pp. 174-183

Author(s):

Aisykha Reisla Rayhan ◽

Widya Astuti ◽

Zakiyatush Shufila ◽

Edy Widodo

Keyword(s):

Self Organizing Map ◽

Corona Virus ◽

Cluster 2 ◽

Self Organizing

Covid-19 adalah penyakit menular yang disebabkan oleh jenis coronavirus. Penyakit menular baru yang disebabkan oleh sindrom pernafasan akut yang parah corona virus 2 (SARS-CoV-2), menyebar pesat ke beberapa negara di seluruh dunia termasuk negara Indonesia. Provinsi Jawa Barat merupakan salah satu provinsi sebagai penyumbang terbesar kasus covid-19 di Indonesia. Penelitian ini bertujuan untuk mengelompokkan wilayah kabupaten/kota di Provinsi Jawa Barat menurut perkembangan kasus covid-19. Metode yang digunakan dalam proses pengelompokan ini adalah metode Self Organizing Map (SOM). SOM merupakan perangkat visualisasi dan analisis untuk data berdimensi tinggi dan dalam pengelompokannya tidak diperlukan uji asumsi. Data sekunder yang digunakan pada penelitian ini adalah 5 variabel pada 27 kabupaten/kota di Provinsi Jawa Barat dari tanggal 1 Agustus 2020 sampai dengan 22 Juni 2021 yang bersumber dari Pusat Informasi dan Koordinasi Covid-19 Provinsi Jawa Barat. Berdasarkan hasil analisis menggunakan SOM diperoleh sebanyak dua cluster yang masing-masing memiliki karakteristik yang berbeda-beda untuk mengelompokkan 27 kabupaten/kota di Provinsi Jawa Barat. Cluster yang terbentuk meliputi cluster 1 yang terdiri dari 2 kota yaitu kota Depok dan kota Bekasi memiliki tingkat kasus covid-19 yang tertinggi, sedangkan Cluster 2 terdiri dari 25 kabupaten/kota di Jawa Barat dengan tingkat kasus covid-19 terendah.

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Day-Ahead Electric Load Forecasting for the Residential Building with a Small-Size Dataset Based on a Self-Organizing Map and a Stacking Ensemble Learning Method

Applied Sciences ◽

10.3390/app9061231 ◽

2019 ◽

Vol 9 (6) ◽

pp. 1231 ◽

Cited By ~ 6

Author(s):

Jaehyun Lee ◽

Jinho Kim ◽

Woong Ko

Keyword(s):

Ensemble Learning ◽

Load Forecasting ◽

Similar Data ◽

Electric Load ◽

Self Organizing Map ◽

Learning Method ◽

Forecasting Models ◽

Consumption Data ◽

Self Organizing ◽

Electric Load Forecasting

Electric load forecasting for buildings is important as it assists building managers or system operators to plan energy usage and strategize accordingly. Recent increases in the adoption of advanced metering infrastructure (AMI) have made building electrical consumption data available, and this has increased the feasibility of data-driven load forecasting. Self-organizing map (SOM) has been successfully utilized to cluster a dataset into subsets containing similar data points. These subsets are then used to train the forecasting models to improve forecasting accuracy. However, some buildings may have insufficient data since newly installed monitoring devices such as AMI have no choice but to collect a limited amount of data. Using a clustering technique on small datasets could lead to overfitting when using forecasting models following an SOM network to be trained with clusters. This results in a relatively high generalization error. In this study, we propose to address this problem by employing the stacking ensemble learning method (SELM) that is well-known for its generalization ability. An experimental study was conducted using the electricity consumption data of an actual institutional building and meteorological data. Our proposed model outperformed other baseline models, which means it successfully mitigates the effect of overfitting.

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PENGELOMPOKKAN DAN PEMETAAN KARAKTERISTIK KEMISKINAN DI KABUPATEN MALUKU BARAT DAYA PROVINSI MALUKU DENGAN MENGGUNAKAN SELF-ORGANIZING MAP DAN ANALISIS BIPLOT

BAREKENG JURNAL ILMU MATEMATIKA DAN TERAPAN ◽

10.30598/barekengvol13iss2pp093-106ar810 ◽

2019 ◽

Vol 13 (2) ◽

pp. 093-106

Author(s):

Zeth Arthur Leleury ◽

Berny Pebo Tomasouw

Keyword(s):

Self Organizing Map ◽

Cluster 2 ◽

Self Organizing

Berdasarkan hasil survei sosial ekonomi nasional (SUSENAS) BPS Provinsi Maluku menunjukkan bahwa kabupaten Maluku Barat Daya (MBD) merupakan kabupaten yang memiliki persentase tingkat kemiskinan tertinggi di provinsi Maluku dengan persentase tingkat kemiskinan 30,18 persen. Penelitian ini bertujuan untuk melakukan pengelompokkan desa/kecamatan di Kabupaten MDB guna melihat karakteristik kemiskinan pada setiap cluster. Selain itu, juga dilakukan pemetaan karakteristik kemiskinan untuk setiap desa/kecamatan di Kabupaten MBD sebagai upaya untuk mengetahui keragaman karakteristik kemiskinan. Metode pengelompokkan yang digunakan adalah algoritma jaringan syaraf tiruan Self Organizing Map (SOM) dan Biplot. Hasil penelitian memperlihatkan bahwa hasil pengelompokkan terbaik adalah dengan metode Biplot karena memiliki rasio yang lebih kecil. Adapun hasil pengelompokkan 17 kecamatan di kabupaten MBD terbagi dalam 4 cluster yakni cluster 1 terdiri kecamatan Pulau-Pulau Terselatan, Letti dan Moa; cluster 2 terdiri dari kecamatan Pulau-Pulau Babar dan Babar Timur; cluster 3 terdiri dari kecamatan Pulau Masela, Mdona Hyera, Kepulauan Romang, Damer, Wetar, dan Wetar Barat; sedangkan cluster 4 terdiri dari kecamatan Wetar Utara, Wetar Timur, Kisar Utara, Lakor, Dawelor Dawera dan Pulau Wetang. Ada 11 faktor yang mempengaruhi sehingga cluster 4 tergolong dalam cluster dengan karakteristik kemiskinan tertinggi. Sedangkan ada 4 faktor yang mempengaruhi sehingga cluster 3 tergolong dalam cluster dengan karakteristik kemiskinan cukup tinggi.

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