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All Journal Jurnal Gaussian Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Statistika dan Matematika (Statmat) Journal of International Conference Proceedings KOLONI STATISTIKA
Allo, Caecilia Bintang Girik
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Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Public Health Social Sciences Other

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Performance Single Linkage and K-Medoids on Data with Outliers Allo, Caecilia Bintang Girik; B, Winda Ade Fitriya; Paranoan, Nicea Roona
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 4 (2024): Article Research Volume 8 Issue 4, October 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i4.14072

Abstract

One way to assess the economic growth of a province is by examining its Gross Regional Domestic Product (GRDP). GRDP calculated through the production approach reflects the total value added by goods and services from various sectors within a particular region over a specified period. To determine the GRDP, 17 business sectors are considered. In 2023, the GRDP growth rate in Papua has decreased to 3.44%, down from 4.11% the previous year. To help the government improve Papua’s GRDP, an analysis is required. Clustering methods can group regencies and cities with similar characteristics. Boxplots are used to identify outliers in the data. The data contains outliers, so one method that can be used is K-Medoids. Euclidean Distance is used to calculate the distance matrix. Before calculating the distances, standardization using z-score normalization is performed to ensure that the data ranges are the same. This article aims to identify the most effective method for clustering regencies and cities in Papua using GRDP at constant price data. Both Single Linkage and K-Medoids methods are applied in this study. The DBI is used for evaluation, with lower DBI values indicating better methods. According to the DBI results, Single Linkage outperforms K-Medoids for clustering regencies and cities in Papua, with the optimal number of clusters being three. Keywords: Euclidean Distance; Davies Bouldin Index (DBI); Gross Regional Domestic Bruto; K-Medoids; Single Linkage; z-score Normalization
Clustering Kabupaten/Kota di Provinsi Papua Berdasarkan Produk Domestik Regional Bruto Menurut Lapangan Usaha Menggunakan Single Linkage dan K-Medoids Allo, Caecilia Bintang Girik
Jurnal Gaussian Vol 13, No 1 (2024): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.13.1.111-120

Abstract

Gross Regional Domestic Product (GRDP) using the production approach represents the total value added from goods and services produced by different sectors within a specific region over a defined timeframe. There are 17 business sectors used to obtain the GRDP. The growth rate of GRDP in Papua is decrease in 2023. The growth rate is only 3,44%,  whereas the previous year it reached 4,11%. An analysis is needed to assist the government to enhance the GRDP in Papua. Clustering method can group districts/cities that have similar characteristics. The aims of this article is to determine the best method for clustering districts/cities in Papua using GRDP data. Single Method and K-Medoids is used in this article. Based on silhouette coefficients, Single Methods is better than K-Medoids to clustering districts/cities in Papua. Based on criteria of silhouette coefficients, number of clusters formed is three.
Performance of K-Means and DBSCAN Algorithm in Clustering Gross Regional Domestic Product Wororomi, Jonathan K.; Allo, Caecilia Bintang Girik; Paranoan, Nicea Roona; Gusthvi, Wickly
Journal of International Conference Proceedings Vol 6, No 5 (2023): 2023 UICEB Papua Proceeding
Publisher : AIBPM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32535/jicp.v6i5.2710

Abstract

Gross Regional Domestic Product (GRDP) is one of important indicator to determine the economic conditions of a region. GRDP are obtained from sum of value added produced by all unit of production in a region. This study use GRDP by production approach that grouped into seventeen categories of Industry. The government always put the big efforts to increase the economic growth after Covid-19 pandemic. According publication of BPS - Statistics Indonesian, in the year of 2021 and 2022 it’s growth between 3.70% and 5.31%. The aim of these study are determined the cluster GDRB based on province in Indonesia at current prices and analyses the performance of the cluster method. The results showed that by using the DBSCAN, two clusters were formed and one province can be detected as an outlier. On the other hands, performance of the method by K-Means showed two clusters. The silhouette value using K-Means is higher than the DBSCAN. For these case, the performance of K-Means is more appropriate than DBSCAN to use in clustering province in Indonesia based on GRDP at Current Market Prices. Moreover, performance of DBSCAN shows more sensitive on outliers detection.
PENENTUAN RUTE di APLIKASI GOOGLE MAPS DENGAN MENGGUNAKAN GRAF DAN ALGORITMA PRIM B, Winda Ade Fitriya; Sumardi, Sitti Rosnafi’an; Paranoan, Nicea Roona; Allo, Caecilia Bintang Girik
KOLONI Vol. 2 No. 1 (2023): MARET 2023
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/koloni.v2i1.434

Abstract

Along with the times, many technologies and applications were created to meet human needs. Applications that are quite developed at this time is a navigation application. One of the well-known and frequently used navigation applications is Google Maps. By using the Google Maps application, people can find out where they are and know the route to get to their destination very easily. This paper discusses route selection in the Google Maps application using the prim graph and algorithm. Keywords: Graph, Prims’s Algorithm Prim, Route, Application
PERBANDINGAN METODE KLASIFIKASI KEGAGALAN SIMULASI MODEL IKLIM Perbandingan Metode Klasifikasi Kegagalan Simulasi Model Iklim Allo, Caecilia Bintang Girik; Paranoan, Nicea Roona; B, Winda Ade Fitriya; Sumardi, Sitti Rosnafi’an
KOLONI Vol. 2 No. 1 (2023): MARET 2023
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/koloni.v2i1.438

Abstract

Simulation of climate model is used to produce climate models used to estimate climate in the future using some software. Simulation of climate model has two probability, they are success or failure. The problem is when the simulation is fail. There are 18 variables that used to predict the simulation. Feature selection is used to reduce the dimension of variables using RFECV method. There are 11 variables that important to simulation of climate. There are 46 from 540 simulations that fail. Furthermore, SMOTE is used to handle imbalance cases. The classification method used in this paper are Logistic Regression, Naïve Bayes, Support Vector Machine (SVM), and Random Forest. The AUC value were not significantly different for the four methods when using SMOTE. However, the highest AUC was obtained by SVM method, so the simulation of climate model can be predicted by SVM method. Keywords: AUC, SMOTE, RFECV, Logistic Regression, SVM, Random Forest, Naïve Bayes
ANALISIS KOMPARATIF ALGORITMA DECISION TREE DAN RANDOM FOREST UNTUK KLASIFIKASI PENJUALAN PRODUK PADA DATASET SUPERSTORE Kurniawan, Rezza Debby; Sukarman, Dian Natasia Dewanti; Rumaropen, Kartini Wika; Allo, Caecilia Bintang Girik
STATMAT : JURNAL STATISTIKA DAN MATEMATIKA Vol 7 No 2 (2025)
Publisher : Math Program, Math and Science faculty, Pamulang University

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

Abstract

Penelitian ini bertujuan untuk membandingkan kinerja dua algoritma klasifikasi machine learning, yaitu Decision Tree dan Random Forest, dalam mengklasifikasikan produk berdasarkan data penjualan pada dataset Sample Superstore. Tujuan klasifikasi adalah untuk mengelompokkan produk ke dalam dua kategori, yaitu “laris” dan “tidak laris”, berdasarkan perbandingan antara nilai Quantity dengan nilai rata-rata dataset. Penelitian diawali dengan tahapan pra-pemrosesan seperti penghapusan data duplikat, pengolahan variabel kategorikal dengan label encoding, standardisasi fitur numerik, dan pembentukan variabel target biner. Model klasifikasi dilatih dan dievaluasi menggunakan confusion matrix serta metrik performa seperti akurasi, precision, recall, dan F1-score. Tuning parameter dilakukan pada kedua model menggunakan GridSearchCV dengan validasi silang lima lipat. Hasil evaluasi menunjukkan bahwa algoritma Decision Tree memperoleh akurasi tertinggi sebesar 73,1%, sedikit lebih baik dibandingkan Random Forest yang mencapai akurasi 72,7% setelah dilakukan tuning. Meskipun Random Forest dikenal sebagai algoritma ensemble yang lebih stabil, pada kasus ini performanya tidak menunjukkan peningkatan signifikan. Temuan ini memperkuat pentingnya pemilihan algoritma yang disesuaikan dengan karakteristik dan struktur data.
Analysis of Stunting Data in Indonesia Using K-Means and Self Organizing Map (SOM) Allo, Caecilia Bintang Girik; Nicea Roona Paranoan; Winda Ade Fitriya B; Bobi Frans Kuddi; Feby Seru
Statistika Vol. 25 No. 2 (2025): Statistika
Publisher : Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Islam Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/statistika.v25i2.7778

Abstract

Abstract. Stunting is a global public health concern, including in Indonesia. The Indonesian government establishes a target for stunting prevalence reduction every year. The government is aiming for a stunting prevalence of 18% in 2025. The government certainly requires policy recommendations to achieve this target. Clustering analysis can be used to identify provinces with similar characteristics or those that still require special attention based on stunting related indicators. There are several clustering methods, including K-Means and Self-Organizing Map (SOM). This study aims to classify provinces in Indonesia based on indicators related to stunting and to compare the performance of two clustering methods. Based on the obtained data, it was found that the data contains outliers. The best clustering method can be determined using the Silhouette Coefficient (SC) and Davies Bouldin Index (DBI). The results showed that the highest SC value, 0.62, was obtained using the SOM method and the lowest DBI, 0.75, was obtained also using SOM method. Two clusters were formed using the SOM method. Cluster 1 consisted of 36 provinces in Indonesia. Cluster 2 consisted of 2 provinces, namely Highland Papua and Central Papua.
Co-Authors Bobi Frans Kuddi FEBY SERU Gusthvi, Wickly Jonathan k. Wororomi Kurniawan, Rezza Debby Nicea Roona Paranoan Paranoan, Nicea Roona Rumaropen, Kartini Wika Sitti Rosnafi’an Sumardi Sukarman, Dian Natasia Dewanti Winda Ade Fitriya B