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All Journal EDUMATIC: Jurnal Pendidikan Informatika Kappa Journal Jurnal Varian Jurnal Abdi Insani JAMBURA JOURNAL OF PROBABILITY AND STATISTICS International Journal of Educational Research and Social Sciences (IJERSC) Multica Science and Technology Jurnal Statistika dan Komputasi (STATKOM) Jurnal Sains dan Kesehatan
Nurmayanti, Wiwit Pura
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Penerapan Metode OPTICS dan ST-DBSCAN untuk Klasterisasi Data Kesehatan Hastuti, Siti Hariati; Septiani, Ayu; Hendrayani, Hendrayani; Nurmayanti, Wiwit Pura
Jurnal Pendidikan Informatika (EDUMATIC) Vol 8 No 1 (2024): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v8i1.25765

Abstract

One way to extract valuable insights from large datasets is through cluster analysis. This statistical technique involves grouping data objects based on their similarities, aiming to create distinct groups where objects within each group share high similarities but differ significantly from objects in other groups. Cluster analysis, such as the OPTICS and ST-DBSCAN methods, can be utilized in various domains, including healthcare workforce and demographic data. In a case study focusing on health workers in East Lombok, these clustering methods were employed. The study aimed to present the outcomes of clustering health workers using OPTICS and ST-DBSCAN and determine the superior method through internal validation. The results from OPTICS revealed the formation of 5 clusters: cluster-1 with two sub-district members, cluster-2 with three members, cluster-3 with two members, cluster-4 with three members, and cluster-5 with seven members. Conversely, ST-DBSCAN produced only 2 clusters: cluster-1 with six members and cluster-2 with four members. Based on the internal validation findings, OPTICS emerged as the more effective method for categorizing health workers in East Lombok.
Optimalisasi Peramalan Total Aset PT. BPD Kaltim Kaltara dengan Double Exponential Smoothing Brown Ningsih, Eva Lestari; Nurmayanti, Wiwit Pura; Widyaningrum, Erlyne Nadhilah; Pangruruk, Thesya Atarezcha
Jurnal Statistika dan Komputasi Vol. 3 No. 2 (2024): Jurnal Statistika dan Komputasi
Publisher : Universitas Nahdlatul Ulama Sunan Giri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32665/statkom.v3i2.3525

Abstract

Background: Total assets can provide a comprehensive picture of the wealth owned by a company or institution, with total assets also helping to assess the scale of operations, stability, and the company’s ability to meet its financial responsibilities. Study on the total assets held by PT. BPD Kaltim Kaltara is interesting to do because it has an important role in advancing economic growth in the East Kalimantan and North Kalimantan regions. Digital transformation can influence how assets grow and how capital is structured. Objective: Predicting PT BPD Kaltim Kaltara’s total assets over the next three periods using the DES Brown method with the optimal constant. Methods: Double Exponential Smoothing Brown (DES Brown) with constants α = β = 0.3; 0.6; 0.7; 0.8. Results: The smallest MAPE value is obtained at the constant α = β = 0.3, indicating that the DES Brown method with this constant provides the most accurate forecasting results. Conclusion: The forecasting results for the next three periods show a stable upward trend, namely September at Rp48,389,055.93, October at Rp48,480,301.62, and November at Rp48,571,547.30. Thus, the DES Brown method has proven effective in forecasting the total assets of PT. BPD Kaltim Kaltara and can be used to support the company's financial decision making.
PELATIHAN ANALISIS DATA DENGAN SOFTWARE R BAGI SISWA SMA NEGERI 8 SAMARINDA Sari, Nariza Wanti Wulan; Sifriyani, Sifriyani; Suyitno, Suyitno; Wahyuningsih, Sri; Yuniarti, Desi; Purnamasari, Ika; Mahmudah, Siti; Nurmayanti, Wiwit Pura; Widyaningrum, Erlyne Nadhilah; Nugraha, Pratama Yuly; Pangruruk, Thesya Atarezcha; Hidayanty, Nurul Ilma; Kosasih, Raditya Arya; Bahriah, Ayu
Jurnal Abdi Insani Vol 12 No 7 (2025): Jurnal Abdi Insani
Publisher : Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/abdiinsani.v12i7.2136

Abstract

Students of SMA Negeri 8 Samarinda have received material on statistics since grade X. In the learning process, teachers use Microsoft Office Excel software which is closed source. So through this community service activity, a solution is provided by disseminating data analysis and alternative open source software 'R'. Community service activities are packaged in the form of training. Evaluation of activities in the form of pretest and posttest questionnaires and activity feedback surveys. This activity was carried out on September 11, 2024 in the Computer Laboratory Room of SMA Negeri 8 Samarinda. The number of students who participated in this activity consisted of 36 students. Based on the analysis of the pre-test and post-test data, it was concluded that there was an increase in student understanding after the training. The results of the feedback stated that the training material was easy, the explanations given were considered interesting, and the training activities were considered useful by the participants. Furthermore, participants hope that there will be follow-up activities to hold similar activities again.
IMPLEMENTATION OF NEURAL NETWORK IN PREDICTING STOCK PRICE OF PT BANK RAKYAT INDONESIA (PERSERO) TBK Nurmayanti, Wiwit Pura; Ni Luh Desvita Pratiwi; Nariza Wanti Wulan Sari; Desi Yuniarti; Erlyne Nadhilah Widyaningrum; Thesya Atarezcha Pangruruk
Multica Science and Technology (ACCREDITED-SINTA 5) Vol. 5 No. 1 (2025): Multica Science and Technology
Publisher : Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/dwkza342

Abstract

Forecasting involves estimating future outcomes by examining patterns in both historical and present data. A commonly used data type in forecasting is time series data, characterized by observations collected at consistent time intervals. One forecasting technique that has gained significant attention is the Neural Network, particularly through the Backpropagation method utilized in this study. In the context of the stock market, price fluctuations are influenced by a variety of factors, including shareholder rights, company performance, and the balance between supply and demand. Typically, a rise in stock prices leads to decreased demand, while a decline tends to stimulate it. Predicting stock prices, such as those of Bank Rakyat Indonesia (BRI), can support investors in making well-informed decisions. This research seeks to identify the optimal number of neurons in the hidden layer for forecasting BRI stock prices by minimizing error metrics such as MAPE, MSE, and MAE. The analysis revealed that forecasting the stock price of PT Bank Rakyat Indonesia (Persero) Tbk. using a neural network with one hidden neuron resulted in a MAPE of 1.22248 and an MAE of 61.30548.
Peramalan Jumlah Sampah di Kabupaten Lombok Timur dengan Metode ARIMA dan Dekomposisi Nurmayanti, Wiwit Pura; Kertanah, Kertanah; Hasanah, Siti Hadijah; Rahim, Abdul; hendrayani, hendrayani
Jambura Journal of Probability and Statistics Vol 4, No 2 (2023): Jambura Journal Of Probability and Statistics
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjps.v4i2.19954

Abstract

AbstractForecasting is the science of predicting events that will occur using historical data and projecting them into the future with some form of mathematical model that aims to handle and policy in the future. In forecasting there are several methods, two of which are Autoregeressive Integrated Moving Average (ARIMA) and Decomposition. ARIMA is a method developed by George Box and Gwilym Jenkins in 1970. The Decomposition Method is a method that decomposes (breaks) time series data into several patterns, namely trend, cyclical and seasonal, and identifies each of these components separately. Both of these methods can be applied in various fields, one of which is in the field of environmental health, especially data on the amount of waste. Problems related to the amount of waste in East Lombok are still a concern of the government because as the population increases and the needs of the community each year have the potential to cause waste problems. The final disposal site (TPA) in East Lombok is located in Ijo Balit, this TPA is the only one in East Lombok. The purpose of this research is to see which method is the best between ARIMA and Decomposition, and to see the forecasting results of the amount of waste entering TPA Ijo Balit from the best method. Based on the results of the analysis carried out by the Decomposition model, it gives the best performance in terms of the smallest error value so that it can be used for Forecasting and produces an RMSE value of 5201.694, a MAPE of 0.955827 and a MASE of 0.0129691. The results of forecasting using the Decomposition method are that the highest forecast occurs in December, while the lowest occurs in January with a total of 1,439,439 (tons) and 1,117,000 (tons). Keywords:  Forecasting, ARIMA, Decomposition, Waste
Analisis Data Transaksi Penjualan Obat di Apotek X Samarinda Menggunakan Algoritma Apriori dan FP-Growth Berbasis Association Rule Mining: Analysis of Drug Sales Transaction Data at Pharmacy X Samarinda Using Apriori and FP-Growth Algorithms Based on Association Rule Mining Nurmayanti, Wiwit Pura; Hasanah, Siti Hadijah; Rahim, Abdul
Jurnal Sains dan Kesehatan Vol. 6 No. 3 (2024): J. Sains Kes.
Publisher : Fakultas Farmasi, Universitas Mulawarman, Samarinda, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25026/jsk.v6i3.2353

Abstract

Association Rule Mining is a data mining technique that is used to search for a group of items that often appear together in an event and is often analogous to a market basket. Algorithms in the association rule include apriori and frequent pattern growth (fp-growth). We can apply these two algorithms in various fields, one of which is in the pharmaceutical sector, namely related to drug sales transactions in pharmacies. The aim of this research is to see a picture of drug sales transactions at Pharmacy X, Samarinda City, and to find out the best algorithm for determining drug sales transaction patterns at the pharmacy. Based on the results of the analysis, information was obtained that out of 100 drug sales transactions at Pharmacy The product that consumers purchased the most was the ChargeR type of medicine, namely 7 transactions and the one that was purchased the least was Grape Tempra Syrup 60 ml which was purchased in only 1 transaction, and seen from the higher support and confidence values, the fp-growth algorithm could produce rules better to the apriori algorithm. Keywords:          association rule, fp-growth algorithm, apriori algorithm, pharmacies   Abstrak Association Rule Mining merupakan teknik data mining untuk menemukan aturan asosiasi antara suatu kombinasi item. Algoritma dalam Association Rule dapat diterapkan diberbagai bidang, salah satunya adalah bidang farmasi terkait transaksi penjualan obat di Apotek, adapun algoritma tersebut adalah Apriori dan Frequent pattern Growth (Fp-Growth). Tujuan penelitian ini adalah untuk melihat gambaran transaksi penjualan obat di Apotek X Kota Samarinda, dan mengetahui algoritma terbaik dalam menentukan pola transaksi penjualan obat di apotek tersebut. Berdasarkan hasil analisis diperoleh informasi bahwa dari 100 transaksi penjualan obat di Apotek X kota Samarinda, obat yang paling banyak terjual dalam 1 transaksi terdapat pada transaksi ke 41 dengan jenis obat sebanyak 17 jenis. Produk yang paling banyak dibeli konsumen adalah jenis obat ChargeR yaitu sebanyak 7 transaksi dan yang paling sedikit dibeli adalah Sirup Tempra Anggur 60 ml yang dibeli hanya dalam 1 transaksi, dan dilihat dari nilai support dan confident yang lebih tinggi algoritma fp-growth mampu menghasilkan aturan algoritma yang lebih baik dibandingkan dengan algoritma apriori. Kata Kunci:         association rule, apriori, frequent pattern growth, apotek
Comparison of Algorithms K-Means and DBSCAN for Clustering Student Cognitive Learning Outcomes in Physics Subject Kertanah, Kertanah; Nurmayanti, Wiwit Pura; Aini, Sri Rahmatun; Amrullah, Lalu Muh.; Sya'roni, Muhammad
Kappa Journal Vol 7 No 2 (2023): Agustus
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/kpj.v7i2.18428

Abstract

Clustering is an activity of grouping data into the same group based on similarity. The purpose of the study is to cluster and determine student cognitive learning outcomes characteristics. Cluster analysis was conducted on student cognitive learning outcomes using algorithms K-Means and DBSCAN. Both algorithms are appropriate to have been applied to the overlapping data such as student learning outcomes data. Also, their advantages are scaling large datasets and outliers. The data used in this study is student cognitive learning outcomes - final and mid-term exams grade X in physics subject. Applying the two proposed algorithms K-Means and DBSCAN, the best cluster algorithm to have been used for clustering analysis is K-Means which is based on the highest silhouette score of 0.43, while the silhouette score of DBSCAN is 0.39 respectively. Using the best cluster, the K-Means algorithm, found two types of clusters – cluster 1 consists of 132 students who have a high average score, and cluster 2 shows 183 students who have a low average score in both final and mid-term exams respectively. From the analysis results, most students still have low cognitive learning outcomes in physics subject.
Market Basket Analysis with Apriori Algorithm and Frequent Pattern Growth (Fp-Growth) on Outdoor Product Sales Data Nurmayanti, Wiwit Pura; Sastriana, Hanipar Mahyulis; Rahim, Abdul; Gazali, Muhammad; Hirzi, Ristu Haiban; Ramdani, Zuhut; Malthuf, Muhammad
International Journal of Educational Research & Social Sciences Vol. 2 No. 1 (2021): February 2021
Publisher : CV. Inara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51601/ijersc.v2i1.45

Abstract

Indonesia is an equatorial country that has abundant natural wealth from the seabed to the top of the mountains, the beauty of the country of Indonesia also lies in the mountains that it has in various provinces, for example in the province of West Nusa Tenggara known for its beautiful mountain, namely Rinjani. The increase in outdoor activities has attracted many people to open outdoor shops in the West Nusa Tenggara region. Sales transaction data in outdoor stores can be processed into information that can be profitable for the store itself. Using a market basket analysis method to see the association (rules) between a number of sales attributes. The purpose of this study is to determine the pattern of relationships in the transactions that occur. The data used is the transaction data of outdoor goods. The analysis used is the Association Rules with the Apriori algorithm and the frequent pattern growth (FP-growth) algorithm. The results of this study are formed 10 rules in the Apriori algorithm and 4 rules in the FP-Growth algorithm. The relationship pattern or association rule that is formed is in the item "if a consumer buys a portable stove, it is possible that portable gas will also be purchased" at the strength level of the rules with a minimum support of 0.296 and confidence 0.774 at Apriori and 0.296 and 0.750 at FP-Growth.
Comparing SOM, DBSCAN, and K-Affinity Propagation in Labor Economic Patterns Nurmayanti, Wiwit Pura; Yuniarti, Desi; Siringoringo, Meiliyani; Purnamasari, Ika; Putri, Desi Febriani; Hasanah, Siti Hadijah
Jurnal Varian Vol. 9 No. 1 (2026)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v9i1.5933

Abstract

The objective of this research is to identify the most effective clustering method for grouping Indonesian provinces by labor–economic indicators to support more precise, data-driven policy formulation. Regional disparities in Indonesia’s economic growth, driven by unequal labor characteristics, remain a significant obstacle to achieving inclusive development. An analytical approach capable of grouping provinces by labor and economic indicators is therefore essential. This study applies a comparative clustering analysis using three unsupervised algorithms: Self-Organizing Maps (SOM), Density-Based Spatial Clustering of Applications with Noise (DBSCAN), and K-Affinity Propagation (K-AP). The dataset consists of five key indicators, namely economic growth, total population, labor force, employment rate, and average wage level obtained from Statistics Indonesia (BPS) for the year 2024. The clustering performance is evaluated using internal validation criteria based on the ratio of within-cluster variation (Sw) to between-cluster variation (Sb), where a smaller ratio indicates more compact, well-separated clusters. The results show that each method produces different clustering structures. SOM and DBSCAN generate three clusters with varying provincial distributions, whereas K-AP produces five clusters with more balanced, representative groupings. The evaluation results indicate ratios of 3.1906 for SOM, 0.2000 for DBSCAN, and 0.1779 for K-AP, indicating that K-AP provides the most optimal clustering performance. These findings confirm that K-Affinity Propagation is the most effective and stable method for classifying Indonesian provinces by labor and economic characteristics. The outcomes of this study provide empirical insights and analytical references for labor-driven economic policy formulation and data-driven regional development planning in Indonesia.
Co-Authors Abda Abda Abdul Rahim Aini, Sri Rahmatun Amrullah, Lalu Muh. Ayu Septiani, Ayu Bahriah, Ayu Desi Yuniarti Erlyne Nadhilah Widyaningrum Hanipar Mahyulis Sastriana Hasanah, Siti Hadijah Hendrayani, Hendrayani Hidayanty, Nurul Ilma Ika Purnamasari Kertanah Kosasih, Raditya Arya Muhammad Gazali Muhammad Malthuf Nariza Wanti Wulan Sari Ni Luh Desvita Pratiwi Ningsih, Eva Lestari Nugraha, Pratama Yuly Pangruruk , Thesya Atarezcha Pangruruk Putri, Desi Febriani Ristu Haiban Hirzi Sifriyani, Sifriyani Siringoringo, Meiliyani Siti Hariati Hastuti SITI MAHMUDAH Suyitno Suyitno Sya'roni, Muhammad Yuniarti, Desi Zuhut Ramdani