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All Journal EDUMATIC: Jurnal Pendidikan Informatika Jurnal Abdi Insani Multica Science and Technology Parameter: Journal of Statistics Jurnal Statistika dan Komputasi (STATKOM)
Nurmayanti, Wiwit Pura
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Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Physics Social Sciences

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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.
FORECASTING TOTAL ASSETS OF PT. BPD KALTIM KALTARA USING THE SINGLE EXPONENTIAL SMOOTHING METHOD Nurmayanti, Wiwit Pura; Ningsih, Eva Lestari; Arif, Zainul; Fathurahman, M; Hasanah, Siti Hadijah
Parameter: Journal of Statistics Vol. 4 No. 2 (2024)
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Tadulako

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22487/27765660.2024.v4.i2.17473

Abstract

PT. BPD Kaltim Kaltara is one of the regional development banks that plays a crucial role in supporting regional economic development in East Kalimantan and North Kalimantan. The company's total assets reflect significant financial stability and growth, making it an interesting topic to analyze in the context of strategic financial planning. The purpose of this study is to use the Single Exponential Smoothing (SES) approach to forecast PT. BPD Kaltim Kaltara's total assets. In the forecasting process, alpha 0,3, alpha 0,6, alpha 0,7, and alpha 0,8 are tested to determine the best value that gives the most accurate results. Based on the forecasting accuracy analysis, the SES method with alpha = 0,7 proved to be the most optimal in predicting the company's total assets, achieving MAE = 1454272,737, MSE = 4764920751283, and MAPE = 4,0433% (excellent forecasting ability). The forecasting results show an upward trend in assets, with total assets in September 2024 estimated to reach IDR 48.440.683,75. This method provides valuable guidance in thecompany's financial strategic planning, helping to anticipate future asset developments more precisely.These forecasting results also emphasize the importance of selecting the right parameters in the forecasting model to improve prediction accuracy.
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.
Co-Authors Arif, Zainul Ayu Septiani, Ayu Bahriah, Ayu Desi Yuniarti Erlyne Nadhilah Widyaningrum Hasanah, Siti Hadijah Hendrayani, Hendrayani Hidayanty, Nurul Ilma Ika Purnamasari Kosasih, Raditya Arya M. Fathurahman Nariza Wanti Wulan Sari Ni Luh Desvita Pratiwi Ningsih, Eva Lestari Nugraha, Pratama Yuly Pangruruk , Thesya Atarezcha Pangruruk Sifriyani, Sifriyani Siti Hariati Hastuti SITI MAHMUDAH Sri Wahyuningsih Suyitno Suyitno Thesya Atarezcha Pangruruk Yuniarti, Desi