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All Journal EDUMATIC: Jurnal Pendidikan Informatika Indonesian Physical Review Kappa Journal JAMBURA JOURNAL OF PROBABILITY AND STATISTICS Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Jurnal Statistika dan Komputasi (STATKOM)
Kertanah, Kertanah
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Astronomy Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Environmental Science Industrial & Manufacturing Engineering Mathematics Physics Social Sciences Other

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Modeling of Gross Domestic Product Growth in Indonesia by Using Multi-Input Intervention Model Chandrawati, Chandrawati; Kertanah, Kertanah; Ramli, Tri Juliantin; Chintyana, Alissa; Hirzi, Ristu Haiban
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Volume 12 Issue 2 December 2024
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/euler.v12i2.27949

Abstract

The Gross Domestic Product (GDP) Growth of Indonesia has fluctuated over time due to established policies, economic crises, changes in political direction, and natural disasters. In 1998, due to the fall of the New Order regime, the Indonesian economy contracted by -13.13 percent, leading to hyperinflation. In 2020 the COVID-19 pandemic occurred which caused Indonesia's GDP Growth to contract again. Accurate forecasting of GDP Growth is crucial for government to formulate effective future policy strategies to maintain the stability of Indonesia's economy. There are several outliers in Indonesia's GDP Growth data, so the proper analysis is a multi-input intervention. The best model analysis is ARIMA (1,0,0) with non-zero mean using the first order intervention b=0, r=0, s=0 and the second order intervention b=0, r=0, and s=0 which resulted in a Mean Absolute Percentage Error (MAPE) of 23.47 percent. The outlier effect on Indonesia's GDP Growth data is both direct and temporary.
Pemetaan Kasus DBD di Pulau Lombok menggunakan Regresi Binomial Negatif berbasis Geografis Ayundasari, Dita Septiana; Hastuti, Siti Hariati; Kertanah, Kertanah
Jurnal Pendidikan Informatika (EDUMATIC) Vol 8 No 2 (2024): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v8i2.27460

Abstract

According to the Indonesia Health Profile Report 2022, NTB Province is among the 11 provinces with the highest incidence rate of dengue hemorrhagic fever (DHF). On Lombok Island, there were 2,074 cases with 4 deaths in 2022. DHF remains a serious threat in Lombok, so this study aims to map sub-districts based on significant factors for the spread of DHF in 54 sub-districts throughout Lombok Island. This study used quantitative analysis with one response variable, the number of DHF cases, and three predictor variables: the ratio of medical personnel (nurses) (X1), the percentage of proper sanitation facilities (healthy latrines) (X2) and the percentage of standard drinking water facilities (X3) in 54 sub-districts. Data were obtained from the Health Office throughout Lombok Island. Analysis techniques include descriptive analysis, GWNBR modeling, and significant variable mapping. The mapping results showed six groups of sub-districts with a combination of significant variables, which included variables X1, X2, and X3. The findings suggest the need for additional studies or prevention policies that are more focused on hygiene to reduce the risk of DHF spread. Related parties also need to be informed to take strategic steps based on these findings.
Penerapan Algoritma Self Organizing Maps (SOM) Dan K-Means Untuk Mengelompokkan Akseptor KB Di NTB Yahya, Lalu Muhammad; Kertanah, Kertanah; Hidayaturrohman, Umam
Jurnal Statistika dan Komputasi Vol. 3 No. 1 (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.v3i1.2960

Abstract

Latar Belakang: Salah satu permasalahan utama terkait penggunaan KB yaitu berhubungan dengan ketersediaan layanan kesehatan, sehingga untuk memberikan akses yang lebih baik kepada masyarakat terhadap informasi dan layanan dapat dilakuakn analsis clustering yang membantu mengidentifikasi wilayah-wilayah di NTB yang memiliki akses terbatas terhadap layanan kesehatan reproduksi. Tujuan: Tujuan penelitian ini, pertama adalah untuk mengetahui gambaran umum akseptor keluarga berencana seluruh kecamatan di NTB. Kedua adalah untuk mengetahui hasil cluster akseptor keluarga berencana di kecamatan seluruh NTB 2022 dengan algoritma SOM dan K-means serta mengetahui algoritma terbaik pada data akseptor keluarga berencana di kecamatan seluruh NTB ditinjau dari nilai validasi internal. Metode: Algoritma clustering yang digunakan pada penelitian ini yaitu SOM dan K-means. Hasil: Berdasarkan hasil analisis didapatkan bahwa suntik merupakan akseptor tertinggi di NTB sebanyak 299.344. Sedangkan akseptor terendah adalah kondom sebanyak 7.333. Hasil penelitian dengan algoritma SOM memiliki 2 cluster yaitu cluster 1 terdapat 103 kecamatan dan cluster 2 terdapat 14 kecamatan. Algoritma K-means memiliki 2 cluster yaitu cluster 1 terdapat 84 kecamatan dan cluster 2 terdapat 33 kecamatan. Kesimpulan: Algoritma terbaik untuk pengelompokan akseptor keluarga berencana di kecamatan seluruh Provinsi NTB adalah algoritma SOM.  
APPLYING K-MEANS ALGORITHM FOR CLUSTERING ANALYSIS EARTHQUAKES DATA IN WEST NUSA TENGGARA PROVINCE Kertanah, Kertanah; Rahadi, Irwan; Aryani Novianti, Baiq; Syahidi, Khaerus; Sapiruddin, Sapiruddin; Mandala Putra, Hadian; Gazali, Muhammad; Haiban Hirzi, Ristu; Sabar, Sabar
Indonesian Physical Review Vol. 5 No. 3 (2022)
Publisher : Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/ipr.v5i3.148

Abstract

This study aims to cluster and visualize the earthquake data on a geographical map to determine earthquakes' characteristics using the k-means algorithm. Cluster analysis using the k-means algorithm was carried out on the earthquake data. K-means algorithm is familiar and is one of the well-known techniques to have been applied in cluster analysis. One of Its advantages in cluster analysis is scaling large datasets, for example, earthquake data. The data used in this study is earthquake data in the West Nusa Tenggara from 1991 to 2021. Applying the proposed k-means algorithm, the optimal number of clusters (k) used in this clustering is 2, based on the highest silhouette score of 0.749. The cluster analysis on the geographical map showed that the epicenters of the earthquakes were pretty spread out before 2018, and the number of earthquakes in the eastern region of West Nusa Tenggara is more than in the western area. However, in 2018, the clusters were all bunched in the northern Lombok region. There were a few earthquakes in the west region in 2018, but they happened before August 5. Even after 2019, most earthquakes continue to occur, with the epicenters clustered close to the northern Lombok region  
Support Vector Machine-Radial Basis Function Kernel and K-Nearest Neighbor Differences for Classification Superior Varieties of Rice in Indonesia Chintyana, Alissa; Kertanah, Kertanah; Hastuti, Siti Hariati; Khotimah, Husnul
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Volume 12 Issue 2 December 2024
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/euler.v12i2.27605

Abstract

Rice is the primary food source for the Indonesian population, making it a priority commodity in Indonesia. Rice production plays a significant role in Indonesia's economic development, with high-yield rice varieties being crucial for enhancing national rice output. Ensuring food security requires the selection of superior rice varieties with optimal quality. This study evaluates various high-yield rice varieties, including INPARA, INPARI, INPAGO, and HIPA, based on characteristic data collected in 2023. Machine learning algorithms, increasingly central to data analysis, were applied, leveraging labeled data suitable for supervised learning methods. During the pre-processing stage, it was determined that the data did not meet the linearity assumption. Thus the Support Vector Machine (SVM) algorithm was modified with the Radial Basis Function (RBF) kernel to better handle non-linear data. Additionally, the K-Nearest Neighbor (KNN) algorithm, a traditional method, was used for comparison. The results indicate that SVM with the RBF kernel achieved faster processing times and the accuracy value reaches 96%, nearly 10% higher than the KNN algorithm.
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
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.
Co-Authors Abdul Rahim Aini, Sri Rahmatun Amrullah, Lalu Muh. Aryani Novianti, Baiq Ayundasari, Dita Septiana Chandrawati, Chandrawati Chintyana, Alissa Hadian Mandala Putra Hasanah, Siti Hadijah Hendrayani, Hendrayani Husnul Khotimah Khaerus Syahidi Muhammad Gazali Nurmayanti, Wiwit Pura Rahadi, Irwan Ramli, Tri Juliantin Ristu Haiban Hirzi, Ristu Sabar Sabar Sapiruddin, Sapiruddin Siti Hariati Hastuti Sya'roni, Muhammad Umam Hidayaturrohman Yahya, Lalu Muhammad