Article Per Year (5 Year)
p-Index From 2020 - 2025
2.338
P-Index
This Author published in this journals
All Journal Pythagoras: Jurnal Matematika dan Pendidikan Matematika Media Statistika Mechatronics, Electrical Power, and Vehicular Technology Al-Jabar : Jurnal Pendidikan Matematika BAREKENG: Jurnal Ilmu Matematika dan Terapan Jurnal Elektronika dan Telekomunikasi Majalah Ilmiah Matematika dan Statistika (MIMS) Jurnal TIKOMSIN (Teknologi Informasi dan Komunikasi Sinar Nusantara) Jurnal Ilmiah Matematika Komatika: Jurnal Pengabdian Kepada Masyarakat Prosiding University Research Colloquium Prosiding Konferensi Nasional Penelitian Matematika dan Pembelajarannya STATISTIKA
pratiwi, hasih
Unknown Affiliation
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Medicine & Pharmacology Physics Social Sciences Transportation

Published : 15 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 15 Documents
Search

 1   2 
CLUSTERING OF EARTHQUAKE RISK IN INDONESIA USING K-MEDOIDS AND K-MEANS ALGORITHMS Rifa, Isna Hidayatur; Pratiwi, Hasih; Respatiwulan, Respatiwulan
MEDIA STATISTIKA Vol 13, No 2 (2020): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.13.2.194-205

Abstract

Earthquake is the shaking of the earth's surface due to the shift in the earth's plates. This disaster often happens in Indonesia due to the location of the country on the three largest plates in the world and nine small others which meet at an area to form a complex plate arrangement. An earthquake has several impacts which depend on the magnitude and depth. This research was, therefore, conducted to classify earthquake data in Indonesia based on the magnitudes and depths using one of the data mining techniques which is known as clustering through the application of k-medoids and k-means algorithms. However, k-medoids group data into clusters with medoid as the centroid and it involves using clustering large application (CLARA) algorithm while k-means divide data into k clusters where each object belongs to the cluster with the closest average. The results showed the best clustering for earthquake data in Indonesia based on magnitude and depth is the CLARA algorithm and five clusters were found to have total members of 2231, 1359, 914, 2392, and 199 objects for cluster 1 to cluster 5 respectively.
Pemodelan Produksi Padi di Provinsi Jawa Timur dengan Regresi Non Parametrik B-Spline Handajani, Sri Sulistijowati; Pratiwi, Hasih; Susanti, Yuliana; Respatiwulan, Respatiwulan; Nirwana, Muhammad Bayu; Mahmudah, Arik
PYTHAGORAS Jurnal Matematika dan Pendidikan Matematika Vol. 18 No. 2: December 2023
Publisher : Department of Mathematics Education, Faculty of Mathematics and Natural Sciences, UNY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/pythagoras.v18i2.67475

Abstract

Kebutuhan pangan merupakan kebutuhan primer masyarakat yang harus terpenuhi. Makanan pokok yang banyak dikonsumsi masyarakat Indonesia salah satunya beras. Beras yang berasal dari padi selalu diusahakan memenuhi untuk kebutuhan konsumsi masyarakat terutama di sekitarnya. Jawa Timur adalah salah satu provinsi penyumbang terbesar produksi padi di Indonesia.  Oleh sebab itu perlunya melihat pengaruh faktor-faktor iklim di beberapa wilayah produksi padi terbesar di provinsi Jawa Timur yaitu kabupaten Tuban, Nganjuk dan Gresik terhadap besarnya produksi padi di wilayah tersebut. Tujuan penelitian ini adalah menganalisis faktor-faktor meliputi suhu, kelembaban, curah hujan dan luas panen padi terhadap jumlah prodiksi padi. Data diambil dari website BMKG dan BPS tahun 2020-2022 di Kabupaten Tuban, Nganjuk dan Gresik. Metode analisis yang digunakan dengan memodelkan regresi non parametrik B-spline dengan beberapa kombinasi titik knot dari beberapa variable prediktor yang menghasilkan GCV terkecil dari kemungkinan banyaknya titik knot yang dicobakan. Hasil pemodelan mendapatkan knot optimum pada variabel X1 (suhu) berorde 2 dengan tiga titik knot bernilai 23,45584; 24,32467; 26,93116. Knot optimum pada variabel X2 (kelembaban) berorde 2 dengan satu titik knot bernilai 83,3828. Knot optimum pada variabel X3 (curah hujan) berorde 2 dengan dua titik knot bernilai 5,177247 dan 15,51238. Knot optimum pada variabel X4 (luas panen padi) berorde 2 dengan satu titik knot bernilai 16939,25. Nilai GCV minimum yang diperoleh adalah 18462458. Hasil analisis menunjukkan semua variable berpengaruh signifikan walaupun untuk variable iklim terdapat beberapa segmen yang kurang signifikan, dengan nilai adjusted R-Square sebesar 0,987. The need for food is a primary requirement of society that must be fulfilled. One of the staple foods widely consumed by the Indonesian society is rice. Rice, which comes from paddy fields, is always cultivated to fufill  the consumption needs of the community, especially in the surrounding areas. East Java is one of the largest contributors to rice production in Indonesia. Therefore, it is necessary to examine the influence of climate factors in several rice-producing regions in East Java, namely Tuban, Nganjuk, and Gresik regencies, on the level of rice production in those areas. The aim of this research is to analyze factors such as rainfall, humidity, temperature, and rice cultivation area on rice production quantity.  The data was collected from BMKG (Meteorology, Climatology, and Geophysics Agency) and BPS (Central Statistics Agency) websites for the years 2020-2022 in Tuban, Nganjuk, and Gresik regencies. The analysis method used involves modeling non-parametric B-splines with various combinations of knot points from multiple predictor variables, resulting in the smallest Generalized Cross-Validation (GCV) among the possible knot points tested. The modeling results obtained the optimal knots for variable X1 (temperature) of order 2 with three knot points at values 23.45584, 24.32467, and 26.93116. The optimal knot for variable X2 (humidity) of order 2 was at one knot point with a value of 83.3828. The optimal knots for variable X3 (rainfall) of order 2 were two knot points with values of 5.177247 and 15.51238. The optimal knot for variable X4 (rice cultivation area) of order 2 was at one knot point with a value of 16,939.25. The minimum GCV value obtained was 18,462,458. The analysis results indicate that all variables have a significant influence, although for climate variables, there were some segments that were less significant, with an value adjusted R-Square of 0.987.
Distracted driver behavior recognition using modified capsule networks Kadar, Jimmy Abdel; Dewi, Margareta Aprilia Kusuma; Suryawati, Endang; Heryana, Ana; Zilfan, Vicky; Kusumo, Budiarianto Suryo; Yuwana, Raden Sandra; Supianto, Ahmad Afif; Pratiwi, Hasih; Pardede, Hilman Ferdinandus
Journal of Mechatronics, Electrical Power, and Vehicular Technology Vol 14, No 2 (2023)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14203/j.mev.2023.v14.177-185

Abstract

Human activity recognition (HAR) is an increasingly active study field within the computer vision community. In HAR, driver behavior can be detected to ensure safe travel. Detect driver behaviors using a capsule network with leave-one-subject-out validation. The study was done using CapsNet with leave-one-subject-out validation to identify driving habits. The proposed method in this study consists of two parts, namely encoder and decoder. The encoder used in this study modifies Sabour’s capsule network architecture by adding a convolution layer before going to the primary capsule layer. The proposed method is evaluated using a primary dataset with 10 classes and 300 images for each class. The dataset is split based on hold-out validation and leave-one-subject-out validation. The resulting models were then compared to conventional CNN architecture. The objective of the research is to identify driving behavior. In this study, the proposed method results an accuracy rate of 97.83 % in the split dataset using hold-out validation. However, the accuracy decreased by 53.11 % when the proposed method was used on a split dataset using leave-one-subject-out validation. This is because the proposed method extracts all features including the attributes of each participant contained in the input image (user-independent). Thus, the resulting model in this study tends to overfit.
Penerapan Metode Hierarchical Clustering Untuk Pengelompokan Kota/Kabupaten di Indonesia Berdasarkan Indikator Kemiskinan Kumarahadi, Brigitta Melati; Pratiwi, Hasih; Subanti, Sri
Jurnal Teknologi Informasi dan Komunikasi (TIKomSiN) Vol 11, No 2 (2023): Jurnal Tikomsin, Vol. 11, No. 2, Oktober 2023
Publisher : STMIK Sinar Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30646/tikomsin.v11i2.754

Abstract

In 2024, the government sets a poverty target in Indonesia of 6-7%. Until September 2022, poverty still shows a figure of 9.57%. To achieve the target, it is necessary to determine priority areas so that government policies can be right on target. This study aims to group cities/regencies in Indonesia based on poverty indicators as a solution to obtain priority areas using the clustering method. This method is used to collect data into several groups based on the same criteria. Hierarchical clustering consists of several methods, including agglomerative nesting such as single linkage, complete linkage, average linkage, and Ward linkage, and divisive analysis. The results showed that the agglomerative nesting average linkage method is the better method because it has a greater cophenetic value and silhouette coefficient value, which is 0.90 and 0.71. The clustering results consist of two clusters, cluster 1 contains 493 areas with low poverty and cluster 2 contains 21 areas with high poverty.
Two-Stage Object Detection for Autonomous Vehicles With VGG-16 Based Faster R-CNN Dewi, Arnetta Listiana; Pardede, Hilman F.; Suryawati, Endang; Pratiwi, Hasih; Heryana, Ana; Yuliani, Asri R; Ramdan, Ade
Jurnal Elektronika dan Telekomunikasi Vol 24, No 1 (2024)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/jet.551

Abstract

The implementation of object detection for autonomous vehicles is essential as it is necessary to identify common object on the street so proper response could be designed. While single stage object may be smaller in computations, two-stage object detection is preferred due to the ability to localize the object. In this paper, we propose to use Faster R-CNN with VGG-16 backbone for detections of object on the street. We evaluate the method with open image subset by selecting objects that are common on street. We explore several hyper-parameters setup such as learning rate and the number of ROI regions to find the optimum set-up. We found that the use of learning rate 10-6 with Adam optimizer to be the optimum value for this task. We also found that increasing the number of ROI may benefit the performance. This shows that there is potential for getting a higher mAP with increase the amount of RoI.
Pemodelan faktor-faktor yang memengaruhi angka kesembuhan tuberkulosis di Jawa Barat menggunakan regresi spline truncated Evitasari, Niken; Handajani, Sri Sulistijowati; Pratiwi, Hasih
Majalah Ilmiah Matematika dan Statistika Vol 22 No 2 (2022): Majalah Ilmiah Matematika dan Statistika
Publisher : Jurusan Matematika FMIPA Universitas Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19184/mims.v22i2.30356

Abstract

Tuberculosis is a bacterial infection caused by Mycobacterium tuberculosis. Transmission of tuberculosis (TBC) can occur due to environmental factors and community behavior. West Java is Indonesia's province with the highest number of tuberculosis cases. Curing tuberculosis is critical to reducing cases and breaking the transmission chain. The Human Development Index (IPM), good sanitation, comprehensive tuberculosis treatment, public spaces (PS) meeting health criteria, and residents having health insurance are all assumed to influence the tuberculosis cure rate. This research aimed to model the elements that have a substantial impact on tuberculosis cure rates.The tuberculosis cure rate in West Java in 2020 was modeled using nonparametric spline truncated linear regression with a combination of knot points (3,3,3,3,2). The lowest Generalized Cross Validation (GCV) value of 26.7579 was used to find the best knot point. The adjusted coefficient of determination for this study was 96.35 percent, indicating that the linear truncated spline regression model with a combination of knot points is feasible to use in modeling. The five predictor variables simultaneously affect the tuberculosis cure rate of 96.35 percent, while 3.65 percent is influenced by other variables not used in the study. Keywords: Spline truncated, tuberculosis cure, knots, GCVMSC2020: 62G08
Pelatihan Manajemen dan Visualisasi Data Menggunakan Excel untuk Guru Matematika SMP di Kabupaten Karanganyar: Data Management and Visualization Training using Excel for Junior High School Mathematics Teacher in Karanganyar Regency Nirwana, Muhammad Bayu; Pratiwi, Hasih; Susanti, Yuliana; Respatiwulan, Respatiwulan; Handayani, Sri Sulistijowati; Wijaya, Andreas Rony; Pratama, Alfito Putra Fajar; Ferawati, Kiki
Komatika: Jurnal Pengabdian Kepada Masyarakat Vol. 4 No. 2 (2024): November 2024
Publisher : Pusat Penelitian dan Pengabdian Kepada Masyarakat, Institut Informatika Indonesia Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34148/komatika.v4i2.1023

Abstract

Literasi statistik merupakan kemampuan untuk memahami beragam informasi statistik yang dimunculkan di berbagai media. Kemampuan ini meliputi keterampilan dalam menginterpretasikan grafik dan tabel, serta mampu membaca dan memahami statistik dalam berita, media, jajak pendapat, dan lain-lain. Kabupaten Karanganyar merupakan salah satu kabupaten di Provinsi Jawa Tengah yang berbatasan dengan Kota Surakarta dan termasuk sebagai wilayah Karesidenan Surakarta. Pengetahuan mengenai literasi statistik dan implementasinya di wilayah Kabupaten Karanganyar merupakan hal yang penting untuk disampaikan kepada masyarakat, karena berkaitan langsung dengan pemahaman mengenai informasi data statistika dan bagaimana merepresentasikannya. Sebagai ilmu yang mempelajari tentang cara pengumpulan, analisis, dan pengambilan keputusan dari data, pengetahuan tentang statistika merupakan ilmu penunjang yang penting untuk dimiliki oleh masyarakat. Sebagai sasaran peningkatan literasi statistika kali ini Grup Riset Statistika dan Sains Data Bidang Lingkungan dan Kesehatan Program Studi Statistika FMIPA UNS akan melaksanakan pengabdian masyarakat dengan bentuk pelatihan untuk guru dan siswa SMP di Kabupaten Karanganyar melalui forum Musyawarah Guru Mata Pelajaran (MGMP) Matematika. Literasi statistik memerlukan pengetahuan tentang analisis dan visualisasi data yang diberikan untuk meningkatkan pemahaman terkait penerapan metode statistika dengan menggunakan Excel yang sudah banyak dikenal oleh masyarakat.
Perbandingan Algoritma Density-Based Spatial Clustering Algorithm with Noise (DBSCAN) dan Self-Organizing Map (SOM) untuk Clustering Data Gempa Bumi Wati, Rosita Kurnia; Pratiwi, Hasih; Winita Sulandari
Statistika Vol. 24 No. 2 (2024): 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.v24i2.3645

Abstract

ABSTRAK Gempa bumi merupakan bencana alam yang kerap melanda Indonesia karena letak geografisnya berada pada batas pertemuan tiga lempeng aktif dunia. Dampak kerusakan yang timbul akibat gempa bumi bergantung pada magnitudo dan kedalamannya. Oleh karena itu, perlu upaya mitigasi bencana dan manajemen risiko bencana melalui pengolahan data untuk mengetahui karakteristik dari data gempa tersebut. Penelitian ini bertujuan untuk clustering data gempa bumi di Indonesia berdasarkan magnitudo dan kedalaman dengan menerapkan algoritma Density-Based Spatial Clustering Algorithm With Noise (DBSCAN) dan Self-Organizing Map  (SOM) dengan validasi kebaikan cluster menggunakan koefisien silhouette. Penerapan algoritma DBSCAN dengan nilai Eps dan MinPts optimal sebesar 1,6 dan 12 membentuk dua cluster dan 23 data diidentifikasi sebagai noise, sedangkan menggunakan algoritma SOM dengan learning rate 0,05 dan maksimal epoch 1.000 membentuk dua cluster. Pada analisis ini SOM mampu  melakukan clustering yang lebih baik jika dibandingkan dengan DBSCAN karena memberikan  nilai koefisien silhouette yang lebih besar, yaitu sebesar 0,717 sedangkan DBSCAN sebesar  0,677. Hasil clustering terbaik memiliki karakteristik yaitu cluster 1 dikategorikan sebagai gempa sedang berkekuatan sedang dan cluster 2 dikategorikan sebagai gempa dangkal berkekuatan sedang. ABSTRACT Earthquakes are natural disasters that occur frequently in Indonesia because of the geographical location at the convergence of three active tectonic plates. The severity of an earthquake's impact is influenced by magnitude and depth. Therefore, disaster mitigation efforts and disaster risk management through data mining are needed to understand the characteristics of earthquakes. This research aims to cluster earthquake data in Indonesia based on magnitude and depth by applying a Density-Based Spatial Clustering Algorithm with Noise (DBSCAN) and Self-Organizing Map (SOM) algorithms and cluster results are evaluated using the silhouette coefficient. Using the DBSCAN algorithm with optimal Eps and MinPts values of 1.6 and 12 formed two clusters and 23 data were identified as noise while using the SOM algorithm with a learning rate of 0.05 and a maximum epoch of 1000 formed two clusters. SOM can perform clustering better than DBSCAN because it provides a larger silhouette coefficient value, which is 0.717 while DBSCAN is 0.677. The clustering results obtained show that cluster 1 is categorized as moderate earthquakes of moderate intensity and cluster 2 is categorized as shallow earthquakes of moderate intensity.
Penerapan Model Epidemic Type Aftershock Sequence (ETAS) pada Data Gempa Bumi Sulawesi dan Jawa Mutiah, Siti; Pratiwi, Hasih; Handajani, Sri Sulistijowati
Prosiding Konferensi Nasional Penelitian Matematika dan Pembelajarannya 2019: Prosiding Konferensi Nasional Penelitian Matematika dan Pembelajarannya
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (672.23 KB)

Abstract

Gempa bumi merupakan suatu kejadian yang bersifat acak baik dalam waktu maupun lokasi. Suatu kejadian gempa bumi yang berkekuatan besar, biasanya diikuti oleh kejadian gempa susulan. Oleh karana itu, diperlukan upaya untuk meminimalkan dampak yang diakibatkkan peristiwa gempa bumi, salah satunya menggunakan pendekatan probabilistik yaitu proses titik. Model yang dibahas pada penelitian ini adalah model epidemic type aftershock sequence (ETAS), dimana model ini merupakan fungsi intensitas bersyarat yang memberikan informasi tentang laju kejadian gempa bumi dengan mempertimbangkan variabel magnitudo dan waktu. Penelitian ini bertujuan untuk membahas model ETAS dengan magnitudo mengikuti distribusi gamma dan penerapannya pada gempa bumi yang terjadi di Pulau Sulawesi dan Pulau Jawa dari tahun 2000 sampai 2018. Langkah yang dilakukan adalah mengestimasi parameter model ETAS dengan metode estimasi likelihood maksimum. Hasil estimasi parameter gempa bumi di Pulau Sulawesi menunjukkan bahwa laju kegempaan dasar, produktivitas gempa susulan, dan efisiensi gempa bumi dengan magnitudo tertentu yang menghasilkan gempa susulan lebih tinggi dari hasil estimasi di Pulau Jawa. Akan tetapi, pada laju peluruhan gempa susulan menurut waktu dan secara keseluruhan di Pulau Sulawesi lebih rendah dari Pulau Jawa.
The Effect of Self-Esteem on Students’ Mathematical Communication Skills Mahani, Ibud; budiyono, budiyono; pratiwi, hasih
Al-Jabar: Jurnal Pendidikan Matematika Vol 10 No 1 (2019): Al-Jabar: Jurnal Pendidikan Matematika
Publisher : Universitas Islam Raden Intan Lampung, INDONESIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/ajpm.v10i1.4294

Abstract

This study aims to find out between students with high, medium, and low self-esteem in term of better mathematical communication. This type of research is quantitative research. The data analysis technique used was one-way variance analysis with inequivalent cells. The collection of students' mathematical communication data was obtained from the score of mathematical communication tests, and the data of self-esteem was collected through questionnaires. The subjects of this study were the eighth-grade junior high school students in Ponorogo with the high, medium and low schools categories. The results of this study indicate that students with high self-esteem possess better mathematical communication skills compared to students with moderate self-esteem and students with low self-esteem. Students with low self-esteem are having the same level of mathematical communication skills as students with moderate self-esteem. 
Co-Authors Ade Ramdan Ahmad Afif Supianto Andreas Rony Wijaya Apsari, Nindya Eka Budiyono Budiyono Dewi, Arnetta Listiana Dewi, Margareta Aprilia Kusuma Evitasari, Niken Evitasi, Niken Febrianti, Tri Kartika Handajani, Sri Sulistijowati Handayani, Sri Sulistijowati Heryana, Ana Kadar, Jimmy Abdel Kiki Ferawati Kumarahadi, Brigitta Melati Kusumo, Budiarianto Suryo Mahani, Ibud Mahmudah, Arik mutiah, siti Nafi'udin, Fauzi Nareswari, Lintang Pramesti Nirwana, Muhammad Bayu Pardede, Hilman F. Pardede, Hilman Ferdinandus Pratama, Alfito Putra Fajar Putri Ayu Pramesti, Putri Ayu Qona’ah, Niswatul Ramadhania, Monica Respatiwulan, Respatiwulan Rifa, Isna Hidayatur Sri Subanti Suryawati, Endang Wati, Rosita Kurnia Winita Sulandari Yuliana Susanti, Yuliana Yuliani, Asri R Yuwana, Raden Sandra Zilfan, Vicky Zukhronah, Etik