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All Journal Jurnal Gaussian Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Kreativitas PKM JURNAL PANGAN Jurnal Informatika dan Rekayasa Elektronik Jurnal Keluarga Berencana Abdi: Jurnal Pengabdian dan Pemberdayaan Masyarakat Al-Muqayyad Mitra Ash-Shibyan: Jurnal Pendidikan dan Konseling Unri Conference Series: Community Engagement Parameter: Journal of Statistics International Journal of Industrial Engineering and Engineering Management Journal of Community Engagement Research for Sustainability Sagu VARIANSI: Journal of Statistics and Its Application on Teaching and Research Jurnal Ilmiah Niagara Jurnal Statistika Terapan (JSTAR)
Anne Mudya Yolanda
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Agriculture, Biological Sciences & Forestry Humanities Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Environmental Science Health Professions Industrial & Manufacturing Engineering Mathematics Medicine & Pharmacology Nursing Public Health Social Sciences Other

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SEGMENTASI WILAYAH BERDASARKAN KETERSEDIAAN FASILITAS PUBLIK (STUDI KASUS: KABUPATEN/KOTA DI PROVINSI NUSA TENGGARA TIMUR) Anne Mudya Yolanda; Agung Satrio Wicaksono
NIAGARA Scientific Journal Vol 14 No 1 (2022): Jurnal Ilmiah Niagara Vol. 14 No. 1 Tahun 2022
Publisher : LPPM STIA Banten

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

Abstract

The availability of public facilities could be an indicator of regional development. The purpose of this study was to describe the segmentation of the availability of public facilities in Nusa Tenggara Timur province by regency/city. The K-Means Clustering analysis was used to divide the regencies/cities in Nusa Tenggara Timur province into three groups. The variables used in this K-Means Clustering analysis based on Nusa Tenggara Timur Village Potential Statistics are Public Spaces, Integrated Health Counseling Post, The Source of Main Street Illumination, Availability of Public Transportation, and Availability of Communication Facility. According to the findings and discussions, the first, second, and third clusters of the availability of public facilities byregencies/cities in Nusa Tenggara Timur Province consist of 8, 5, and 9 regencies/cities, respectively. Percentage of variability for this data is about 61.4 %. The segments with the lowest availability of public facilities are Sumba Barat regency, Belu regency, Sumba Tengah regency, Sabu Raijua regency, and Kupang City. The medium segment consists of Sumba Timur regency, Alor regency, Sikka regency, Ngada regency, Rote Ndao regency, Sumba Barat Daya regency, Nagekeo regency, and Malacca regency. The areas with the highest availability of facilities are Kupang regency, Timor Tengah Selatan regency, Timor Tengah Utara Regency, Lembata regency, Flores Timur regency, Ende regency, Manggarai regency, Manggarai Barat regency, and Manggarai Timur regency.
The Comparison of Accuracy on Classification Climate Change Data with Logistic Regression Adnan, Arisman; Yolanda, Anne Mudya; Erda, Gustriza; Goldameir, Noor Ell; Indra, Zul
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 1 (2023): Articles Research Volume 7 Issue 1, 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i1.11914

Abstract

Machine learning methods can be used to generate climate change models. The goal of this study is to use logistic regression machine learning algorithms to classify data on greenhouse gas emissions. The data used is climate change data of several countries obtained from The World Bank, with total greenhouse gas emissions as the response variable and 61 other attributes as explanatory variables. This data is preprocessed using min-max normalization to handle unbalanced ranges, and then the data is split into 70% training data and 30% testing data. Based on the logistic regression modeling, it was discovered that the data from the min-max transformation resulted in better modeling than the data modeling without the transformation process. The accuracy, precision, sensitivity, and specificity of the transformation are 87.60%, 87.76%, 87.04%, and 88.14%, respectively
EXPLORATION OF STUDENTS INTERESTS IN MBKM AT RIAU UNIVERSITY USING A MACHINE LEARNING APPROACH Safitri, Nuraini; Zahra, Lathifah; Lafina, Melanie Maria; Erda, Gustriza; Yolanda, Anne Mudya
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.i2.17158

Abstract

This study aims to analyze the factors that have a significant influence on the interest of Riau University students in the Merdeka Belajar Kampus Merdeka (MBKM) program using a machine learning approach. MBKM is an innovation initiated by the Ministry of Education and Culture with the aim of improving student competence through its various programs. The Riau University as one of the universities supports this program by providing opportunities for its students to participate in various activities provided in the MBKM program. This study will specifically use a machine learning approach by utilizing several methods to analyze significant factors that have not been analyzed in depth by previous studies. The methods used in this analysis are logistic regression, decision trees, random forests, and naive bayes by utilizing secondary data on the level of interest of Riau University students to participate in the MBKM program in 2023. The variables used in this study include gender, generation, faculty, knowledge, self-confidence, feeling benefits, family support, friend support, lecturer support, self-ability, and facilities as independent variables and MBKM interest as a dependent variable. The results of the analysis of several methods show that the logistic regression method provides the best performance in modeling with an accuracy level of 95%. Variables that have a significant influence on students' interest in the MBKM program have also been successfully identified. The variables that have a significant effect are self-ability and family support. The development strategy of MBKM at the University of Riau can be optimized by paying attention to and focusing on these variables. The optimization of this strategy aims to make the implementation of the program more effective and efficient. Supportive policies such as workshops for the development of students' soft skills can be one of the strategic steps to improve students' abilities to the maximum
Pengembangan Aplikasi Berbasis Data untuk Optimalisasi Posyandu Pucuk Rebung Bersiku Keluang Yolanda, Anne Mudya; Erda, Gustriza; T, Nurhannifah Rizky; Finda, Ingla; Tata, Tata
Unri Conference Series: Community Engagement Vol 6 (2024): Seminar Nasional Pemberdayaan Masyarakat
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat Universitas Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31258/unricsce.6.635-639

Abstract

Integrated Service Posts (Posyandu) play an important role in improving community health, especially in rural and remote areas. Posyandu provides basic health services for mothers and children, including growth monitoring, pregnancy check-ups, immunizations, and counseling on nutrition and health. However, many Posyandu, including Posyandu Pucuk Rebung Bersiku Keluang in Sail Sub-district, Pekanbaru, still use a manual recording system in data management. This often results in inaccurate data and makes it difficult to make informed decisions, which in turn hinders effective health policy development. This service activity aims to design and implement an information system specifically for Posyandu, with a focus on data digitization to facilitate recording, management, and visualization of information. The system is expected to provide more accurate data and comprehensive information on maternal and child health conditions. With a user-friendly dashboard-based design, the system is expected to improve the efficiency of data management and support more informed decision-making at the local level. The program is also expected to have a positive impact on the quality of health services in Posyandu and provide recommendations for similar implementation in other Posyandu.
Penguatan Kapasitas Komunitas Statistika Bantar dalam Tata Kelola Data Desa untuk Pembangunan Berkelanjutan Adnan, Arisman; Yolanda, Anne Mudya; Erda, Gustriza; Syamsudhuha, Syamsudhuha; Indra, Zul; Solfitri, Titi; T, Masrina Munawarah
Unri Conference Series: Community Engagement Vol 6 (2024): Seminar Nasional Pemberdayaan Masyarakat
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat Universitas Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31258/unricsce.6.640-645

Abstract

This activity aims to strengthen the capacity of the statistical community in Bantar Village in supporting the transformation and management of data for sustainable development. The program focuses on assisting village officials in effectively managing data at the village level, in line with the Desa Cantik initiative and Indonesia One Data (SDI) program. The goal is to improve data accuracy and the effectiveness of village development planning. As a result, the statistical community, which also includes village officials, has shown increased capabilities in managing sectoral statistics and digitalizing data integrated with the Desa Cantik program. The village officials actively participated in this assistance, supported by the provincial and district BPS, who acted as facilitators. BPS provided training, monitoring, evaluation, and assistance in the preparation of program materials and outputs. One of the key outputs of this program is the creation of an infographic summarizing the statistics and potential of Bantar Village, covering demographic profiles, population density, and key commodities. This infographic serves as a visual communication tool that supports data-driven development planning. The program successfully established a strong foundation for better data management, supporting sustainable village development.
Forecasting Non-Oil and Gas Exports in Indonesia Using Double and Triple Exponential Smoothing Methods Bustami; Yolanda, Anne Mudya; Thahira, Nisha
International Journal of Industrial Engineering and Engineering Management Vol. 5 No. 1 (2023)
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/ijieem.v5i1.6211

Abstract

Non-oil and gas exports could be forecasted using exponential smoothing for future periods. This study examines non-oil and gas export data in Indonesia from January 2015 to May 2021, indicating trends and seasonality. Based on the data characteristics, the obtained data were analyzed using Holt's double exponential smoothing method and triple exponential smoothing with multiplicative and additives Holt-Winters. The MAPE for all three models is less than 10%, indicating that the method is very good and could be used to forecast the next period. Using MAPE as a comparison, the best model for non-oil and gas exports is the additive Holt-Winters method triple exponential smoothing, which has the lowest MAPE of any model. The best method was employed to forecast data, making it possible for us to anticipate the pattern of non-oil and gas exports. This forecast data could be used as the basis for policymakers' decision-making. The forecast results using this method indicate that the value of non-oil exports will increase for the next period.
Implementasi Metode Support Vector Machine untuk Analisis Sentimen pada Ulasan Aplikasi Sayurbox di Google Play Store Yolanda, Anne Mudya; Mulya, Ridho Tri
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 6 No. 02 (2024)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm258

Abstract

Penelitian ini bertujuan untuk menganalisis kinerja metode Support Vector Machine (SVM) dalam mengklasifikasikan ulasan pengguna aplikasi Sayurbox yang terkenal di Indonesia. Data ulasan diperoleh melalui scraping dari Google Play Store antara tahun 2017 hingga 2023. Ulasan dan rating yang diberikan pengguna digunakan sebagai indikator untuk mengevaluasi kepuasan terhadap layanan yang disediakan. Dalam penelitian ini, metode SVM digunakan untuk memproses data ulasan tersebut. Hasil klasifikasi menunjukkan bahwa metode SVM mencapai akurasi sebesar 89,29%. Selain itu, berdasarkan Confusion Matrix, nilai precision yang diperoleh adalah 91,42%, recall 95,58%, dan f1-score 93,50%. Temuan ini menunjukkan bahwa SVM merupakan metode yang efektif dalam mengklasifikasikan ulasan pengguna, yang dapat memberikan wawasan berharga untuk meningkatkan kualitas pelayanan Sayurbox.
The Effect of Increasing Daily Case COVID-19 as Moderating Variable on Coal Stock Price Dewi, Ratna Mustika; Yolanda, Anne Mudya
International Journal of Industrial Engineering and Engineering Management Vol. 3 No. 2 (2021)
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/ijieem.v3i2.5547

Abstract

Stock investments in the time of the COVID-19 pandemic have a considerable risk. This happens because of the increasingly uncontrolled movement of stock prices. The potential for steep charts can occur at any time. Sentiment analysis of increasing daily cases of COVID-19 was analyzed to see how much effect it has on stock price movements. This research will analyze stock prices from coal commodities in Indonesia. Researchers choose to discuss coal commodities because, in April 2021, there was a significant increase and highest in November 2021. Because of the data, researchers want to see the influence of some coal companies using selling price data with moderating variables for estimating the stock price. There are 26 coal companies that are listed on Indonesia Stock Exchange. The analysis will be a check on five companies that have the largest investors. The analysis is also carried out on coal sales price movements. Furthermore, five different coal mining companies were analyzed based on the rate of price changes to new selling prices with variable moderation in Indonesia. Increasing daily cases of COVID-19 being variable moderation. The method used for finding the relationship is a linear regression with a moderating variable. According to the analysis, the increasing daily case of COVID-19 as a moderating variable is enough to affect the relationship between the selling price of coal and the stock price of HRUM.JK and PTBA.JK. In stock price HRUM.JK, there is an increasing adjusted R square from 0.5254 to 0.5451. The same conditions apply to PTBA.JK has increased by 0.4040 to 0.4444.
Segmentasi Provinsi di Indonesia berdasarkan Data Runtun Waktu Produksi Padi dengan Algoritma DTW dan K-Medoids Clustering Yolanda, Anne Mudya; Savira, Husna
JURNAL PANGAN Vol. 33 No. 3 (2024): PANGAN
Publisher : Perum BULOG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33964/jp.v33i3.847

Abstract

        Sektor pertanian, khususnya tanaman pangan padi, memiliki peranan penting bagi perekonomian Indonesia. Analisis data historis produksi padi atau data runtun waktu produksi padi dapat memberikan gambaran pola produksi dan segmentasi wilayah berdasarkan karakteristik tanaman padi. Penelitian ini menggunakan algoritma DTW dan K-Medoids Clustering untuk melakukan segmentasi provinsi di Indonesia berdasarkan data produksi padi tahun 2013-2021. Hasil penelitian menunjukkan tiga cluster wilayah dengan karakteristik produksi padi yang berbeda. Setiap cluster menunjukkan pola produksi yang berbeda dengan anggota cluster lainnya, yang disebabkan oleh perbedaan jarak DTW dalam mengukur kesamaan pola produksi. Cluster 1 memiliki produksi tertinggi, diikuti oleh Cluster 2 dan Cluster 3, masing-masing terdiri dari 16, 7, dan 11 provinsi. Temuan ini dapat digunakan sebagai dasar pengembangan kebijakan pemerintah sesuai karakteristik masing-masing segmen.             The agricultural sector, particularly rice crops, plays a crucial role in Indonesia’s economy. Analyzing rice production historical data, or rice production time series data, can provide insights into production patterns and regional segmentation based on rice crop characteristics. This study employed the DTW algorithm and K-Medoids Clustering to segment provinces in Indonesia based on rice production data from 2013 to 2021. The results of the study indicated three clusters of regions with distinct rice production characteristics. Some regions exhibited different production patterns from other cluster members, attributed to variations in DTW distances used to measure pattern similarity. Cluster 1 had the highest production, followed by Cluster 2 and Cluster 3, with 16, 7, and 11 provinces respectively. These findings can serve as a basis for government policy development tailored to the characteristics of each segment.
Pengembangan Desa Cinta Statistik Sebagai Upaya Percepatan Penguatan Statistik Sektoral di Desa Selatbaru SITI SUHAILA; Anne Mudya Yolanda; Rustam Efendi; Sukamto; Musraini M; Syamsudhuha; Gustriza Erda; Yenita Roza; Gumanti; M. SYIFA RAMADHAN; Althoff Hibban; Dimas Abyan Fatkhin Al-Aswad; Fandi Gusriyanda; Sarasmita Apriyenti; Adia Syaputri; Nusantri Purba; Nurhaliza; Okta Bella Syuhada
Journal of Community Engagement Research for Sustainability Vol. 4 No. 1 (2024): Januari
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat Universitas Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31258/cers.4.1.32-44

Abstract

The Desa Cinta Statistik (Cantik) program aims to improve the quality of statistical data produced by increasing literacy, awareness and the active role of village officials and the village statistical community. This program is also a form of the Statistics Study Program's participation in the implementation of community service and is a collaboration between the Statistics Study Program (lecturers and students of Real Work Lectures) with BPS and Villages. The activities carried out in the form of assisting village officials in improving sectoral data and village-specific data are expected to suffice the availability of data for village development as one of the efforts to make One Data Indonesia successful. This program will be implemented in Selat Baru Village, Bengkalis Regency. Based on the data that has been collected, there are several work programs carried out by the Selatbaru Integrated Community Service Program (Cinta Statistik) Selatbaru Team in 2022. Village officials as non-productive partners have improved their sectoral data management capabilities. The outputs of the activity are the publication of Selatbaru Village statistics, Selatbaru Village Profiles and Statistics, Infographics, Monographs, and Videographics.
Co-Authors Achmad Isya Alfassa Adia Syaputri Agung Satrio Wicaksono Agung Satrio Wicaksono Agus Kurniawati Althoff Hibban Andriani, Yulia Arisman Adnan Ayu Agustiani Azra Aulia Dwiputri Bustami Bustami Bustami Dewi, Ratna Mustika Dimas Abyan Fatkhin Al-Aswad Ecelly Indriani Putri Eva Eriani Fandi Gusriyanda Febrianti, Lusi Finda, Ingla Goldameir, Noor Ell Gumanti Gunawan, Chandra Gustriza Erda Haposan Sirait Haposan Sirait Imtikhanah Anis Mahmudiati Indra, Zul Irfansyah Irfansyah Isti Yuliani Kristiana Yunitaningtyas Lafina, Melanie Maria M. Ridhwan M. SYIFA RAMADHAN Mulya, Ridho Tri Musraini M Noor Ell Goldameir Nopiani, Yanti Nurhaliza Nurhannifah Rizky Tampubolon Nurul Imani Nusantri Purba Okta Bella Syuhada Okta Bella Syuhada Putri Sion Cahayana Rahmad Ramadhan Laska Raudatul Yusra Ridho Tri Mulya Riko Febrian Rustam Efendi Rustam Efendi Safitri, Nuraini Sarasmita Apriyenti Savira, Husna Septya, Fanny Siti Farika Sari SITI SUHAILA Sukamto Syaftiani Dwi Astuti Syamsudhuha Syamsudhuha Syamsudhuha T, Masrina Munawarah T, Nurhannifah Rizky Tata, Tata Thahira, Nisha Thearas, Lia Tira Mei Darnis Titi Solfitri Windy Maya Sari Wulansuci, Trivira Yelmira Zalfiatri Yenita Roza Zahra, Lathifah