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All Journal Owner : Riset dan Jurnal Akuntansi Data Science: Journal of Computing and Applied Informatics Abdimas: Jurnal Pengabdian Masyarakat Universitas Merdeka Malang Djtechno: Jurnal Teknologi Informasi Jurnal Nasional Holistic Science Journal of Software Engineering, Information and Communication Technology Jurnal Pengabdian Masyarakat Gemilang (JPMG) Wahana NUMBERS: Jurnal Pendidikan Matematika dan Ilmu Pengetahuan Alam JS (Jurnal Sekolah) STATISTIKA Jurnal Asimilasi Pendidikan Hanif Journal of Information Systems Journal of Mathematics, Computation and Statistics (JMATHCOS) Konstanta : Jurnal Matematika dan Ilmu Pengelatuan Alam Interdisciplinary Explorations in Research Journal (IERJ)
Sisti Nadia Amalia
Universitas Negeri Medan
Religion Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Immunology & microbiology Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Physics Public Health Social Sciences Other

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Evaluasi Perencanaan Produksi Kubis Di Sumatera Utara Dengan Metode Rantai Markov Waktu Diskrit Witri Wardani Hulu; Talitha Nakhwan Hasibuan; Widya Narti Lubis; Sudianto Manullang; Sisti Nadia Amalia
Konstanta : Jurnal Matematika dan Ilmu Pengetahuan Alam Vol. 2 No. 3 (2024): September : Jurnal Matematika dan Ilmu Pengetahuan Alam
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59581/konstanta.v2i3.3736

Abstract

This research aims to evaluate cabbage production planning in North Sumatra using the discrete-time Markov chain method. Cabbage is one of the horticultural agricultural products that plays an important role in North Sumatra's exports. Proper evaluation of production plans is necessary to ensure sustainability and increase productivity and export volume. The Discrete Time Markov Chain method is used to predict changes in cabbage production conditions over time by considering the factors that influence them. Data on cabbage production and harvested area in North Sumatra from 2020 to 2022 were analyzed using one-step and n-step transition opportunity matrices. The results of the analysis show that in 2023, cabbage production and harvested land area are predicted to experience a significant increase compared to the previous year. This research provides a more accurate and efficient planning strategy for cabbage production, which can ultimately improve agricultural management in North Sumatra.
Analisis Uji Median K-Sampel Independen untuk Mengidentifikasi Jumlah Penduduk di Sumatera Utara Septi Melani Putri Tambunan; Shalsha Nazillah; Talitha Nakhwan Hasibuan; Sisti Nadia Amalia
Interdisciplinary Explorations in Research Journal Vol. 2 No. 3 (2024)
Publisher : PT. Sharia Journal and Education Center

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

Abstract

This study aims to analyze the distribution of the population in North Sumatra using the Independent K-Sample Median Test on the 2023 data. The statistical test results show that there is no significant difference in the population numbers across the districts/cities in North Sumatra, with a Chi-Square value of 33 and a P-Value of 0.418, which is greater than 0.05. The Post Hoc analysis revealed that all groups have the same value, indicating a relatively even distribution of the population. Additionally, the overall median population in North Sumatra is 312,540, with minimal variation around this value. The study concludes that the population distribution in North Sumatra does not show significant differences among the tested groups.
Hubungan Antara Tingkat Penggunaan Media Sosial dengan Tingkat Produktivitas Mahasiswa dalam Menyelesaikan Tugas Akademik Suci Ramadhani; Surya Alenta Nababan; Yasmin Azzahra; Sisti Nadia Amalia
Interdisciplinary Explorations in Research Journal Vol. 2 No. 3 (2024)
Publisher : PT. Sharia Journal and Education Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62976/ierj.v2i3.790

Abstract

Social media has become an integral part of students' lives, serving as a tool for communication, a source of information, and a means of entertainment. This study aims to analyze the relationship between the level of social media usage and students' productivity in completing academic tasks. The research employs a descriptive correlational quantitative approach, with data collected through online questionnaires using ordinal scales. The respondents were active students from various universities. The analysis was conducted using Spearman's Rank correlation test and the t-test for significance. The results show a moderate positive relationship between social media usage and academic productivity, with a Spearman's Rank correlation coefficient (rₛ) of 0.483. The significance test indicates a t-value of 3.819, which is greater than the critical t-value of 1.677 at the 5% significance level. This demonstrates that the relationship found is statistically significant. The study concludes that prudent use of social media can contribute to enhancing students' productivity in completing academic tasks, although the relationship is not particularly strong. The implications of this study highlight the importance of time management and social media activity regulation to optimally support academic productivity.
Development of Batik Motifs using Symatrig Application to enhance productivity and competitiveness of SMEs Dinda Kartika; Debi Yandra Niska; Fevi Rahmawati Suwanto; Sisti Nadia Amalia; Nurhasanah Siregar; Anita Talia; Fikri Syahputra
Abdimas: Jurnal Pengabdian Masyarakat Universitas Merdeka Malang Vol. 10 No. 1 (2025): February 2025
Publisher : University of Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/abdimas.v10i1.14812

Abstract

The Village Development Index (IDM) of the North Padang Lawas Regency in 2023 remained underdeveloped. The status has been upheld since 2018, although many craft and tourism industries in North Padang Lawas Regency have the potential to contribute to the regional economy, one of which is batik small medium enterprises (SMEs). Batik SMEs in North Padang Lawas Regency are predicted to be the featured product of the regency. One of the batik SMEs, i.e., Batik Sekar Najogi, becomes the icon of North Padang Lawas Regency. However, limited knowledge and resources in developing varied batik motifs have made the batik SMEs in this regency unpopular, both provincial and national. The Abdimas Team and partners, i.e., the Department of Industry and Trade of North Padang Lawas Regency, hold training and coaching for the Symatrig computer application to develop batik motifs based on symmetrical patterns in mathematics. The application also has different motif development patterns to produce various batik motifs. Of 30 batik makers joining the training, 13 agree to utilize the Symatrig application in their businesses, while the others strongly agree. Batik makers also offer recommendations to continue conducting such activity to support those involved in the industry.
Spatial Clustering Analysis of Stunting in North Sumatra Based on Environmental Factors Using K-Means Algorithm Fanny Ramadhani; Dian Septiana; Sisti Nadia Amalia; Putri Maulidina Fadilah; Andy Satria
Data Science: Journal of Computing and Applied Informatics Vol. 9 No. 2 (2025): Data Science: Journal of Computing and Applied Informatics (JoCAI)
Publisher : Talenta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32734/jocai.v9.i2-17179

Abstract

This research aims to analyze the spatial grouping of stunting events in North Sumatra based on environmental factors using the K-Means algorithm. The data used in this research includes the incidence of stunting, environmental factors (such as access to health services, living environment conditions, water use and sanitation), and spatial data (geographical coordinates). The data comes from Basic Health Research (RISKESDAS 2018, then processed and normalized. The elbow method and silhouette analysis are used to determine the optimal number of clusters, resulting in four different clusters. The application of the K-Means algorithm produces the following cluster characteristics: Cluster 1, with good environmental conditions and access to health services, shows low levels of stunting; Cluster 2, with moderate environmental conditions, shows moderate levels of stunting; Cluster 3, which is characterized by poor living conditions and limited access to health services, has levels high stunting; and Cluster 4, with varied environmental conditions but very limited access to health and sanitation services, also shows a high stunting rate. Validation using the Silhouette Coefficient produces an average score of 0.65 which indicates good clustering quality shows that environmental factors, access to health services, and sanitation conditions have a significant impact on the incidence of stunting. Based on these findings, policy and intervention recommendations are focused on Clusters 3 and 4, which have high stunting rates. The interventions carried out include increasing access and quality of nutrition, health services, sanitation conditions, economic empowerment, and health education.
Multivariate Analysis of Regional Economic Resilience Capacity Using PCA, Gaussian Mixture Model, and Random Forest Dian Septiana; Fanny Ramadhani; Sisti Nadia Amalia; Fahmi Ashari S. Sihaloho
Journal of Mathematics, Computations and Statistics Vol. 9 No. 2 (2026): Volume 09 Issue 02 (June 2026)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/Jmathcos12213

Abstract

Economic resilience capacity has become an important issue in regional development because socio-economic disparities influence the ability of regions to adapt to structural pressures and external disturbances. However, measuring regional resilience capacity remains challenging due to the multidimensional and interrelated nature of socio-economic indicators. This study analyses regional economic resilience capacity in North Sumatra using an integrated multivariate statistical and machine learning framework combining Principal Component Analysis (PCA), Gaussian Mixture Model (GMM), and Random Forest. PCA was employed to construct a composite Economic Resilience Capacity Index (ERCI) from socio-economic indicators, while GMM clustering was applied to identify regional typologies within the reduced dimensional space. The initial clustering estimation identified North Nias as an extreme singleton cluster, indicating the presence of an outlier observation. After excluding the outlier, the final GMM model selected a four-cluster spherical covariance structure based on the Bayesian Information Criterion (BIC). A comparison with K-means clustering produced different optimal grouping structures, indicating sensitivity to clustering assumptions and the complexity of regional socio-economic patterns. The first two principal components explained approximately 72% of the total variance, indicating adequate representation of the dominant socio-economic structure. The geographical distribution of clusters reveals substantial regional heterogeneity, where regions in the Nias area are concentrated within the low resilience capacity cluster, while urban and economically integrated regions form distinct growth-oriented clusters. Random Forest analysis indicates that unemployment and poverty related indicators are the most influential variables in distinguishing regional resilience typologies. Furthermore, the comparison between ERCI and GMM results shows that regions with relatively similar index values may still belong to different clusters, indicating that regional resilience patterns do not necessarily follow a single linear socio-economic structure. These findings suggest that regional economic resilience capacity in North Sumatra is shaped by multidimensional structural disparities rather than by a single composite index alone.
Pendampingan AWS IoT untuk Literasi Digital Petani Kopi Perteguhan Nurul Maulida Surbakti; Muhammad Ashari; Fanny Ramadhani; Dian Septiana; Sisti Nadia Amalia; Erita Astrid; Arnah Ritonga; Dinda Kartika; Nadrah Afiati; Ade Andriani
Wahana Jurnal Pengabdian kepada Masyarakat Vol. 5 No. 1 (2026): Edisi Juni
Publisher : Ilmu Bersama Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56211/wahana.v5i1.1910

Abstract

Petani kopi di Desa Perteguhan, Kabupaten Karo, masih menghadapi produktivitas yang rendah, keterbatasan pencatatan mikroklimat, dan pengambilan keputusan agronomis yang dominan berbasis pengalaman. Artikel pengabdian ini bertujuan mengevaluasi dampak pendampingan pemanfaatan Automatic Weather Station (AWS) berbasis Internet of Things (IoT) yang terintegrasi dengan aplikasi mobile Temani Kopi. Kegiatan dilaksanakan pada akhir Mei 2025 melalui survei pendahuluan, perakitan sistem, instalasi lapangan, pelatihan petani, pendampingan tiga bulan, dan evaluasi dampak terhadap 10 petani mitra. Data dikumpulkan melalui observasi, wawancara terstruktur, serta kuesioner pre-test dan post-test sebanyak 15 butir menggunakan skala Likert. Analisis dilakukan dengan persentase capaian, persentase perubahan, dan normalized gain. Hasil menunjukkan literasi teknologi meningkat dari 45% menjadi 85% (N-Gain=0,73), kemampuan mengoperasikan aplikasi meningkat dari 50% menjadi 90% (N-Gain=0,80), dan pengambilan keputusan agronomis berbasis data meningkat dari 48% menjadi 88% (N-Gain = 0,77). Pendampingan juga meningkatkan efisiensi penggunaan air dan pupuk sekitar 20% serta memperkuat kesiapan adopsi pertanian digital. Temuan ini menunjukkan bahwa pendampingan IoT partisipatif dapat mengubah praktik budidaya kopi dari berbasis intuisi menuju keputusan berbasis data.
A Structural Equation Modeling Approach to Exploring the Role of Emotional Factors in Student Anxiety amid Public Demonstrations Sisti Nadia Amalia; Hafizha Zahra; Zul Amry
JURNAL ASIMILASI PENDIDIKAN Vol. 4 No. 2 (2026): Jurnal Asimilasi Pendidikan
Publisher : LEMBAGA PENELITIAN DAN PENDIDIKAN (LPP) ARROSYIDIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61924/jasmin.v4i2.100

Abstract

This study investigates the role of emotional factors in student anxiety amid public demonstrations using a Structural Equation Modeling (SEM) approach. The study focuses on emotional intensity and emotional regulation as key psychological constructs within a socio-political context. The sample consisted of approximately 202 students from the Mathematics Department of the Faculty of Mathematics and Natural Sciences at Universitas Negeri Medan, selected through stratified random sampling. Data were collected using standardized instruments, including the Affect Intensity Measure (AIM), the Emotion Regulation Questionnaire (ERQ), and an adapted student anxiety scale measured on a five-point Likert scale. The SEM analysis indicates that the proposed model achieved an acceptable level of fit based on established goodness-of-fit criteria. However, the findings reveal that environmental stressors related to public demonstrations, emotional intensity, and emotional regulation do not have a significant direct effect on student anxiety. These results suggest the presence of adaptive psychological mechanisms, such as resilience and desensitization, among students in response to recurring socio-political disturbances. This study highlights the importance of considering contextual and psychological complexity when modeling student anxiety using SEM.
KLASIFIKASI RISIKO GIZI BURUK PADA IBU HAMIL MENGGUNAKAN METODE RANDOM FOREST Fanny Ramadhani; Dian Septiana; Sisti Nadia Amalia; Putri Maulidina Fadilah; Andy Satria
Djtechno: Jurnal Teknologi Informasi Vol 5, No 2 (2024): Agustus
Publisher : Universitas Dharmawangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46576/djtechno.v5i2.4815

Abstract

Penelitian ini bertujuan untuk mengidentifikasi ibu hamil yang berisiko mengalami gizi buruk menggunakan metode klasifikasi machine learning, khususnya Random Forest, dengan memanfaatkan data dari RISKESDAS 2018. Dataset yang digunakan mencakup informasi demografi dan pola makan, termasuk usia, pendidikan, pekerjaan, status ekonomi, pola makan, dan akses ke layanan kesehatan. Data tersebut diolah melalui proses preprocessing yang meliputi penanganan nilai yang hilang, transformasi variabel kategori menggunakan OneHotEncoder, dan normalisasi fitur numerik. Model Random Forest kemudian dilatih dan dievaluasi menggunakan metrik akurasi, precision, recall, dan F1-score, serta confusion matrix untuk memahami kinerja klasifikasi. Hasil penelitian menunjukkan bahwa model Random Forest memiliki akurasi sebesar 0.67, precision sebesar 0.6, recall sebesar 0.67, dan F1-score sebesar 0.63 dalam mengklasifikasikan risiko gizi buruk pada ibu hamil. Confusion matrix memperlihatkan distribusi prediksi yang benar dan salah, sedangkan feature importance analysis mengidentifikasi fitur pola makan dan status ekonomi sebagai yang paling berpengaruh dalam prediksi risiko gizi buruk. Model Random Forest ini dapat digunakan sebagai alat yang efektif untuk mengidentifikasi ibu hamil yang berisiko tinggi mengalami gizi buruk, memungkinkan intervensi dini dan terarah dalam program kesehatan ibu hamil, sehingga dapat membantu meningkatkan kesehatan ibu dan anak. Penelitian ini juga menyediakan dasar untuk studi lanjutan yang dapat menggunakan dataset yang lebih luas dan beragam untuk memperbaiki akurasi dan generalisasi model.
Comparative Evaluation of Remote Sensing, Socioeconomic, and Integrated Data for Predicting Regional Economic Growth in North Sumatra Using Machine Learning Dian Septiana; Sisti Nadia Amalia; Fahmi Ashari S Sihaloho
Hanif Journal of Information Systems Vol. 4 No. 1 (2026): August Edition
Publisher : Ilmu Bersama Center

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

Abstract

This study evaluates the predictive performance of remote sensing variables, socioeconomic indicators, and their integration for estimating regional economic growth across 33 districts and municipalities in North Sumatra, Indonesia. A quantitative cross-sectional design was employed using three predictor scenarios: remote sensing variables (Nighttime Light, NDVI, Built-up Area, and Land Surface Temperature), socioeconomic indicators (Human Development Index, Open Unemployment Rate, Fiscal Capacity Index, and Disaster Risk Index), and an integrated dataset. Four regression algorithms (Linear Regression, Support Vector Regression, Random Forest, and K-Nearest Neighbors) were optimized using RandomizedSearchCV and validated through Leave-One-Out Cross Validation. Model performance was evaluated using RMSE, MAE, and R², while permutation importance assessed predictor contributions. Support Vector Regression achieved the best predictive performance across all predictor scenarios. The socioeconomic dataset yielded the highest prediction accuracy (RMSE = 0.625, MAE = 0.480, R² = 0.198), outperforming the remote sensing-only dataset (RMSE = 0.685, MAE = 0.471, R² = 0.038) and the integrated dataset (RMSE = 0.647, MAE = 0.492, R² = 0.140). Although the R² values were relatively low, they reflect the complexity of regional economic growth and the influence of factors beyond those included in this study. Permutation importance identified the Human Development Index and Disaster Risk Index as the most influential predictors. These findings indicate that socioeconomic indicators are stronger predictors of regional economic growth, while remote sensing variables provide complementary spatial information. Although integrating remote sensing variables did not improve predictive accuracy, it offers valuable environmental context for more comprehensive data-driven regional development planning.
Co-Authors Ade Andriani Afiati Nasution, Nadrah Andy Satria Andy Satria Anita Talia ARNAH RITONGA Debi Yandra Niska Dian Septiana Dinda Kartika Erita Astrid Fahmi Ashari S. Sihaloho Fanny Ramadhani Fevi Rahmawati Suwanto Fikri Syahputra Hafizha Zahra Hutapea, Risca Octaviyani Ichwanul Muslim Karo Karo Juliana Tampubolon Manullang, Sudianto Marwan Faiz Hilmi Muhammad Ashari Nadrah Afiati Nurhasanah Siregar Nurul Maulida Surbakti Panggabean, Natanael Putri Maulidina Fadilah Ramadhani, Fanny Septi Melani Putri Tambunan Septia Cahaya Sari Sipayung Shalsha Nazillah Sitepu, Eliya Suci Ramadhani Sudianto Manulang Sukma Dermawan Saragih Surya Alenta Nababan Susi Setiawati Talitha Nakhwan Hasibuan Tarigan, Dandi Rifa'i Thanaya Lovry Lastiar Trinita Melyana Hutagalung Triyunita, Gizka Wahyuni Susi Sulastri Berasa Widya Narti Lubis Witri Wardani Hulu Yasifati Hia Yasmin Azzahra Yohana Sitorus Zul Amry