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All Journal Jurnal Ilmu Pendidikan JMM (Jurnal Masyarakat Mandiri) Data Science: Journal of Computing and Applied Informatics Jurnal Sistem Komputer dan Informatika (JSON) Yayasan Cita Cendikiawan Al Khwarizmi Djtechno: Jurnal Teknologi Informasi Majalah Ilmiah UPI YPTK Journal of Software Engineering, Information and Communication Technology Jurnal Pengabdian Masyarakat Gemilang (JPMG) Deli Sains Informatika Hanif Journal of Information Systems Journal of Mathematics, Computation and Statistics (JMATHCOS)
Dian Septiana
Statistika, Universitas Negeri Medan
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Hybrid DAC-GA and K-Means for Spatial Clustering of Stunting Risk in North Sumatra Andy Satria; Ibnu Rusydi; Dian Septiana; Fanny Ramadhani
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 7 No. 1 (2025): September 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v7i1.9071

Abstract

Stunting continues to pose a severe global health concern, particularly in Indonesia, where prevalence rates persist above international standards despite recent advances in reduction initiatives. Accurately documenting the regional variation of stunting is critical to facilitate targeted interventions and successful policymaking. This paper offers a hybrid clustering framework that merges the classic K-Means approach with the Dynamic Artificial Chromosomes Genetic approach (DAC-GA) to increase the resilience and reliability of spatial analysis. The dataset used combines demographic and population statistics from the Central Bureau of Statistics (BPS), strategic policy documents from the Regional Medium-Term Development Plan (RPJMD) of North Sumatra, and health indicators including stunting prevalence data from the Ministry of Health of the Republic of Indonesia. The research approach consists of four primary phases: data preparation, clustering model construction, cluster evaluation, and geographical visualization. Three evaluation metrics Sum of Squared Errors (SSE), Davies–Bouldin Index (DBI), and Silhouette Coefficient were applied to validate clustering performance. Results demonstrate that DAC-GA dynamically determined the ideal number of clusters at k=2 in just 1.171677 seconds, classifying Kota Medan and Deli Serdang into the low-risk cluster, while all other districts were consistently put into the high-risk cluster. Both DAC-GA and standard K-Means yielded similar spatial maps, giving significant methodological validation and strengthening the dependability of the findings. The study reveals not just the technical advantages of DAC-GA in maximizing clustering but also its practical utility in guiding spatially targeted health interventions. Future research is recommended to add dimensionality reduction utilizing Principal Component Analysis (PCA) to improve computing efficiency and enhance the interpretability of clustering results.
INTEGRASI TEKNOLOGI INFORMASI UNTUK DIGITALISASI BISNIS DAN MANAJEMEN PRODUKSI UMKM SILMARILS Fanny Ramadhani; Dian Septiana; Andy Satria
JMM (Jurnal Masyarakat Mandiri) Vol 9, No 5 (2025): Oktober
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jmm.v9i5.34586

Abstract

Abstrak: UMKM Silmarils di Kota Medan menghadapi permasalahan pencatatan inventori manual, ketiadaan sistem Key Performance Indicator (KPI), pemasaran yang masih bergantung pada marketplace pihak ketiga, serta proses pemotongan roti yang manual. Program pengabdian ini bertujuan mengintegrasikan teknologi digital untuk meningkatkan efisiensi dan daya saing. Metode meliputi sosialisasi, penyuluhan, dan pelatihan dengan 14 peserta inti. Evaluasi dilakukan melalui observasi, wawancara, dan angket berisi 14 pertanyaan yang mencakup aspek relevansi, pelaksanaan, manfaat dan hasil, serta dampak dan kepuasan. Target minimal satu orang peserta memahami alur sistem tercapai dengan pemilik UMKM yang sudah mampu memahami alur dan mengoperasikan sistem, sementara karyawan masih dalam tahap adaptasi. Pada aspek produksi, mesin pemotong roti otomatis meningkatkan efisiensi waktu pemotongan 1 roti tawar sebesar 98,3% dengan hasil seragam. Seluruh responden menilai program relevan, bermanfaat, dan dapat langsung diterapkan, dengan 100% menyatakan keterampilan baru digunakan dalam pekerjaan harian. Program ini berdampak nyata pada peningkatan hardskill, efisiensi operasional, serta potensi nilai ekonomis UMKM.Abstract: UMKM Silmarils in Medan faces challenges of manual inventory recording, the absence of a Key Performance Indicator (KPI) system, marketing that still relies on third-party marketplaces, and manual bread cutting processes. This community service program aims to integrate digital technology to enhance efficiency and competitiveness. Methods included socialization, counseling, and training with 14 core participants. Evaluation was carried out through observation, interviews, and a questionnaire consisting of 14 questions covering aspects of relevance, implementation, benefits and outcomes, as well as impact and satisfaction. The target of having at least one participant understand the system workflow was achieved, as the UMKM owner successfully operated the system, while the employees are still in the process of adaptation. Evaluation was carried out through observation, interviews, and satisfaction surveys. The results showed improved digital skills among participants in operating the web-based inventory system, KPI dashboard, and online store management. In terms of production, the automatic bread slicer increased cutting efficiency by 98.3% with uniform results. All respondents rated the program relevant, useful, and directly applicable, with 100% reporting that the new skills were applied in daily work. The program had a tangible impact on hardskill development, operational efficiency, and the economic potential of UMKM.
PENERAPAN SISTEM KONTROL HAMA PADI DAN MONITORING SAWAH BERBASIS INTERNET OF THINGS (IOT) DI SUMATERA UTARA Surbakti, Nurul Maulida; Dewi, Sri; Ramadhani, Fanny; Septiana, Dian; Pahlawan, Riza
JMM (Jurnal Masyarakat Mandiri) Vol 8, No 4 (2024): Agustus
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jmm.v8i4.25241

Abstract

Abstrak: Artikel ini membahas tentang tantangan yang dihadapi petani padi di Sumatera Utara akibat serangan hama wereng dan belalang yang menurunkan secara signifikan hasil panen padi. Untuk mengatasi permasalahan ini, pengabdian ini dilakukan dengan menerapkan teknologi Internet of Things (IoT) dalam bentuk sistem kontrol hama padi dan monitoring sawah. Teknologi ini menggunakan perangkat berbasis IoT yang dilengkapi dengan sensor lingkungan dan aktuator seperti ultrasonik dan LED UV untuk mengusir dan menangkap hama, serta aplikasi mobile untuk pemantauan real-time. Implementasi teknologi ini diharapkan dapat meningkatkan efisiensi pengelolaan pertanian, mengoptimalkan penggunaan sumber daya, dan mengurangi dampak lingkungan dari penggunaan pestisida kimia. Hasil uji coba awal menunjukkan bahwa sistem ini efektif dalam mengendalikan populasi hama, dengan potensi besar untuk meningkatkan produksi padi secara berkelanjutan di Desa Petumbukan dan daerah-daerah pertanian lainnya di Indonesia. Kelompok tani Kenanga yang terdiri dari 10 orang (4 laki-laki dan 6 perempuan) menjadi mitra dalam kegiatan ini. Mereka berada di Desa Petumbukan, Kecamatan Galang, Kabupaten Deli Serdang. Metode pelaksanaan meliputi penyajian materi, praktik, dan pendampingan selama pelatihan. Evaluasi dilakukan melalui pemantauan lapangan dan analisis data hasil uji coba, dengan indikator keberhasilan berupa peningkatan produksi padi dan efektivitas pengendalian hama. Hasil menunjukkan peningkatan keterampilan mitra sebesar 80% dalam menggunakan teknologi IoT, yang juga berhasil menurunkan populasi hama wereng dan belalang, berpotensi meningkatkan produksi padi secara berkelanjutan.Abstract: This article discusses the challenges faced by rice farmers in North Sumatra due to attacks by brown planthoppers and grasshoppers that significantly reduce rice yields. To overcome this problem, this community service is carried out by implementing Internet of Things (IoT) technology in the form of a rice pest control system and rice field monitoring. This technology uses IoT-based devices equipped with environmental sensors and actuators such as ultrasonic and UV LEDs to repel and capture pests, as well as mobile applications for real-time monitoring. The implementation of this technology is expected to improve the efficiency of agricultural management, optimize resource use, and reduce the environmental impact of chemical pesticide use. Initial trial results show that this system is effective in controlling pest populations, with great potential to increase sustainable rice production in Petumbukan Village and other agricultural areas in Indonesia. The Kenanga farmer group consisting of 10 people (4 men and 6 women) is a partner in this activity. They are located in Petumbukan Village, Galang District, Deli Serdang Regency. The implementation method includes presentation of materials, practice, and assistance during training. The evaluation was conducted through field monitoring and analysis of trial data, with success indicators in the form of increased rice production and effectiveness of pest control. The results showed an 80% increase in partner skills in using IoT technology, which also succeeded in reducing the population of brown planthoppers and grasshoppers, potentially increasing rice production sustainably.
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/qbm5kx46

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.
Co-Authors Adidtya Perdana, Adidtya Amalia, Sisti Nadia Andy Satria Andy Satria Arnita Arnita Diah Retno Wahyuningrum Eri Widyastuti Fadilah, Putri Maulidina Fahmi Ashari S. Sihaloho Fanny Ramadhani Fanny Ramadhani Ibnu Rusydi Ichwanul Muslim Karo Karo Maulida Surbakti, Nurul Maulidina Fadila, Putri Melly Br Bangun Nasution, Nadrah Afiati Nurul Ain Farhana Nurul Maulida Surbakti Pahlawan, Riza Panggabean, Suvriadi Prihatin Ningsih Sagala Putri Maulidina Fadilah Ramadhani, Fanny Said . Iskandar Sisti Nadia Amalia Sisti Nadia Amalia Sisti Nadia Amalia Sri Dewi