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Buaton, Relita
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ANALISIS CLUSTER STUNTING DENGAN METODE K-MEANS DI KOTA BINJAI Buaton, Relita; Maulidya, Adek; Simanjuntak, Magdalena; Sinaga, Ayu Puspita Sari
Journal of Information System, Informatics and Computing Vol 9 No 1 (2025): JISICOM (June 2025)
Publisher : Sekolah Tinggi Manajemen Informatika dan Komputer Jayakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52362/jisicom.v9i1.1927

Abstract

Puskesmas berperan penting dalam meningkatkan kesehatan masyarakat. Analisis data kesehatan yang tepat dapat membantu dalam mengidentifikasi kelompok populasi yang membutuhkan perhatian khusus. Sumber daya manusia yang unggul dan berkualitas didasari dengan sumber daya manusia yang sehat dengan indikator tercukupinya asupan gizi sesuai dengan perkembangan usianya. Namun masalah kelaparan dan kekurangan gizi masih dihadapi oleh dunia hingga saat ini. Menurut laporan Unicef, jumlah penduduk yang menderita kekurangan gizi di dunia mencapai 767,9 juta orang pada tahun 2021. Organisasi Kesehatan Dunia (WHO) mengatakan, kekurangan gizi menjadi salah satu ancaman berbahaya bagi kesehatan penduduk dunia. Stunting juga berdampak di Indonesia, prevalensi balita yang mengalami stunting di Indonesia sebanyak 21,6% pada tahun 2022. Penelitian ini bertujuan untuk mengklasifikasikan data kesehatan dari Puskesmas di Binjai menggunakan algoritma K-Means untuk memahami karakteristik setiap kluster.Dilakukan studi lapangan dengan mengolah data hasil penimbangan anak, data diolah dengan menggunakan metode cluster sehingga diperoleh cluster stunting untuk wilayah Kota Binjai yakni kluster 1: mencerminkan kondisi kesehatan yang baik, dengan nilai rata-rata yang rendah pada indikator risiko gizi dan gizi buruk, kluster 2: menunjukkan kondisi yang sangat buruk, dengan nilai yang tinggi pada hampir semua indikator, mencerminkan masalah kesehatan yang serius di populasi tersebut dan kluster 3: menunjukkan kondisi moderat, dengan nilai yang berada di antara kluster 1 dan kluster 2. Hasil analisis menunjukkan bahwa terdapat tiga kluster yang berbeda, masing-masing dengan karakteristik kesehatan yang unik. Pengujian kluster dilakukan dengan menggunakan metode cluster analysis untuk memastikan validitas hasil. Temuan ini diharapkan dapat memberikan rekomendasi bagi pihak dians kesehatan dalam merancang program intervensi kesehatan yang lebih tepat sasaran dengan hasil pengujian Silhouette Score: 0.65, menunjukkan bahwa kluster yang terbentuk cukup baik. Davies-Bouldin Index: 0.3, menunjukkan pemisahan kluster yang baik. Inertia: 1500 menandakan bahwa data terdistribusi dengan baik di sekitar centroid.
Perancangan Sistem Penentuan Peluang Usaha pada Usaha Mikro di Kota Binjai Menggunakan Metode Topsis: Studi Kasus; Dinas Koperasi & Umkm Kota Binjai Heka Herawati Br Tarigan; Relita Buaton; Lina Arliana Nur Kadim
Saturnus : Jurnal Teknologi dan Sistem Informasi Vol. 2 No. 4 (2024): Oktober : Saturnus : Jurnal Teknologi dan Sistem Informasi
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/saturnus.v2i4.336

Abstract

As a developing city, Binjai has a variety of business potential that can be exploited by micro entrepreneurs. However, in identifying and exploiting these opportunities, they are often faced with various obstacles, such as lack of access to market information, intense competition, and changes in consumer needs. Therefore, determining effective business opportunities is the key to the growth and sustainability of micro businesses in Binjai City. Determining business opportunities for micro businesses in Binjai City includes an understanding of the complexities and challenges faced by the MSME sector in identifying and exploiting business opportunities. Determining business opportunities requires alternative types of business in the TOPSIS method to compare various business opportunities based on important factors so that you can choose the one with the most potential and profit. In this context, the use of the TOPSIS method is important to assist in making more informed and effective decisions for authorities such as the Department of Cooperatives and MSMEs. This method will provide a systematic framework for evaluating various existing business opportunities, enabling a more objective and accurate assessment to support the development of MSMEs in Binjai City.
Prediksi Tingkat Stunting Anak di Kabupaten Langkat Menggunakan Metode Regresi Linear Berganda : (Studi Kasus : Dinas PPKB-PPA Kab.Langkat) Dhea Alfiya Ningsih; Relita Buaton; Anton Sihombing
Saturnus : Jurnal Teknologi dan Sistem Informasi Vol. 2 No. 4 (2024): Oktober : Saturnus : Jurnal Teknologi dan Sistem Informasi
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/saturnus.v2i4.344

Abstract

Stunting is a growth and development disorder in children caused by chronic malnutrition over a long period of time, especially in the first 1,000 days of life, namely from pregnancy to the first 2 years of life. There are more than 149 million (22%) toddlers worldwide who are stunted, of which 6.3 million are Indonesian toddlers. Based on data from the Ministry of Health, the stunting rate in Indonesia in 2023 was recorded at 21.5 percent, only down 0.1 percent from the previous year which amounted to 21.6 percent. Predicting the number of stunted toddlers is very important and necessary to know the stunting rate in Langkat Regency in 2024, and the prediction results can help health workers in handling and preventing the spread of stunting. The method applied to this prediction system is Multiple Linear Regression where this analysis determines whether each independent variable is positively or negatively related, the direction of the relationship between variables, and estimates the value of the dependent variable will increase or decrease. The prediction system is carried out using the RapidMiner application because this application is very appropriate to produce information output in the form of prediction results for the coming year. The prediction results obtained are an increase and decrease in 2024 in each sub-district and there are sub-districts that do not experience an increase and decrease. The sub-district with the highest number was Secanggang with approximately 177 people, and the sub-district with the lowest number of stunted children was West Berandan with approximately 55 people. Then Stabat sub-district became the sub-district that experienced the most increase in the number of stunting, which was around 15 people, and the sub-district that experienced the most decrease was Kuala sub-district with a total of approximately 23 people. From the overall results it can be calculated that the number of stunting in all districts in Langkat Regency amounted to approximately 2453 people in 2024. And testing the error rate of prediction results using RMSE in the RapidMiner application of 7.63%, where the level of accuracy in the prediction of child stunting in Langkat Regency is 92.46%.
Klasifikasi Tingkat Pemahaman Siswa pada Pelajaran Matematika di MTSS PAB 5 Klambir Lima Auni Patrisyah; Relita Buaton; Juliana Naftali Sitompul
Saturnus : Jurnal Teknologi dan Sistem Informasi Vol. 2 No. 4 (2024): Oktober : Saturnus : Jurnal Teknologi dan Sistem Informasi
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/saturnus.v2i4.345

Abstract

According to academic data, student math ability tests at MTSS PAB 5 Klambir Lima yield mixed results. There are students who understand math well, but there are also those who have difficulty understanding the mathematical concepts themselves. Math teachers at this school have difficulty designing lessons that can meet the needs of students with different levels of understanding. So, it is necessary to group student data to produce educational decision-making and improve learning effectiveness, such as through data mining. Data mining is a semi-automated process that uses machine learning techniques, mathematics, statistics, and artificial intelligence to identify and organize information contained in large databases. The process of finding information can be done by determining the decision rule based based on the level of student understanding in mathematics lessons using the Decision Tree Algorithm C4.5 method. The use of the Decision Tree algorithm C4.5 aims to make it easier to determine decision rules based on gender, Predicate, teacher teaching methods, student learning interest, and level of understanding. Based on the results of the study, it was found that if the teacher's teaching method is good, the predicate value is B, the student's learning interest is less interested, and the gender is male, then the student's level of understanding in mathematics lessons is not understood.
Diagnosis Penyakit Dispepsia menggunakan Metode Dempster-Shafer Nurul Syahrani; Relita Buaton; Husnul Khair
Saturnus : Jurnal Teknologi dan Sistem Informasi Vol. 2 No. 4 (2024): Oktober : Saturnus : Jurnal Teknologi dan Sistem Informasi
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/saturnus.v2i4.361

Abstract

Dyspepsia is a gastroduodenal disorder that is often characterized by symptoms such as epigastric pain, burning, bloating, and a feeling of fullness after eating. Treatment of dyspepsia often requires examination by a specialist doctor, which may not always be easily accessible due to distance, cost, or time constraints. Therefore, this study aims to diagnose dyspepsia using the Dempster-Shafer method to identify possible dyspeptic diseases such as GERD, gastritis, dyspepsia, and gastric ulcers based on 16 detected symptoms and 4 different treatments. to make it easier for patients to consult and get an initial diagnosis without having to see a specialist doctor directly. From this study, it is expected to help patients get information on the initial diagnosis of the patient.
Clustering Tindak Kekerasan Pada Anak Menggunakan Algoritma K-Means Dengan Perbandingan Jarak Kedekatan Manhattan City Dan Euclidean Buaton, Relita; Sundari, Yeni; Maulita, Yani
MEANS (Media Informasi Analisa dan Sistem) Volume 1 Nomor 2
Publisher : LPPM UNIKA Santo Thomas Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (833.695 KB) | DOI: 10.54367/means.v1i2.8

Abstract

ABSTRAK Kekerasan terhadap anak dari tahun ketahun semakin meningkat dan menjadi perhatian di kalangan masyarakat. Hasil pemantauan Komisi Perlindungan Anak Indonesia (KPAI) terhadap kekerasan pada anak dari tahun 2011 sampai 2014, terjadi peningkatan yang signifikan. Tahun 2011 terjadi 2178 kasus kekerasan, tahun 2012 ada 3512 kasus, tahun 2013 ada 4311 kasus dan tahun 2014 ada 5066 kasus kekerasan pada anak. Metode yang digunakan untuk mengclusterkan tindak kekerasan pada anak adalah algoritma k-means menggunakan 2 jarak kedekatan yakni Manhattan City dan Euclidean dengan jumlah data 280 kejadian yang diperoleh dari POLRES BINJAI bekerja sama dengan Badan KBPP Kab.Langkat dengan variable usia korban, jenis kekerasan dan faktor penyebab. Hasil cluster menunjukkan dengan jarak kedekatan Manhattan City bahwa korban kekerasan anak cenderung terjadi pada remaja dengan jenis kekerasan psikis dan pelecehan seksual karena faktor ekonomi dan kesempatan, sedangkan dengan jarak kedekatan Euclidean bahwa korban kekerasan anak cenderung terjadi pada anak-anak dengan usia 5 sampai 12 tahun mengalami kekerasan seksual karena faktor ekonomi.
Cluster Pelanggan Listrik PLN UP3 Binjai Dengan Metode K-Means Rohana, Sherly; Buaton, Relita; Annatasia , Kristina
Jurnal Nasional Teknologi Komputer Vol 5 No 3 (2025): Juli 2025
Publisher : CV. Hawari

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61306/jnastek.v5i3.227

Abstract

PLN UP3 Binjai faces challenges in understanding customer characteristics in each rayon so the service strategy is not fully effective. This study aims to group electricity user areas based on customer type and installed power so that PLN can develop a more targeted and efficient service strategy. The method used in this study is clustering with the K-Means algorithm, with customer data for 2024 from 9 rayons analyzed using the MATLAB R2014b application. Grouping is done with 3, 4 and 5 clusters. The results of grouping 3 clusters produce a variance value of 1.0890, in grouping 4 clusters it produces a variance value of 0.8047 and in grouping 5 clusters it produces a variance value of 0.6093. The results of the Grouping that has been carried out show that 5 clusters provide the most optimal results with the smallest variance value of 0.6093 which shows that the distribution of data between clusters is more homogeneous and representative. Each cluster shows a different combination pattern of rayon, installed power and customer type, which can be used as a basis for developing a more effective service strategy. Keywords: Electricity Customers, Electricity Power, K-Means Clustering.
Pengelompokkan Penyakit Tuberkulosis Paru Berdasarkan Penyebabnya Menggunakan Metode Clustering: Studi Kasus : UPT Puskesmas Selesai Cinta Apriliza; Relita Buaton; Hermansyah Sembiring
Neptunus: Jurnal Ilmu Komputer Dan Teknologi Informasi Vol. 3 No. 3 (2025): Agustus : Neptunus : Jurnal Ilmu Komputer Dan Teknologi Informasi
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/neptunus.v3i3.995

Abstract

Pulmonary tuberculosis remains a pressing public health problem, particularly in the work area of the Duduk Health Center (UPT Puskesmas). Effective management of this disease requires a thorough understanding of the characteristics of the causes of pulmonary TB in patients. This study aims to classify pulmonary TB cases based on the main causes such as diabetes mellitus, irritant factors, pleural effusion, and family environmental conditions. The research method used is a clustering technique with the K-Means algorithm. The data used are data on pulmonary TB patients in 2020–2025 with variables of age, gender, and causative factors collected from medical records. The analysis process was carried out using MATLAB R2014b software. The clustering model was carried out in 3, 4, and 5 clusters to compare the level of segmentation efficiency. Based on the calculation results, the model with 5 clusters showed the lowest cluster variance value of 0.4889 compared to the 3-cluster model (0.7333) and 4-cluster models (0.6151), which indicates that the division into 5 clusters produces the most compact and representative data group. Each cluster shows a different combination of characteristics of pulmonary TB patients, for example: (1) elderly male patients with comorbid diabetes; (2) adolescent females with the negative influence of environmental factors; (3) adult males exposed to irritants; (4) patients with pleural effusion; and (5) groups with multiple factors. The results of this study can provide strategic input for the Finished Community Health Center UPT in formulating more targeted and targeted intervention policies in order to prevent, control, and handle pulmonary tuberculosis cases in a sustainable and effective manner.  
Penerapan Metode Apriori untuk Mengidentifikasi Korelasi Nilai Siswa di Sekolah Menengah Pertama Novita Anggraini; Relita Buaton; Imeldawaty Gultom
Jurnal ilmiah Sistem Informasi dan Ilmu Komputer Vol. 5 No. 3 (2025): November: Jurnal ilmiah Sistem Informasi dan Ilmu Komputer
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/juisik.v5i3.1443

Abstract

Currently, education is developing very rapidly, starting from the school level even up to the university level. Quality human resources depend on education. Student learning achievement which is usually indicated by report card grades is one of the benchmarks of educational success. However, student grade data for strategic decision making in schools is often done manually and is not optimal. This study aims to identify correlations between student grades in Junior High Schools and apply the Apriori method in data analysis to determine the relationship between student grades and other variables such as subjects and achievement levels. This study involved data collection consisting of 508 students. The Apriori method successfully identified relevant correlations, such as students in Pancasila Education subjects received a B, ICT received a B, Mathematics received a B, English received a B, Social Studies received a B, Craft received a B, Indonesian received a B, Physical Education received a B, Science received a B, SBK received a B, Religious Education received a B, then the Achievement Level is Low with support 3.90% and confidence 95.20%. The use of RapidMiner software in data analysis provides recommendations for robust relationships or correlations. This research is expected to provide sound recommendations to support the achievement of national education goals by identifying the relationship between student grades and subject matter, as well as improving learning outcomes based on student achievement levels.
Penerapan Metode Teorema Bayes untuk Memprediksi Penyakit pada Tanaman Kopi Zulkifli Zulkifli; Relita Buaton; I Gusti Prahmana
Neptunus: Jurnal Ilmu Komputer Dan Teknologi Informasi Vol. 3 No. 3 (2025): Agustus : Neptunus : Jurnal Ilmu Komputer Dan Teknologi Informasi
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/neptunus.v3i3.1025

Abstract

Coffee is a leading commodity in Indonesia's agricultural sector, possessing high economic value and providing a livelihood for many farmers. However, coffee plant productivity often declines significantly due to various diseases affecting the leaves, stems, and berries. This situation is exacerbated by the lack of knowledge among most farmers in recognizing early disease symptoms, resulting in delayed treatment. Consequently, crop losses are unavoidable. Based on these challenges, this study aims to design and build an expert system capable of diagnosing coffee plant diseases quickly, precisely, and accurately using the Bayesian Theorem method. This method was chosen because it can calculate the probability of a disease occurring based on observed symptoms in plants. The Bayesian approach allows the system to provide more reliable diagnostic results by updating the probability values ​​as new evidence is introduced. The developed expert system is web-based, making it easily accessible to users, both farmers and other interested parties. Users simply select the symptoms observed in coffee plants, and the system will then provide a diagnostic result in the form of possible diseases and their probability levels. Test results indicate that the system is capable of providing fairly accurate diagnostic results and can be used as a basis for farmers in making initial decisions regarding coffee plant disease management. With this expert system, farmers are expected to improve their ability to detect coffee plant diseases early, thereby maintaining crop productivity. This expert system is expected to be an effective decision support tool for farmers to reduce crop losses and improve agricultural sustainability.
Co-Authors Achmad Fauzi Ade Chairany Adek Maulidya Adinda Maudia Savira Ajisro Siringoringo Alma Diana Rangkuti Alma Diana Rangkuti Ambarita, Indah Ami Dilham Ana, Putri Andri Kristiawan Anisa Anisa Anisa Anisa Anisa Putri Pratiwi anjelia alsar anjeliaalsharlubis Anjelia Alsar Lubis Annatasia , Kristina Aprillianda Pasaribu Aula, Nurhasanah Auni Patrisyah Ayu Rahayu Febria Ayu Rahayu Febria Br. Ginting, Rosa Lina Budi Serasi Ginting Budi Serasi Ginting Cinta Apriliza Clara Rosa Wijaya David Jumpa Malem Sembiring Dea, Dea Puspita Deny Jollyta Deri Kurniawan Desva Karliana br Sembiring Dhea Agustina Akmal Dhea Alfiya Ningsih Dhovan Damara Santoso Dicha Mutia Dhani Dita Mawarni Diva Alifya Dwi ASTUTI Elviwani Fadillah Fadillah Fajar Amalia Putri Fany Juliawati Farid Reza Malau Fauzi, Achmad Febi Andini Fuji Dodo Aritonang Gultom, Imeldawaty Haryanto, Septian Hayati, Radhiah Heka Herawati Br Tarigan Herman Mawengkang Hermansyah Sembiring Hermansyah Sembiring Husnul K I Gusti Prahmana Indah Malasari Ivan Candra Dinata Kadim, Lina Arliana Nur Khadapi, Muammar Khair, Husnul Khairul, Habib Kristina Ananatasia Kristina Annatasia Leni Tri Ramadhayanti Lestari, Chintiya Wahyuni Indah Lidya Hasna lidya hasna Lishayani, Putri Lubis, Anjelia Alsar Lumbanbatu, Katen magdalena simanjuntak Magdalena Simanjuntak Magdalena Simanjuntak Malau, Farid Reza Marto Sihombing Mayaza, Suha Baby Melda Pita Uli Sitompul Mili Alfhi Syari Muhammad Arif Ridho Muhammad Zarlis, Muhammad Muhammad, Zarlis N Novriyenni Nadila Rahmawati Nike Alpio Rizky Ningsih, Novia Novita Anggraini Novriyenni Nur Fariza Khairani Nurhayati Nurlaila Nurlaila Nurlaila Nurlaila Nurul Syahrani Pardede, Akim Manaor Hara PASARIBU, TIO RIA Prahmana , I Gusti Prahmana, I Gusti Pramudhita, Chika Prisa Abela Purba, Ramen Antonov Putri Purwani, Dea Nanda Raja Rizki Alanta Nasution Ramadani, Suci Rani Lestari Rani Nuraini Rani Nuraini Ratih Ratih Puspadini Reza Alexandra Rianty Zabitha Siregar rifa'i, Muhammad Rohana, Sherly Rusmin Saragih, Rusmin Sany Lubis, Fauzan Al An Sari Suwandi, Ema Selfira Selfira Selfira Selfira, Selfira Sembiring, Hermansyah Sembiring, Indri Aurellia Apsari septian haryanto Septian Haryanto Sherly Eka Wahyuni Sihombing, Anton Sihombing, Marto Sihombing, Novena Putri Antonia Sima, Brema Arisma Simanjuntak, Magdalena Sinaga, Ayu Puspita Sari Sinek Mehuli Br Perangin-Angin Siswan Syahputra Siti Nur Azizah, Siti Nur Solikhun Solikhun Solikhun Solikhun, Solikhun Sri Astuti Sri Hardiningsih Sundari, Yeni Sundari, Yeni Suria Alamsyah Putra Syahputra, Siswan Syahputra, Suria Alam Syahril Effendi Syari, Milli Alfhi T. Reza Pahlevi Teuku Reza Pahlefi Tiara Jelita Windy Indah Sary Sinaga Windy, Windy Alfira Yani Maulita Yel, Mesra Yusnan Sepriadi Ginting Yusnan Sepriadi Ginting Yusrina, Eli Yuyun Arnia Zuliani - Zulkifli Zulkifli