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Buaton, Relita
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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.
Grouping of Toddler Nutritional Status Based on Anthropometric Data in Pekan Kuala Village Using the K-Means Clustering Method Dita Mawarni; Relita Buaton; Kristina Annatasia
International Journal of Information Engineering and Science Vol. 2 No. 3 (2025): August : International Journal of Information Engineering and Science
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijies.v2i3.300

Abstract

Nutritional issues among toddlers continue to be a pressing public health challenge in Indonesia, including in Kelurahan Pekan Kuala, where although anthropometric data have been systematically collected through the e-PPGBM application, they have not been thoroughly explored in terms of clustering patterns that may provide deeper insights. This study seeks to classify toddler nutritional status by applying the K-Means Clustering method to anthropometric indicators such as age, weight, height, and weight-to-height index. A dataset consisting of 648 entries recorded between January and March 2025 was processed using MATLAB R2014b with cluster variations set at 5, 7, and 9. The analysis revealed that the majority of toddlers were categorized as having good nutritional status, while a portion of the sample was identified as undernourished and some at risk of overnutrition, indicating the diverse nutritional challenges faced by this community. Furthermore, testing the variance across cluster configurations demonstrated that the 9-cluster model yielded the lowest variance score of 0.20, thereby representing the most optimal solution since it produced more homogeneous, balanced, and stable clusters compared to other configurations. These outcomes highlight the importance of data-driven approaches in public health planning, as the clustering results not only provide a clearer picture of nutritional distribution among toddlers but also serve as a foundation for more evidence-based and targeted intervention strategies. By offering a more granular understanding of nutritional variations, this research is expected to support local health authorities in developing customized nutrition programs, allocating resources more effectively, and ultimately improving child health outcomes in Kelurahan Pekan Kuala and similar communities across Indonesia, where malnutrition and overnutrition risks continue to coexist.
Penerapan Metode Case Based Reasoning dalam Diagnosis Penyakit Sinusitis pada Anak Ningsih, Novia; Relita Buaton; Kristina Ananatasia
Jurnal Nasional Teknologi Komputer Vol 5 No 4 (2025): Oktober 2025
Publisher : CV. Hawari

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61306/jnastek.v5i4.289

Abstract

Sinusitis pada anak merupakan gangguan kesehatan dengan angka kejadian yang meningkat dan berpotensi menimbulkan komplikasi serius jika tidak terdeteksi dini. Keterbatasan metode diagnosis konvensional dalam memberikan hasil cepat dan tepat mendorong perlunya solusi berbasis teknologi. Penelitian ini mengembangkan sistem diagnosis sinusitis berbasis web menggunakan metode Case-Based Reasoning (CBR) yang memanfaatkan riwayat kasus terdahulu untuk menganalisis kasus baru melalui perhitungan kemiripan gejala. Data gejala pasien anak diperoleh dari dokter spesialis THT, kemudian diolah menggunakan pembobotan gejala dominan, sedang, dan ringan untuk menghitung nilai similarity. Hasil pengujian menunjukkan sistem menghasilkan diagnosis dengan tingkat kemiripan tertinggi 90,91% pada kasus sinusitis kronis, sesuai dengan evaluasi pakar. Temuan ini membuktikan sistem mampu memberikan diagnosis cepat, akurat, dan dapat digunakan sebagai sarana deteksi dini. Pengembangan selanjutnya disarankan untuk memperluas basis data kasus dan menambahkan fitur pembelajaran adaptif guna meningkatkan akurasi sistem.
Penerapan Internet of Things pada Mekanik Prototipe Robot Pengaduk Gabah Ade Chairany; Relita Buaton; Ratih Puspadini
Repeater : Publikasi Teknik Informatika dan Jaringan Vol. 3 No. 4 (2025): Oktober : Repeater : Publikasi Teknik Informatika dan Jaringan
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/repeater.v3i3.617

Abstract

Manual post-harvest paddy stirring requires significant time and labor and often results in uneven mixing, which can affect grain quality. To address this issue, this study designed and implemented a prototype of an Internet of Things (IoT)-based paddy stirring robot to simplify the process and improve efficiency. The system utilizes an ESP32 microcontroller as the main controller, DC motors as the stirring mechanism, and an IoT module for wireless connectivity to a mobile application. The research stages included hardware design, control system programming, IoT platform integration, and performance testing. Testing was conducted to evaluate response time, mixing uniformity, and power consumption. The results showed that the system could be operated remotely via a local Wi-Fi network with an average delay of less than 1 second, enabling real-time control. The prototype successfully stirred 0.3 kg of paddy with a mixing uniformity rate of 92% and an average power consumption of 12 watts. The application of IoT in the paddy stirring mechanism significantly improved time efficiency, reduced manual labor requirements, and maintained grain quality compared to traditional methods. These findings indicate the potential for further development into a large-scale automated paddy processing system with integrated humidity and temperature sensors for real-time quality monitoring, supporting the modernization of post-harvest processing through digital technology.
Clustering of Deleted Binjai City Government Asset Data Using the K-Means Algorithm Lestari, Chintiya Wahyuni Indah; Buaton, Relita; Syahputra, Suria Alam
Journal of Engineering, Technology and Computing (JETCom) Vol. 4 No. 2 (2025): Journal of Engineering, Tecnology and Computing (JETCom)
Publisher : Yayasan Bina Internusa Mabarindo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63893/jetcom.v4i2.306

Abstract

Regional assets are a crucial component in managing local government resources. However, their management often encounters various obastacles, such as the accumulation of unproductive assets and the difficulty of mapping assets that must be written off. The Binjai City Government, through the Regional Finance, Revenue, and Asset Management Agency (BPKPAD), is obliigated to manage its assets, including those that have reached the end of their useful life. However, without in-depth analysis, the management of written-off asset data can become disorganized, potentially hampering the transparency and efficiency of overall asset management. To address these issuses, this study applied the K-Means algorithm with 3, 4, and 5 clusters as a method for grouping deleted asset data. The data characteristics used included the type of item, year of acquisition, and method of acquisition. The test results showed that grouping with 3 clusters resulted in a cluster variance value of 208,6587, indicating a high level of data diversity. With 4 clusters, the cluster variance value decreased to 110,5156, resulting in a better and more compact grouping. Meanwhile, testing with 5 clusters provided the most optimal results, with a cluster variance value of 79,2477. This shows that the use of 5 clusters can minimize the spread of data within each cluster, resulting in higher similarity between data compared to 3 and 4 clusters. Therefore, the application of the K-Means Algorithm to deleted Binjai City Government asset data can assist the data analysis and grouping process, where the best results were obtained in testing with 5 clusters. Keywords: Asset Data, K-Means Clustering, MATLAB, Binjai City Government
Implementasi Metode Promethee Untuk Rekomendasi Destinasi Objek Wisata Unggulan Provinsi Sumatera Utara Purba, Ramen Antonov; David Jumpa Malem Sembiring; Sinek Mehuli Br Perangin-Angin; Relita Buaton
JST (Jurnal Sains dan Teknologi) Vol. 13 No. 1 (2024): April
Publisher : Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/jstundiksha.v13i1.82395

Abstract

Tidak semua objek wisata telah dipersiapkan dengan apik dan memiliki daya tarik yang cukup. Ini menyebabkan kesulitan bagi wisatawan dalam memilih destinasi yang tepat, yang sering kali berujung pada kekecewaan dan kerugian. Agar masyarakat dapat memilih objek wisata yang tepat, diperlukan media yang dapat memberikan rekomendasi. Tujuan penelitian ini yaitu, menganalisis efektivitas implementasi metode Promethee dalam memberikan rekomendasi destinasi objek wisata unggulan berdasarkan kriteria yang telah ditetapkan. Subjek yang terlibat dalam penelitian ini berjumlah sebanyak 32 orang. Penelitian ini termasuk penelitian deskriptif kuantitatif. Adapun metode pengumpulan data yang digunakan adalah kuesioner, sedangkan instrumen yang digunakan adalah lembar kuesioner. Setelah data dikumpulkan kemudian dianalisis menggunakan metode analisis data kuantitatif dengan metode promethee yang bertujuan untuk melakukan perangkingan destinasi wisata berdasarkan kriteria yang ditetapkan. Hasil analisis menunjukkan bahwa metode Promethee berhasil menghasilkan perangkingan destinasi wisata yang akurat dan stabil berdasarkan kriteria yang telah ditetapkan. Sehingga, dapat disimpulkan bahwa metode Promethee merupakan alat yang efektif dan andal untuk memberikan rekomendasi destinasi objek wisata unggulan. Penelitian ini memberikan kontribusi yang berharga dalam upaya meningkatkan pemasukan daerah melalui sektor pariwisata.
Co-Authors ., Novriyenni Achmad Fauzi ACHMAD FAUZI Ade Chairany Adek Maulidya Adian Fahreza Surbakti Adinda Maudia Savira Ajisro Siringoringo Akbar, Yuma 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 Betty Yel, Mesra 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 Elisa Br. Sembiring Elviwani Elviwani Ema Sari Suwandi 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 I Gusti Prahmana Indah Malasari Ivan Candra Dinata Kadim, Lina Arliana Nur Katen Lumbanbatu Khadapi, Muammar Khair, Husnul Khairul, Habib Kristina Ananatasia Kristina Annatasia Leni Tri Ramadhayanti Lestari, Chintiya Wahyuni Indah Lidya Hasna lidya hasna Lubis, Anjelia Alsar Luta, Devi Andriani M. Yogi Riyantama Isjoni Magdalena Simanjuntak Malau, Farid Reza Marto Sihombing mayla zuhrina Melda Pita Uli Sitompul Mesra Yel Mili Alfhi Syari Muhammad Arif Ridho Muhammad Rifa'i Muhammad Zarlis Muhammad Zarlis, Muhammad N Novriyenni Nadila Rahmawati Nike Alpio Rizky Ningsih, Novia Novita Anggraini Novriyenni Nur Fariza Khairani Nurhayati Nurlaila Nurlaila Nurul Syahrani Pardede, Akim Manaor Hara Prahmana , I Gusti Prahmana, I Gusti Pramudhita, Chika Prisa Abela Purba, Ramen Antonov Putri Lishayani Putri Purwani, Dea Nanda Raja Rizki Alanta Nasution Ramadani, Suci Rani Lestari Rani Nuraini Rani Nuraini Ratih Ratih Puspadini Reza Alexandra Rianty Zabitha Siregar Rohana, Sherly Rusmin Saragih, Rusmin Sany Lubis, Fauzan Al An Selfira Selfira Selfira Selfira Selfira Yap 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 Simanjuntak, Magdalena Sinaga, Ayu Puspita Sari Sinek Mehuli Br Perangin-Angin Siswan Syahputra Siti Nur Azizah Siti Nur Azizah, Siti Nur Solikhun Solikhun Sri Astuti Sri Hardiningsih Suha Baby Mayaza 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 Tio Ria Pasaribu Windy Indah Sary Sinaga Windy, Windy Alfira Yani Maulita Yusnan Sepriadi Ginting Yusnan Sepriadi Ginting Yusrina, Eli Yuyun Arnia Zarlis Muhammad Zuliani - Zulkifli Zulkifli