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Buaton, Relita
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PERANCANGAN SISTEM PENDETEKSI BERITA HOAX MENGGUNAKAN ALGORITMA LEVENSHTEIN DISTANCE BERBASIS PHP Aprillianda Pasaribu; Marto Sihombing; Relita Buaton
Jurnal Informatika Kaputama (JIK) Vol 4 No 1 (2020): Volume 4, Nomor 1, Januari 2020
Publisher : STMIK KAPUTAMA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59697/jik.v4i1.352

Abstract

In the 4.0 era where the Internet is an important part of life today, information can be easily accessed anytime, anywhere. But not all information distributed through the internet is in the form of facts. Data presented by the Ministry of Communication and Information based on a survey conducted in 2018 said that as many as 800,000 sites in Indonesia indicated that non-fact or hoax news spreaders were indicated. As a result of hoax news generated is very dangerous because it attacks the minds of the human subconscious, so it is needed a system that can detect hoax news. In this study used a database containing hoax news documents. The algorithm applied is the TF-IDF algorithm to measure the weight of a word in a hoax document and combined with the Levenshtein Distance (LD) algorithm to measure the distance between words in a document. The application of the Levenshtein Distance Method in the Hoax Detection System has several stages that begin with the pre-processing of the word (prepocessing text) followed by the TF-IDF calculation phase and then the minimum distance calculation between words using the Levenshtein Distance algorithm. The result of a limit of 0.1 on 40 documents that have been classified as test data has high Precision, Recall and Accuracy values, namely Precision 1; Recall 0.71; and Accuracy 80%.
Korelasi Kecerdasan Emosional Dengan Prestasi Belajar Siswa Menggunakan Metode A Priori (Studi Kasus: SMPIT Alkaffah Binjai) Relita Buaton; Yani Maulita; Ayu Rahayu Febria
Jurnal Informatika Kaputama (JIK) Vol 1 No 1 (2017): Volume 1, Nomor 1, Januari 2017
Publisher : STMIK KAPUTAMA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59697/jik.v1i1.439

Abstract

Sering ditemukan siswa yang tidak dapat meraih prestasi belajar yang setara dengan kemampuan inteligensinya. Ada siswa yang mempunyai kemampuan inteligensi tinggi tetapi memperoleh prestasi belajar yang relatif rendah, namun ada siswa yang walaupun kemampuan inteligensinya relatif rendah tetapi dapat meraih prestasi belajar yang relatif tinggi. Itu sebabnya taraf inteligensi bukan merupakan satu-satunya faktor yang menentukan keberhasilan seseorang, karena ada faktor lain yang mempengaruhi, maka perlu digali dengan metode A Priori, bagaimana cara menentukan korelasi nilai kecerdasan emosional dan prestasi belajar siswa. Metodologi yang digunakan adalah analisis pola frekkuensi tinggi dan pembentukan aturan asosiasi. Hasil yang ditemukan adalah faktor-faktor yang paling sering terjadi dan yang paling banyak muncul secara bersamaan adalah kemampuan siswa untuk mengenal emosi diri mau bertanggung jawab atas kesalahan yang dilakukan dan kemampuan siswa untuk memotivasi diri sendiri mau mendahulukan belajar daripada bermain dan mau memperbaiki kegagalan menjadi suatu keberhasilan dan kemampuan siswa untuk mengenal emosi orang lain mau mendengar keluh kesah teman dan Afektif mengikuti nilai-nilai yang telah ditentukan then Psikomotorik siswa ulet dalam mengikuti latihan dengan nilai Support 90% dan Confidence 100%.
Data Mining Pengelompokan Pasien Rawat Inap Berdasarkan Kelas Bpjs Menggunakan Metode Clustering (Studi Kasus : Rumah Sakit Umum Daerah Dr. Rm. Djoelham Binjai) Anjelia Alsar Lubis; Relita Buaton; Indah Ambarita
Jurnal Sistem Informasi Kaputama (JSIK) Vol. 6 No. 2 (2022): Volume 6, Nomor 2, Juli 2022
Publisher : STMIK KAPUTAMA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59697/jsik.v6i2.189

Abstract

RSUD Dr. R.M. Djoelham Kota Binjai merupakan lembaga penyedia jasa layanan kesehatan berdiri sejak tahun 1927 di kota Binjai yang menyediakan pelayanan rawat inap bagi pasien yang sedang sakit, kecelakaan maupun pemulihan kondisi (pasca operasi). RSUD Dr. R.M. Djoelham memberikan pelayanan rawat inap yang baik, dari segi pelayanan yang diberikan perawat, pelayanan medis, pelayanan kamar, maupun fasilitas lainnya. BPJS kesehatan membantu ketersediaan untuk semua kebutuhan biaya dokter, obat-obatan, rawat inap, sampai dengan tindakan operasi. Pengelompokkan pasien rawat inap berdasarkan kelas BPJS menjadi hal yang penting pada database rumah sakit terdiri dari banyak kelas BPJS yang digunakan dalam kegiatan rawat inap rumah sakit. Namun, dalam kegiatan ini masih susah untuk didentifikasikan karena disetiap harinya banyak pasien masuk. Teknik data mining dapat menggali data kasus yang berjumlah besar dan menghasilkan informasi tentang pengelompokkan pasien rawat inap berdasarkan kelas BPJS sesuai dengan clustering masing-masing.
Penerapan Sistem Pakar Menentukan Covid-19 Dengan Metode KNN (K Nearest Neighbor) Berbasis Web (Studi Kasus : RSU Sylvani) Septian Haryanto; Relita Buaton; Indah Ambarita
Jurnal Sistem Informasi Kaputama (JSIK) Vol. 6 No. 2 (2022): Volume 6, Nomor 2, Juli 2022
Publisher : STMIK KAPUTAMA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59697/jsik.v6i2.198

Abstract

Covid-19 adalah virus yang baru muncul di wuhan pada akhir tahun 2019. Gejala yang di timbulkan oleh covid-19 bervariasi antara suhu tubu meningkat, demam, batuk dan lain nya. Untuk mengatasi faktor ketidakpastian dalam mendiagnosis gejala covid-19, system pakar dirancang untuk menemukan kasus serupa mengenai covid 19 tersebut. Gejala-gejala akan dimasukkan dan dicocokan dengan data penelitian untuk diolah dengan data latih, yaitu data lama pasien yang telah terdiagnosi. suatu sistem yang dapat mencegah sejak dini, sehingga membantu mengatasi penyakit yang disebabkan oleh virus covid-19 lebih dini. Subjek penelitian ini adalah sistem pakar untuk menentukan covid-19. Tahap pengembangan sistem dimulai dengan menganalisis kebutuhan sistem, merancang sistem, antara lain membangun basis pengetahuan, pengambilan tabel keputusan, tabel aturan, memonitor kesimpulan, merancang aliran data, diagram relasional entitas yang kemudian melakukan implementasi dan pengujian. dari sistem. Dengan black box test dan alpha test. Hasil penelitian menunjukkan bahwa aplikasi layak dan bermanfaat
Pengelompokan Bidang Usaha Terhadap Bantuan Produktif Usaha Mikro (BPUM) Berdasarkan Wilayah Deli Serdang Menggunakan Metode Clustering K-Means (Studi Kasus: Dinas Koperasi Dan UMKM Kabupaten Deli Serdang) Tiara Jelita; Relita Buaton; Magdalena Simanjuntak
Explorer Vol 3 No 2 (2023): July 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/explorer.v3i2.783

Abstract

Micro Business Productive Assistance is a program that is assistance from the government to MSME workers throughout Indonesia. Every year, MSMEs can receive this assistance, without exception for those who have received it in previous years. The Office of Cooperatives and MSMEs of Deli Serdang Regency is a regional apparatus in North Sumatra Province which has the main task of carrying out government affairs in the field of cooperatives and small businesses including saving and loan business permits, empowerment and development of small businesses. Micro, Small and Medium Enterprises (MSMEs) are individual business entities which contributed significantly to increasing exports, increasing and equalizing income, forming national products and expanding employment opportunities. Based on these conditions, the authors provide a solution that needs to be built a clustering that can classify fields in each business owned by the community, because not all types of business fields in the community will receive this assistance, including agriculture and animal husbandry. Grouping data can apply the data mining process with the K-Means Algorithm clustering method which is a process of processing very large amounts of data using statistical methods, mathematics, and utilizing Artificial Intelligence technology to produce a group of data. By utilizing the data mining process using the clustering method, it is hoped that clustering can solve the problem of grouping business fields owned by the community. From the test results with 1004 data, which was carried out with MATLAB, it was found that group 1 had 383 data, group 2 had 261 data and group 3 had 360 data. Meanwhile, based on the results of the trial with RapidMiner, it was found that group 1 had 371 data, group 2 had 281 data and group 3 had 352 data.
Pengelompokan Data Mining Penerimaan Bantuan Pangan Non Tunai (BPNT) Menggunakan Metode Clustering (Studi Kasus : Kantor Desa Payabakung Hamparan Perak) Fany Juliawati; Relita Buaton; Rusmin Saragih
Explorer Vol 3 No 2 (2023): July 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/explorer.v3i2.793

Abstract

Poverty is a problem that is often faced by various countries in the world, including Indonesia. In an effort to overcome poverty and increase people's access to food, in 2017 the Government gradually created a program that was formed to reduce the burden on the community in meeting basic needs, with the Non-Cash Food Assistance Program (BPNT). The problem is that the assistance provided has not been distributed on target / the distribution of assistance has not been objective, due to limited data and information obtained regarding families receiving BPNT assistance, so that families who should be entitled to receive assistance cannot receive assistance due to limited data available. Therefore, the village office is required to record again the families who are entitled to receive BPNT assistance with the existing criteria. The solution offered is to create a system of k-means that displays the clustering results of recipients of Non-Cash Food Assistance, by utilizing a number of data owned by the agency, it can be grouped using data mining technology. The benefit is that data mining can help agencies gain knowledge. by processing existing BPNT beneficiary data. The use of data mining techniques in grouping BPNT recipients is expected to be useful in facilitating the process of searching system data, which was previously still manual. The data group for recipients of Non-Cash Food Assistance (BPNT) in the work group (X) are private employees, for the income group (Y) are 1,400,001 – 1,700,000 and in the home status group (Z) are self-owned homes, and Centroid 2 ( 1,552,861.44), the data group for recipients of Non-Cash Food Assistance (BPNT) in the occupation group (X) is Plantation, for the income group (Y) is 800,001 – 1,100,000 and in the house status group (Z) is Owned house, and Centroid3 (4,592,351.64) data group for recipients of Non-Cash Food Assistance (BPNT) in the occupation group (X) is Labor, for the income group (Y) is 500,001 – 800,000 and in the house status group (Z) is Rent house.
Klasifikasi Data Penduduk Pada Pemilihan Umum Di Kota Binjai Menggunakan Algoritma K-Means (Studi Kasus : KPU Kota Binjai) Windy Indah Sary Sinaga; Relita Buaton; Hermansyah Sembiring
Explorer Vol 3 No 2 (2023): July 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/explorer.v3i2.794

Abstract

Population growth is something that continues in an environment both in rural and urban areas. The rapidly increasing number of residents must be re-recorded in a government agency. Likewise, the Binjai City KPU Office must re-record population data, especially residents in the city of Binjai who have the right to carry out the General Election in 2024 by involving the community that has been previously recorded. Problems were also found with data on residents who had moved domiciles but their personal data had already been recorded for general elections in 2024. With that, data collection had to be re-done to select population data so as to produce a new population data status so that data was not found that did not match what it should be. By observing the problems above Data Mining with the Clustering method is very appropriate to be used to generate knowledge of new population data groups to carry out general elections at the KPU Binjai, using the MATLAB application is also very appropriate to choose in this problem so that it can produce output from data mining that can be used in future decision making. This study aims to process data to produce population data in the city of Binjai in the implementation of general elections, implement a system so that it can classify new population data in the middle of old population data and design data grouping in determining population data groups based on criteria conditions at the KPU Office in Binjai city. By using the clustering method that has been used to process population data at general elections in the city of Binjai, it can produce new information from 1000 data that has been tested. From 1000 population data for general elections in Binjai City, 3 clusters are obtained with the results of 7 tests where cluster 1 totals 225 data, cluster 2 has 436 data and cluster 3 has 339 data.
Student Character Grouping Based on Six Dimensions of Pancasila Student Profile Using Clustering Method (Case Study of SMK Swasta Setia Budi Binjai) Zuliani Zuliani; Suci Ramadani; Relita Buaton
International Journal of Informatics, Economics, Management and Science (IJIEMS) Vol 2 No 2 (2023): IJIEMS (August 2023)
Publisher : Sekolah Tinggi Manajemen Informatika dan Komputer Jayakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52362/ijiems.v2i2.1202

Abstract

Character education is one of the important aspects in developing students into individuals with integrity, ethics, and responsibility. Pancasila as the foundation of the Indonesian state has a central role in shaping students' character. This study aims to categorize student character based on six dimensions of the Pancasila learner profile at SMK Swasta Setia Budi Binjai. The six dimensions of the Pancasila learner profile that are the focus of this study include: 1) Faithful, Devoted to God Almighty and Noble, 2) Global Diversity, 3) Mutual Cooperation, 4) Independent, 5) Creative, 6) Critical Reasoning. The clustering method is used to group students based on the Pancasila learner profile measured through questionnaires distributed to subject teachers. The collected data will be analyzed using relevant clustering algorithms to identify the pattern of student characters present in the school population. This research is expected to provide deeper insight into the character of students at SMK Swasta Setia Budi Binjai based on Pancasila values. The results of this study are expected to be the basis for the development of a more effective character education program that focuses on strengthening the values of Pancasila in an effort to produce a young generation with strong character, love for the country, and contribute positively to society and the nation.
Application of the Monte Carlo Method in Modeling and Simulation of Service Queuing Systems at PT. Pos Indonesia Persero Binjai Eli Yusrina; Relita Buaton; I Gusti Prahmana
International Journal of Informatics, Economics, Management and Science (IJIEMS) Vol 2 No 2 (2023): IJIEMS (August 2023)
Publisher : Sekolah Tinggi Manajemen Informatika dan Komputer Jayakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52362/ijiems.v2i2.1181

Abstract

Queues are events that we often encounter in various places that provide services to the public, one of which is the Post Office. Service to customer satisfaction is a very important thing, so that improving the quality of customer service must always be done. A good queuing system will affect consumer behavior and satisfaction. PT. Pos Indonesia Persero Binjai is a service company where the purpose of PT. Pos Indonesia (Persero) itself is customer satisfaction oriented. The problems that occurred at PT. Pos Indonesia Persero Binjai caused long queues to build up in queues. To overcome these problems, it is necessary to improve the system such as applying the queuing model application by applying the queuing model method and Monte Carlo simulation. Monte Carlo is a probabilistic simulation method that generates random input to mimic the existing conditions of a problem by estimating the same distribution and in accordance with reality. So that with the improvement of the system, the application of the queuing model can be applied, directing customers to take queue numbers, and monitoring the queue card arrangement, adding tellers to serve customers, enlarging the waiting room so that customers are comfortable waiting and also determine the characteristics and performance measures of the queuing system in part of the payment counter and delivery of goods that will help PT. Pos Indonesia Binjai Company.
Implementation of Mechine Learning Eligibility for Customer Credit Payments at Bank BTN Using the K – Nearst Neighbor Algorithm Ema Sari Suwandi; Relita Buaton; Rusmin Saragih
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 3 No. 1 (2023): October 2023
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v3i1.246

Abstract

Credit is the provision of money or bills that can be equated with that, based on a loan agreement or agreement between a bank and another party that requires the borrower to pay off the debt after a certain period of time with interest (Government of Indonesia, 1998). In its initial development, credit had a function in stimulating mutual assistance aimed at meeting needs, both in the field of business and meeting daily needs.In developing applications, it is necessary to predict applications at Bank BTN Medan accurately, accurate prediction results are very important in showing the right rating and decision-making in selecting customers. When customers experience arrears, the system used by Bank BTN Medan is still manual and has not applied predication in credit arrears to customers of Bank BTN Medan. Tests carried out in this test use a credit customer dataset from Bank BTN Medan. This study predicts the eligibility of customer credit payments at Bank BTN with the K – Nearst neighbor algorithm. The prediction of the level of smoothness of credit payments is made using K-Nearest Neighbor in order to be able to predict the smoothness of future credit payments.
Co-Authors Achmad Fauzi 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 Eli Yusrina 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 I Gusti Prahmana I Gusti Prahmana Indah Malasari Ivan Candra Dinata Kadim, Lina Arliana Nur Katen Lumbanbatu Khair, Husnul Kristina Ananatasia Kristina Annatasia Leni Tri Ramadhayanti Lestari, Chintiya Wahyuni Indah Lidya Hasna lidya hasna Lubis, Anjelia Alsar magdalena simanjuntak Magdalena Simanjuntak Malau, Farid Reza Marto Sihombing Melda Pita Uli Sitompul Mesra Yel Mili Alfhi Syari Muammar Khadapi 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 Nurlaila Nurlaila Nurul Syahrani Pardede, Akim Manaor Hara Prahmana , I Gusti 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 Sembiring, Hermansyah Septian Haryanto septian haryanto Sherly Eka Wahyuni Sihombing, Anton Sihombing, Marto Simanjuntak, Magdalena Sinaga, Ayu Puspita Sari Sinek Mehuli Br Perangin-Angin Siswan Syahputra Solikhun Solikhun Solikhun Solikhun, Solikhun Sri Astuti Sri Hardiningsih suci ramadani Suha Baby Mayaza Sundari, Yeni Sundari, Yeni Suria Alamsyah Putra 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 Yuyun Arnia Zarlis Muhammad Zuliani Zuliani Zulkifli Zulkifli