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All Journal IJEBD (International Journal Of Entrepreneurship And Business Development) METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi International Journal of New Media Technology METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Jurnal Widya Jurnal Sains dan Teknologi Widyaloka (JSTekWid) Jurnal Pengabdian Pada Masyarakat METHABDI Majalah Ilmiah METHODA TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi

Rena Nainggolan

Universitas Methodist Indonesia

Author-ID : 3448719

Religion Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Physics Public Health Social Sciences Transportation

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SISTEM PENDUKUNG KEPUTUSAN DALAM PEMILIHAN HOTEL DI TANGERANG MENGGUNAKAN METODE AHP DAN TOPSIS Herry Sukma; Fenina Twince Tobing; Rena Nainggolan
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 5 No. 1 (2021): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Banten Province has a city, namely Tangerang, to be precise in the northern part of Banten City. There are many tourist attractions that we can visit in the city of Tangerang, such as natural scenery, historical places, natural scenery, photo spots and culinary tours. With various tourist attractions in the city of Tangerang, it attracts tourists who want to travel to these places. With so many interested tourists visiting the city of Tangerang, the existing hotel is one of the destinations for tourists who are visited for a place to stay and rest. This has had an impact on the increasing number of hotels in the area, which has led to an increasing variety of choices for tourists. To make it easier for tourists to choose a hotel according to their needs, a decision support system is needed in choosing a hotel to use for a place to stay and rest. Tourists can choose hotels according to their desired needs by using a decision support system with various criteria. The decision support system applies the initial criteria weighting by using the AHP method and hotel alternative ranking using the TOPSIS method. The system has been tested and implemented by distributing questionnaires to 30 respondents using the USE Questionnaire and applying the Sala Likert Method to perform the questionnaire calculations, and the final result obtained in the calculation of the questionnaire is 84.51%.

OPTIMASI PERFORMA CLUSTER K-MEANS MENGGUNAKAN SUM OF SQUARED ERROR (SSE) Rena Nainggolan; Gortap Lumbantoruan
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 2 No. 2 (2018): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

K-Means merupakan suatu algoritma pengklasteran yang cukup sederhana yang mempunyai kemampuan mengelempokkan data dalam jumlah yang cukup besar, mempartisi dataset kedalam beberapa klaster k. Algoritmanya cukup mudah untuk diimplementasikan dan dijalankan, relative cepat, dan efisien. Disi lain K-Means masih memiliki beberapa kelemahan, yaitu dalam menentukan jumlah cluster. Hasil cluster yang terbentuk dari metode K-means ini sangatlah tergantung pada inisiasi nilai pusat awal cluster yang diberikan. Hal ini menyebabkan hasil clusternya berupa solusi yang sifatnya local optimal. Pada penelitian ini dilakukan untuk mengatasi kelemahan yang ada pada algoritma K-Means yaitu: perbaikan pada algoritma K-Means menghasilkan cluster yang lebih baik yaitu penerapan Sum Of Squared Error (SSE) untuk membatu K-Means Clustering dalam menetukan jumlah cluster yang paling optimum, dari proses modifikasi ini, diharapkan pusat cluster yang diperoleh nantinya akan menghasilkan cluster-cluster, dimana antar anggota cluster memiliki tingkat kemiripan yang tinggi. Perbaikan performa cluster K-Means akan diterapkan pada penentuan pusat cluster.

EVALUASI CLUSTER SOCIAL MEDIA DATA IN TOURISM DOMAIN MENGGUNAKAN K-MEANS CLUSTERING Rena Nainggolan; Fenina Adline Twince Tobing; Emma Rosinta Simarmata; Resianta Perangin-angin
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 4 No. 1 (2020): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Clustering adalah salah satu teknik Data Mining. Clustering bekerja dengan cara menggabungkan sejumlah data atau objek kedalam satu klaster, dengan tujuan setiap data dalam satu klaster tersebut akan mempunyai data yang semirip mungkin dan berbeda dengan data atai objek yang berada pada kelompok lain. K-Means Clustering memiliki kemampuan untuk melakukan komputasi yang relatif cepat dan efisien dalam mengabungkan data dalam jumlah yang cukup besar. Dalam penelitian ini, peneliti akan menggunakan metode K-mean clustering yang diterapkan pada data mining pada Online Reviews pada data TripAdvisor. Implementasi proses K-Means Clustring menggunakan Weka, Atribut yang digunakan adalah galeri seni, klub dansa, bar jus, restoran, museum, resor, taman atau tempat piknik, pantai, teater, dan lembaga keagamaan. Menghasilkan jumlah cluster 4 (k=4) dengan cluster pertama sebanyak 178 (18%) reviews traveler, cluster kedua 243 (25%) reviews traveler, cluster ketiga 228 (23%) reviews traveler, cluster keempat 331(34%) reviews traveler.

ANALISIS PERBANDINGAN PENGGUNAAN METODE BINARY SEARCH DENGAN REGULAR SEARCH EXPRESSION Fenina Adline Twince Tobing; Rena Nainggolan
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 4 No. 2 (2020): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

The search system is a feature that is indispensable for an application or website. By comparing two algorithms that are often used, namely Binary Search and Regular Search Expression (REGEX) algorithms in a simple search system is a problem that will be discussed in this journal. Analysis of the two algorithms is carried out to solve problems in the search system so that the search algorithm can be applied more precisely and effectively. The results prove that the Binary Search has the advantage of searching large amounts of data in an ordered state and has a more effective iteration. While the Regular Expression Search has the advantage of performing searches that are not completely known about the results and keys, besides that this algorithm also allows you to search based on certain patterns in the data.

ANALISIS CLUSTER DENGAN MENGGUNAKAN K-MEANS UNTUK PENGELOMPOKKAN ONLINE CUSTOMER REVIEWS (OCR) PADA ONLINE MARKETPLACE Rena Nainggolan; Fenina A.T Tobing
METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Vol. 6 No. 1 (2020): Maret 2020
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/mtk.v6i1.246

Technological advances at this time are very influential on people's shopping culture, plus during the current pandemic, it has resulted in an increasing number of people shopping for daily necessities online. There are many conveniences offered in online shopping that make people switch to using these facilities. Besides the advantages of online shopping, there are also some disadvantages of online shopping, including the rise of online sales fraud such as goods not being shipped, damaged goods, items not as ordered, and much more. For this reason, in conducting online transactions, trust is needed between the seller and the buyer, and one of the factors that greatly affect the prospective buyer is to know the history of the seller, namely by looking at the reviews given by the buyer on the seller's homepage which is called Online Customers Reviews (OCR). OCR is considered to be very influential on customer buying interest. One of the indicators that are considered very important in influencing consumer buying interest and trust is OCR. This study aims to analyze OCR clustering in one of the marketplaces in Indonesia using the K-Means Clustering Method. K-Means is a clustering algorithm that is quite effective because it has the ability to group large amounts of data and with high speed, the K-Means algorithm partitions data into clusters so that they have the similarity of being in one cluster.

Cluster Analysis of Online Shop Product Reviews Using K-Means Clustering Rena Nainggolan; Eviyanty Purba
IJEBD (International Journal of Entrepreneurship and Business Development) Vol 3 No 2 (2020): March 2020
Publisher : LPPM of NAROTAMA UNIVERSITY

Purpose:This research aims to mine review data on one of the e-commerce sites which ultimately produces clusters using the K-Means Clustering algorithm that can help potential customers to make a decision before deciding to buy a product or service. Design/methodology/approach: By using Octoparse we mine opinion or comment data in the form of customer online reviews, after getting the data we group the data using the k-emans clustering methode to obtain cluster Findings: Cluster Analysys can can help potential customers to make a decision before deciding to buy a product or service Research limitations/implications: WWW.Lazada.Com Practical implications: State your implication here. Originality/value: Paper type: This paper can be categorized as case study paper

Penerapan Algoritma Knuth Morris Pratt (KMP) pada Pencarian Data di SQL LIKE Operators Fenina Adline Twince Tobing; Alex Chandra; Rena Nainggolan
JURNAL WIDYA Vol. 3 No. 1 (2022): Jurnal Widya, April 2022
Publisher : Akademi Manajemen Informatika dan Komputer Widya Loka Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54593/awl.v3i1.82

Penelitian ini membahas bagaimana database tracking system dapat menemukan data yang diinginkan berdasarkan SQL LIKE Operator yang telah ditentukan dengan menggunakan Knuth Morris Pratt Algorithm (KMP) dalam pencarian data pada SQL. Structured Query Language (SQL) adalah sekumpulan perintah khusus yang digunakan untuk mengakses data dalam database relasional. Untuk mencari data SQL pada query operator LIKE yang telah ditentukan dapat dilakukan melalui pencocokan string pada data yang ada untuk mendapatkan hasil. Pencocokan String adalah algoritma untuk mencari semua kemunculan string pendek yang disebut pola dalam string yang lebih panjang yang disebut teks. Knuth Morris Pratt Algorithm (KMP) adalah pencocokan string dalam teks dari kiri ke kanan dengan mencocokkan karakter per pola karakter dengan karakter dalam teks yang sesuai. Hasil penelitian menggunakan metode KMP pada query SQL berjalan dengan baik dalam melakukan pencarian data menggunakan Operator LIKE dan kemudahan dalam mengimplementasikan algoritma KMP dalam pencarian data pada SQL harus disesuaikan dengan wildcard pada operator LIKE.

Analisis efisiensi pencarian greatest common divisor dengan metode euclidean algorithms, middle school procedure dan CIC Fenisa Lourence Br Tobing; Alex Chandra; Fenina Adline Twince Tobing; Rena Nainggolan; Prayogo
Jurnal Sains dan Teknologi Widyaloka (JSTekWid) Vol. 1 No. 1 (2022): JSTekWid (January 2022)
Publisher : Akademi Manajemen Informatika dan Komputer Widya Loka Medan

Permasalahan yang ada saat mencari GCD sangatlah beragam, untuk itu perlu diteliti metode mana yang efisiensinya paling tinggi untuk setiap masalah yang ada saat mencarinya. Efisiensi yang kita cari dilihat dari faktor pemakaian memori dan waktu dalam menjalankan algoritma tersebut. Dalam penelitian ini, digunakam tiga metode tersebut dalam mencari Greatest Common Divisor (GCD) yaitu Euclidean Algorithms, Consecutive Integer Checking (CIC) dan Middle School Procedure. Hasil penelitian menunjukkan bahwa metode Consecutive Integer Checking menggunakan waktu yang paling sedikit dibandingkan dua metode lainnya, tetapi metode ini menggunakan memori yang sangat banyak daripada metode lain sehingga metode ini tidak dapat dikatakan sebagai metode yang paling efisien. Metode Euclidean Algorithms adalah metode yang paling efektif karena tidak memerlukan waktu yang banyak dan memori yang digunakan juga sedikit.

Sentiment; Clustering; K-Means Analysis Sentiment in Bukalapak Comments with K-Means Clustering Method Rena Nainggolan; Fenina Adline Twince Tobing; Eva J.G Harianja
IJNMT (International Journal of New Media Technology) Vol 9 No 2 (2022): IJNMT : International Journal of New Media Technology)
Publisher : Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ijnmt.v9i2.2914

Technological development are very fast ini this era of globalization, to facilitate the work of many aspevt that can be utilized, as well as for the flow of information. By applying computer technology in varios fields, such as educations, entertainment, healt, tourism, culinary and so on. Clustering id one of the Data Mining techniques. Clustering works by combining a number of data or objects into one cluster, with the aim that each data ini one cluster will have data that is a similar as possible and different from data or objects in other groups. K-Means Clustering has the ability to perform computations that are relatively fast and efficient in combining large amounts of data. In this research, there are 1407 comments which will training data and testing data.

PELATIHAN PEMBUATAN KONTEN PEMBELAJARAN DARING PADA SMK SWASTA GELORA JAYA NUSANTARA MEDAN Rena Nainggolan; Roni Jhonson Simamora; Rasmulia Sembiring; Mendarissan Aritonang; Siti Normi; Junika Napitupulu; Melanthon Rumapea; Mufria J. Purba; Rijois I. E. Saragih
Jurnal Pengabdian Pada Masyarakat METHABDI Vol 1 No 1 (2021): Jurnal Pengabdian Pada Masyarakat METHABDI
Publisher : Universitas Methodist Indonesia

The purpose of community service activities is to implement the Tri Dharma of Higher Education and to contribute ideas and transfer technology to teaching staff at SMK Gelora Jaya Nusantara Medan. This service activity was carried out for 2 days. The training materials are Video Recording and Video Editing. The topic given in this training is the creation of online learning content. The material given is the use of Filmora X software. This topic is very much needed to equip teachers in preparing and delivering subject matter during this covid-19 pandemic. This topic was deliberately chosen considering that currently, teachers have difficulty in delivering subject matter face to face.

Co-Authors Adela Bintang Asari Alex Chandra Apriani Magdalena Sibarani Arina P. Silalahi Aritonang, Mendarissan Elisabeth, Duma Megaria Emma Rosinta Simarmata Eva Julia G. Harianja Eva Julia Gunawati Harianja Eviyanty Purba F. Vipriani Manurung Felisa Deswana Sianturi Fenina Adline Twince Tobing Fenina Twince Tobing Fenisa Lourence Br Tobing Gortap Lumbantoruan Herry Sukma Imelda S. Dumayanti Jamaluddin Jepriyanta N. Brahmana Jimmy F. Naibaho Junika Napitupulu Lyna M. N. Hutapea Mahendra Tlapta Sitepu Melanthon Rumapea Mufria J. Purba Petty Exclesia Pardosi Posma S. M. Lumbanraja Prayogo Purba, Eviyanti Novita Rasmulia Sembiring Resianta Perangin-Angin Rijois I. E. Saragih Rimbun Siringoringo, Rimbun Roni J Simamora Siti Normi Stevani L. Z. Simanjuntak Thomson J. Napitupulu Tryposa Br. Simatupang Yessy Dearni C. Saragih Yohana Angelita Manullang

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