Garuda - Garba Rujukan Digital

p-Index From 2020 - 2025

5.793

P-Index

This Author published in this journals

All Journal Jurnal TIMES Sinkron : Jurnal dan Penelitian Teknik Informatika Journal Information System Development METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Jurnal Informatika Kaputama (JIK) Jurnal Pengabdian Pada Masyarakat METHABDI TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi

Resianta Perangin-Angin

Universitas Methodist Indonesia

Author-ID : 2546611

Religion Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Immunology & microbiology Languange, Linguistic, Communication & Media Medicine & Pharmacology Physics Public Health

Published : 45 Documents Claim Missing Document

Claim Missing Document

Articles

Title

MODEL HIBRID GENETIC-XGBOOST DAN PRINCIPAL COMPONENT ANALYSIS PADA SEGMENTASI DAN PERAMALAN PASAR Siringoringo, Rimbun; Perangin-angin, Resianta; Jamaluddin, Jamaluddin
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 5 No. 2 (2021): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Extreme Gradient Boosting(XGBoost) is a popular boosting algorithm based on decision trees. XGBoost is the best in the boosting group. XGBoost has excellent convergence. On the other hand, XGBoost is a Hyper parameterized model. Determining the value of each parameter is classified as difficult, resulting in the results obtained being trapped in the local optimum situation. Determining the value of each parameter manually, of course, takes a lot of time. In this study, a Genetic Algorithm (GA) is applied to find the optimal value of the XGBoost hyperparameter on the market segmentation problem. The evaluation of the model is based on the ROC curve. Test result. The ROC test results for several SVM, Logistic Regression, and Genetic-XGBoost models are 0.89; 0.98; 0.99. The results show that the Genetic-XGBoost model can be applied to market segmentation and forecasting.

SEGMENTASI DAN PERAMALAN PASAR RETAIL MENGGUNAKAN XGBOOST DAN PRINCIPAL COMPONENT ANALYSIS Rimbun Siringoringo; Resianta Perangin-angin; Mufria J. Purba
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 5 No. 1 (2021): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

The growth of the online retail market in Indonesia is an excellent business opportunity. It is predicted that this growth will continue to move upward due to the increasing internet penetration. With greater exposure to brands, products and offerings, consumers become smarter and wiser in their purchasing decisions. Offering goods and services that match the tastes and behavior of consumers is very important to maintain business continuity. So far, the models developed are divided into two major parts, namely the time series approach and machine learning. In this study, segmentation and forecasting of online retail sector sales were carried out using extreme gradient boosting (XGBoost). The data used in this study is an online retail dataset obtained from the UCI repository. The k-means clustering (KMC) method is applied to determine the target or data class. Principal component analysis (PCA) is applied to reduce data dimensions by eliminating irrelevant features. Model evaluation is based on confusion matrix and macro average ROC curve. Based on the research results, XGBoost can perform retail data classification well, this can be seen through confusion matrix metrics and ROC curves.

APLIKASI PENGADUAN MASYARAKAT BERBASIS MOBILE WEB DI KECAMATAN TARUTUNG Sofya C. Sitompul; Jamaluddin Jamaluddin; Roni Jhonson Simamora; Resianta Perangin-angin
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 3 No. 2 (2019): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Public complaints are an important element in government agencies because complaints aim to improve the shortcomings of activities carried out by the government. Complaints from the Tarutung Subdistrict community have not been fully maximized regarding administrative activities in the sub-district including the schedule for distributing Raskin, taking family card files, ID cards, and others. Based on the background of the above thoughts can be identified as a problem that is how to design an application for public complaints services and information that can be felt directly by the community without having to spend much time on the complaints process. The purpose of this study is to build a mobile web-based application that can be accessed through a web browser. This application is built with the PHP programming language and uses the MySQL database as a database server. The results of this research are complaints applications that provide convenience in terms of online public complaints.

PENERAPAN ALGORITMA SAFE-LEVEL-SMOTE UNTUK PENINGKATAN NILAI G-MEAN DALAM KLASIFIKASI DATA TIDAK SEIMBANG Resianta Perangin-angin; Eva Julia Gunawati Harianja; Indra Kelana Jaya; Benget Rumahorbo
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 4 No. 1 (2020): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Klasifikasi data yang tidak seimbang merupakan masalah yang krusial pada bidang machine learning dan data mining. Ketidakseimbangan data memberikan dampak yang buruk pada hasil klasifikasi dimana kelas minoritas sering disalah klasifikasikan sebagai kelas mayoritas. Dimana kelompok kelas minoritas (minority) adalah kelompok kelas yang memiliki data lebih sedikit, dan kelompok kelas mayoritas (mayority) adalah kelompok kelas yang memiliki jumlah data lebih banyak. Data tidak seimbang adalah suatu kondisi dimana jumlah contoh dari salah satu kelas jauh lebih banyak dari kelas yang lain. Alasan buruknya kinerja metode klasifikasi biasa yang digunakan pada data tidak seimbang adalah bahwa tujuan metode klasifikasi dalam meminimumkan galat secara keseluruhan tidak dapat tercapai karena kelas minoritas hanya sedikit memberikan kontribusi, selain itu keputusan akhir yang dihasilkan tidak tepat karena terjadinya bias. Hal ini disebabkan oleh salah satu kelas mendominasi dalam hal jumlah. Dalam penelitian ini akan berfokus pada peningkatan nilai G-Mean dari dataset yang digunakan, dengan menerapkan algoritma Safe-Level-Smote. Dari hasil ujicoba yang dilakukan terhadap dua dataset yakni Abalon dan Vowel, untuk skema Smote + k-NN nilai G-Mean yang didapat yakni 0,47 untuk dataset Abalon dan 0.94 untuk dataset Vowel. Seletah dilakukan ujicoba terhadap dataset yang sama menggunakan skema Safe-Level-Smote menggunakan algoritma klasifikasi k-NN didapat hasil G-Mean 0,59 untuk dataset Abalon dan 1.00 Untuk dataset Vowel, rerata dari kenaikan nilai G-Mean terhadap algoritma Smote sebesar 12,68%. Hal ini membuktikan bahwasanya algoritma Safe-Level-Smote dapat meningkatkan nilai G-Mean pada klasifikasi data tidak seimbang menggunakan algoritma klasifikasi k-Nearst Neighbors.

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.

SIMULASI MONTE CARLO DALAM MEMPREDIKSI PEMAKAIAN OBAT PENYAKIT GIGI DAN MULUT PADA RUMAH SAKIT Resianta Perangin-angin; Ika Yusnita Sari; Elvika Rahmi; Roni Jhonson Simamora
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 6 No. 2 (2022): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

The use of drugs in patients with dental disease is a necessity that needs to be considered by the hospital in providing medical services to patients. Adequate and well-managed drug supply prevents shortages or excess drug stocks. So it needs good planning in managing and monitoring drug stocks appropriately. This study aims to make predictions in the use of dental disease drugs by using a monte carlo simulation. The data used is data on the use of drugs for dental diseases from 2020 to 2022. The data on drug use processed were 12 types of drugs. The data will be processed based on the Monte Carlo simulation stages. The results of using the Monte Carlo Simulation are to obtain predictions of the use of dental disease drugs with an accuracy value reaching 89.14%. Based on the accuracy value obtained, the Monte Carlo simulation can be used to predict drug use in the future. So that the supply of dental disease medicine is maintained.

TEXT MINING DAN KLASIFIKASI MULTI LABEL MENGGUNAKAN XGBOOST Rimbun Siringoringo; Jamaluddin Jamaluddin; Resianta Perangin-angin
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 6 No. 2 (2022): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

The conventional classification process is applied to find a single criterion or label. The multi-label classification process is more complex because a large number of labels results in more classes. Another aspect that must be considered in multi-label classification is the existence of mutual dependencies between data labels. In traditional binary classification, classification analysis only aims to determine the label in the text, whether positive or negative. This method is sub-optimal because the relationship between labels cannot be determined. To overcome the weaknesses of these traditional methods, multi-label classification is one of the solutions in data labeling. With multi-label text classification, it allows the existence of many labels in a document and there is a semantic correlation between these labels. This research performs multi-label classification on research article texts using the ensemble classifier approach, namely XGBoost. Classification performance evaluation is based on several metrics criteria of confusion matrix, accuracy, and f1 score. Model evaluation is also carried out by comparing the performance of XGBoost with Logistic Regression. The results of the study using the train test split and cross-validation obtained an average accuracy of training and testing for Regression Logistics of 0.81, and an average f1 score of 0.47. The average accuracy for XGBoost is 0.88, and the average f1 score is 0.78. The results show that the XGBoost classifier model can be applied to produce a good classification performance.

MODEL BIDIRECTIONAL LSTM UNTUK PEMROSESAN SEKUENSIAL DATA TEKS SPAM Siringoringo, Rimbun; Jamaluddin, Jamaluddin; Perangin-angin, Resianta; Harianja, Eva Julia Gunawati; Lumbantoruan, Gortap; Purba, Eviyanti Novita
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 7 No. 2 (2023): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol7No2.pp265-271

This study examines the LSTM-based model for processing spam in text data. Spam poses several dangers and risks, both for individuals and organizations. Spam can be a nuisance that hampers both individual and organizational productivity. Much spam contains fraudulent or phishing attempts to obtain sensitive information. Spam detection using deep learning involves the utilization of algorithms and deep neural network models to accurately classify messages as either spam or not spam. Typically, spam detection systems use a combination of these methods to improve the accuracy of identifying spam messages. This study applies the Bi-LSTM deep learning model to sequentially process text (sequencing). The performance of the model is determined based on the loss and accuracy. The data used are the Spam SMS and Spam Email datasets. The test results show that the Bi-LSTM model demonstrates better performance on all tested datasets. Bi-LSTM is able to capture textual patterns from both the context and the text itself, as it can combine information from both directions. The test results prove that the Bi-LSTM model is more effective in text comprehension. So we need to use Snort to maintain network security. Snort is a useful software for observing activity in a computer network. Snort can be used as a lightweight Network Intrusion Detection System (NIDS). Detection is carried out based on the rules that have been described by the administrator in the directory rules contained in the configuration file. Snort can analyze real time alerts, where the mechanism for entering alerts can be in the form of a user syslog, file or through a database. So we can detect attacks on computer networks early.

PREDICTION OF FUTURE FLIGHT DELAYS BASED ON CURRENT DATA ANALYSIS USING MACHINE LEARNING Nainggolan, Rena; Tobing, Fenina A. T.; Lumbantoruan, Gortap; Harianja, Eva Julia G.; Perangin-angin, Resianta
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 8 No. 1 (2024): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol8No1.pp110-116

Airplane transportation has many advantages compared to other means of transportation, where this means of transportation is able to cover long distances in a short time. Besides having advantages, of course, air transportation often experiences flight delays which can be caused by several things, including delayed flights due to late arrival (Arrival Delay), delays of the airline itself (Carrier Delay), previously late arrivals (Late Aircraft Delay), air traffic congestion (Nas Delay), security issues (Security Delay) and weather conditions (Weather Delay) The tests carried out were 101,315 US flight delay data from 2017 to 2022. By using the Machine Learning method, the results obtained were that the largest flight delays were caused by late arrivals, namely 386,124,672, where the largest flight delays were by airlines. The airline, namely Southwest Airlines, is 61,474,379 of the total airlines, which is 20, and the biggest departure delay is at Chicago O'Hare International airport, which is in the city of Chicago, IL province, which is 20,912,928 of the total airport, which is 596. This research aims to predict future flights by analyzing current flight data, so that future flights can be better by overcoming or avoiding previous flight delay problems

Co-Authors Bangun, Joy Erivan Pratama Berlian Juni R Marpaung Br. Batubara, Anggi Natasya Br. Karo, Selli Afnita Br. Sembiring Pelawi, Pindi Alpioninta Darwis Robinson Manalu Delvi Natalina Br Tarigan Elisabeth, Duma Megaria Elvika Rahmi Emma Rosinta Simarmata Eva Julia G. Harianja Eva Julia Gunawati Harianja Eva Julia Gunawati Harianja, Eva Julia Gunawati Eviyanti N. Purba Fenina Twince Tobing Ginting Babo, Aris Franata Giska Yufani Gortap Lumbantoruan Harianja, Eva J. G. Harianja, Eva Julia G. Hutagalung, Estri Aprilia Hutagaol, Ryan Philip Hutapea, Marlyna I. Ika Yusnita Sari Indra Kelana Jaya Jamaluddin Jamaluddin Jamaluddin Jepriyanta N. Brahmana Jimmy F. Naibaho Jonathan H. Saragih Jonathan Hamonangan Saragih Jujur Marentha Nababan Lyna M. N. Hutapea Mahendra Tlapta Sitepu Marpaung, Berlian Juni R Marpaung, Flora Moris Raichel Sitanggang Mufria J. Purba Nainggolan, Rena Napitupulu, Thomson Januari Paiman Nababan Panjaitan, Calvin Nicolas Petty Exclesia Pardosi Purba, Eviyanti Novita Rasmulia Sembiring Rena Nainggolan Reynaldi Pantun Sianturi Rijois I. E. Saragih Rimbun Siringoringo Rimbun Siringoringo, Rimbun Robert Simangunsong Rumahorbo, Benget Siboro, Yohana Natalia Sidabalok, Valentino Sihaloho, Senta Egrioni Simanjuntak, Stevani L. Z. Simanullang, Sanco Siringoriongo, Rimbun Sitepu, Fernanda Jekita F. Sitorus, Hegi Audria Sofya C. Sitompul Tobing, Fenina A. T. Torong, Yepta Efraim Yohana Angelita Manullang Zalukhu, Delianus

Title Search

Found 9 Documents Search Journal : METHOMIKA: Jurnal Manajemen Informatika

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title

Found 9 Documents
Search
Journal : METHOMIKA: Jurnal Manajemen Informatika